From 3469b7e8efb18f49b7285ca4fde4422875f38c29 Mon Sep 17 00:00:00 2001 From: ooonush Date: Sat, 5 Mar 2022 13:51:08 +0500 Subject: [PATCH 1/5] =?UTF-8?q?=D0=94=D0=97=20Numpy?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Numpy (26.02)/Numpy_Task.ipynb | 434 ++++++++++++++++++++++++++------- 1 file changed, 351 insertions(+), 83 deletions(-) diff --git a/Numpy (26.02)/Numpy_Task.ipynb b/Numpy (26.02)/Numpy_Task.ipynb index 593ba20..1949baa 100644 --- a/Numpy (26.02)/Numpy_Task.ipynb +++ b/Numpy (26.02)/Numpy_Task.ipynb @@ -2,7 +2,8 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "id": "feb7f6f2", "metadata": { "id": "medieval-detail" }, @@ -13,6 +14,7 @@ }, { "cell_type": "markdown", + "id": "4f80823f", "metadata": { "id": "abstract-istanbul" }, @@ -25,20 +27,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, + "id": "54d8a235", "metadata": { "id": "entertaining-automation" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n" + ] + } + ], "source": [ "python_list = [1, 12, 13, 45, 76, 45, 98, 0]\n", - "print()\n", - "python_list = \n", - "print()" + "print(type(python_list))\n", + "python_list = np.array(python_list)\n", + "print(type(python_list))" ] }, { "cell_type": "markdown", + "id": "0f235321", "metadata": { "id": "loose-tobago" }, @@ -49,18 +62,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, + "id": "07b9bc7e", "metadata": { "id": "included-polymer" }, - "outputs": [], - "source": [ - "z = \n", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5]\n" + ] + } + ], + "source": [ + "z = np.full(10, 1.5)\n", "print(z)" ] }, { "cell_type": "markdown", + "id": "3b15fd7d", "metadata": { "id": "threatened-theme" }, @@ -71,18 +94,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, + "id": "963f2be0", "metadata": { "id": "alert-endorsement" }, - "outputs": [], - "source": [ - "z = \n", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0.]]\n" + ] + } + ], + "source": [ + "z = z = np.zeros((5, 5))\n", + "\n", "print(z)" ] }, { "cell_type": "markdown", + "id": "b4d9ed58", "metadata": { "id": "federal-blackberry" }, @@ -93,18 +131,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, + "id": "5be6c607", "metadata": { "id": "static-filing" }, - "outputs": [], - "source": [ - "ones = \n", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n" + ] + } + ], + "source": [ + "ones = np.ones(12)\n", "print(ones)" ] }, { "cell_type": "markdown", + "id": "dd144105", "metadata": { "id": "whole-chassis" }, @@ -116,18 +164,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, + "id": "977bb23a", "metadata": { "id": "outstanding-deviation" }, - "outputs": [], - "source": [ - "ones = \n", + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 4)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ones = ones.reshape(3,4)\n", "ones.shape" ] }, { "cell_type": "markdown", + "id": "117bb1bc", "metadata": { "id": "cubic-noise" }, @@ -139,20 +200,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, + "id": "1892658d", "metadata": { "id": "foster-memory" }, - "outputs": [], - "source": [ - "Z = \n", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 1 2 3 4 5]\n", + " [ 6 7 8 9 10]\n", + " [11 12 13 14 15]\n", + " [16 17 18 19 20]]\n", + "[[ 1 2 3 4 5]\n", + " [ 6 7 8 9 10]\n", + " [ 11 12 13 -99 15]\n", + " [ 16 17 18 19 20]]\n" + ] + } + ], + "source": [ + "Z = np.arange(1, 21, 1).reshape(4, 5)\n", "print(Z)\n", - "\n", + "Z[2, 3] = -99\n", "print(Z)" ] }, { "cell_type": "markdown", + "id": "4dcf81f8", "metadata": { "id": "helpful-table" }, @@ -164,20 +242,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, + "id": "84b1215a", "metadata": { "id": "magnetic-leone" }, - "outputs": [], - "source": [ - "first = \n", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ -3 -10 -10 10 2 4 -9 10 4 1 -7 7 3 -5 -3]\n", + "[ -3 -5 3 7 -7 1 4 10 -9 4 2 10 -10 -10 -3]\n" + ] + } + ], + "source": [ + "first = np.random.randint(-10, 11, 15)\n", "print(first)\n", - "second = \n", + "second = first[::-1]\n", "print(second)" ] }, { "cell_type": "markdown", + "id": "0dbd17ef", "metadata": { "id": "executed-september" }, @@ -189,20 +278,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, + "id": "455f7019", "metadata": { "id": "pharmaceutical-sigma" }, - "outputs": [], - "source": [ - "first = \n", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 2 -1 -15 -11 4]\n", + " [ 11 -5 -9 0 -13]\n", + " [ -7 1 -12 -12 -3]\n", + " [ 1 -7 -15 2 -9]\n", + " [ 0 3 -12 0 13]]\n", + "[[ 2 1 225 121 4]\n", + " [ 11 25 81 0 169]\n", + " [ 49 1 144 144 9]\n", + " [ 1 49 225 2 81]\n", + " [ 0 3 144 0 13]]\n" + ] + } + ], + "source": [ + "first = np.random.randint(-15, 16, 25).reshape(5, 5)\n", "print(first)\n", - "\n", + "first = np.where(first < 0, first**2, first)\n", "print(first)" ] }, { "cell_type": "markdown", + "id": "5d36be00", "metadata": { "id": "floral-difference" }, @@ -216,18 +324,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, + "id": "acb86da0", "metadata": { "id": "saving-conference" }, - "outputs": [], - "source": [ - "first = \n", - "print(first)\n" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 12 5 -3 11 6]\n", + " [ 4 -5 -3 0 14]\n", + " [ 15 -1 -10 -9 3]]\n" + ] + }, + { + "data": { + "text/plain": [ + "(15,\n", + " -10,\n", + " array([10.33333333, -0.33333333, -5.33333333, 0.66666667, 7.66666667]),\n", + " array([ 6.2, 2. , -0.4]))" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "first = np.random.randint(-15, 16, 15).reshape(3, 5)\n", + "print(first)\n", + "first.max(), first.min(), first.mean(axis=0), first.mean(axis=1)" ] }, { "cell_type": "markdown", + "id": "45907f32", "metadata": { "id": "diagnostic-departure" }, @@ -240,23 +374,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, + "id": "cc76e88c", "metadata": { "id": "olympic-qatar" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 36 -54 96]\n", + " [ -47 -137 79]]\n" + ] + } + ], "source": [ "a = np.random.randint(-10, 10, (2, 5))\n", "first_axis = np.random.randint(4, 6)\n", "b = np.random.randint(-10, 10, (first_axis, 3))\n", - "if :\n", + "if a.shape[1] == b.shape[0]:\n", " print(a @ b)\n", "else:\n", - " " + " print('err')" ] }, { "cell_type": "markdown", + "id": "2ded5995", "metadata": { "id": "governmental-austin" }, @@ -268,20 +413,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, + "id": "9b454b9c", "metadata": { "id": "suffering-mauritius" }, - "outputs": [], - "source": [ - "mask = \n", - "matrix = \n", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ -2.61775722 -1.67086735 -2.27801851 -6.93616721 -7.45792731]\n", + " [ 0. -6.15690924 0.62688566 0.60474397 -7.53560122]\n", + " [ 0. 0. -11.70636778 -3.90126739 -1.98764308]\n", + " [ 0. 0. 0. -9.70455908 -10.8963226 ]\n", + " [ 0. 0. 0. 0. -2.1782692 ]]\n" + ] + } + ], + "source": [ + "mask = np.random.uniform(2, -12, (5, 5)) \n", + "matrix = np.copy(mask)\n", + "for i in range(5):\n", + " for j in range(5):\n", + " if j < i:\n", + " matrix[i, j] = 0\n", "\n", "print(matrix)" ] }, { "cell_type": "markdown", + "id": "40985b25", "metadata": { "id": "altered-baghdad" }, @@ -293,20 +456,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, + "id": "cec7e7ea", "metadata": { "id": "refined-stuff" }, - "outputs": [], - "source": [ - "mask = \n", - "matrix = \n", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0. 10.18238614 11.91385332 8.31314191 7.16254416]\n", + " [ 9.49416669 0. 8.87762329 11.12641883 10.55408449]\n", + " [ 6.60673358 6.12302958 0. 11.78786698 11.4615063 ]\n", + " [10.08653577 10.05468692 10.00824118 0. 12.99399982]\n", + " [12.01515492 7.92418921 9.0942127 9.01347768 0. ]]\n" + ] + } + ], + "source": [ + "mask = np.random.normal(10, 2, 25).reshape(5, 5)\n", + "matrix = np.copy(mask)\n", + "\n", + "for i in range(5):\n", + " for j in range(5):\n", + " if j == i:\n", + " matrix[i, j] = 0\n", "\n", "print(matrix)" ] }, { "cell_type": "markdown", + "id": "2d9b132e", "metadata": { "id": "quiet-complement" }, @@ -317,22 +499,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, + "id": "be1e172f", "metadata": { "id": "french-fighter" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 0 0 0 0]\n", + "[1 1 0 1 0]\n" + ] + }, + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "a = np.random.randint(0,2,5)\n", "print(a)\n", "b = np.random.randint(0,2,5)\n", "print(b)\n", - "equal = \n", + "equal = sum(a == b) == len(a)\n", "equal" ] }, { "cell_type": "markdown", + "id": "eb176893", "metadata": { "id": "color-amplifier" }, @@ -347,23 +550,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, + "id": "a0255fb7", "metadata": { "id": "close-daisy" }, - "outputs": [], - "source": [ - "r, c = \n", - "a = \n", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[61 2 49 59 9 31 40 42 45 4 70]\n", + " [11 22 73 31 14 48 51 26 96 69 69]\n", + " [18 80 30 20 67 73 68 82 12 83 21]\n", + " [ 7 42 72 85 57 36 66 63 74 37 18]\n", + " [ 2 87 58 27 79 71 24 67 80 18 29]]\n", + "20\n", + "[67 70 42 37 69 24 72 18 48 57 31 21 85 69 2 67 74 79 40 96]\n" + ] + } + ], + "source": [ + "import random\n", + "import math\n", + "\n", + "r, c = random.randint(3, 7), random.randint(2, 12)\n", + "a = np.random.randint(0, 100, r * c).reshape(r, c)\n", "print(a)\n", - "N = \n", + "N = random.randint(1, math.floor(r * c / 2))\n", "print(N)\n", - "sample = \n", + "sample = np.random.choice(a.flatten(), N)\n", "print(sample)" ] }, { "cell_type": "markdown", + "id": "80f200c2", "metadata": { "id": "patent-african" }, @@ -376,20 +598,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, + "id": "f653032e", "metadata": { "id": "taken-fabric" }, - "outputs": [], - "source": [ - "a = np.array([1, np.NaN, np.Inf], float)\n", - "\n", - "\n", + "outputs": [ + { + "data": { + "text/plain": [ + "array([1., 0., 0.])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = np.array([1, np.NaN, np.Inf], float)\n", + "for i, e in enumerate(a):\n", + " if np.isnan(e) or np.isinf(e):\n", + " a[i] = 0\n", "a" ] }, { "cell_type": "markdown", + "id": "136ca513", "metadata": { "id": "analyzed-ireland" }, @@ -401,20 +637,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, + "id": "113bddd8", "metadata": { "id": "imposed-digest" }, - "outputs": [], - "source": [ - "axis = \n", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24\n", + "24\n" + ] + } + ], + "source": [ + "axis = random.randint(1, 32)\n", "print(axis)\n", - "matrix = \n", - "print(...)" + "matrix = np.zeros(tuple(np.ones((axis), dtype=int)))\n", + "print(len(matrix.shape))" ] }, { "cell_type": "markdown", + "id": "dd32794d", "metadata": { "id": "regulation-colleague" }, @@ -427,17 +674,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, + "id": "1321e582", "metadata": { "id": "concerned-anthropology" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[37.40050676 50.32378478 27.00206948]\n", + " [53.26304936 49.19480125 50.78815875]\n", + " [56.14512038 62.83737387 37.25231629]\n", + " [66.89832777 49.51868862 60.56248615]\n", + " [46.90543492 43.61393481 57.70402322]\n", + " [56.99272333 73.20515882 45.65732603]\n", + " [41.55582669 52.51231989 32.21895004]\n", + " [48.05808254 50.78640518 37.77281941]\n", + " [57.72747562 66.61338244 37.49564976]\n", + " [67.92895952 38.51751088 65.11972469]]\n", + "[2, 1, 2, 1, 1, 2, 2, 2, 2, 1]\n", + "[27.00206948 49.19480125 37.25231629 49.51868862 43.61393481 45.65732603\n", + " 32.21895004 37.77281941 37.49564976 38.51751088]\n" + ] + } + ], "source": [ "matrix = np.random.normal(50, 10, (10,3))\n", "print(matrix)\n", - "indexes = \n", + "indexes = ([list(item).index(min(item)) for item in matrix])\n", "print(indexes)\n", - "print(...)" + "print(matrix.min(axis=1))" ] } ], @@ -462,7 +730,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.8.8" } }, "nbformat": 4, From 54c43a89d3c8313e1e1aec9f5ee8285880f8a7d3 Mon Sep 17 00:00:00 2001 From: ooonush Date: Sat, 12 Mar 2022 10:43:37 +0500 Subject: [PATCH 2/5] =?UTF-8?q?pd=20=D0=B4=D0=B7?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Pandas (06.03)/Pandas. Lecture. Part 1.ipynb | 5765 +++++++++++++++++- 1 file changed, 5764 insertions(+), 1 deletion(-) diff --git a/Pandas (06.03)/Pandas. Lecture. Part 1.ipynb b/Pandas (06.03)/Pandas. Lecture. Part 1.ipynb index a0c1d04..f1f44db 100644 --- a/Pandas (06.03)/Pandas. Lecture. Part 1.ipynb +++ b/Pandas (06.03)/Pandas. Lecture. Part 1.ipynb @@ -1 +1,5764 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"01_Pandas.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyPGZA72+5Brg/wHtKFk27jK"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"qCUpgW4Chxlt"},"source":["# Игрушечные наборы данных\n","https://scikit-learn.org/stable/datasets/index.html"]},{"cell_type":"code","metadata":{"id":"6-e8Ub9ghvMA","executionInfo":{"status":"ok","timestamp":1632403984813,"user_tz":-300,"elapsed":867,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}}},"source":["import sklearn.datasets as sets\n","datasets = {0:'boston', 1:'iris', 2:'diabets', 3:'digits', 4:'linnerud', 5:'wine', 6:'cancer', 7:'olivetti_faces', 8:'20_newsgroups',\n"," 9:'20_newsgroups_vec', 10:'people_labeled_faces', 11:'pairs_labeled_faces', 12:'covertype', 13:'RCV1_multilabel',\n"," 14:'kddcup99', 15:'california_housing', }\n","choise = 1\n","if choise == 0:\n"," ds = sets.load_boston() #regression\n","elif choise == 1:\n"," ds = sets.load_iris() # classification\n","elif choise == 2:\n"," ds = sets.load_diabetes() # regression\n","elif choise == 3:\n"," ds = sets.load_digits() # classification\n","elif choise == 4:\n"," ds = sets.load_linnerud() # multivariate regression\n","elif choise == 5:\n"," ds = sets.load_wine() # classification\n","elif choise == 6:\n"," ds = sets.load_breast_cancer() # classification\n","elif choise == 7:\n"," ds = sets.fetch_olivetti_faces() # classification\n","elif choise == 8:\n"," ds = sets.fetch_20newsgroups() # classification\n","elif choise == 9:\n"," ds = sets.fetch_20newsgroups_vectorized() # classification\n","elif choise == 10:\n"," ds = sets.fetch_lfw_people() # classification\n","elif choise == 11:\n"," ds = sets.fetch_lfw_pairs() # classification\n","elif choise == 12:\n"," ds = sets.fetch_covtype() # classification\n","elif choise == 13:\n"," ds = sets.fetch_rcv1() # classification\n","elif choise == 14:\n"," ds = sets.fetch_kddcup99() # classification\n","elif choise == 15:\n"," ds = sets.fetch_california_housing() # regression"],"execution_count":1,"outputs":[]},{"cell_type":"code","metadata":{"id":"rHDZmzjAiy7N","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1615295304765,"user_tz":-300,"elapsed":1064,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"160c86a8-b336-429a-b12b-52cf5bb6a14b"},"source":["print(ds.DESCR)"],"execution_count":null,"outputs":[{"output_type":"stream","text":[".. _iris_dataset:\n","\n","Iris plants dataset\n","--------------------\n","\n","**Data Set Characteristics:**\n","\n"," :Number of Instances: 150 (50 in each of three classes)\n"," :Number of Attributes: 4 numeric, predictive attributes and the class\n"," :Attribute Information:\n"," - sepal length in cm\n"," - sepal width in cm\n"," - petal length in cm\n"," - petal width in cm\n"," - class:\n"," - Iris-Setosa\n"," - Iris-Versicolour\n"," - Iris-Virginica\n"," \n"," :Summary Statistics:\n","\n"," ============== ==== ==== ======= ===== ====================\n"," Min Max Mean SD Class Correlation\n"," ============== ==== ==== ======= ===== ====================\n"," sepal length: 4.3 7.9 5.84 0.83 0.7826\n"," sepal width: 2.0 4.4 3.05 0.43 -0.4194\n"," petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\n"," petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\n"," ============== ==== ==== ======= ===== ====================\n","\n"," :Missing Attribute Values: None\n"," :Class Distribution: 33.3% for each of 3 classes.\n"," :Creator: R.A. Fisher\n"," :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n"," :Date: July, 1988\n","\n","The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n","from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n","Machine Learning Repository, which has two wrong data points.\n","\n","This is perhaps the best known database to be found in the\n","pattern recognition literature. Fisher's paper is a classic in the field and\n","is referenced frequently to this day. (See Duda & Hart, for example.) The\n","data set contains 3 classes of 50 instances each, where each class refers to a\n","type of iris plant. One class is linearly separable from the other 2; the\n","latter are NOT linearly separable from each other.\n","\n",".. topic:: References\n","\n"," - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n"," Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n"," Mathematical Statistics\" (John Wiley, NY, 1950).\n"," - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n"," (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\n"," - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n"," Structure and Classification Rule for Recognition in Partially Exposed\n"," Environments\". IEEE Transactions on Pattern Analysis and Machine\n"," Intelligence, Vol. PAMI-2, No. 1, 67-71.\n"," - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\". IEEE Transactions\n"," on Information Theory, May 1972, 431-433.\n"," - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al\"s AUTOCLASS II\n"," conceptual clustering system finds 3 classes in the data.\n"," - Many, many more ...\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"59mLor4WoeZg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1632404056458,"user_tz":-300,"elapsed":683,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"3548322c-6765-4349-8dea-66ab12f3f7d9"},"source":["print(ds.feature_names)\n","print(ds.target_names)"],"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n","['setosa' 'versicolor' 'virginica']\n"]}]},{"cell_type":"code","metadata":{"id":"9Yt4tJ2_otjm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1632404071563,"user_tz":-300,"elapsed":420,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"b471a124-b71b-456d-de41-fe29676b6604"},"source":["data = ds.data\n","type(data)"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":["numpy.ndarray"]},"metadata":{},"execution_count":3}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZgxY_56q3YVG","executionInfo":{"status":"ok","timestamp":1632404086557,"user_tz":-300,"elapsed":402,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"b4e3ee4f-16b7-4b1e-f5be-34d0e5f4dd31"},"source":["data"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[5.1, 3.5, 1.4, 0.2],\n"," [4.9, 3. , 1.4, 0.2],\n"," [4.7, 3.2, 1.3, 0.2],\n"," [4.6, 3.1, 1.5, 0.2],\n"," [5. , 3.6, 1.4, 0.2],\n"," [5.4, 3.9, 1.7, 0.4],\n"," [4.6, 3.4, 1.4, 0.3],\n"," [5. , 3.4, 1.5, 0.2],\n"," [4.4, 2.9, 1.4, 0.2],\n"," [4.9, 3.1, 1.5, 0.1],\n"," [5.4, 3.7, 1.5, 0.2],\n"," [4.8, 3.4, 1.6, 0.2],\n"," [4.8, 3. , 1.4, 0.1],\n"," [4.3, 3. , 1.1, 0.1],\n"," [5.8, 4. , 1.2, 0.2],\n"," [5.7, 4.4, 1.5, 0.4],\n"," [5.4, 3.9, 1.3, 0.4],\n"," [5.1, 3.5, 1.4, 0.3],\n"," [5.7, 3.8, 1.7, 0.3],\n"," [5.1, 3.8, 1.5, 0.3],\n"," [5.4, 3.4, 1.7, 0.2],\n"," [5.1, 3.7, 1.5, 0.4],\n"," [4.6, 3.6, 1. , 0.2],\n"," [5.1, 3.3, 1.7, 0.5],\n"," [4.8, 3.4, 1.9, 0.2],\n"," [5. , 3. , 1.6, 0.2],\n"," [5. , 3.4, 1.6, 0.4],\n"," [5.2, 3.5, 1.5, 0.2],\n"," [5.2, 3.4, 1.4, 0.2],\n"," [4.7, 3.2, 1.6, 0.2],\n"," [4.8, 3.1, 1.6, 0.2],\n"," [5.4, 3.4, 1.5, 0.4],\n"," [5.2, 4.1, 1.5, 0.1],\n"," [5.5, 4.2, 1.4, 0.2],\n"," [4.9, 3.1, 1.5, 0.2],\n"," [5. , 3.2, 1.2, 0.2],\n"," [5.5, 3.5, 1.3, 0.2],\n"," [4.9, 3.6, 1.4, 0.1],\n"," [4.4, 3. , 1.3, 0.2],\n"," [5.1, 3.4, 1.5, 0.2],\n"," [5. , 3.5, 1.3, 0.3],\n"," [4.5, 2.3, 1.3, 0.3],\n"," [4.4, 3.2, 1.3, 0.2],\n"," [5. , 3.5, 1.6, 0.6],\n"," [5.1, 3.8, 1.9, 0.4],\n"," [4.8, 3. , 1.4, 0.3],\n"," [5.1, 3.8, 1.6, 0.2],\n"," [4.6, 3.2, 1.4, 0.2],\n"," [5.3, 3.7, 1.5, 0.2],\n"," [5. , 3.3, 1.4, 0.2],\n"," [7. , 3.2, 4.7, 1.4],\n"," [6.4, 3.2, 4.5, 1.5],\n"," [6.9, 3.1, 4.9, 1.5],\n"," [5.5, 2.3, 4. , 1.3],\n"," [6.5, 2.8, 4.6, 1.5],\n"," [5.7, 2.8, 4.5, 1.3],\n"," [6.3, 3.3, 4.7, 1.6],\n"," [4.9, 2.4, 3.3, 1. ],\n"," [6.6, 2.9, 4.6, 1.3],\n"," [5.2, 2.7, 3.9, 1.4],\n"," [5. , 2. , 3.5, 1. ],\n"," [5.9, 3. , 4.2, 1.5],\n"," [6. , 2.2, 4. , 1. ],\n"," [6.1, 2.9, 4.7, 1.4],\n"," [5.6, 2.9, 3.6, 1.3],\n"," [6.7, 3.1, 4.4, 1.4],\n"," [5.6, 3. , 4.5, 1.5],\n"," [5.8, 2.7, 4.1, 1. ],\n"," [6.2, 2.2, 4.5, 1.5],\n"," [5.6, 2.5, 3.9, 1.1],\n"," [5.9, 3.2, 4.8, 1.8],\n"," [6.1, 2.8, 4. , 1.3],\n"," [6.3, 2.5, 4.9, 1.5],\n"," [6.1, 2.8, 4.7, 1.2],\n"," [6.4, 2.9, 4.3, 1.3],\n"," [6.6, 3. , 4.4, 1.4],\n"," [6.8, 2.8, 4.8, 1.4],\n"," [6.7, 3. , 5. , 1.7],\n"," [6. , 2.9, 4.5, 1.5],\n"," [5.7, 2.6, 3.5, 1. ],\n"," [5.5, 2.4, 3.8, 1.1],\n"," [5.5, 2.4, 3.7, 1. ],\n"," [5.8, 2.7, 3.9, 1.2],\n"," [6. , 2.7, 5.1, 1.6],\n"," [5.4, 3. , 4.5, 1.5],\n"," [6. , 3.4, 4.5, 1.6],\n"," [6.7, 3.1, 4.7, 1.5],\n"," [6.3, 2.3, 4.4, 1.3],\n"," [5.6, 3. , 4.1, 1.3],\n"," [5.5, 2.5, 4. , 1.3],\n"," [5.5, 2.6, 4.4, 1.2],\n"," [6.1, 3. , 4.6, 1.4],\n"," [5.8, 2.6, 4. , 1.2],\n"," [5. , 2.3, 3.3, 1. ],\n"," [5.6, 2.7, 4.2, 1.3],\n"," [5.7, 3. , 4.2, 1.2],\n"," [5.7, 2.9, 4.2, 1.3],\n"," [6.2, 2.9, 4.3, 1.3],\n"," [5.1, 2.5, 3. , 1.1],\n"," [5.7, 2.8, 4.1, 1.3],\n"," [6.3, 3.3, 6. , 2.5],\n"," [5.8, 2.7, 5.1, 1.9],\n"," [7.1, 3. , 5.9, 2.1],\n"," [6.3, 2.9, 5.6, 1.8],\n"," [6.5, 3. , 5.8, 2.2],\n"," [7.6, 3. , 6.6, 2.1],\n"," [4.9, 2.5, 4.5, 1.7],\n"," [7.3, 2.9, 6.3, 1.8],\n"," [6.7, 2.5, 5.8, 1.8],\n"," [7.2, 3.6, 6.1, 2.5],\n"," [6.5, 3.2, 5.1, 2. ],\n"," [6.4, 2.7, 5.3, 1.9],\n"," [6.8, 3. , 5.5, 2.1],\n"," [5.7, 2.5, 5. , 2. ],\n"," [5.8, 2.8, 5.1, 2.4],\n"," [6.4, 3.2, 5.3, 2.3],\n"," [6.5, 3. , 5.5, 1.8],\n"," [7.7, 3.8, 6.7, 2.2],\n"," [7.7, 2.6, 6.9, 2.3],\n"," [6. , 2.2, 5. , 1.5],\n"," [6.9, 3.2, 5.7, 2.3],\n"," [5.6, 2.8, 4.9, 2. ],\n"," [7.7, 2.8, 6.7, 2. ],\n"," [6.3, 2.7, 4.9, 1.8],\n"," [6.7, 3.3, 5.7, 2.1],\n"," [7.2, 3.2, 6. , 1.8],\n"," [6.2, 2.8, 4.8, 1.8],\n"," [6.1, 3. , 4.9, 1.8],\n"," [6.4, 2.8, 5.6, 2.1],\n"," [7.2, 3. , 5.8, 1.6],\n"," [7.4, 2.8, 6.1, 1.9],\n"," [7.9, 3.8, 6.4, 2. ],\n"," [6.4, 2.8, 5.6, 2.2],\n"," [6.3, 2.8, 5.1, 1.5],\n"," [6.1, 2.6, 5.6, 1.4],\n"," [7.7, 3. , 6.1, 2.3],\n"," [6.3, 3.4, 5.6, 2.4],\n"," [6.4, 3.1, 5.5, 1.8],\n"," [6. , 3. , 4.8, 1.8],\n"," [6.9, 3.1, 5.4, 2.1],\n"," [6.7, 3.1, 5.6, 2.4],\n"," [6.9, 3.1, 5.1, 2.3],\n"," [5.8, 2.7, 5.1, 1.9],\n"," [6.8, 3.2, 5.9, 2.3],\n"," [6.7, 3.3, 5.7, 2.5],\n"," [6.7, 3. , 5.2, 2.3],\n"," [6.3, 2.5, 5. , 1.9],\n"," [6.5, 3. , 5.2, 2. ],\n"," [6.2, 3.4, 5.4, 2.3],\n"," [5.9, 3. , 5.1, 1.8]])"]},"metadata":{},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"-7ejnqmmwr_J","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1615295357693,"user_tz":-300,"elapsed":855,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"e22abd6b-c840-4e43-aa62-d9c1a5cdd231"},"source":["data.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(150, 4)"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"RmRL0mZ3o5ri","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1632404107395,"user_tz":-300,"elapsed":420,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"bcace884-7ac8-49ce-d14e-05c8f625bb38"},"source":["target = ds.target\n","target[:5], target.shape"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([0, 0, 0, 0, 0]), (150,))"]},"metadata":{},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"fpcR7aEBJoGq"},"source":["# Pandas"]},{"cell_type":"code","metadata":{"id":"FVTPYh-hhvah","executionInfo":{"status":"ok","timestamp":1632404228644,"user_tz":-300,"elapsed":546,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}}},"source":["import pandas as pd\n","import numpy as np"],"execution_count":7,"outputs":[]},{"cell_type":"code","metadata":{"id":"CZzMZXcyDnCx","colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"status":"ok","timestamp":1632404365934,"user_tz":-300,"elapsed":20,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"55d262ac-6243-4338-a45e-57217f23a610"},"source":["df = pd.DataFrame(data, columns=ds.feature_names) # data - может быть как лист, так и numpy array\n","df['target'] = ds.target\n","df"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)target
05.13.51.40.20
14.93.01.40.20
24.73.21.30.20
34.63.11.50.20
45.03.61.40.20
..................
1456.73.05.22.32
1466.32.55.01.92
1476.53.05.22.02
1486.23.45.42.32
1495.93.05.11.82
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150 rows × 5 columns

\n","
"],"text/plain":[" sepal length (cm) sepal width (cm) ... petal width (cm) target\n","0 5.1 3.5 ... 0.2 0\n","1 4.9 3.0 ... 0.2 0\n","2 4.7 3.2 ... 0.2 0\n","3 4.6 3.1 ... 0.2 0\n","4 5.0 3.6 ... 0.2 0\n",".. ... ... ... ... ...\n","145 6.7 3.0 ... 2.3 2\n","146 6.3 2.5 ... 1.9 2\n","147 6.5 3.0 ... 2.0 2\n","148 6.2 3.4 ... 2.3 2\n","149 5.9 3.0 ... 1.8 2\n","\n","[150 rows x 5 columns]"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"id":"WMx25DeePe80","executionInfo":{"status":"ok","timestamp":1632404401169,"user_tz":-300,"elapsed":1482,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"ca1eb41f-18e0-47de-cc77-b8648b89cec5"},"source":["df.head(2) #tail()"],"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)target
05.13.51.40.20
14.93.01.40.20
\n","
"],"text/plain":[" sepal length (cm) sepal width (cm) ... petal width (cm) target\n","0 5.1 3.5 ... 0.2 0\n","1 4.9 3.0 ... 0.2 0\n","\n","[2 rows x 5 columns]"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","metadata":{"id":"yY02uqmWhvlj","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1632404414446,"user_tz":-300,"elapsed":580,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"f4adccbb-22f7-4192-a8f7-67d00c8ff7c3"},"source":["df.sample(5)"],"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)target
1206.93.25.72.32
75.03.41.50.20
656.73.14.41.41
776.73.05.01.71
985.12.53.01.11
\n","
"],"text/plain":[" sepal length (cm) sepal width (cm) ... petal width (cm) target\n","120 6.9 3.2 ... 2.3 2\n","7 5.0 3.4 ... 0.2 0\n","65 6.7 3.1 ... 1.4 1\n","77 6.7 3.0 ... 1.7 1\n","98 5.1 2.5 ... 1.1 1\n","\n","[5 rows x 5 columns]"]},"metadata":{},"execution_count":13}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"L0oDISZyHqUh","executionInfo":{"status":"ok","timestamp":1632404445651,"user_tz":-300,"elapsed":486,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"f2586af7-7f30-4106-861b-539f5ed618d6"},"source":["type(df)"],"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["pandas.core.frame.DataFrame"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"arTjJfy442ss","executionInfo":{"status":"ok","timestamp":1632404485030,"user_tz":-300,"elapsed":433,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"6d630c99-cbed-42e1-d69f-c71e595be995"},"source":["type(df[\"target\"])"],"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["pandas.core.series.Series"]},"metadata":{},"execution_count":15}]},{"cell_type":"markdown","metadata":{"id":"xX_Qut-QR_ia"},"source":["### Индексация и срезы данных"]},{"cell_type":"code","metadata":{"id":"jXimDZePWyIp","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1614783881358,"user_tz":-300,"elapsed":3256,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"00860947-6e2c-484e-90ae-8149d6c2bb45"},"source":["df['sepal length (cm)'] # выбор столбца по названию"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0 5.1\n","1 4.9\n","2 4.7\n","3 4.6\n","4 5.0\n"," ... \n","145 6.7\n","146 6.3\n","147 6.5\n","148 6.2\n","149 5.9\n","Name: sepal length (cm), Length: 150, dtype: float64"]},"metadata":{"tags":[]},"execution_count":96}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rOBV0RUtHxLh","executionInfo":{"status":"ok","timestamp":1615295621844,"user_tz":-300,"elapsed":619,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"2e25e363-6fd5-477f-9e38-afe8f91522ac"},"source":["type(df['sepal length (cm)'])"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["pandas.core.series.Series"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sq2YmKFr5m-1","executionInfo":{"status":"ok","timestamp":1632404667952,"user_tz":-300,"elapsed":523,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"e9f125e0-3f1f-4a4b-d39c-5e6091047c86"},"source":["df.columns"],"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n"," 'petal width (cm)', 'target'],\n"," dtype='object')"]},"metadata":{},"execution_count":18}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"o5CI-Ha6P4AX","executionInfo":{"status":"ok","timestamp":1614783884339,"user_tz":-300,"elapsed":1699,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"ee350cf3-212a-4bdd-daf8-f0decfe313c0"},"source":["{name : '_'.join(name.split(' ')) for name in df.columns}"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'petal length (cm)': 'petal_length_(cm)',\n"," 'petal width (cm)': 'petal_width_(cm)',\n"," 'sepal length (cm)': 'sepal_length_(cm)',\n"," 'sepal width (cm)': 'sepal_width_(cm)',\n"," 'target': 'target'}"]},"metadata":{"tags":[]},"execution_count":97}]},{"cell_type":"code","metadata":{"id":"ztRKBaVlxM8d","executionInfo":{"status":"ok","timestamp":1632404857471,"user_tz":-300,"elapsed":585,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}}},"source":["# df = df.rename(columns={name : '_'.join(name.split(' ')) for name in df.columns}) # смена имен столбцов\n","df.rename(columns={name : '_'.join(name.split(' ')) for name in df.columns}, inplace=True)"],"execution_count":21,"outputs":[]},{"cell_type":"code","metadata":{"id":"Bryqf6bCxNC5","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1632404863328,"user_tz":-300,"elapsed":29,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"2fb81e40-0667-4c5b-9b50-4ba23010385b"},"source":["df.columns"],"execution_count":22,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['sepal_length_(cm)', 'sepal_width_(cm)', 'petal_length_(cm)',\n"," 'petal_width_(cm)', 'target'],\n"," dtype='object')"]},"metadata":{},"execution_count":22}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uesXOV19QcNX","executionInfo":{"status":"ok","timestamp":1615295826923,"user_tz":-300,"elapsed":438,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"6476924c-249d-4876-89be-920b127e125b"},"source":["df.target"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0 0\n","1 0\n","2 0\n","3 0\n","4 0\n"," ..\n","145 2\n","146 2\n","147 2\n","148 2\n","149 2\n","Name: target, Length: 150, dtype: int64"]},"metadata":{"tags":[]},"execution_count":20}]},{"cell_type":"code","metadata":{"id":"J2il4fodbWLb","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1614777840378,"user_tz":-300,"elapsed":566,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"b6d5c2a4-dc69-497d-997c-8127f174765a"},"source":["df.target[-10:] # возможен такой стиль обращения к столбцам, если его имя не содержит пробелов"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["140 2\n","141 2\n","142 2\n","143 2\n","144 2\n","145 2\n","146 2\n","147 2\n","148 2\n","149 2\n","Name: target, dtype: int64"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"code","metadata":{"id":"2IaGUtDoYIAO","colab":{"base_uri":"https://localhost:8080/","height":357},"executionInfo":{"status":"ok","timestamp":1614777891289,"user_tz":-300,"elapsed":607,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"c64f553c-27a2-4f0d-a1e3-aa82ee895acf"},"source":["df.loc[140: , 'sepal_width_(cm)':'petal_width_(cm)'] # возможность среза данных по ИМЕНАМ строк и столбцов"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal_width_(cm)petal_length_(cm)petal_width_(cm)
1403.15.62.4
1413.15.12.3
1422.75.11.9
1433.25.92.3
1443.35.72.5
1453.05.22.3
1462.55.01.9
1473.05.22.0
1483.45.42.3
1493.05.11.8
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"],"text/plain":[" sepal_width_(cm) petal_length_(cm) petal_width_(cm)\n","140 3.1 5.6 2.4\n","141 3.1 5.1 2.3\n","142 2.7 5.1 1.9\n","143 3.2 5.9 2.3\n","144 3.3 5.7 2.5\n","145 3.0 5.2 2.3\n","146 2.5 5.0 1.9\n","147 3.0 5.2 2.0\n","148 3.4 5.4 2.3\n","149 3.0 5.1 1.8"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"code","metadata":{"id":"RwTuNV_BxNtH","colab":{"base_uri":"https://localhost:8080/","height":357},"executionInfo":{"status":"ok","timestamp":1614777918498,"user_tz":-300,"elapsed":735,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"89004bbc-fd5d-4bb9-fbdc-6756fa31cb1b"},"source":["df.iloc[:10,:4] # возможность среза данных по ПОРЯДКОВЫМ НОМЕРАМ строк и столбцов "],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal_length_(cm)sepal_width_(cm)petal_length_(cm)petal_width_(cm)
05.13.51.40.2
14.93.01.40.2
24.73.21.30.2
34.63.11.50.2
45.03.61.40.2
55.43.91.70.4
64.63.41.40.3
75.03.41.50.2
84.42.91.40.2
94.93.11.50.1
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"],"text/plain":[" sepal_length_(cm) sepal_width_(cm) petal_length_(cm) petal_width_(cm)\n","0 5.1 3.5 1.4 0.2\n","1 4.9 3.0 1.4 0.2\n","2 4.7 3.2 1.3 0.2\n","3 4.6 3.1 1.5 0.2\n","4 5.0 3.6 1.4 0.2\n","5 5.4 3.9 1.7 0.4\n","6 4.6 3.4 1.4 0.3\n","7 5.0 3.4 1.5 0.2\n","8 4.4 2.9 1.4 0.2\n","9 4.9 3.1 1.5 0.1"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QSAbGcDbJP9B","executionInfo":{"status":"ok","timestamp":1632405184550,"user_tz":-300,"elapsed":413,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"20274561-ff6c-4031-e1a7-a26a2399cea5"},"source":["[column for column in df.columns if column.startswith('sepal')]"],"execution_count":23,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['sepal_length_(cm)', 'sepal_width_(cm)']"]},"metadata":{},"execution_count":23}]},{"cell_type":"code","metadata":{"id":"pytaw0cAxNp8","colab":{"base_uri":"https://localhost:8080/","height":424},"executionInfo":{"status":"ok","timestamp":1614784351268,"user_tz":-300,"elapsed":1370,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"81983e96-8834-40e4-b828-6706a4f3bbb6"},"source":["df[[column for column in df.columns if column.startswith('sepal')]] # выбор столбцов по условию"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal_length_(cm)sepal_width_(cm)
05.13.5
14.93.0
24.73.2
34.63.1
45.03.6
.........
1456.73.0
1466.32.5
1476.53.0
1486.23.4
1495.93.0
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150 rows × 2 columns

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"],"text/plain":[" sepal_length_(cm) sepal_width_(cm)\n","0 5.1 3.5\n","1 4.9 3.0\n","2 4.7 3.2\n","3 4.6 3.1\n","4 5.0 3.6\n",".. ... ...\n","145 6.7 3.0\n","146 6.3 2.5\n","147 6.5 3.0\n","148 6.2 3.4\n","149 5.9 3.0\n","\n","[150 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":102}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bHiE8tk872bY","executionInfo":{"status":"ok","timestamp":1632405255702,"user_tz":-300,"elapsed":666,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"5cab46f0-7d00-4c5a-a435-ecd145b8c82c"},"source":["df.target==1.0"],"execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0 False\n","1 False\n","2 False\n","3 False\n","4 False\n"," ... \n","145 False\n","146 False\n","147 False\n","148 False\n","149 False\n","Name: target, Length: 150, dtype: bool"]},"metadata":{},"execution_count":24}]},{"cell_type":"code","metadata":{"id":"YNxRaJqqavOz","colab":{"base_uri":"https://localhost:8080/","height":347},"executionInfo":{"status":"ok","timestamp":1615296046504,"user_tz":-300,"elapsed":815,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"2baa1152-611c-43a3-eea6-c9ae07cfea4e"},"source":["df[df.target==1.0][:10] # выбор данных по условию. В данном случае хотим увидеть данные у которых целевой класс = 1\n","# так же можно увидеть что обращаться к столбцу можно"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal_length_(cm)sepal_width_(cm)petal_length_(cm)petal_width_(cm)target
507.03.24.71.41
516.43.24.51.51
526.93.14.91.51
535.52.34.01.31
546.52.84.61.51
555.72.84.51.31
566.33.34.71.61
574.92.43.31.01
586.62.94.61.31
595.22.73.91.41
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"],"text/plain":[" sepal_length_(cm) sepal_width_(cm) ... petal_width_(cm) target\n","50 7.0 3.2 ... 1.4 1\n","51 6.4 3.2 ... 1.5 1\n","52 6.9 3.1 ... 1.5 1\n","53 5.5 2.3 ... 1.3 1\n","54 6.5 2.8 ... 1.5 1\n","55 5.7 2.8 ... 1.3 1\n","56 6.3 3.3 ... 1.6 1\n","57 4.9 2.4 ... 1.0 1\n","58 6.6 2.9 ... 1.3 1\n","59 5.2 2.7 ... 1.4 1\n","\n","[10 rows x 5 columns]"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"markdown","metadata":{"id":"i4V1_5AOgmB9"},"source":["### Описательная статистика"]},{"cell_type":"code","metadata":{"id":"EuwQ-U54xNnA","colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"status":"ok","timestamp":1614766986724,"user_tz":-300,"elapsed":1283,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"5ed73970-f852-49b2-82a7-bfe43b1ad3c3"},"source":["df.describe() # статистическое описание набора данных"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal_length_(cm)sepal_width_(cm)petal_length_(cm)petal_width_(cm)target
count150.000000150.000000150.000000150.000000150.000000
mean5.8433333.0573333.7580001.1993331.000000
std0.8280660.4358661.7652980.7622380.819232
min4.3000002.0000001.0000000.1000000.000000
25%5.1000002.8000001.6000000.3000000.000000
50%5.8000003.0000004.3500001.3000001.000000
75%6.4000003.3000005.1000001.8000002.000000
max7.9000004.4000006.9000002.5000002.000000
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"],"text/plain":[" sepal_length_(cm) sepal_width_(cm) ... petal_width_(cm) target\n","count 150.000000 150.000000 ... 150.000000 150.000000\n","mean 5.843333 3.057333 ... 1.199333 1.000000\n","std 0.828066 0.435866 ... 0.762238 0.819232\n","min 4.300000 2.000000 ... 0.100000 0.000000\n","25% 5.100000 2.800000 ... 0.300000 0.000000\n","50% 5.800000 3.000000 ... 1.300000 1.000000\n","75% 6.400000 3.300000 ... 1.800000 2.000000\n","max 7.900000 4.400000 ... 2.500000 2.000000\n","\n","[8 rows x 5 columns]"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"code","metadata":{"id":"X4ykTpKtxNiG","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1614778091397,"user_tz":-300,"elapsed":627,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"e62b683d-f476-4422-d691-774ead34e63f"},"source":["df.info() # информация об индексах, пропусках в данных, типах данных и объеме оперативной памяти занимаемой данными"],"execution_count":null,"outputs":[{"output_type":"stream","text":["\n","RangeIndex: 150 entries, 0 to 149\n","Data columns (total 5 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 sepal_length_(cm) 150 non-null float64\n"," 1 sepal_width_(cm) 150 non-null float64\n"," 2 petal_length_(cm) 150 non-null float64\n"," 3 petal_width_(cm) 150 non-null float64\n"," 4 target 150 non-null int64 \n","dtypes: float64(4), int64(1)\n","memory usage: 6.0 KB\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"b7khmMfj8mDB","executionInfo":{"status":"ok","timestamp":1632405484185,"user_tz":-300,"elapsed":51,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"8e7ccfa9-cffa-4872-c0a5-d00635211e12"},"source":["df.target.unique(), df.target.nunique()"],"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([0, 1, 2]), 3)"]},"metadata":{},"execution_count":26}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":167},"id":"n1XzQbdFRx7Z","executionInfo":{"status":"ok","timestamp":1615296303195,"user_tz":-300,"elapsed":783,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"a4acff70-40cf-4462-f2b6-03546318b29b"},"source":["df.groupby('target').mean() #df.groupby('target')['petal_length_(cm)'].mean()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal_length_(cm)sepal_width_(cm)petal_length_(cm)petal_width_(cm)
target
05.0063.4281.4620.246
15.9362.7704.2601.326
26.5882.9745.5522.026
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"],"text/plain":[" sepal_length_(cm) ... petal_width_(cm)\n","target ... \n","0 5.006 ... 0.246\n","1 5.936 ... 1.326\n","2 6.588 ... 2.026\n","\n","[3 rows x 4 columns]"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"code","metadata":{"id":"MRiTYhiixNfC","colab":{"base_uri":"https://localhost:8080/","height":217},"executionInfo":{"status":"ok","timestamp":1615296321113,"user_tz":-300,"elapsed":724,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"3bd6da21-1bde-404e-b9fb-d4e36e94634c"},"source":["df.groupby('target').agg([min, max, np.mean, np.std, np.size]) # применение общих функций группировки для всех столбцов"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal_length_(cm)sepal_width_(cm)petal_length_(cm)petal_width_(cm)
minmaxmeanstdsizeminmaxmeanstdsizeminmaxmeanstdsizeminmaxmeanstdsize
target
04.35.85.0060.35249050.02.34.43.4280.37906450.01.01.91.4620.17366450.00.10.60.2460.10538650.0
14.97.05.9360.51617150.02.03.42.7700.31379850.03.05.14.2600.46991150.01.01.81.3260.19775350.0
24.97.96.5880.63588050.02.23.82.9740.32249750.04.56.95.5520.55189550.01.42.52.0260.27465050.0
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"],"text/plain":[" sepal_length_(cm) ... petal_width_(cm) \n"," min max mean std ... max mean std size\n","target ... \n","0 4.3 5.8 5.006 0.352490 ... 0.6 0.246 0.105386 50.0\n","1 4.9 7.0 5.936 0.516171 ... 1.8 1.326 0.197753 50.0\n","2 4.9 7.9 6.588 0.635880 ... 2.5 2.026 0.274650 50.0\n","\n","[3 rows x 20 columns]"]},"metadata":{"tags":[]},"execution_count":25}]},{"cell_type":"code","metadata":{"id":"w_oHay4KxNdC","colab":{"base_uri":"https://localhost:8080/","height":197},"executionInfo":{"status":"ok","timestamp":1615296592781,"user_tz":-300,"elapsed":511,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"2b52fff3-b9c7-4c74-ea6f-e52b965f4e6b"},"source":["df.groupby('target').agg({'sepal_length_(cm)':[np.mean, np.std], 'petal_width_(cm)':[min, max]}) # индивидуальное применение функций группировки"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal_length_(cm)petal_width_(cm)
meanstdminmax
target
05.0060.3524900.10.6
15.9360.5161711.01.8
26.5880.6358801.42.5
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"],"text/plain":[" sepal_length_(cm) petal_width_(cm) \n"," mean std min max\n","target \n","0 5.006 0.352490 0.1 0.6\n","1 5.936 0.516171 1.0 1.8\n","2 6.588 0.635880 1.4 2.5"]},"metadata":{"tags":[]},"execution_count":30}]},{"cell_type":"markdown","metadata":{"id":"NCfoXnc41fmW"},"source":["### Полезные функции, которые конкретно сейчас не нужны, но часто применимы"]},{"cell_type":"code","metadata":{"id":"KV8EM_b41m0m","colab":{"base_uri":"https://localhost:8080/","height":197},"executionInfo":{"status":"ok","timestamp":1615296494311,"user_tz":-300,"elapsed":747,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"b898ccdb-16f0-415b-a629-25b794f42859"},"source":["d = df.copy()\n","d.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal_length_(cm)sepal_width_(cm)petal_length_(cm)petal_width_(cm)target
05.13.51.40.20
14.93.01.40.20
24.73.21.30.20
34.63.11.50.20
45.03.61.40.20
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"],"text/plain":[" sepal_length_(cm) sepal_width_(cm) ... petal_width_(cm) target\n","0 5.1 3.5 ... 0.2 0\n","1 4.9 3.0 ... 0.2 0\n","2 4.7 3.2 ... 0.2 0\n","3 4.6 3.1 ... 0.2 0\n","4 5.0 3.6 ... 0.2 0\n","\n","[5 rows x 5 columns]"]},"metadata":{"tags":[]},"execution_count":26}]},{"cell_type":"code","metadata":{"id":"pGOooxXo1xqA","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1615296536700,"user_tz":-300,"elapsed":737,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"a3a5fe7b-d857-49c8-8d59-ed08472c37e9"},"source":["targets = {float(i):target for i, target in enumerate(ds.target_names)}\n","targets"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{0.0: 'setosa', 1.0: 'versicolor', 2.0: 'virginica'}"]},"metadata":{"tags":[]},"execution_count":27}]},{"cell_type":"code","metadata":{"id":"1qI4cEd81xxK","colab":{"base_uri":"https://localhost:8080/","height":197},"executionInfo":{"status":"ok","timestamp":1615296574079,"user_tz":-300,"elapsed":474,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"7e62a1d9-dc06-4fc5-8270-6da0236d7341"},"source":["d.target = d.target.map(targets) # заменим цифровые обозначения классов на буквенные подписи\n","d.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal_length_(cm)sepal_width_(cm)petal_length_(cm)petal_width_(cm)target
05.13.51.40.2setosa
14.93.01.40.2setosa
24.73.21.30.2setosa
34.63.11.50.2setosa
45.03.61.40.2setosa
\n","
"],"text/plain":[" sepal_length_(cm) sepal_width_(cm) ... petal_width_(cm) target\n","0 5.1 3.5 ... 0.2 setosa\n","1 4.9 3.0 ... 0.2 setosa\n","2 4.7 3.2 ... 0.2 setosa\n","3 4.6 3.1 ... 0.2 setosa\n","4 5.0 3.6 ... 0.2 setosa\n","\n","[5 rows x 5 columns]"]},"metadata":{"tags":[]},"execution_count":28}]},{"cell_type":"code","metadata":{"id":"q1W6kwXe1xuc","colab":{"base_uri":"https://localhost:8080/","height":197},"executionInfo":{"status":"ok","timestamp":1615296637939,"user_tz":-300,"elapsed":647,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"cbd628aa-1e1b-4a98-e5f5-b9ef80aa9544"},"source":["d['sepal_length_on_width'] = d['sepal_length_(cm)'] / d['sepal_width_(cm)'] # операции непосредственно со столбцами много быстрее поэлементных операций \n","d.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal_length_(cm)sepal_width_(cm)petal_length_(cm)petal_width_(cm)targetsepal_length_on_width
05.13.51.40.2setosa1.457143
14.93.01.40.2setosa1.633333
24.73.21.30.2setosa1.468750
34.63.11.50.2setosa1.483871
45.03.61.40.2setosa1.388889
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"],"text/plain":[" sepal_length_(cm) sepal_width_(cm) ... target sepal_length_on_width\n","0 5.1 3.5 ... setosa 1.457143\n","1 4.9 3.0 ... setosa 1.633333\n","2 4.7 3.2 ... setosa 1.468750\n","3 4.6 3.1 ... setosa 1.483871\n","4 5.0 3.6 ... setosa 1.388889\n","\n","[5 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":31}]},{"cell_type":"code","metadata":{"id":"dRp4-vhV1xmt"},"source":["d.sepal_length_on_width = d.sepal_length_on_width.apply(np.sin)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"357_A4ny1xjb","colab":{"base_uri":"https://localhost:8080/","height":197},"executionInfo":{"status":"ok","timestamp":1615296813029,"user_tz":-300,"elapsed":767,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"196d0d5d-1883-4552-ec7c-890f592130de"},"source":["def bias(x):\n"," if x < 1.0:\n"," return 0\n"," return 1\n","d['petal_width_(cm)'] = d['petal_width_(cm)'].apply(bias)\n","d.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal_length_(cm)sepal_width_(cm)petal_length_(cm)petal_width_(cm)targetsepal_length_on_width
05.13.51.40setosa0.993548
14.93.01.40setosa0.998045
24.73.21.30setosa0.994798
34.63.11.50setosa0.996224
45.03.61.40setosa0.983500
\n","
"],"text/plain":[" sepal_length_(cm) sepal_width_(cm) ... target sepal_length_on_width\n","0 5.1 3.5 ... setosa 0.993548\n","1 4.9 3.0 ... setosa 0.998045\n","2 4.7 3.2 ... setosa 0.994798\n","3 4.6 3.1 ... setosa 0.996224\n","4 5.0 3.6 ... setosa 0.983500\n","\n","[5 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":33}]},{"cell_type":"code","metadata":{"id":"aeUhqZEX1xey"},"source":["d.drop([column for column in d.columns if column.endswith('length_(cm)')], axis=1, inplace=True)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"gQJ6De486fsw","colab":{"base_uri":"https://localhost:8080/","height":197},"executionInfo":{"status":"ok","timestamp":1615296912439,"user_tz":-300,"elapsed":684,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"f39caff3-2866-4a3b-b6ac-8510ddad127f"},"source":["d.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal_width_(cm)petal_width_(cm)targetsepal_length_on_width
03.50setosa0.993548
13.00setosa0.998045
23.20setosa0.994798
33.10setosa0.996224
43.60setosa0.983500
\n","
"],"text/plain":[" sepal_width_(cm) petal_width_(cm) target sepal_length_on_width\n","0 3.5 0 setosa 0.993548\n","1 3.0 0 setosa 0.998045\n","2 3.2 0 setosa 0.994798\n","3 3.1 0 setosa 0.996224\n","4 3.6 0 setosa 0.983500"]},"metadata":{"tags":[]},"execution_count":36}]},{"cell_type":"code","metadata":{"id":"H6wlNTB76hoP","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1615296981297,"user_tz":-300,"elapsed":589,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"ef4c98c9-53fc-403d-e5ad-91b96ab8f864"},"source":["f = pd.concat([d,d], axis=0)\n","d.shape, f.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((150, 4), (300, 4))"]},"metadata":{"tags":[]},"execution_count":37}]},{"cell_type":"code","metadata":{"id":"8wvhQgCh6stP","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1615297019618,"user_tz":-300,"elapsed":572,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"ed87bb36-d869-49c6-cc1a-516cd9daa65b"},"source":["f = pd.concat([d,d], axis=1)\n","d.shape, f.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((150, 4), (150, 8))"]},"metadata":{"tags":[]},"execution_count":38}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"hYXfdNRds8wc","executionInfo":{"status":"ok","timestamp":1632405950884,"user_tz":-300,"elapsed":476,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"2b21ce4b-5d17-4800-ea16-396dc95557c3"},"source":["df_1 = pd.DataFrame(index=[1,2,3], data=[[1,2],[1,2],[1,2]], columns=[4,5])\n","df_2 = pd.DataFrame(index=[5,6,3], data=[[1,2],[1,2],[1,2]], columns=[5,7])\n","df_1"],"execution_count":27,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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45
112
212
312
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"],"text/plain":[" 4 5\n","1 1 2\n","2 1 2\n","3 1 2"]},"metadata":{},"execution_count":27}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"-bALOOiOs_xk","executionInfo":{"status":"ok","timestamp":1632405952831,"user_tz":-300,"elapsed":12,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"56478aab-30e8-477d-8628-2352f3ed3ac4"},"source":["df_2"],"execution_count":28,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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57
512
612
312
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"],"text/plain":[" 5 7\n","5 1 2\n","6 1 2\n","3 1 2"]},"metadata":{},"execution_count":28}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":237},"id":"nspfyfjMUepW","executionInfo":{"status":"ok","timestamp":1632405958777,"user_tz":-300,"elapsed":400,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"946cd0f5-3470-4620-a1ea-52221f5a06b1"},"source":["df_1 = pd.DataFrame(index=[1,2,3], data=[[1,2],[1,2],[1,2]], columns=[4,5])\n","df_2 = pd.DataFrame(index=[5,6,3], data=[[1,2],[1,2],[1,2]], columns=[5,7])\n","\n","pd.concat([df_1,df_2], axis=0)"],"execution_count":29,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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457
11.02NaN
21.02NaN
31.02NaN
5NaN12.0
6NaN12.0
3NaN12.0
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"],"text/plain":[" 4 5 7\n","1 1.0 2 NaN\n","2 1.0 2 NaN\n","3 1.0 2 NaN\n","5 NaN 1 2.0\n","6 NaN 1 2.0\n","3 NaN 1 2.0"]},"metadata":{},"execution_count":29}]},{"cell_type":"code","metadata":{"id":"hfsafxqc6wl0","colab":{"base_uri":"https://localhost:8080/","height":217},"executionInfo":{"status":"ok","timestamp":1615297123302,"user_tz":-300,"elapsed":594,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"80ab6214-48dd-4847-9637-c8eda376ce2b"},"source":["f.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal_width_(cm)petal_width_(cm)targetsepal_length_on_widthsepal_width_(cm)petal_width_(cm)targetsepal_length_on_width
03.50setosa0.9935483.50setosa0.993548
13.00setosa0.9980453.00setosa0.998045
23.20setosa0.9947983.20setosa0.994798
33.10setosa0.9962243.10setosa0.996224
43.60setosa0.9835003.60setosa0.983500
\n","
"],"text/plain":[" sepal_width_(cm) petal_width_(cm) ... target sepal_length_on_width\n","0 3.5 0 ... setosa 0.993548\n","1 3.0 0 ... setosa 0.998045\n","2 3.2 0 ... setosa 0.994798\n","3 3.1 0 ... setosa 0.996224\n","4 3.6 0 ... setosa 0.983500\n","\n","[5 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":44}]},{"cell_type":"code","metadata":{"id":"HRY-rDbb8gGk"},"source":["g = d.drop(['sepal_width_(cm)', 'petal_width_(cm)'], axis=1)\n","h = d.drop(['sepal_length_on_width'], axis=1)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"tsgVE2Si8oFG","colab":{"base_uri":"https://localhost:8080/","height":197},"executionInfo":{"status":"ok","timestamp":1615297139175,"user_tz":-300,"elapsed":429,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"dea93f74-7d0d-4030-c81c-84ea655c5f6d"},"source":["g.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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targetsepal_length_on_width
0setosa0.993548
1setosa0.998045
2setosa0.994798
3setosa0.996224
4setosa0.983500
\n","
"],"text/plain":[" target sepal_length_on_width\n","0 setosa 0.993548\n","1 setosa 0.998045\n","2 setosa 0.994798\n","3 setosa 0.996224\n","4 setosa 0.983500"]},"metadata":{"tags":[]},"execution_count":46}]},{"cell_type":"code","metadata":{"id":"kny_HFf489cy","colab":{"base_uri":"https://localhost:8080/","height":197},"executionInfo":{"status":"ok","timestamp":1615297148886,"user_tz":-300,"elapsed":628,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"59ae8694-c22e-4118-e25f-f31a2e148c4e"},"source":["h.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal_width_(cm)petal_width_(cm)target
03.50setosa
13.00setosa
23.20setosa
33.10setosa
43.60setosa
\n","
"],"text/plain":[" sepal_width_(cm) petal_width_(cm) target\n","0 3.5 0 setosa\n","1 3.0 0 setosa\n","2 3.2 0 setosa\n","3 3.1 0 setosa\n","4 3.6 0 setosa"]},"metadata":{"tags":[]},"execution_count":47}]},{"cell_type":"code","metadata":{"id":"ZAKyHnni8_wx","colab":{"base_uri":"https://localhost:8080/","height":197},"executionInfo":{"status":"ok","timestamp":1615297241757,"user_tz":-300,"elapsed":588,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"cc83133f-1f83-4c7a-f041-b23d01f14cf4"},"source":["d = g.merge(h, on='target')\n","d.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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targetsepal_length_on_widthsepal_width_(cm)petal_width_(cm)
0setosa0.9935483.50
1setosa0.9935483.00
2setosa0.9935483.20
3setosa0.9935483.10
4setosa0.9935483.60
\n","
"],"text/plain":[" target sepal_length_on_width sepal_width_(cm) petal_width_(cm)\n","0 setosa 0.993548 3.5 0\n","1 setosa 0.993548 3.0 0\n","2 setosa 0.993548 3.2 0\n","3 setosa 0.993548 3.1 0\n","4 setosa 0.993548 3.6 0"]},"metadata":{"tags":[]},"execution_count":49}]},{"cell_type":"code","metadata":{"id":"m6ec0Exh9K8V","colab":{"base_uri":"https://localhost:8080/","height":424},"executionInfo":{"status":"ok","timestamp":1614767389654,"user_tz":-300,"elapsed":712,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"1c97b950-0ba5-4b63-f8b7-2560f8decceb"},"source":["pd.get_dummies(d.target)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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setosaversicolorvirginica
0100
1100
2100
3100
4100
............
7495001
7496001
7497001
7498001
7499001
\n","

7500 rows × 3 columns

\n","
"],"text/plain":[" setosa versicolor virginica\n","0 1 0 0\n","1 1 0 0\n","2 1 0 0\n","3 1 0 0\n","4 1 0 0\n","... ... ... ...\n","7495 0 0 1\n","7496 0 0 1\n","7497 0 0 1\n","7498 0 0 1\n","7499 0 0 1\n","\n","[7500 rows x 3 columns]"]},"metadata":{"tags":[]},"execution_count":46}]},{"cell_type":"code","metadata":{"id":"Hrp_HGEb9t4d","colab":{"base_uri":"https://localhost:8080/","height":197},"executionInfo":{"status":"ok","timestamp":1615297478580,"user_tz":-300,"elapsed":440,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"b3b9983b-e598-4288-90ee-7b0d1abe5ff8"},"source":["d = pd.get_dummies(data=d, columns=['target'])\n","d.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal_length_on_widthsepal_width_(cm)petal_width_(cm)target_setosatarget_versicolortarget_virginica
00.9935483.50100
10.9935483.00100
20.9935483.20100
30.9935483.10100
40.9935483.60100
\n","
"],"text/plain":[" sepal_length_on_width sepal_width_(cm) ... target_versicolor target_virginica\n","0 0.993548 3.5 ... 0 0\n","1 0.993548 3.0 ... 0 0\n","2 0.993548 3.2 ... 0 0\n","3 0.993548 3.1 ... 0 0\n","4 0.993548 3.6 ... 0 0\n","\n","[5 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":50}]},{"cell_type":"markdown","metadata":{"id":"Ym2h89BMguk6"},"source":["### Графическое представление"]},{"cell_type":"code","metadata":{"id":"EB8GRu9XxNaZ"},"source":["%matplotlib inline\n","import seaborn as sns\n","from matplotlib import pyplot as plt"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"hbipgoEZxNOg"},"source":["sns.set_style(\"whitegrid\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"VlMb-EWdxNMn","colab":{"base_uri":"https://localhost:8080/","height":122},"executionInfo":{"status":"ok","timestamp":1614779517504,"user_tz":-300,"elapsed":587,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"9907624b-bf04-4f40-f152-94951d92a782"},"source":["print(sns.color_palette())\n","sns.palplot(sns.color_palette())"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[(0.12156862745098039, 0.4666666666666667, 0.7058823529411765), (1.0, 0.4980392156862745, 0.054901960784313725), (0.17254901960784313, 0.6274509803921569, 0.17254901960784313), (0.8392156862745098, 0.15294117647058825, 0.1568627450980392), (0.5803921568627451, 0.403921568627451, 0.7411764705882353), (0.5490196078431373, 0.33725490196078434, 0.29411764705882354), (0.8901960784313725, 0.4666666666666667, 0.7607843137254902), (0.4980392156862745, 0.4980392156862745, 0.4980392156862745), (0.7372549019607844, 0.7411764705882353, 0.13333333333333333), (0.09019607843137255, 0.7450980392156863, 0.8117647058823529)]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"4umRGJuKqHuO","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1615297767532,"user_tz":-300,"elapsed":622,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"49a1d76f-c4ba-4088-817f-e1bdce211bdc"},"source":["targets = {float(i):target for i, target in enumerate(ds.target_names)}\n","targets"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{0.0: 'setosa', 1.0: 'versicolor', 2.0: 'virginica'}"]},"metadata":{"tags":[]},"execution_count":54}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"SDeuDnTEXKQk","executionInfo":{"status":"ok","timestamp":1615297774179,"user_tz":-300,"elapsed":456,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"53cf3a73-56d9-42cc-f715-9dee1f23fd15"},"source":["df[df.target==1]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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sepal_length_(cm)sepal_width_(cm)petal_length_(cm)petal_width_(cm)target
507.03.24.71.41
516.43.24.51.51
526.93.14.91.51
535.52.34.01.31
546.52.84.61.51
555.72.84.51.31
566.33.34.71.61
574.92.43.31.01
586.62.94.61.31
595.22.73.91.41
605.02.03.51.01
615.93.04.21.51
626.02.24.01.01
636.12.94.71.41
645.62.93.61.31
656.73.14.41.41
665.63.04.51.51
675.82.74.11.01
686.22.24.51.51
695.62.53.91.11
705.93.24.81.81
716.12.84.01.31
726.32.54.91.51
736.12.84.71.21
746.42.94.31.31
756.63.04.41.41
766.82.84.81.41
776.73.05.01.71
786.02.94.51.51
795.72.63.51.01
805.52.43.81.11
815.52.43.71.01
825.82.73.91.21
836.02.75.11.61
845.43.04.51.51
856.03.44.51.61
866.73.14.71.51
876.32.34.41.31
885.63.04.11.31
895.52.54.01.31
905.52.64.41.21
916.13.04.61.41
925.82.64.01.21
935.02.33.31.01
945.62.74.21.31
955.73.04.21.21
965.72.94.21.31
976.22.94.31.31
985.12.53.01.11
995.72.84.11.31
\n","
"],"text/plain":[" sepal_length_(cm) sepal_width_(cm) ... petal_width_(cm) target\n","50 7.0 3.2 ... 1.4 1\n","51 6.4 3.2 ... 1.5 1\n","52 6.9 3.1 ... 1.5 1\n","53 5.5 2.3 ... 1.3 1\n","54 6.5 2.8 ... 1.5 1\n","55 5.7 2.8 ... 1.3 1\n","56 6.3 3.3 ... 1.6 1\n","57 4.9 2.4 ... 1.0 1\n","58 6.6 2.9 ... 1.3 1\n","59 5.2 2.7 ... 1.4 1\n","60 5.0 2.0 ... 1.0 1\n","61 5.9 3.0 ... 1.5 1\n","62 6.0 2.2 ... 1.0 1\n","63 6.1 2.9 ... 1.4 1\n","64 5.6 2.9 ... 1.3 1\n","65 6.7 3.1 ... 1.4 1\n","66 5.6 3.0 ... 1.5 1\n","67 5.8 2.7 ... 1.0 1\n","68 6.2 2.2 ... 1.5 1\n","69 5.6 2.5 ... 1.1 1\n","70 5.9 3.2 ... 1.8 1\n","71 6.1 2.8 ... 1.3 1\n","72 6.3 2.5 ... 1.5 1\n","73 6.1 2.8 ... 1.2 1\n","74 6.4 2.9 ... 1.3 1\n","75 6.6 3.0 ... 1.4 1\n","76 6.8 2.8 ... 1.4 1\n","77 6.7 3.0 ... 1.7 1\n","78 6.0 2.9 ... 1.5 1\n","79 5.7 2.6 ... 1.0 1\n","80 5.5 2.4 ... 1.1 1\n","81 5.5 2.4 ... 1.0 1\n","82 5.8 2.7 ... 1.2 1\n","83 6.0 2.7 ... 1.6 1\n","84 5.4 3.0 ... 1.5 1\n","85 6.0 3.4 ... 1.6 1\n","86 6.7 3.1 ... 1.5 1\n","87 6.3 2.3 ... 1.3 1\n","88 5.6 3.0 ... 1.3 1\n","89 5.5 2.5 ... 1.3 1\n","90 5.5 2.6 ... 1.2 1\n","91 6.1 3.0 ... 1.4 1\n","92 5.8 2.6 ... 1.2 1\n","93 5.0 2.3 ... 1.0 1\n","94 5.6 2.7 ... 1.3 1\n","95 5.7 3.0 ... 1.2 1\n","96 5.7 2.9 ... 1.3 1\n","97 6.2 2.9 ... 1.3 1\n","98 5.1 2.5 ... 1.1 1\n","99 5.7 2.8 ... 1.3 1\n","\n","[50 rows x 5 columns]"]},"metadata":{"tags":[]},"execution_count":55}]},{"cell_type":"markdown","metadata":{"id":"Rg_HMRSVzGz-"},"source":["Строим гистограммы"]},{"cell_type":"code","metadata":{"id":"mx_PNSF8xNKe","colab":{"base_uri":"https://localhost:8080/","height":406},"executionInfo":{"status":"ok","timestamp":1615297826988,"user_tz":-300,"elapsed":1244,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"5d46e25e-fb29-467c-d88f-b3b689306815"},"source":["for target in targets:\n"," sns.distplot(df[df.target==target]['sepal_length_(cm)'],kde=True,kde_kws={\"label\":targets[target]})"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n"," warnings.warn(msg, FutureWarning)\n","/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n"," warnings.warn(msg, FutureWarning)\n","/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n"," warnings.warn(msg, FutureWarning)\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"bNUuVXgzhvz1","colab":{"base_uri":"https://localhost:8080/","height":406},"executionInfo":{"status":"ok","timestamp":1615297848522,"user_tz":-300,"elapsed":1136,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"7ef13877-988b-4be0-a9b2-b4983762d161"},"source":["for target in targets:\n"," sns.distplot(df[df.target==target]['sepal_width_(cm)'],kde=True,kde_kws={\"label\":targets[target]})"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n"," warnings.warn(msg, FutureWarning)\n","/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n"," warnings.warn(msg, FutureWarning)\n","/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n"," warnings.warn(msg, FutureWarning)\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"6Li1iREOhvts","colab":{"base_uri":"https://localhost:8080/","height":408},"executionInfo":{"status":"ok","timestamp":1615297853838,"user_tz":-300,"elapsed":923,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"56d8c257-b464-465f-c365-a0dbc90b03b6"},"source":["for target in targets:\n"," sns.distplot(df[df.target==target]['petal_length_(cm)'],kde=True,kde_kws={\"label\":targets[target]})"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n"," warnings.warn(msg, FutureWarning)\n","/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n"," warnings.warn(msg, FutureWarning)\n","/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n"," warnings.warn(msg, FutureWarning)\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"oCQEu59thvri","colab":{"base_uri":"https://localhost:8080/","height":404},"executionInfo":{"status":"ok","timestamp":1614779712345,"user_tz":-300,"elapsed":1286,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"1e523154-41f1-4e2c-ca8e-0aebf1cee232"},"source":["for target in targets:\n"," sns.distplot(df[df.target==target]['petal_width_(cm)'],kde=True,kde_kws={\"label\":targets[target]})"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n"," warnings.warn(msg, FutureWarning)\n","/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n"," warnings.warn(msg, FutureWarning)\n","/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n"," warnings.warn(msg, FutureWarning)\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"48op7eXwzKb4"},"source":["Строим точечные графики взаимного влияния параметров"]},{"cell_type":"code","metadata":{"id":"hKh-KV27whqi","colab":{"base_uri":"https://localhost:8080/","height":225},"executionInfo":{"status":"ok","timestamp":1614767506092,"user_tz":-300,"elapsed":1461,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"b6d7f703-3029-4c58-e075-ba67e8307bcf"},"source":["g = sns.FacetGrid(df, hue='target')\n","g.map(plt.scatter, 'sepal_length_(cm)', 'sepal_width_(cm)');\n","g.add_legend();"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"ArJjRTF6ySuO","colab":{"base_uri":"https://localhost:8080/","height":225},"executionInfo":{"status":"ok","timestamp":1614767511831,"user_tz":-300,"elapsed":1523,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"a857d46d-33d9-417c-8a78-e3b3cf5dcbd9"},"source":["g = sns.FacetGrid(df, hue='target')\n","g.map(plt.scatter, 'petal_length_(cm)', 'petal_width_(cm)');\n","g.add_legend();"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"5KszRYQ0yaaV","colab":{"base_uri":"https://localhost:8080/","height":225},"executionInfo":{"status":"ok","timestamp":1614767516166,"user_tz":-300,"elapsed":1157,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"ffcba165-ccb3-4f61-e7ae-c413017c2e8c"},"source":["g = sns.FacetGrid(df, hue='target')\n","g.map(plt.scatter, 'petal_length_(cm)', 'sepal_width_(cm)');\n","g.add_legend();"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"SE3c3sS0yfgl","colab":{"base_uri":"https://localhost:8080/","height":225},"executionInfo":{"status":"ok","timestamp":1614767522475,"user_tz":-300,"elapsed":1668,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"ee44eed9-08f4-495a-e122-a0910757f984"},"source":["g = sns.FacetGrid(df, hue='target')\n","g.map(plt.scatter, 'sepal_length_(cm)', 'petal_width_(cm)');\n","g.add_legend();"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"dnoGFA4MzW9o"},"source":["Можно все предыдущие графики вывести одной строчкой кода"]},{"cell_type":"code","metadata":{"id":"izSb9tJThvhk","colab":{"base_uri":"https://localhost:8080/","height":743},"executionInfo":{"status":"ok","timestamp":1614779829439,"user_tz":-300,"elapsed":12239,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"0a4d8076-27df-4520-bac8-004f756b4670"},"source":["sns.pairplot(df,hue='target',diag_kind=\"kde\",kind=\"scatter\",palette=\"husl\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":69},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"MOtqb-wJhvfD","colab":{"base_uri":"https://localhost:8080/","height":296},"executionInfo":{"status":"ok","timestamp":1614767558267,"user_tz":-300,"elapsed":714,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"bc730c65-b8f4-4417-d1f5-68fcc327dbcb"},"source":["sns.boxplot(x=\"target\", y=\"sepal_length_(cm)\", data=df)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":62},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"pop0xJy808kv","colab":{"base_uri":"https://localhost:8080/","height":299},"executionInfo":{"status":"ok","timestamp":1614767566285,"user_tz":-300,"elapsed":648,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"0c951aff-d432-4cab-ba5c-fd913d8256c9"},"source":["sns.boxplot(x=\"target\", y=\"sepal_width_(cm)\", data=df)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":63},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"zL6zrC0108t4","colab":{"base_uri":"https://localhost:8080/","height":296},"executionInfo":{"status":"ok","timestamp":1614767569562,"user_tz":-300,"elapsed":1021,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"41a4f4ed-c685-4b02-adec-77ede4cf6761"},"source":["sns.boxplot(x=\"target\", y=\"petal_length_(cm)\", data=df)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":64},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"gt-CS-v80841","colab":{"base_uri":"https://localhost:8080/","height":296},"executionInfo":{"status":"ok","timestamp":1614767574695,"user_tz":-300,"elapsed":672,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"17e5b682-a469-43a9-fe36-3ad50c2b4449"},"source":["sns.boxplot(x=\"target\", y=\"petal_width_(cm)\", data=df)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":65},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[]}}]}]} \ No newline at end of file +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "qCUpgW4Chxlt" + }, + "source": [ + "# Игрушечные наборы данных\n", + "https://scikit-learn.org/stable/datasets/index.html" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "executionInfo": { + "elapsed": 867, + "status": "ok", + "timestamp": 1632403984813, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "6-e8Ub9ghvMA" + }, + "outputs": [], + "source": [ + "import sklearn.datasets as sets\n", + "datasets = {0:'boston', 1:'iris', 2:'diabets', 3:'digits', 4:'linnerud', 5:'wine', 6:'cancer', 7:'olivetti_faces', 8:'20_newsgroups',\n", + " 9:'20_newsgroups_vec', 10:'people_labeled_faces', 11:'pairs_labeled_faces', 12:'covertype', 13:'RCV1_multilabel',\n", + " 14:'kddcup99', 15:'california_housing', }\n", + "choise = 1\n", + "if choise == 0:\n", + " ds = sets.load_boston() #regression\n", + "elif choise == 1:\n", + " ds = sets.load_iris() # classification\n", + "elif choise == 2:\n", + " ds = sets.load_diabetes() # regression\n", + "elif choise == 3:\n", + " ds = sets.load_digits() # classification\n", + "elif choise == 4:\n", + " ds = sets.load_linnerud() # multivariate regression\n", + "elif choise == 5:\n", + " ds = sets.load_wine() # classification\n", + "elif choise == 6:\n", + " ds = sets.load_breast_cancer() # classification\n", + "elif choise == 7:\n", + " ds = sets.fetch_olivetti_faces() # classification\n", + "elif choise == 8:\n", + " ds = sets.fetch_20newsgroups() # classification\n", + "elif choise == 9:\n", + " ds = sets.fetch_20newsgroups_vectorized() # classification\n", + "elif choise == 10:\n", + " ds = sets.fetch_lfw_people() # classification\n", + "elif choise == 11:\n", + " ds = sets.fetch_lfw_pairs() # classification\n", + "elif choise == 12:\n", + " ds = sets.fetch_covtype() # classification\n", + "elif choise == 13:\n", + " ds = sets.fetch_rcv1() # classification\n", + "elif choise == 14:\n", + " ds = sets.fetch_kddcup99() # classification\n", + "elif choise == 15:\n", + " ds = sets.fetch_california_housing() # regression" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 1064, + "status": "ok", + "timestamp": 1615295304765, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "rHDZmzjAiy7N", + "outputId": "160c86a8-b336-429a-b12b-52cf5bb6a14b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".. _iris_dataset:\n", + "\n", + "Iris plants dataset\n", + "--------------------\n", + "\n", + "**Data Set Characteristics:**\n", + "\n", + " :Number of Instances: 150 (50 in each of three classes)\n", + " :Number of Attributes: 4 numeric, predictive attributes and the class\n", + " :Attribute Information:\n", + " - sepal length in cm\n", + " - sepal width in cm\n", + " - petal length in cm\n", + " - petal width in cm\n", + " - class:\n", + " - Iris-Setosa\n", + " - Iris-Versicolour\n", + " - Iris-Virginica\n", + " \n", + " :Summary Statistics:\n", + "\n", + " ============== ==== ==== ======= ===== ====================\n", + " Min Max Mean SD Class Correlation\n", + " ============== ==== ==== ======= ===== ====================\n", + " sepal length: 4.3 7.9 5.84 0.83 0.7826\n", + " sepal width: 2.0 4.4 3.05 0.43 -0.4194\n", + " petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\n", + " petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\n", + " ============== ==== ==== ======= ===== ====================\n", + "\n", + " :Missing Attribute Values: None\n", + " :Class Distribution: 33.3% for each of 3 classes.\n", + " :Creator: R.A. Fisher\n", + " :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n", + " :Date: July, 1988\n", + "\n", + "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n", + "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n", + "Machine Learning Repository, which has two wrong data points.\n", + "\n", + "This is perhaps the best known database to be found in the\n", + "pattern recognition literature. Fisher's paper is a classic in the field and\n", + "is referenced frequently to this day. (See Duda & Hart, for example.) The\n", + "data set contains 3 classes of 50 instances each, where each class refers to a\n", + "type of iris plant. One class is linearly separable from the other 2; the\n", + "latter are NOT linearly separable from each other.\n", + "\n", + ".. topic:: References\n", + "\n", + " - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n", + " Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n", + " Mathematical Statistics\" (John Wiley, NY, 1950).\n", + " - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n", + " (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\n", + " - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n", + " Structure and Classification Rule for Recognition in Partially Exposed\n", + " Environments\". IEEE Transactions on Pattern Analysis and Machine\n", + " Intelligence, Vol. PAMI-2, No. 1, 67-71.\n", + " - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\". IEEE Transactions\n", + " on Information Theory, May 1972, 431-433.\n", + " - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al\"s AUTOCLASS II\n", + " conceptual clustering system finds 3 classes in the data.\n", + " - Many, many more ...\n" + ] + } + ], + "source": [ + "print(ds.DESCR)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 683, + "status": "ok", + "timestamp": 1632404056458, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "59mLor4WoeZg", + "outputId": "3548322c-6765-4349-8dea-66ab12f3f7d9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n", + "['setosa' 'versicolor' 'virginica']\n" + ] + } + ], + "source": [ + "print(ds.feature_names)\n", + "print(ds.target_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 420, + "status": "ok", + "timestamp": 1632404071563, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "9Yt4tJ2_otjm", + "outputId": "b471a124-b71b-456d-de41-fe29676b6604" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = ds.data\n", + "type(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 402, + "status": "ok", + "timestamp": 1632404086557, + "user": { + "displayName": "Александр Аксёнов", + 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выбор столбца по названию" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 619, + "status": "ok", + "timestamp": 1615295621844, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "rOBV0RUtHxLh", + "outputId": "2e25e363-6fd5-477f-9e38-afe8f91522ac" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.series.Series" + ] + }, + "execution_count": 14, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "type(df['sepal length (cm)'])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 523, + "status": "ok", + "timestamp": 1632404667952, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "sq2YmKFr5m-1", + "outputId": "e9f125e0-3f1f-4a4b-d39c-5e6091047c86" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n", + " 'petal width (cm)', 'target'],\n", + " dtype='object')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 1699, + "status": "ok", + "timestamp": 1614783884339, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "o5CI-Ha6P4AX", + "outputId": "ee350cf3-212a-4bdd-daf8-f0decfe313c0" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'petal length (cm)': 'petal_length_(cm)',\n", + " 'petal width (cm)': 'petal_width_(cm)',\n", + " 'sepal length (cm)': 'sepal_length_(cm)',\n", + " 'sepal width (cm)': 'sepal_width_(cm)',\n", + " 'target': 'target'}" + ] + }, + "execution_count": 97, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "{name : '_'.join(name.split(' ')) for name in df.columns}" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "executionInfo": { + "elapsed": 585, + "status": "ok", + "timestamp": 1632404857471, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "ztRKBaVlxM8d" + }, + "outputs": [], + "source": [ + "# df = df.rename(columns={name : '_'.join(name.split(' ')) for name in df.columns}) # смена имен столбцов\n", + "df.rename(columns={name : '_'.join(name.split(' ')) for name in df.columns}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 29, + "status": "ok", + "timestamp": 1632404863328, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "Bryqf6bCxNC5", + "outputId": "2fb81e40-0667-4c5b-9b50-4ba23010385b" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['sepal_length_(cm)', 'sepal_width_(cm)', 'petal_length_(cm)',\n", + " 'petal_width_(cm)', 'target'],\n", + " dtype='object')" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 438, + "status": "ok", + "timestamp": 1615295826923, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "uesXOV19QcNX", + "outputId": "6476924c-249d-4876-89be-920b127e125b" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 0\n", + "2 0\n", + "3 0\n", + "4 0\n", + " ..\n", + "145 2\n", + "146 2\n", + "147 2\n", + "148 2\n", + "149 2\n", + "Name: target, Length: 150, dtype: int64" + ] + }, + "execution_count": 20, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "df.target" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 566, + "status": "ok", + "timestamp": 1614777840378, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "J2il4fodbWLb", + "outputId": "b6d5c2a4-dc69-497d-997c-8127f174765a" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "140 2\n", + "141 2\n", + "142 2\n", + "143 2\n", + "144 2\n", + "145 2\n", + "146 2\n", + "147 2\n", + "148 2\n", + "149 2\n", + "Name: target, dtype: int64" + ] + }, + "execution_count": 22, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "df.target[-10:] # 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sepal_length_(cm)sepal_width_(cm)petal_length_(cm)petal_width_(cm)target
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sepal_length_(cm)sepal_width_(cm)petal_length_(cm)petal_width_(cm)target
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mean5.8433333.0573333.7580001.1993331.000000
std0.8280660.4358661.7652980.7622380.819232
min4.3000002.0000001.0000000.1000000.000000
25%5.1000002.8000001.6000000.3000000.000000
50%5.8000003.0000004.3500001.3000001.000000
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max7.9000004.4000006.9000002.5000002.000000
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" + ], + "text/plain": [ + " sepal_length_(cm) sepal_width_(cm) ... petal_width_(cm) target\n", + "count 150.000000 150.000000 ... 150.000000 150.000000\n", + "mean 5.843333 3.057333 ... 1.199333 1.000000\n", + "std 0.828066 0.435866 ... 0.762238 0.819232\n", + "min 4.300000 2.000000 ... 0.100000 0.000000\n", + "25% 5.100000 2.800000 ... 0.300000 0.000000\n", + "50% 5.800000 3.000000 ... 1.300000 1.000000\n", + "75% 6.400000 3.300000 ... 1.800000 2.000000\n", + "max 7.900000 4.400000 ... 2.500000 2.000000\n", + "\n", + "[8 rows x 5 columns]" + ] + }, + "execution_count": 19, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe() # статистическое описание набора данных" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 627, + "status": "ok", + "timestamp": 1614778091397, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "X4ykTpKtxNiG", + "outputId": "e62b683d-f476-4422-d691-774ead34e63f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 150 entries, 0 to 149\n", + "Data columns (total 5 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 sepal_length_(cm) 150 non-null float64\n", + " 1 sepal_width_(cm) 150 non-null float64\n", + " 2 petal_length_(cm) 150 non-null float64\n", + " 3 petal_width_(cm) 150 non-null float64\n", + " 4 target 150 non-null int64 \n", + "dtypes: float64(4), int64(1)\n", + "memory usage: 6.0 KB\n" + ] + } + ], + "source": [ + "df.info() # информация об индексах, пропусках в данных, типах данных и объеме оперативной памяти занимаемой данными" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 51, + "status": "ok", + "timestamp": 1632405484185, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "b7khmMfj8mDB", + "outputId": "8e7ccfa9-cffa-4872-c0a5-d00635211e12" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([0, 1, 2]), 3)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.target.unique(), df.target.nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 167 + }, + "executionInfo": { + "elapsed": 783, + "status": "ok", + "timestamp": 1615296303195, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "n1XzQbdFRx7Z", + "outputId": "a4acff70-40cf-4462-f2b6-03546318b29b" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sepal_length_(cm)sepal_width_(cm)petal_length_(cm)petal_width_(cm)
target
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sepal_length_(cm)sepal_width_(cm)petal_length_(cm)petal_width_(cm)
minmaxmeanstdsizeminmaxmeanstdsizeminmaxmeanstdsizeminmaxmeanstdsize
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14.97.05.9360.51617150.02.03.42.7700.31379850.03.05.14.2600.46991150.01.01.81.3260.19775350.0
24.97.96.5880.63588050.02.23.82.9740.32249750.04.56.95.5520.55189550.01.42.52.0260.27465050.0
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sepal_length_(cm)petal_width_(cm)
meanstdminmax
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" + ], + "text/plain": [ + " sepal_length_(cm) petal_width_(cm) \n", + " mean std min max\n", + "target \n", + "0 5.006 0.352490 0.1 0.6\n", + "1 5.936 0.516171 1.0 1.8\n", + "2 6.588 0.635880 1.4 2.5" + ] + }, + "execution_count": 30, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby('target').agg({'sepal_length_(cm)':[np.mean, np.std], 'petal_width_(cm)':[min, max]}) # индивидуальное применение функций группировки" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NCfoXnc41fmW" + }, + "source": [ + "### Полезные функции, которые конкретно сейчас не нужны, но часто применимы" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 197 + }, + "executionInfo": { + "elapsed": 747, + "status": "ok", + "timestamp": 1615296494311, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "KV8EM_b41m0m", + "outputId": "b898ccdb-16f0-415b-a629-25b794f42859" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sepal_length_(cm)sepal_width_(cm)petal_length_(cm)petal_width_(cm)target
05.13.51.40.2setosa
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24.73.21.30.2setosa
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sepal_length_(cm)sepal_width_(cm)petal_length_(cm)petal_width_(cm)targetsepal_length_on_width
05.13.51.40.2setosa1.457143
14.93.01.40.2setosa1.633333
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34.63.11.50.2setosa1.483871
45.03.61.40.2setosa1.388889
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sepal_length_(cm)sepal_width_(cm)petal_length_(cm)petal_width_(cm)targetsepal_length_on_width
05.13.51.40setosa0.993548
14.93.01.40setosa0.998045
24.73.21.30setosa0.994798
34.63.11.50setosa0.996224
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sepal_width_(cm)petal_width_(cm)targetsepal_length_on_width
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sepal_width_(cm)petal_width_(cm)targetsepal_length_on_widthsepal_width_(cm)petal_width_(cm)targetsepal_length_on_width
03.50setosa0.9935483.50setosa0.993548
13.00setosa0.9980453.00setosa0.998045
23.20setosa0.9947983.20setosa0.994798
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targetsepal_length_on_width
0setosa0.993548
1setosa0.998045
2setosa0.994798
3setosa0.996224
4setosa0.983500
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" + ], + "text/plain": [ + " target sepal_length_on_width\n", + "0 setosa 0.993548\n", + "1 setosa 0.998045\n", + "2 setosa 0.994798\n", + "3 setosa 0.996224\n", + "4 setosa 0.983500" + ] + }, + "execution_count": 46, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "g.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 197 + }, + "executionInfo": { + "elapsed": 628, + "status": "ok", + "timestamp": 1615297148886, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "kny_HFf489cy", + "outputId": "59ae8694-c22e-4118-e25f-f31a2e148c4e" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sepal_width_(cm)petal_width_(cm)target
03.50setosa
13.00setosa
23.20setosa
33.10setosa
43.60setosa
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" + ], + "text/plain": [ + " sepal_width_(cm) petal_width_(cm) target\n", + "0 3.5 0 setosa\n", + "1 3.0 0 setosa\n", + "2 3.2 0 setosa\n", + "3 3.1 0 setosa\n", + "4 3.6 0 setosa" + ] + }, + "execution_count": 47, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "h.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 197 + }, + "executionInfo": { + "elapsed": 588, + "status": "ok", + "timestamp": 1615297241757, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "ZAKyHnni8_wx", + "outputId": "cc83133f-1f83-4c7a-f041-b23d01f14cf4" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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targetsepal_length_on_widthsepal_width_(cm)petal_width_(cm)
0setosa0.9935483.50
1setosa0.9935483.00
2setosa0.9935483.20
3setosa0.9935483.10
4setosa0.9935483.60
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" + ], + "text/plain": [ + " target sepal_length_on_width sepal_width_(cm) petal_width_(cm)\n", + "0 setosa 0.993548 3.5 0\n", + "1 setosa 0.993548 3.0 0\n", + "2 setosa 0.993548 3.2 0\n", + "3 setosa 0.993548 3.1 0\n", + "4 setosa 0.993548 3.6 0" + ] + }, + "execution_count": 49, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "d = g.merge(h, on='target')\n", + "d.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + }, + "executionInfo": { + "elapsed": 712, + "status": "ok", + "timestamp": 1614767389654, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "m6ec0Exh9K8V", + "outputId": "1c97b950-0ba5-4b63-f8b7-2560f8decceb" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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setosaversicolorvirginica
0100
1100
2100
3100
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............
7495001
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7500 rows × 3 columns

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" + ], + "text/plain": [ + " setosa versicolor virginica\n", + "0 1 0 0\n", + "1 1 0 0\n", + "2 1 0 0\n", + "3 1 0 0\n", + "4 1 0 0\n", + "... ... ... ...\n", + "7495 0 0 1\n", + "7496 0 0 1\n", + "7497 0 0 1\n", + "7498 0 0 1\n", + "7499 0 0 1\n", + "\n", + "[7500 rows x 3 columns]" + ] + }, + "execution_count": 46, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "pd.get_dummies(d.target)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 197 + }, + "executionInfo": { + "elapsed": 440, + "status": "ok", + "timestamp": 1615297478580, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "Hrp_HGEb9t4d", + "outputId": "b3b9983b-e598-4288-90ee-7b0d1abe5ff8" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sepal_length_on_widthsepal_width_(cm)petal_width_(cm)target_setosatarget_versicolortarget_virginica
00.9935483.50100
10.9935483.00100
20.9935483.20100
30.9935483.10100
40.9935483.60100
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" + ], + "text/plain": [ + " sepal_length_on_width sepal_width_(cm) ... target_versicolor target_virginica\n", + "0 0.993548 3.5 ... 0 0\n", + "1 0.993548 3.0 ... 0 0\n", + "2 0.993548 3.2 ... 0 0\n", + "3 0.993548 3.1 ... 0 0\n", + "4 0.993548 3.6 ... 0 0\n", + "\n", + "[5 rows x 6 columns]" + ] + }, + "execution_count": 50, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "d = pd.get_dummies(data=d, columns=['target'])\n", + "d.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ym2h89BMguk6" + }, + "source": [ + "### Графическое представление" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "EB8GRu9XxNaZ" + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import seaborn as sns\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hbipgoEZxNOg" + }, + "outputs": [], + "source": [ + "sns.set_style(\"whitegrid\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 122 + }, + "executionInfo": { + "elapsed": 587, + "status": "ok", + "timestamp": 1614779517504, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "VlMb-EWdxNMn", + "outputId": "9907624b-bf04-4f40-f152-94951d92a782" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(0.12156862745098039, 0.4666666666666667, 0.7058823529411765), (1.0, 0.4980392156862745, 0.054901960784313725), (0.17254901960784313, 0.6274509803921569, 0.17254901960784313), (0.8392156862745098, 0.15294117647058825, 0.1568627450980392), (0.5803921568627451, 0.403921568627451, 0.7411764705882353), (0.5490196078431373, 0.33725490196078434, 0.29411764705882354), (0.8901960784313725, 0.4666666666666667, 0.7607843137254902), (0.4980392156862745, 0.4980392156862745, 0.4980392156862745), (0.7372549019607844, 0.7411764705882353, 0.13333333333333333), (0.09019607843137255, 0.7450980392156863, 0.8117647058823529)]\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "print(sns.color_palette())\n", + "sns.palplot(sns.color_palette())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 622, + "status": "ok", + "timestamp": 1615297767532, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "4umRGJuKqHuO", + "outputId": "49a1d76f-c4ba-4088-817f-e1bdce211bdc" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{0.0: 'setosa', 1.0: 'versicolor', 2.0: 'virginica'}" + ] + }, + "execution_count": 54, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "targets = {float(i):target for i, target in enumerate(ds.target_names)}\n", + "targets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "executionInfo": { + "elapsed": 456, + "status": "ok", + "timestamp": 1615297774179, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "SDeuDnTEXKQk", + "outputId": "53cf3a73-56d9-42cc-f715-9dee1f23fd15" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sepal_length_(cm)sepal_width_(cm)petal_length_(cm)petal_width_(cm)target
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976.22.94.31.31
985.12.53.01.11
995.72.84.11.31
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" + ], + "text/plain": [ + " sepal_length_(cm) sepal_width_(cm) ... petal_width_(cm) target\n", + "50 7.0 3.2 ... 1.4 1\n", + "51 6.4 3.2 ... 1.5 1\n", + "52 6.9 3.1 ... 1.5 1\n", + "53 5.5 2.3 ... 1.3 1\n", + "54 6.5 2.8 ... 1.5 1\n", + "55 5.7 2.8 ... 1.3 1\n", + "56 6.3 3.3 ... 1.6 1\n", + "57 4.9 2.4 ... 1.0 1\n", + "58 6.6 2.9 ... 1.3 1\n", + "59 5.2 2.7 ... 1.4 1\n", + "60 5.0 2.0 ... 1.0 1\n", + "61 5.9 3.0 ... 1.5 1\n", + "62 6.0 2.2 ... 1.0 1\n", + "63 6.1 2.9 ... 1.4 1\n", + "64 5.6 2.9 ... 1.3 1\n", + "65 6.7 3.1 ... 1.4 1\n", + "66 5.6 3.0 ... 1.5 1\n", + "67 5.8 2.7 ... 1.0 1\n", + "68 6.2 2.2 ... 1.5 1\n", + "69 5.6 2.5 ... 1.1 1\n", + "70 5.9 3.2 ... 1.8 1\n", + "71 6.1 2.8 ... 1.3 1\n", + "72 6.3 2.5 ... 1.5 1\n", + "73 6.1 2.8 ... 1.2 1\n", + "74 6.4 2.9 ... 1.3 1\n", + "75 6.6 3.0 ... 1.4 1\n", + "76 6.8 2.8 ... 1.4 1\n", + "77 6.7 3.0 ... 1.7 1\n", + "78 6.0 2.9 ... 1.5 1\n", + "79 5.7 2.6 ... 1.0 1\n", + "80 5.5 2.4 ... 1.1 1\n", + "81 5.5 2.4 ... 1.0 1\n", + "82 5.8 2.7 ... 1.2 1\n", + "83 6.0 2.7 ... 1.6 1\n", + "84 5.4 3.0 ... 1.5 1\n", + "85 6.0 3.4 ... 1.6 1\n", + "86 6.7 3.1 ... 1.5 1\n", + "87 6.3 2.3 ... 1.3 1\n", + "88 5.6 3.0 ... 1.3 1\n", + "89 5.5 2.5 ... 1.3 1\n", + "90 5.5 2.6 ... 1.2 1\n", + "91 6.1 3.0 ... 1.4 1\n", + "92 5.8 2.6 ... 1.2 1\n", + "93 5.0 2.3 ... 1.0 1\n", + "94 5.6 2.7 ... 1.3 1\n", + "95 5.7 3.0 ... 1.2 1\n", + "96 5.7 2.9 ... 1.3 1\n", + "97 6.2 2.9 ... 1.3 1\n", + "98 5.1 2.5 ... 1.1 1\n", + "99 5.7 2.8 ... 1.3 1\n", + "\n", + "[50 rows x 5 columns]" + ] + }, + "execution_count": 55, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "df[df.target==1]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Rg_HMRSVzGz-" + }, + "source": [ + "Строим гистограммы" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 406 + }, + "executionInfo": { + "elapsed": 1244, + "status": "ok", + "timestamp": 1615297826988, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "mx_PNSF8xNKe", + "outputId": "5d46e25e-fb29-467c-d88f-b3b689306815" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n", + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n", + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "for target in targets:\n", + " sns.distplot(df[df.target==target]['sepal_length_(cm)'],kde=True,kde_kws={\"label\":targets[target]})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 406 + }, + "executionInfo": { + "elapsed": 1136, + "status": "ok", + "timestamp": 1615297848522, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "bNUuVXgzhvz1", + "outputId": "7ef13877-988b-4be0-a9b2-b4983762d161" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n", + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n", + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "for target in targets:\n", + " sns.distplot(df[df.target==target]['sepal_width_(cm)'],kde=True,kde_kws={\"label\":targets[target]})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 408 + }, + "executionInfo": { + "elapsed": 923, + "status": "ok", + "timestamp": 1615297853838, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "6Li1iREOhvts", + "outputId": "56d8c257-b464-465f-c365-a0dbc90b03b6" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n", + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n", + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "for target in targets:\n", + " sns.distplot(df[df.target==target]['petal_length_(cm)'],kde=True,kde_kws={\"label\":targets[target]})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 404 + }, + "executionInfo": { + "elapsed": 1286, + "status": "ok", + "timestamp": 1614779712345, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "oCQEu59thvri", + "outputId": "1e523154-41f1-4e2c-ca8e-0aebf1cee232" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n", + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n", + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "for target in targets:\n", + " sns.distplot(df[df.target==target]['petal_width_(cm)'],kde=True,kde_kws={\"label\":targets[target]})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "48op7eXwzKb4" + }, + "source": [ + "Строим точечные графики взаимного влияния параметров" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 225 + }, + "executionInfo": { + "elapsed": 1461, + "status": "ok", + "timestamp": 1614767506092, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "hKh-KV27whqi", + "outputId": "b6d7f703-3029-4c58-e075-ba67e8307bcf" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.FacetGrid(df, hue='target')\n", + "g.map(plt.scatter, 'sepal_length_(cm)', 'sepal_width_(cm)');\n", + "g.add_legend();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 225 + }, + "executionInfo": { + "elapsed": 1523, + "status": "ok", + "timestamp": 1614767511831, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "ArJjRTF6ySuO", + "outputId": "a857d46d-33d9-417c-8a78-e3b3cf5dcbd9" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.FacetGrid(df, hue='target')\n", + "g.map(plt.scatter, 'petal_length_(cm)', 'petal_width_(cm)');\n", + "g.add_legend();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 225 + }, + "executionInfo": { + "elapsed": 1157, + "status": "ok", + "timestamp": 1614767516166, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "5KszRYQ0yaaV", + "outputId": "ffcba165-ccb3-4f61-e7ae-c413017c2e8c" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.FacetGrid(df, hue='target')\n", + "g.map(plt.scatter, 'petal_length_(cm)', 'sepal_width_(cm)');\n", + "g.add_legend();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 225 + }, + "executionInfo": { + "elapsed": 1668, + "status": "ok", + "timestamp": 1614767522475, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "SE3c3sS0yfgl", + "outputId": "ee44eed9-08f4-495a-e122-a0910757f984" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.FacetGrid(df, hue='target')\n", + "g.map(plt.scatter, 'sepal_length_(cm)', 'petal_width_(cm)');\n", + "g.add_legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dnoGFA4MzW9o" + }, + "source": [ + "Можно все предыдущие графики вывести одной строчкой кода" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 743 + }, + "executionInfo": { + "elapsed": 12239, + "status": "ok", + "timestamp": 1614779829439, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "izSb9tJThvhk", + "outputId": "0a4d8076-27df-4520-bac8-004f756b4670" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 69, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.boxplot(x=\"target\", y=\"petal_width_(cm)\", data=df)" + ] + } + ], + "metadata": { + "colab": { + "authorship_tag": "ABX9TyPGZA72+5Brg/wHtKFk27jK", + "collapsed_sections": [], + "name": "01_Pandas.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 29da91805833b7a23cd3305bc318b6f052e915bd Mon Sep 17 00:00:00 2001 From: ooonush Date: Sat, 12 Mar 2022 10:44:29 +0500 Subject: [PATCH 3/5] =?UTF-8?q?pd=20=D0=B4=D0=B7?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Pandas (06.03)/Pandas. Task. Part 1.ipynb | 914 +++++++++++++++++++++- 1 file changed, 913 insertions(+), 1 deletion(-) diff --git a/Pandas (06.03)/Pandas. Task. Part 1.ipynb b/Pandas (06.03)/Pandas. Task. Part 1.ipynb index 5172e85..3760429 100644 --- a/Pandas (06.03)/Pandas. Task. Part 1.ipynb +++ b/Pandas (06.03)/Pandas. Task. Part 1.ipynb @@ -1 +1,913 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"anaconda-cloud":{},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.6"},"colab":{"name":"01_task_pandas.ipynb","provenance":[],"collapsed_sections":[]}},"cells":[{"cell_type":"markdown","metadata":{"id":"UTKVH3sMutTM"},"source":["**В задании предлагается с помощью Pandas ответить на несколько вопросов по данным репозитория UCI [Adult](https://archive.ics.uci.edu/ml/datasets/Adult)**"]},{"cell_type":"markdown","metadata":{"id":"3lUT-CqYutTO"},"source":["Уникальные значения признаков (больше информации по ссылке выше):\n","- age: continuous.\n","- workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.\n","- fnlwgt: continuous.\n","- education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.\n","- education-num: continuous.\n","- marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.\n","- occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.\n","- relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.\n","- race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.\n","- sex: Female, Male.\n","- capital-gain: continuous.\n","- capital-loss: continuous.\n","- hours-per-week: continuous.\n","- native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands. \n","- salary: >50K,<=50K"]},{"cell_type":"code","metadata":{"id":"6GzulHvOutTR"},"source":["import pandas as pd"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"SJ3LbaoiutTT","colab":{"base_uri":"https://localhost:8080/","height":380},"executionInfo":{"status":"ok","timestamp":1626441443051,"user_tz":-300,"elapsed":499,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"eab110b9-0f5f-4bcd-db91-328a0b391379"},"source":["data = pd.read_csv(\"https://raw.githubusercontent.com/aksenov7/Kaggle_competition_group/master/adult.data.csv\")\n","data.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipracesexcapital-gaincapital-losshours-per-weeknative-countrysalary
039State-gov77516Bachelors13Never-marriedAdm-clericalNot-in-familyWhiteMale2174040United-States<=50K
150Self-emp-not-inc83311Bachelors13Married-civ-spouseExec-managerialHusbandWhiteMale0013United-States<=50K
238Private215646HS-grad9DivorcedHandlers-cleanersNot-in-familyWhiteMale0040United-States<=50K
353Private23472111th7Married-civ-spouseHandlers-cleanersHusbandBlackMale0040United-States<=50K
428Private338409Bachelors13Married-civ-spouseProf-specialtyWifeBlackFemale0040Cuba<=50K
\n","
"],"text/plain":[" age workclass fnlwgt ... hours-per-week native-country salary\n","0 39 State-gov 77516 ... 40 United-States <=50K\n","1 50 Self-emp-not-inc 83311 ... 13 United-States <=50K\n","2 38 Private 215646 ... 40 United-States <=50K\n","3 53 Private 234721 ... 40 United-States <=50K\n","4 28 Private 338409 ... 40 Cuba <=50K\n","\n","[5 rows x 15 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"code","metadata":{"id":"EpQFv8t1ds05"},"source":["# def married(row):\n","# return \"Married\" in row\n","data[\"married\"] = data[\"marital-status\"].apply(lambda row: \"Married\" in row)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":756},"id":"3Bb2mRTEeoJK","executionInfo":{"status":"ok","timestamp":1626441731759,"user_tz":-300,"elapsed":481,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"9dd7d83b-f51a-4e11-f6dc-035a844f81c9"},"source":["data"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipracesexcapital-gaincapital-losshours-per-weeknative-countrysalarymarried
039State-gov77516Bachelors13Never-marriedAdm-clericalNot-in-familyWhiteMale2174040United-States<=50KFalse
150Self-emp-not-inc83311Bachelors13Married-civ-spouseExec-managerialHusbandWhiteMale0013United-States<=50KTrue
238Private215646HS-grad9DivorcedHandlers-cleanersNot-in-familyWhiteMale0040United-States<=50KFalse
353Private23472111th7Married-civ-spouseHandlers-cleanersHusbandBlackMale0040United-States<=50KTrue
428Private338409Bachelors13Married-civ-spouseProf-specialtyWifeBlackFemale0040Cuba<=50KTrue
...................................................
3255627Private257302Assoc-acdm12Married-civ-spouseTech-supportWifeWhiteFemale0038United-States<=50KTrue
3255740Private154374HS-grad9Married-civ-spouseMachine-op-inspctHusbandWhiteMale0040United-States>50KTrue
3255858Private151910HS-grad9WidowedAdm-clericalUnmarriedWhiteFemale0040United-States<=50KFalse
3255922Private201490HS-grad9Never-marriedAdm-clericalOwn-childWhiteMale0020United-States<=50KFalse
3256052Self-emp-inc287927HS-grad9Married-civ-spouseExec-managerialWifeWhiteFemale15024040United-States>50KTrue
\n","

32561 rows × 16 columns

\n","
"],"text/plain":[" age workclass fnlwgt ... native-country salary married\n","0 39 State-gov 77516 ... United-States <=50K False\n","1 50 Self-emp-not-inc 83311 ... United-States <=50K True\n","2 38 Private 215646 ... United-States <=50K False\n","3 53 Private 234721 ... United-States <=50K True\n","4 28 Private 338409 ... Cuba <=50K True\n","... ... ... ... ... ... ... ...\n","32556 27 Private 257302 ... United-States <=50K True\n","32557 40 Private 154374 ... United-States >50K True\n","32558 58 Private 151910 ... United-States <=50K False\n","32559 22 Private 201490 ... United-States <=50K False\n","32560 52 Self-emp-inc 287927 ... United-States >50K True\n","\n","[32561 rows x 16 columns]"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"markdown","metadata":{"id":"MoK8B5fIutTW"},"source":["**1. Сколько мужчин и женщин (признак *sex*) представлено в этом наборе данных?**"]},{"cell_type":"code","metadata":{"collapsed":true,"id":"hdzky90TutTY"},"source":["# Ваш код здесь"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"adF8lgVbutTZ"},"source":["**2. Каков средний возраст (признак *age*) женщин?**"]},{"cell_type":"code","metadata":{"collapsed":true,"id":"K6C2qZ_zutTb"},"source":["# Ваш код здесь"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"-Cz1S7-HutTd"},"source":["**3. Какова доля граждан Германии (признак *native-country*)?**"]},{"cell_type":"code","metadata":{"collapsed":true,"id":"Y4mmqN6outTf"},"source":["# Ваш код здесь"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Do-rEgaautTg"},"source":["**4-5. Каковы средние значения и среднеквадратичные отклонения возраста тех, кто получает более 50K в год (признак *salary*) и тех, кто получает менее 50K в год? **"]},{"cell_type":"code","metadata":{"collapsed":true,"id":"eSuk0CAnutTh"},"source":["# Ваш код здесь"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"rK9SwvI_utTj"},"source":["**6. Правда ли, что люди, которые получают больше 50k, имеют как минимум высшее образование? (признак *education – Bachelors, Prof-school, Assoc-acdm, Assoc-voc, Masters* или *Doctorate*)**"]},{"cell_type":"code","metadata":{"collapsed":true,"id":"eygYabkdutTj"},"source":["# Ваш код здесь"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"4DqPASEsutTk"},"source":["**7. Выведите статистику возраста для каждой расы (признак *race*) и каждого пола. Используйте *groupby* и *describe*. Найдите таким образом максимальный возраст мужчин расы *Amer-Indian-Eskimo*.**"]},{"cell_type":"code","metadata":{"collapsed":true,"id":"fYkBDZMdutTl"},"source":["# Ваш код здесь"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cn-jYXhzutTl"},"source":["**8. Среди кого больше доля зарабатывающих много (>50K): среди женатых или холостых мужчин (признак *marital-status*)? Женатыми считаем тех, у кого *marital-status* начинается с *Married* (Married-civ-spouse, Married-spouse-absent или Married-AF-spouse), остальных считаем холостыми.**"]},{"cell_type":"code","metadata":{"collapsed":true,"id":"4hIQXgGAutTm"},"source":["# Ваш код здесь"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Rsh8YvoXutTm"},"source":["**9. Какое максимальное число часов человек работает в неделю (признак *hours-per-week*)? Сколько людей работают такое количество часов и каков среди них процент зарабатывающих много?**"]},{"cell_type":"code","metadata":{"collapsed":true,"id":"RK1JQSIZutTn"},"source":["# Ваш код здесь"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"kUXV84AjutTn"},"source":["**10. Посчитайте среднее время работы (*hours-per-week*) зарабатывающих мало и много (*salary*) для каждой страны (*native-country*).**"]},{"cell_type":"code","metadata":{"collapsed":true,"id":"3gzYG3CDutTn"},"source":["# Ваш код здесь"],"execution_count":null,"outputs":[]}]} \ No newline at end of file +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "UTKVH3sMutTM" + }, + "source": [ + "**В задании предлагается с помощью Pandas ответить на несколько вопросов по данным репозитория UCI [Adult](https://archive.ics.uci.edu/ml/datasets/Adult)**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3lUT-CqYutTO" + }, + "source": [ + "Уникальные значения признаков (больше информации по ссылке выше):\n", + "- age: continuous.\n", + "- workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.\n", + "- fnlwgt: continuous.\n", + "- education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.\n", + "- education-num: continuous.\n", + "- marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.\n", + "- occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.\n", + "- relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.\n", + "- race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.\n", + "- sex: Female, Male.\n", + "- capital-gain: continuous.\n", + "- capital-loss: continuous.\n", + "- hours-per-week: continuous.\n", + "- native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands. \n", + "- salary: >50K,<=50K" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "6GzulHvOutTR" + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 380 + }, + "executionInfo": { + "elapsed": 499, + "status": "ok", + "timestamp": 1626441443051, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "SJ3LbaoiutTT", + "outputId": "eab110b9-0f5f-4bcd-db91-328a0b391379" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " age workclass fnlwgt education education-num \\\n", + "0 39 State-gov 77516 Bachelors 13 \n", + "1 50 Self-emp-not-inc 83311 Bachelors 13 \n", + "2 38 Private 215646 HS-grad 9 \n", + "3 53 Private 234721 11th 7 \n", + "4 28 Private 338409 Bachelors 13 \n", + "\n", + " marital-status occupation relationship race sex \\\n", + "0 Never-married Adm-clerical Not-in-family White Male \n", + "1 Married-civ-spouse Exec-managerial Husband White Male \n", + "2 Divorced Handlers-cleaners Not-in-family White Male \n", + "3 Married-civ-spouse Handlers-cleaners Husband Black Male \n", + "4 Married-civ-spouse Prof-specialty Wife Black Female \n", + "\n", + " capital-gain capital-loss hours-per-week native-country salary \n", + "0 2174 0 40 United-States <=50K \n", + "1 0 0 13 United-States <=50K \n", + "2 0 0 40 United-States <=50K \n", + "3 0 0 40 United-States <=50K \n", + "4 0 0 40 Cuba <=50K " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(\"https://raw.githubusercontent.com/aksenov7/Kaggle_competition_group/master/adult.data.csv\")\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "EpQFv8t1ds05" + }, + "outputs": [], + "source": [ + "# def married(row):\n", + "# return \"Married\" in row\n", + "data[\"married\"] = data[\"marital-status\"].apply(lambda row: \"Married\" in row)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 756 + }, + "executionInfo": { + "elapsed": 481, + "status": "ok", + "timestamp": 1626441731759, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "3Bb2mRTEeoJK", + "outputId": "9dd7d83b-f51a-4e11-f6dc-035a844f81c9" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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150Self-emp-not-inc83311Bachelors13Married-civ-spouseExec-managerialHusbandWhiteMale0013United-States<=50KTrue
238Private215646HS-grad9DivorcedHandlers-cleanersNot-in-familyWhiteMale0040United-States<=50KFalse
353Private23472111th7Married-civ-spouseHandlers-cleanersHusbandBlackMale0040United-States<=50KTrue
428Private338409Bachelors13Married-civ-spouseProf-specialtyWifeBlackFemale0040Cuba<=50KTrue
...................................................
3255627Private257302Assoc-acdm12Married-civ-spouseTech-supportWifeWhiteFemale0038United-States<=50KTrue
3255740Private154374HS-grad9Married-civ-spouseMachine-op-inspctHusbandWhiteMale0040United-States>50KTrue
3255858Private151910HS-grad9WidowedAdm-clericalUnmarriedWhiteFemale0040United-States<=50KFalse
3255922Private201490HS-grad9Never-marriedAdm-clericalOwn-childWhiteMale0020United-States<=50KFalse
3256052Self-emp-inc287927HS-grad9Married-civ-spouseExec-managerialWifeWhiteFemale15024040United-States>50KTrue
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" + ], + "text/plain": [ + " age workclass fnlwgt education education-num \\\n", + "0 39 State-gov 77516 Bachelors 13 \n", + "1 50 Self-emp-not-inc 83311 Bachelors 13 \n", + "2 38 Private 215646 HS-grad 9 \n", + "3 53 Private 234721 11th 7 \n", + "4 28 Private 338409 Bachelors 13 \n", + "... ... ... ... ... ... \n", + "32556 27 Private 257302 Assoc-acdm 12 \n", + "32557 40 Private 154374 HS-grad 9 \n", + "32558 58 Private 151910 HS-grad 9 \n", + "32559 22 Private 201490 HS-grad 9 \n", + "32560 52 Self-emp-inc 287927 HS-grad 9 \n", + "\n", + " marital-status occupation relationship race sex \\\n", + "0 Never-married Adm-clerical Not-in-family White Male \n", + "1 Married-civ-spouse Exec-managerial Husband White Male \n", + "2 Divorced Handlers-cleaners Not-in-family White Male \n", + "3 Married-civ-spouse Handlers-cleaners Husband Black Male \n", + "4 Married-civ-spouse Prof-specialty Wife Black Female \n", + "... ... ... ... ... ... \n", + "32556 Married-civ-spouse Tech-support Wife White Female \n", + "32557 Married-civ-spouse Machine-op-inspct Husband White Male \n", + "32558 Widowed Adm-clerical Unmarried White Female \n", + "32559 Never-married Adm-clerical Own-child White Male \n", + "32560 Married-civ-spouse Exec-managerial Wife White Female \n", + "\n", + " capital-gain capital-loss hours-per-week native-country salary \\\n", + "0 2174 0 40 United-States <=50K \n", + "1 0 0 13 United-States <=50K \n", + "2 0 0 40 United-States <=50K \n", + "3 0 0 40 United-States <=50K \n", + "4 0 0 40 Cuba <=50K \n", + "... ... ... ... ... ... \n", + "32556 0 0 38 United-States <=50K \n", + "32557 0 0 40 United-States >50K \n", + "32558 0 0 40 United-States <=50K \n", + "32559 0 0 20 United-States <=50K \n", + "32560 15024 0 40 United-States >50K \n", + "\n", + " married \n", + "0 False \n", + "1 True \n", + "2 False \n", + "3 True \n", + "4 True \n", + "... ... \n", + "32556 True \n", + "32557 True \n", + "32558 False \n", + "32559 False \n", + "32560 True \n", + "\n", + "[32561 rows x 16 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MoK8B5fIutTW" + }, + "source": [ + "**1. Сколько мужчин и женщин (признак *sex*) представлено в этом наборе данных?**" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "hdzky90TutTY" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "21790 10771\n" + ] + } + ], + "source": [ + "women = data[data['sex'] == 'Female']\n", + "men = data[data['sex'] == 'Male']\n", + "print(len(men), len(women))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "adF8lgVbutTZ" + }, + "source": [ + "**2. Каков средний возраст (признак *age*) женщин?**" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "K6C2qZ_zutTb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "36.85823043357163\n" + ] + } + ], + "source": [ + "print(women['age'].mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-Cz1S7-HutTd" + }, + "source": [ + "**3. Какова доля граждан Германии (признак *native-country*)?**" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "Y4mmqN6outTf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.42074874850281013\n" + ] + } + ], + "source": [ + "df=data.groupby(\"native-country\").size()/len(data)*100\n", + "print(df[\"Germany\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Do-rEgaautTg" + }, + "source": [ + "**4-5. Каковы средние значения и среднеквадратичные отклонения возраста тех, кто получает более 50K в год (признак *salary*) и тех, кто получает менее 50K в год? **" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "eSuk0CAnutTh" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "средние <= 50 36.78373786407767\n", + "средние > 50 44.24984058155847\n", + "СКО <= 50df 14.020088490824866\n", + "СКО > 50df 10.519027719851843\n" + ] + } + ], + "source": [ + "df=data.groupby(\"salary\")[\"age\"].mean()\n", + "print('средние <= 50', df[\"<=50K\"])\n", + "print('средние > 50', df[\">50K\"])\n", + "\n", + "df=data.groupby(\"salary\")[\"age\"].std()\n", + "print(\"СКО <= 50df\", df[\"<=50K\"])\n", + "print(\"СКО > 50df\", df[\">50K\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rK9SwvI_utTj" + }, + "source": [ + "**6. Правда ли, что люди, которые получают больше 50k, имеют как минимум высшее образование? (признак *education – Bachelors, Prof-school, Assoc-acdm, Assoc-voc, Masters* или *Doctorate*)**" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "eygYabkdutTj" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n" + ] + } + ], + "source": [ + "df=data[data[\"salary\"]==\">50K\"]\n", + "count_people=len(df[df[\"education\"]==\"Bachelors\"])+len(df[df[\"education\"]==\"Prof-school\"])+len(df[df[\"education\"]==\"Assoc-acdm\"])+len(df[df[\"education\"]==\"Assoc-voc\"])+len(df[df[\"education\"]==\"Masters\"])+len(df[df[\"education\"]==\"Doctorate\"])\n", + "print(len(df)==count_people)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4DqPASEsutTk" + }, + "source": [ + "**7. Выведите статистику возраста для каждой расы (признак *race*) и каждого пола. Используйте *groupby* и *describe*. Найдите таким образом максимальный возраст мужчин расы *Amer-Indian-Eskimo*.**" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "fYkBDZMdutTl" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "82.0\n" + ] + } + ], + "source": [ + "df=data.groupby([\"race\",\"sex\"]).describe().loc['Amer-Indian-Eskimo'].loc['Male']\n", + "print(df[\"age\"][\"max\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cn-jYXhzutTl" + }, + "source": [ + "**8. Среди кого больше доля зарабатывающих много (>50K): среди женатых или холостых мужчин (признак *marital-status*)? Женатыми считаем тех, у кого *marital-status* начинается с *Married* (Married-civ-spouse, Married-spouse-absent или Married-AF-spouse), остальных считаем холостыми.**" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "4hIQXgGAutTm" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "df=data[data[\"salary\"]==\">50K\"]\n", + "df=df[df[\"sex\"]==\"Male\"]\n", + "count_men=len(df[df[\"marital-status\"]==\"Married-civ-spouse\"])+len(df[df[\"marital-status\"]==\"Married-spouse-absent\"])+len(df[df[\"marital-status\"]==\"Married-AF-spouse\"])\n", + "print(count_men>len(df)-count_men)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Rsh8YvoXutTm" + }, + "source": [ + "**9. Какое максимальное число часов человек работает в неделю (признак *hours-per-week*)? Сколько людей работают такое количество часов и каков среди них процент зарабатывающих много?**" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "RK1JQSIZutTn" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "99.0\n", + "85\n", + "0.29411764705882354\n" + ] + } + ], + "source": [ + "print(data['hours-per-week'].describe()['max'])\n", + "h = data[data['hours-per-week'] == data['hours-per-week'].describe()['max']]\n", + "print(len(h))\n", + "print(h['salary'].value_counts(normalize=True).loc['>50K'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kUXV84AjutTn" + }, + "source": [ + "**10. Посчитайте среднее время работы (*hours-per-week*) зарабатывающих мало и много (*salary*) для каждой страны (*native-country*).**" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "3gzYG3CDutTn" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "native-country salary\n", + "? <=50K 40.164760\n", + " >50K 45.547945\n", + "Cambodia <=50K 41.416667\n", + " >50K 40.000000\n", + "Canada <=50K 37.914634\n", + " ... \n", + "United-States >50K 45.505369\n", + "Vietnam <=50K 37.193548\n", + " >50K 39.200000\n", + "Yugoslavia <=50K 41.600000\n", + " >50K 49.500000\n", + "Name: hours-per-week, Length: 82, dtype: float64\n" + ] + } + ], + "source": [ + "print(data.groupby(['native-country', 'salary'])['hours-per-week'].mean())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "colab": { + "collapsed_sections": [], + "name": "01_task_pandas.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 0a3c87d1e685c89f90d466bdc0305d1eedb42547 Mon Sep 17 00:00:00 2001 From: ooonush Date: Sat, 19 Mar 2022 14:49:58 +0500 Subject: [PATCH 4/5] Pandas 2 --- Numpy (26.02)/Numpy_Lecture.ipynb | 34 +- Pandas (06.03)/Pandas. Lecture. Part 2.ipynb | 7354 +++++++++++++++++- Pandas (06.03)/Pandas. Task. Part 2.ipynb | 672 +- 3 files changed, 8039 insertions(+), 21 deletions(-) diff --git a/Numpy (26.02)/Numpy_Lecture.ipynb b/Numpy (26.02)/Numpy_Lecture.ipynb index b7b1491..6665dd0 100644 --- a/Numpy (26.02)/Numpy_Lecture.ipynb +++ b/Numpy (26.02)/Numpy_Lecture.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "id": "m8T33eQfSuf6" }, @@ -1237,7 +1237,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1268,13 +1268,13 @@ }, { "ename": "ValueError", - "evalue": "ignored", + "evalue": "operands could not be broadcast together with shapes (2,3) (3,2) ", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mc\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (2,3) (3,2) " + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0md\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m6\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mc\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (2,3) (3,2) " ] } ], @@ -1330,7 +1330,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1357,10 +1357,8 @@ " [24, 25, 26]])" ] }, - "execution_count": 36, - "metadata": { - "tags": [] - }, + "execution_count": 5, + "metadata": {}, "output_type": "execute_result" } ], @@ -1733,7 +1731,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "id": "q7IVVJ4X9q__" }, @@ -1748,7 +1746,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1774,10 +1772,8 @@ "23" ] }, - "execution_count": 115, - "metadata": { - "tags": [] - }, + "execution_count": 7, + "metadata": {}, "output_type": "execute_result" } ], @@ -3504,7 +3500,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.8.8" } }, "nbformat": 4, diff --git a/Pandas (06.03)/Pandas. Lecture. Part 2.ipynb b/Pandas (06.03)/Pandas. Lecture. Part 2.ipynb index fb80887..e4b09b8 100644 --- a/Pandas (06.03)/Pandas. Lecture. Part 2.ipynb +++ b/Pandas (06.03)/Pandas. Lecture. Part 2.ipynb @@ -1 +1,7353 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"anaconda-cloud":{},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.1"},"name":"seminar02_part2_pandas.ipynb","colab":{"name":"02_Pandas.ipynb","provenance":[],"collapsed_sections":[]}},"cells":[{"cell_type":"markdown","metadata":{"id":"FzQ_ch0ktU7n"},"source":["#
Первичный анализ данных с Pandas
"]},{"cell_type":"code","metadata":{"collapsed":true,"scrolled":true,"id":"Parpx34utU7s","executionInfo":{"status":"ok","timestamp":1633609636856,"user_tz":-300,"elapsed":631,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}}},"source":["import numpy as np\n","import pandas as pd"],"execution_count":5,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"QxIKAzfCtU7u"},"source":["Данные, с которыми работают дата саентисты и аналитики, обычно хранятся в виде табличек — например, в форматах `.csv`, `.tsv` или `.xlsx`. Для того, чтобы считать нужные данные из такого файла, отлично подходит библиотека Pandas.\n","\n","Основными структурами данных в Pandas являются классы `Series` и `DataFrame`. Первый из них представляет собой одномерный индексированный массив данных некоторого фиксированного типа. Второй - это двухмерная структура данных, представляющая собой таблицу, каждый столбец которой содержит данные одного типа. Можно представлять её как словарь объектов типа `Series`. Структура `DataFrame` отлично подходит для представления реальных данных: строки соответствуют признаковым описаниям отдельных объектов, а столбцы соответствуют признакам."]},{"cell_type":"markdown","metadata":{"id":"l_Ell72CtU7w"},"source":["---------\n","\n","## Демонстрация основных методов Pandas \n"]},{"cell_type":"markdown","metadata":{"id":"YMu_ER8WtU7y"},"source":["### Чтение из файла и первичный анализ"]},{"cell_type":"markdown","metadata":{"id":"efGYx1kqtU7z"},"source":["Прочитаем данные и посмотрим на первые 5 строк с помощью метода `head`:"]},{"cell_type":"code","metadata":{"collapsed":true,"scrolled":true,"id":"ByXZK9MFtU71","executionInfo":{"status":"ok","timestamp":1633609637892,"user_tz":-300,"elapsed":597,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}}},"source":["df = pd.read_csv(\"https://raw.githubusercontent.com/Yorko/mlcourse.ai/master/data/telecom_churn.csv\")"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"scrolled":true,"id":"hFaFpz2utU73","colab":{"base_uri":"https://localhost:8080/","height":241},"executionInfo":{"status":"ok","timestamp":1633609637895,"user_tz":-300,"elapsed":77,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"cbd457e9-c2bd-4beb-a1fa-c7ba8a4c5b97"},"source":["df.head()"],"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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StateAccount lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurn
0KS128415NoYes25265.111045.07197.49916.78244.79111.0110.032.701False
1OH107415NoYes26161.612327.47195.510316.62254.410311.4513.733.701False
2NJ137415NoNo0243.411441.38121.211010.30162.61047.3212.253.290False
3OH84408YesNo0299.47150.9061.9885.26196.9898.866.671.782False
4OK75415YesNo0166.711328.34148.312212.61186.91218.4110.132.733False
\n","
"],"text/plain":[" State Account length ... Customer service calls Churn\n","0 KS 128 ... 1 False\n","1 OH 107 ... 1 False\n","2 NJ 137 ... 0 False\n","3 OH 84 ... 2 False\n","4 OK 75 ... 3 False\n","\n","[5 rows x 20 columns]"]},"metadata":{},"execution_count":7}]},{"cell_type":"markdown","metadata":{"id":"CpV496POtU75"},"source":["В Jupyter-ноутбуках датафреймы `Pandas` выводятся в виде вот таких красивых табличек, и `print(df.head())` выглядит хуже.\n","\n","Кстати, по умолчанию `Pandas` выводит всего 20 столбцов и 60 строк, поэтому если ваш датафрейм больше, воспользуйтесь функцией `set_option`:"]},{"cell_type":"code","metadata":{"collapsed":true,"id":"CYFyCCGGtU77","executionInfo":{"status":"ok","timestamp":1633609637897,"user_tz":-300,"elapsed":68,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}}},"source":["# задание проанализировать все опции и выбрать 3-5 самых полезных по личному мнению \n","# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.set_option.html\n","pd.set_option(\"display.max_columns\", 100)\n","pd.set_option(\"display.max_rows\", 100)"],"execution_count":8,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"CbfNn4a9tU78"},"source":["А также укажем значение параметра `presicion` равным 2, чтобы отображать два знака после запятой (а не 6, как установлено по умолчанию."]},{"cell_type":"code","metadata":{"collapsed":true,"id":"-0MCBxGItU78","executionInfo":{"status":"ok","timestamp":1633609637899,"user_tz":-300,"elapsed":67,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}}},"source":["pd.set_option(\"precision\", 2)"],"execution_count":9,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Cu652IOYtU79"},"source":["**Посмотрим на размер данных, названия признаков и их типы**"]},{"cell_type":"code","metadata":{"id":"LQw6THQytU79","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1633609637901,"user_tz":-300,"elapsed":66,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"b2d6d2f1-a6d1-47c6-e4bb-5c5f33834c4a"},"source":["print(df.shape)"],"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["(3333, 20)\n"]}]},{"cell_type":"markdown","metadata":{"id":"LJEPKleBtU7-"},"source":["Видим, что в таблице 3333 строки и 20 столбцов. Выведем названия столбцов:"]},{"cell_type":"code","metadata":{"id":"CQArdzC8tU7_","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1633609637903,"user_tz":-300,"elapsed":57,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"08e4c81f-5a94-4589-c4d3-c6792128de13"},"source":["print(df.columns)"],"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["Index(['State', 'Account length', 'Area code', 'International plan',\n"," 'Voice mail plan', 'Number vmail messages', 'Total day minutes',\n"," 'Total day calls', 'Total day charge', 'Total eve minutes',\n"," 'Total eve calls', 'Total eve charge', 'Total night minutes',\n"," 'Total night calls', 'Total night charge', 'Total intl minutes',\n"," 'Total intl calls', 'Total intl charge', 'Customer service calls',\n"," 'Churn'],\n"," dtype='object')\n"]}]},{"cell_type":"markdown","metadata":{"id":"RoZn1MpBtU8A"},"source":["Чтобы посмотреть общую информацию по датафрейму и всем признакам, воспользуемся методом **`info`**:"]},{"cell_type":"code","metadata":{"scrolled":false,"id":"W_ZF3eM8tU8B","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1633609637906,"user_tz":-300,"elapsed":54,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"b4d58f04-d867-458f-bb5e-7b91fbdc9cd9"},"source":["print(df.info())"],"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","RangeIndex: 3333 entries, 0 to 3332\n","Data columns (total 20 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 State 3333 non-null object \n"," 1 Account length 3333 non-null int64 \n"," 2 Area code 3333 non-null int64 \n"," 3 International plan 3333 non-null object \n"," 4 Voice mail plan 3333 non-null object \n"," 5 Number vmail messages 3333 non-null int64 \n"," 6 Total day minutes 3333 non-null float64\n"," 7 Total day calls 3333 non-null int64 \n"," 8 Total day charge 3333 non-null float64\n"," 9 Total eve minutes 3333 non-null float64\n"," 10 Total eve calls 3333 non-null int64 \n"," 11 Total eve charge 3333 non-null float64\n"," 12 Total night minutes 3333 non-null float64\n"," 13 Total night calls 3333 non-null int64 \n"," 14 Total night charge 3333 non-null float64\n"," 15 Total intl minutes 3333 non-null float64\n"," 16 Total intl calls 3333 non-null int64 \n"," 17 Total intl charge 3333 non-null float64\n"," 18 Customer service calls 3333 non-null int64 \n"," 19 Churn 3333 non-null bool \n","dtypes: bool(1), float64(8), int64(8), object(3)\n","memory usage: 498.1+ KB\n","None\n"]}]},{"cell_type":"markdown","metadata":{"id":"FYDNyB6CtU8C"},"source":["`bool`, `int64`, `float64` и `object` — это типы признаков. Видим, что 1 признак — логический (`bool`), 3 признака имеют тип `object` и 16 признаков — числовые.\n","\n","**Изменить тип колонки** можно с помощью метода `astype`. Применим этот метод к признаку `Churn` и переведём его в `int64`:"]},{"cell_type":"code","metadata":{"collapsed":true,"id":"byRJQVM5tU8D","executionInfo":{"status":"ok","timestamp":1633609637909,"user_tz":-300,"elapsed":48,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}}},"source":["df[\"Churn\"] = df[\"Churn\"].astype(\"int64\")"],"execution_count":13,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"sBTm0lLYtU8D"},"source":["Метод **`describe`** показывает основные статистические характеристики данных по каждому числовому признаку (типы `int64` и `float64`): число непропущенных значений, среднее, стандартное отклонение, диапазон, медиану, 0.25 и 0.75 квартили."]},{"cell_type":"code","metadata":{"id":"bAsmrRI6tU8D","colab":{"base_uri":"https://localhost:8080/","height":335},"executionInfo":{"status":"ok","timestamp":1633609637911,"user_tz":-300,"elapsed":48,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"32a7192a-b49b-4be7-9b6e-9b7f08f57731"},"source":["df.describe()"],"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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Account lengthArea codeNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurn
count3333.003333.003333.003333.003333.003333.003333.003333.003333.003333.003333.003333.003333.003333.003333.003333.003333.00
mean101.06437.188.10179.78100.4430.56200.98100.1117.08200.87100.119.0410.244.482.761.560.14
std39.8242.3713.6954.4720.079.2650.7119.924.3150.5719.572.282.792.460.751.320.35
min1.00408.000.000.000.000.000.000.000.0023.2033.001.040.000.000.000.000.00
25%74.00408.000.00143.7087.0024.43166.6087.0014.16167.0087.007.528.503.002.301.000.00
50%101.00415.000.00179.40101.0030.50201.40100.0017.12201.20100.009.0510.304.002.781.000.00
75%127.00510.0020.00216.40114.0036.79235.30114.0020.00235.30113.0010.5912.106.003.272.000.00
max243.00510.0051.00350.80165.0059.64363.70170.0030.91395.00175.0017.7720.0020.005.409.001.00
\n","
"],"text/plain":[" Account length Area code Number vmail messages Total day minutes \\\n","count 3333.00 3333.00 3333.00 3333.00 \n","mean 101.06 437.18 8.10 179.78 \n","std 39.82 42.37 13.69 54.47 \n","min 1.00 408.00 0.00 0.00 \n","25% 74.00 408.00 0.00 143.70 \n","50% 101.00 415.00 0.00 179.40 \n","75% 127.00 510.00 20.00 216.40 \n","max 243.00 510.00 51.00 350.80 \n","\n"," Total day calls Total day charge Total eve minutes Total eve calls \\\n","count 3333.00 3333.00 3333.00 3333.00 \n","mean 100.44 30.56 200.98 100.11 \n","std 20.07 9.26 50.71 19.92 \n","min 0.00 0.00 0.00 0.00 \n","25% 87.00 24.43 166.60 87.00 \n","50% 101.00 30.50 201.40 100.00 \n","75% 114.00 36.79 235.30 114.00 \n","max 165.00 59.64 363.70 170.00 \n","\n"," Total eve charge Total night minutes Total night calls \\\n","count 3333.00 3333.00 3333.00 \n","mean 17.08 200.87 100.11 \n","std 4.31 50.57 19.57 \n","min 0.00 23.20 33.00 \n","25% 14.16 167.00 87.00 \n","50% 17.12 201.20 100.00 \n","75% 20.00 235.30 113.00 \n","max 30.91 395.00 175.00 \n","\n"," Total night charge Total intl minutes Total intl calls \\\n","count 3333.00 3333.00 3333.00 \n","mean 9.04 10.24 4.48 \n","std 2.28 2.79 2.46 \n","min 1.04 0.00 0.00 \n","25% 7.52 8.50 3.00 \n","50% 9.05 10.30 4.00 \n","75% 10.59 12.10 6.00 \n","max 17.77 20.00 20.00 \n","\n"," Total intl charge Customer service calls Churn \n","count 3333.00 3333.00 3333.00 \n","mean 2.76 1.56 0.14 \n","std 0.75 1.32 0.35 \n","min 0.00 0.00 0.00 \n","25% 2.30 1.00 0.00 \n","50% 2.78 1.00 0.00 \n","75% 3.27 2.00 0.00 \n","max 5.40 9.00 1.00 "]},"metadata":{},"execution_count":14}]},{"cell_type":"markdown","metadata":{"id":"l6MzhnkotU8D"},"source":["Чтобы посмотреть статистику по нечисловым признакам, нужно явно указать интересующие нас типы в параметре `include`. Можно также задать `include`='all', чтоб вывести статистику по всем имеющимся признакам."]},{"cell_type":"code","metadata":{"scrolled":true,"id":"ewJscFGZtU8F","colab":{"base_uri":"https://localhost:8080/","height":175},"executionInfo":{"status":"ok","timestamp":1633609638506,"user_tz":-300,"elapsed":639,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"187fb398-e4bf-4c36-f3ff-e395013e994f"},"source":["df.describe(include=[\"object\", \"bool\"])"],"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StateInternational planVoice mail plan
count333333333333
unique5122
topWVNoNo
freq10630102411
\n","
"],"text/plain":[" State International plan Voice mail plan\n","count 3333 3333 3333\n","unique 51 2 2\n","top WV No No\n","freq 106 3010 2411"]},"metadata":{},"execution_count":15}]},{"cell_type":"markdown","metadata":{"id":"1qbs0vug9TCh"},"source":["Тот же принцип работает при выборе столбцов указанного типа."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"tbL3f9OD9Tg7","executionInfo":{"status":"ok","timestamp":1633609638538,"user_tz":-300,"elapsed":120,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"1489c427-200c-45fa-f127-369a97e46ea8"},"source":["df.select_dtypes(include=['object', 'bool']) # exclude"],"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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StateInternational planVoice mail plan
0KSNoYes
1OHNoYes
2NJNoNo
3OHYesNo
4OKYesNo
............
3328AZNoYes
3329WVNoNo
3330RINoNo
3331CTYesNo
3332TNNoYes
\n","

3333 rows × 3 columns

\n","
"],"text/plain":[" State International plan Voice mail plan\n","0 KS No Yes\n","1 OH No Yes\n","2 NJ No No\n","3 OH Yes No\n","4 OK Yes No\n","... ... ... ...\n","3328 AZ No Yes\n","3329 WV No No\n","3330 RI No No\n","3331 CT Yes No\n","3332 TN No Yes\n","\n","[3333 rows x 3 columns]"]},"metadata":{},"execution_count":16}]},{"cell_type":"markdown","metadata":{"id":"Ge-uZsFvtU8G"},"source":["Для категориальных (тип `object`) и булевых (тип `bool`) признаков можно воспользоваться методом **`value_counts`**. Посмотрим на распределение нашей целевой переменной — `Churn`:"]},{"cell_type":"code","metadata":{"id":"eeDu-JiYtU8G","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1633609638540,"user_tz":-300,"elapsed":115,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"19761b7d-d89b-49eb-e4bd-371bd68907d7"},"source":["df[\"Churn\"].value_counts()"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0 2850\n","1 483\n","Name: Churn, dtype: int64"]},"metadata":{},"execution_count":17}]},{"cell_type":"markdown","metadata":{"id":"KANMt5q2tU8I"},"source":["2850 пользователей из 3333 — лояльные, значение переменной `Churn` у них — `0`.\n","\n","Посмотрим на распределение пользователей по переменной `Area code`. Укажем значение параметра `normalize=True`, чтобы посмотреть не абсолютные частоты, а относительные."]},{"cell_type":"code","metadata":{"id":"pMenDSyHtU8I","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1633609638542,"user_tz":-300,"elapsed":109,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"a99c176c-d2b0-45b9-e54f-653c1f060dd0"},"source":["df[\"Area code\"].value_counts(normalize=True)"],"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/plain":["415 0.50\n","510 0.25\n","408 0.25\n","Name: Area code, dtype: float64"]},"metadata":{},"execution_count":18}]},{"cell_type":"markdown","metadata":{"id":"l4ikQZaptU8I"},"source":["### Сортировка\n","\n","`DataFrame` можно отсортировать по значению какого-нибудь из признаков. В нашем случае, например, по `Total day charge` (`ascending=False` для сортировки по убыванию):"]},{"cell_type":"code","metadata":{"id":"GrbzIXBQtU8J","colab":{"base_uri":"https://localhost:8080/","height":241},"executionInfo":{"status":"ok","timestamp":1633609638544,"user_tz":-300,"elapsed":102,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"7cf76892-8c0d-42fa-fa98-aa49f8c2ab6e"},"source":["df.sort_values(by=\"Total day charge\", ascending=False).head()"],"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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StateAccount lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurn
365CO154415NoNo0350.87559.64216.59418.40253.910011.4310.192.7311
985NY64415YesNo0346.85558.96249.57921.21275.410212.3913.393.5911
2594OH115510YesNo0345.38158.70203.410617.29217.51079.7911.883.1911
156OH83415NoNo0337.412057.36227.411619.33153.91146.9315.874.2701
605MO112415NoNo0335.57757.04212.510918.06265.013211.9312.783.4321
\n","
"],"text/plain":[" State Account length Area code International plan Voice mail plan \\\n","365 CO 154 415 No No \n","985 NY 64 415 Yes No \n","2594 OH 115 510 Yes No \n","156 OH 83 415 No No \n","605 MO 112 415 No No \n","\n"," Number vmail messages Total day minutes Total day calls \\\n","365 0 350.8 75 \n","985 0 346.8 55 \n","2594 0 345.3 81 \n","156 0 337.4 120 \n","605 0 335.5 77 \n","\n"," Total day charge Total eve minutes Total eve calls Total eve charge \\\n","365 59.64 216.5 94 18.40 \n","985 58.96 249.5 79 21.21 \n","2594 58.70 203.4 106 17.29 \n","156 57.36 227.4 116 19.33 \n","605 57.04 212.5 109 18.06 \n","\n"," Total night minutes Total night calls Total night charge \\\n","365 253.9 100 11.43 \n","985 275.4 102 12.39 \n","2594 217.5 107 9.79 \n","156 153.9 114 6.93 \n","605 265.0 132 11.93 \n","\n"," Total intl minutes Total intl calls Total intl charge \\\n","365 10.1 9 2.73 \n","985 13.3 9 3.59 \n","2594 11.8 8 3.19 \n","156 15.8 7 4.27 \n","605 12.7 8 3.43 \n","\n"," Customer service calls Churn \n","365 1 1 \n","985 1 1 \n","2594 1 1 \n","156 0 1 \n","605 2 1 "]},"metadata":{},"execution_count":19}]},{"cell_type":"markdown","metadata":{"id":"apUOhvc_tU8J"},"source":["Сортировать можно и по группе столбцов:"]},{"cell_type":"code","metadata":{"id":"KUU1Xp63tU8K","colab":{"base_uri":"https://localhost:8080/","height":241},"executionInfo":{"status":"ok","timestamp":1633609638545,"user_tz":-300,"elapsed":100,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"0bbacb6a-bbf7-4697-b720-20033f341ff3"},"source":["df.sort_values(by=[\"Churn\", \"Total day charge\"], ascending=[True, False]).head()"],"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StateAccount lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurn
688MN13510NoYes21315.610553.65208.97117.76260.112311.7012.133.2730
2259NC210415NoYes31313.88753.35147.710312.55192.7978.6710.172.7330
534LA67510NoNo0310.49752.7766.51235.65246.59911.099.2102.4840
575SD114415NoYes36309.99052.68200.38917.03183.51058.2614.223.8310
2858AL141510NoYes28308.012352.36247.812821.06152.91036.887.432.0010
\n","
"],"text/plain":[" State Account length Area code International plan Voice mail plan \\\n","688 MN 13 510 No Yes \n","2259 NC 210 415 No Yes \n","534 LA 67 510 No No \n","575 SD 114 415 No Yes \n","2858 AL 141 510 No Yes \n","\n"," Number vmail messages Total day minutes Total day calls \\\n","688 21 315.6 105 \n","2259 31 313.8 87 \n","534 0 310.4 97 \n","575 36 309.9 90 \n","2858 28 308.0 123 \n","\n"," Total day charge Total eve minutes Total eve calls Total eve charge \\\n","688 53.65 208.9 71 17.76 \n","2259 53.35 147.7 103 12.55 \n","534 52.77 66.5 123 5.65 \n","575 52.68 200.3 89 17.03 \n","2858 52.36 247.8 128 21.06 \n","\n"," Total night minutes Total night calls Total night charge \\\n","688 260.1 123 11.70 \n","2259 192.7 97 8.67 \n","534 246.5 99 11.09 \n","575 183.5 105 8.26 \n","2858 152.9 103 6.88 \n","\n"," Total intl minutes Total intl calls Total intl charge \\\n","688 12.1 3 3.27 \n","2259 10.1 7 2.73 \n","534 9.2 10 2.48 \n","575 14.2 2 3.83 \n","2858 7.4 3 2.00 \n","\n"," Customer service calls Churn \n","688 3 0 \n","2259 3 0 \n","534 4 0 \n","575 1 0 \n","2858 1 0 "]},"metadata":{},"execution_count":20}]},{"cell_type":"markdown","metadata":{"id":"VCTKeJUYtU8L"},"source":["### Индексация и извлечение данных"]},{"cell_type":"markdown","metadata":{"id":"lveNXBbztU8L"},"source":["`DataFrame` можно индексировать по-разному. В связи с этим рассмотрим различные способы индексации и извлечения нужных нам данных из датафрейма на примере простых вопросов.\n","\n","Для извлечения отдельного столбца можно использовать конструкцию вида `DataFrame['Name']`. Воспользуемся этим для ответа на вопрос: **какова доля нелояльных пользователей в нашем датафрейме?**"]},{"cell_type":"code","metadata":{"id":"FLaA5u1ztU8L","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1633609638547,"user_tz":-300,"elapsed":98,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"d1b61bde-7b0c-45d0-c2e9-32d9bb9539c0"},"source":["df[\"Churn\"].mean()"],"execution_count":21,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.14491449144914492"]},"metadata":{},"execution_count":21}]},{"cell_type":"markdown","metadata":{"id":"QiJUnpEJtU8M"},"source":["14,5% — довольно плохой показатель для компании, с таким процентом оттока можно и разориться."]},{"cell_type":"markdown","metadata":{"id":"2v6CRyJ3tU8M"},"source":["Очень удобной является логическая индексация `DataFrame` по одному столбцу. Выглядит она следующим образом: `df[P(df['Name'])]`, где `P` - это некоторое логическое условие, проверяемое для каждого элемента столбца `Name`. Итогом такой индексации является `DataFrame`, состоящий только из строк, удовлетворяющих условию `P` по столбцу `Name`. \n","\n","Воспользуемся этим для ответа на вопрос: **каковы средние значения числовых признаков среди нелояльных пользователей?**"]},{"cell_type":"code","metadata":{"scrolled":true,"id":"0G0_4zPytU8O","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1633609638548,"user_tz":-300,"elapsed":90,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"79d763ca-3a4e-4408-f218-e5996dbd68bb"},"source":["df[df[\"Churn\"] == 1].mean()"],"execution_count":22,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Account length 102.66\n","Area code 437.82\n","Number vmail messages 5.12\n","Total day minutes 206.91\n","Total day calls 101.34\n","Total day charge 35.18\n","Total eve minutes 212.41\n","Total eve calls 100.56\n","Total eve charge 18.05\n","Total night minutes 205.23\n","Total night calls 100.40\n","Total night charge 9.24\n","Total intl minutes 10.70\n","Total intl calls 4.16\n","Total intl charge 2.89\n","Customer service calls 2.23\n","Churn 1.00\n","dtype: float64"]},"metadata":{},"execution_count":22}]},{"cell_type":"markdown","metadata":{"id":"vX7Kv82ztU8O"},"source":["Скомбинировав предыдущие два вида индексации, ответим на вопрос: **сколько в среднем в течение дня разговаривают по телефону нелояльные пользователи**?"]},{"cell_type":"code","metadata":{"id":"ZmpzMz9LtU8O","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1633609638551,"user_tz":-300,"elapsed":87,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"f4ef2f49-5d18-4228-b513-96402e23b1b4"},"source":["df[df[\"Churn\"] == 1][\"Total day minutes\"].mean()"],"execution_count":23,"outputs":[{"output_type":"execute_result","data":{"text/plain":["206.91407867494814"]},"metadata":{},"execution_count":23}]},{"cell_type":"markdown","metadata":{"id":"rME2EKe8tU8P"},"source":["**Какова максимальная длина международных звонков среди лояльных пользователей (`Churn == 0`), не пользующихся услугой международного роуминга (`'International plan' == 'No'`)?**"]},{"cell_type":"code","metadata":{"id":"DQ0H-bJttU8Q","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1633609638552,"user_tz":-300,"elapsed":82,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"3c8a6304-7ede-495b-f2cc-dcf70beb252f"},"source":["df[(df[\"Churn\"] == 0) & (df[\"International plan\"] == \"No\")][\"Total intl minutes\"].max()"],"execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/plain":["18.9"]},"metadata":{},"execution_count":24}]},{"cell_type":"markdown","metadata":{"id":"f6IelrO4tU8Q"},"source":["Датафреймы можно индексировать как по названию столбца или строки, так и по порядковому номеру. Для индексации **по названию** используется метод **`loc`**, **по номеру** — **`iloc`**.\n","\n","В первом случае мы говорим _«передай нам значения для id строк от 0 до 5 и для столбцов от State до Area code»_, а во втором — _«передай нам значения первых пяти строк в первых трёх столбцах»_. \n","\n","В случае `iloc` срез работает как обычно, однако в случае `loc` учитываются и начало, и конец среза. Да, неудобно, да, вызывает путаницу."]},{"cell_type":"code","metadata":{"scrolled":true,"id":"Pp82lj7ktU8R","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1633609638554,"user_tz":-300,"elapsed":78,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"8e2a9392-b3f0-44ee-e383-b19a46f8d708"},"source":["d = df.copy()\n","d = d.drop_duplicates('State')\n","d = d.set_index('State')\n","# d = d.reset_index() # сбрасываем столбец-индекс не удаляя его\n","d = d.reset_index(drop=True) # сбрасываем столбец-индекс удаляя его\n","d\n","# d.loc['KS':'OK','Area code':'Total day minutes']"],"execution_count":25,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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Account lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurn
0128415NoYes25265.111045.07197.49916.78244.79111.0110.032.7010
1107415NoYes26161.612327.47195.510316.62254.410311.4513.733.7010
2137415NoNo0243.411441.38121.211010.30162.61047.3212.253.2900
375415YesNo0166.711328.34148.312212.61186.91218.4110.132.7330
4118510YesNo0223.49837.98220.610118.75203.91189.186.361.7000
5121510NoYes24218.28837.09348.510829.62212.61189.577.572.0330
6147415YesNo0157.07926.69103.1948.76211.8969.537.161.9200
7117408NoNo0184.59731.37351.68029.89215.8909.718.742.3510
8141415YesYes37258.68443.96222.011118.87326.49714.6911.253.0200
965415NoNo0129.113721.95228.58319.42208.81119.4012.763.4341
1074415NoNo0187.712731.91163.414813.89196.0948.829.152.4600
11168408NoNo0128.89621.90104.9718.92141.11286.3511.223.0210
1295510NoNo0156.68826.62247.67521.05192.31158.6512.353.3230
13161415NoNo0332.96756.59317.89727.01160.61287.235.491.4641
1485408NoYes27196.413933.39280.99023.8889.3754.0213.843.7310
1593510NoNo0190.711432.42218.211118.55129.61215.838.132.1930
1676510NoYes33189.76632.25212.86518.09165.71087.4610.052.7010
1773415NoNo0224.49038.15159.58813.56192.8748.6813.023.5110
18147415NoNo0155.111726.37239.79320.37208.81339.4010.642.8600
1977408NoNo062.48910.61169.912114.44209.6649.435.761.5451
20130415NoNo0183.011231.1172.9996.20181.8788.189.5192.5700
21111415NoNo0110.410318.77137.310211.67189.61058.537.762.0820
22174415NoNo0124.37621.13277.111223.55250.711511.2815.554.1930
2357408NoYes39213.011536.21191.111216.24182.71158.229.532.5700
2449510NoNo0119.311720.28215.110918.28178.7908.0411.113.0010
25142415NoNo084.89514.42136.76311.62250.514811.2714.263.8320
2675510NoNo0226.110538.44201.510717.13246.29811.0810.352.7810
2772415NoYes37220.08037.40217.310218.47152.8716.8814.763.9730
2836408NoYes30146.312824.87162.58013.81129.31095.8214.563.9200
29135408YesYes41173.18529.43203.910717.33122.2785.5014.6153.9401
3034510NoNo0124.88221.22282.29823.99311.57814.0210.042.7020
3164510NoNo0154.06726.18225.811819.19265.38611.943.530.9510
3259408NoYes28120.99720.55213.09218.11163.11167.348.552.3020
3365415NoNo0211.312035.92162.612213.82134.71186.0613.253.5630
34142408NoNo0187.013331.79134.67411.44242.212710.907.452.0020
3596415NoNo0160.211727.23267.56722.74228.56810.289.352.5120
36116415NoYes34268.68345.66178.214215.15166.31067.4811.633.1320
3774510NoYes33193.79132.93246.19620.92138.0926.2114.633.9420
38149408NoYes28180.79230.72187.86415.96265.55311.9512.633.4030
3938408NoNo0131.29822.30162.99713.85159.01067.158.262.2120
4040415NoYes41148.17425.18169.58814.41214.11029.636.251.6720
41147510NoNo0248.68342.26148.98512.66172.51097.768.042.1630
4290415NoNo0203.414634.58226.711719.27152.41056.867.341.9710
4382415NoNo0300.310951.05181.010015.39270.17312.1511.743.1601
4474415NoYes35154.110426.20123.48410.49202.1579.0910.992.9420
4578415NoNo0252.99342.99178.411215.16263.910511.889.572.5730
46120408NoNo0212.113136.06209.410417.80167.2967.525.351.4311
4778415NoNo0149.711925.45182.211515.49261.512611.779.782.6200
4882415NoYes24155.213126.38244.510620.78122.4685.5110.732.8910
49199415NoYes34230.612139.20219.49918.65299.39413.478.022.1600
5079408NoNo0205.712334.97214.510818.23226.110610.176.7181.8110
\n","
"],"text/plain":[" Account length Area code International plan Voice mail plan \\\n","0 128 415 No Yes \n","1 107 415 No Yes \n","2 137 415 No No \n","3 75 415 Yes No \n","4 118 510 Yes No \n","5 121 510 No Yes \n","6 147 415 Yes No \n","7 117 408 No No \n","8 141 415 Yes Yes \n","9 65 415 No No \n","10 74 415 No No \n","11 168 408 No No \n","12 95 510 No No \n","13 161 415 No No \n","14 85 408 No Yes \n","15 93 510 No No \n","16 76 510 No Yes \n","17 73 415 No No \n","18 147 415 No No \n","19 77 408 No No \n","20 130 415 No No \n","21 111 415 No No \n","22 174 415 No No \n","23 57 408 No Yes \n","24 49 510 No No \n","25 142 415 No No \n","26 75 510 No No \n","27 72 415 No Yes \n","28 36 408 No Yes \n","29 135 408 Yes Yes \n","30 34 510 No No \n","31 64 510 No No \n","32 59 408 No Yes \n","33 65 415 No No \n","34 142 408 No No \n","35 96 415 No No \n","36 116 415 No Yes \n","37 74 510 No Yes \n","38 149 408 No Yes \n","39 38 408 No No \n","40 40 415 No Yes \n","41 147 510 No No \n","42 90 415 No No \n","43 82 415 No No \n","44 74 415 No Yes \n","45 78 415 No No \n","46 120 408 No No \n","47 78 415 No No \n","48 82 415 No Yes \n","49 199 415 No Yes \n","50 79 408 No No \n","\n"," Number vmail messages Total day minutes Total day calls \\\n","0 25 265.1 110 \n","1 26 161.6 123 \n","2 0 243.4 114 \n","3 0 166.7 113 \n","4 0 223.4 98 \n","5 24 218.2 88 \n","6 0 157.0 79 \n","7 0 184.5 97 \n","8 37 258.6 84 \n","9 0 129.1 137 \n","10 0 187.7 127 \n","11 0 128.8 96 \n","12 0 156.6 88 \n","13 0 332.9 67 \n","14 27 196.4 139 \n","15 0 190.7 114 \n","16 33 189.7 66 \n","17 0 224.4 90 \n","18 0 155.1 117 \n","19 0 62.4 89 \n","20 0 183.0 112 \n","21 0 110.4 103 \n","22 0 124.3 76 \n","23 39 213.0 115 \n","24 0 119.3 117 \n","25 0 84.8 95 \n","26 0 226.1 105 \n","27 37 220.0 80 \n","28 30 146.3 128 \n","29 41 173.1 85 \n","30 0 124.8 82 \n","31 0 154.0 67 \n","32 28 120.9 97 \n","33 0 211.3 120 \n","34 0 187.0 133 \n","35 0 160.2 117 \n","36 34 268.6 83 \n","37 33 193.7 91 \n","38 28 180.7 92 \n","39 0 131.2 98 \n","40 41 148.1 74 \n","41 0 248.6 83 \n","42 0 203.4 146 \n","43 0 300.3 109 \n","44 35 154.1 104 \n","45 0 252.9 93 \n","46 0 212.1 131 \n","47 0 149.7 119 \n","48 24 155.2 131 \n","49 34 230.6 121 \n","50 0 205.7 123 \n","\n"," Total day charge Total eve minutes Total eve calls Total eve charge \\\n","0 45.07 197.4 99 16.78 \n","1 27.47 195.5 103 16.62 \n","2 41.38 121.2 110 10.30 \n","3 28.34 148.3 122 12.61 \n","4 37.98 220.6 101 18.75 \n","5 37.09 348.5 108 29.62 \n","6 26.69 103.1 94 8.76 \n","7 31.37 351.6 80 29.89 \n","8 43.96 222.0 111 18.87 \n","9 21.95 228.5 83 19.42 \n","10 31.91 163.4 148 13.89 \n","11 21.90 104.9 71 8.92 \n","12 26.62 247.6 75 21.05 \n","13 56.59 317.8 97 27.01 \n","14 33.39 280.9 90 23.88 \n","15 32.42 218.2 111 18.55 \n","16 32.25 212.8 65 18.09 \n","17 38.15 159.5 88 13.56 \n","18 26.37 239.7 93 20.37 \n","19 10.61 169.9 121 14.44 \n","20 31.11 72.9 99 6.20 \n","21 18.77 137.3 102 11.67 \n","22 21.13 277.1 112 23.55 \n","23 36.21 191.1 112 16.24 \n","24 20.28 215.1 109 18.28 \n","25 14.42 136.7 63 11.62 \n","26 38.44 201.5 107 17.13 \n","27 37.40 217.3 102 18.47 \n","28 24.87 162.5 80 13.81 \n","29 29.43 203.9 107 17.33 \n","30 21.22 282.2 98 23.99 \n","31 26.18 225.8 118 19.19 \n","32 20.55 213.0 92 18.11 \n","33 35.92 162.6 122 13.82 \n","34 31.79 134.6 74 11.44 \n","35 27.23 267.5 67 22.74 \n","36 45.66 178.2 142 15.15 \n","37 32.93 246.1 96 20.92 \n","38 30.72 187.8 64 15.96 \n","39 22.30 162.9 97 13.85 \n","40 25.18 169.5 88 14.41 \n","41 42.26 148.9 85 12.66 \n","42 34.58 226.7 117 19.27 \n","43 51.05 181.0 100 15.39 \n","44 26.20 123.4 84 10.49 \n","45 42.99 178.4 112 15.16 \n","46 36.06 209.4 104 17.80 \n","47 25.45 182.2 115 15.49 \n","48 26.38 244.5 106 20.78 \n","49 39.20 219.4 99 18.65 \n","50 34.97 214.5 108 18.23 \n","\n"," Total night minutes Total night calls Total night charge \\\n","0 244.7 91 11.01 \n","1 254.4 103 11.45 \n","2 162.6 104 7.32 \n","3 186.9 121 8.41 \n","4 203.9 118 9.18 \n","5 212.6 118 9.57 \n","6 211.8 96 9.53 \n","7 215.8 90 9.71 \n","8 326.4 97 14.69 \n","9 208.8 111 9.40 \n","10 196.0 94 8.82 \n","11 141.1 128 6.35 \n","12 192.3 115 8.65 \n","13 160.6 128 7.23 \n","14 89.3 75 4.02 \n","15 129.6 121 5.83 \n","16 165.7 108 7.46 \n","17 192.8 74 8.68 \n","18 208.8 133 9.40 \n","19 209.6 64 9.43 \n","20 181.8 78 8.18 \n","21 189.6 105 8.53 \n","22 250.7 115 11.28 \n","23 182.7 115 8.22 \n","24 178.7 90 8.04 \n","25 250.5 148 11.27 \n","26 246.2 98 11.08 \n","27 152.8 71 6.88 \n","28 129.3 109 5.82 \n","29 122.2 78 5.50 \n","30 311.5 78 14.02 \n","31 265.3 86 11.94 \n","32 163.1 116 7.34 \n","33 134.7 118 6.06 \n","34 242.2 127 10.90 \n","35 228.5 68 10.28 \n","36 166.3 106 7.48 \n","37 138.0 92 6.21 \n","38 265.5 53 11.95 \n","39 159.0 106 7.15 \n","40 214.1 102 9.63 \n","41 172.5 109 7.76 \n","42 152.4 105 6.86 \n","43 270.1 73 12.15 \n","44 202.1 57 9.09 \n","45 263.9 105 11.88 \n","46 167.2 96 7.52 \n","47 261.5 126 11.77 \n","48 122.4 68 5.51 \n","49 299.3 94 13.47 \n","50 226.1 106 10.17 \n","\n"," Total intl minutes Total intl calls Total intl charge \\\n","0 10.0 3 2.70 \n","1 13.7 3 3.70 \n","2 12.2 5 3.29 \n","3 10.1 3 2.73 \n","4 6.3 6 1.70 \n","5 7.5 7 2.03 \n","6 7.1 6 1.92 \n","7 8.7 4 2.35 \n","8 11.2 5 3.02 \n","9 12.7 6 3.43 \n","10 9.1 5 2.46 \n","11 11.2 2 3.02 \n","12 12.3 5 3.32 \n","13 5.4 9 1.46 \n","14 13.8 4 3.73 \n","15 8.1 3 2.19 \n","16 10.0 5 2.70 \n","17 13.0 2 3.51 \n","18 10.6 4 2.86 \n","19 5.7 6 1.54 \n","20 9.5 19 2.57 \n","21 7.7 6 2.08 \n","22 15.5 5 4.19 \n","23 9.5 3 2.57 \n","24 11.1 1 3.00 \n","25 14.2 6 3.83 \n","26 10.3 5 2.78 \n","27 14.7 6 3.97 \n","28 14.5 6 3.92 \n","29 14.6 15 3.94 \n","30 10.0 4 2.70 \n","31 3.5 3 0.95 \n","32 8.5 5 2.30 \n","33 13.2 5 3.56 \n","34 7.4 5 2.00 \n","35 9.3 5 2.51 \n","36 11.6 3 3.13 \n","37 14.6 3 3.94 \n","38 12.6 3 3.40 \n","39 8.2 6 2.21 \n","40 6.2 5 1.67 \n","41 8.0 4 2.16 \n","42 7.3 4 1.97 \n","43 11.7 4 3.16 \n","44 10.9 9 2.94 \n","45 9.5 7 2.57 \n","46 5.3 5 1.43 \n","47 9.7 8 2.62 \n","48 10.7 3 2.89 \n","49 8.0 2 2.16 \n","50 6.7 18 1.81 \n","\n"," Customer service calls Churn \n","0 1 0 \n","1 1 0 \n","2 0 0 \n","3 3 0 \n","4 0 0 \n","5 3 0 \n","6 0 0 \n","7 1 0 \n","8 0 0 \n","9 4 1 \n","10 0 0 \n","11 1 0 \n","12 3 0 \n","13 4 1 \n","14 1 0 \n","15 3 0 \n","16 1 0 \n","17 1 0 \n","18 0 0 \n","19 5 1 \n","20 0 0 \n","21 2 0 \n","22 3 0 \n","23 0 0 \n","24 1 0 \n","25 2 0 \n","26 1 0 \n","27 3 0 \n","28 0 0 \n","29 0 1 \n","30 2 0 \n","31 1 0 \n","32 2 0 \n","33 3 0 \n","34 2 0 \n","35 2 0 \n","36 2 0 \n","37 2 0 \n","38 3 0 \n","39 2 0 \n","40 2 0 \n","41 3 0 \n","42 1 0 \n","43 0 1 \n","44 2 0 \n","45 3 0 \n","46 1 1 \n","47 0 0 \n","48 1 0 \n","49 0 0 \n","50 1 0 "]},"metadata":{},"execution_count":25}]},{"cell_type":"code","metadata":{"scrolled":true,"id":"qGN5gaALtU8R","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1633609638556,"user_tz":-300,"elapsed":75,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"1f803cde-6306-4ebe-cd3a-1cb7ad85010e"},"source":["df.iloc[0:5, 0:3]"],"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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StateAccount lengthArea code
0KS128415
1OH107415
2NJ137415
3OH84408
4OK75415
\n","
"],"text/plain":[" State Account length Area code\n","0 KS 128 415\n","1 OH 107 415\n","2 NJ 137 415\n","3 OH 84 408\n","4 OK 75 415"]},"metadata":{},"execution_count":26}]},{"cell_type":"markdown","metadata":{"id":"UCMKdcx9tU8S"},"source":["Метод `ix` индексирует и по названию, и по номеру, но он вызывает путаницу, и поэтому был объявлен устаревшим (deprecated)."]},{"cell_type":"markdown","metadata":{"id":"HnMAXWTAtU8S"},"source":["Если нам нужна первая или последняя строчка датафрейма, пользуемся конструкцией `df[:1]` или `df[-1:]`:"]},{"cell_type":"code","metadata":{"scrolled":true,"id":"OrwoqAGPtU8U","colab":{"base_uri":"https://localhost:8080/","height":115},"executionInfo":{"status":"ok","timestamp":1633609638558,"user_tz":-300,"elapsed":74,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"53bc6332-8c03-4b98-9335-295812d859cd"},"source":["df[-1:]"],"execution_count":27,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StateAccount lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurn
3332TN74415NoYes25234.411339.85265.98222.6241.47710.8613.743.700
\n","
"],"text/plain":[" State Account length Area code International plan Voice mail plan \\\n","3332 TN 74 415 No Yes \n","\n"," Number vmail messages Total day minutes Total day calls \\\n","3332 25 234.4 113 \n","\n"," Total day charge Total eve minutes Total eve calls Total eve charge \\\n","3332 39.85 265.9 82 22.6 \n","\n"," Total night minutes Total night calls Total night charge \\\n","3332 241.4 77 10.86 \n","\n"," Total intl minutes Total intl calls Total intl charge \\\n","3332 13.7 4 3.7 \n","\n"," Customer service calls Churn \n","3332 0 0 "]},"metadata":{},"execution_count":27}]},{"cell_type":"markdown","metadata":{"id":"Ur_--vTVtU8W"},"source":["### Применение функций: `apply`, `map` и др."]},{"cell_type":"markdown","metadata":{"id":"da6UVfVjtU8W"},"source":["**Применение функции к каждому столбцу:**"]},{"cell_type":"code","metadata":{"id":"LIlX4ORVtU8W","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1633609638559,"user_tz":-300,"elapsed":71,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"315e42ff-9efa-4fa9-e41f-cba08a9534d2"},"source":["df.apply(np.max)"],"execution_count":28,"outputs":[{"output_type":"execute_result","data":{"text/plain":["State WY\n","Account length 243\n","Area code 510\n","International plan Yes\n","Voice mail plan Yes\n","Number vmail messages 51\n","Total day minutes 3.5e+02\n","Total day calls 165\n","Total day charge 60\n","Total eve minutes 3.6e+02\n","Total eve calls 170\n","Total eve charge 31\n","Total night minutes 4e+02\n","Total night calls 175\n","Total night charge 18\n","Total intl minutes 20\n","Total intl calls 20\n","Total intl charge 5.4\n","Customer service calls 9\n","Churn 1\n","dtype: object"]},"metadata":{},"execution_count":28}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":478},"id":"p-mUIP9HQakx","executionInfo":{"status":"ok","timestamp":1633609638561,"user_tz":-300,"elapsed":66,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"bbe218f1-eddb-4ff4-eb09-6a148e04a29c"},"source":["def make_feature(row):\n"," if row['Voice mail plan'] == 'Yes':\n"," return row['Number vmail messages'] * 4\n"," return row['Number vmail messages'] + 4\n","df['new_Number_vmail_messages'] = df.apply(make_feature, axis=1)\n","df"],"execution_count":29,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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StateAccount lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurnnew_Number_vmail_messages
0KS128415NoYes25265.111045.07197.49916.78244.79111.0110.032.7010100
1OH107415NoYes26161.612327.47195.510316.62254.410311.4513.733.7010104
2NJ137415NoNo0243.411441.38121.211010.30162.61047.3212.253.29004
3OH84408YesNo0299.47150.9061.9885.26196.9898.866.671.78204
4OK75415YesNo0166.711328.34148.312212.61186.91218.4110.132.73304
..................................................................
3328AZ192415NoYes36156.27726.55215.512618.32279.18312.569.962.6720144
3329WV68415NoNo0231.15739.29153.45513.04191.31238.619.642.59304
3330RI28510NoNo0180.810930.74288.85824.55191.9918.6414.163.81204
3331CT184510YesNo0213.810536.35159.68413.57139.21376.265.0101.35204
3332TN74415NoYes25234.411339.85265.98222.60241.47710.8613.743.7000100
\n","

3333 rows × 21 columns

\n","
"],"text/plain":[" State Account length Area code International plan Voice mail plan \\\n","0 KS 128 415 No Yes \n","1 OH 107 415 No Yes \n","2 NJ 137 415 No No \n","3 OH 84 408 Yes No \n","4 OK 75 415 Yes No \n","... ... ... ... ... ... \n","3328 AZ 192 415 No Yes \n","3329 WV 68 415 No No \n","3330 RI 28 510 No No \n","3331 CT 184 510 Yes No \n","3332 TN 74 415 No Yes \n","\n"," Number vmail messages Total day minutes Total day calls \\\n","0 25 265.1 110 \n","1 26 161.6 123 \n","2 0 243.4 114 \n","3 0 299.4 71 \n","4 0 166.7 113 \n","... ... ... ... \n","3328 36 156.2 77 \n","3329 0 231.1 57 \n","3330 0 180.8 109 \n","3331 0 213.8 105 \n","3332 25 234.4 113 \n","\n"," Total day charge Total eve minutes Total eve calls Total eve charge \\\n","0 45.07 197.4 99 16.78 \n","1 27.47 195.5 103 16.62 \n","2 41.38 121.2 110 10.30 \n","3 50.90 61.9 88 5.26 \n","4 28.34 148.3 122 12.61 \n","... ... ... ... ... \n","3328 26.55 215.5 126 18.32 \n","3329 39.29 153.4 55 13.04 \n","3330 30.74 288.8 58 24.55 \n","3331 36.35 159.6 84 13.57 \n","3332 39.85 265.9 82 22.60 \n","\n"," Total night minutes Total night calls Total night charge \\\n","0 244.7 91 11.01 \n","1 254.4 103 11.45 \n","2 162.6 104 7.32 \n","3 196.9 89 8.86 \n","4 186.9 121 8.41 \n","... ... ... ... \n","3328 279.1 83 12.56 \n","3329 191.3 123 8.61 \n","3330 191.9 91 8.64 \n","3331 139.2 137 6.26 \n","3332 241.4 77 10.86 \n","\n"," Total intl minutes Total intl calls Total intl charge \\\n","0 10.0 3 2.70 \n","1 13.7 3 3.70 \n","2 12.2 5 3.29 \n","3 6.6 7 1.78 \n","4 10.1 3 2.73 \n","... ... ... ... \n","3328 9.9 6 2.67 \n","3329 9.6 4 2.59 \n","3330 14.1 6 3.81 \n","3331 5.0 10 1.35 \n","3332 13.7 4 3.70 \n","\n"," Customer service calls Churn new_Number_vmail_messages \n","0 1 0 100 \n","1 1 0 104 \n","2 0 0 4 \n","3 2 0 4 \n","4 3 0 4 \n","... ... ... ... \n","3328 2 0 144 \n","3329 3 0 4 \n","3330 2 0 4 \n","3331 2 0 4 \n","3332 0 0 100 \n","\n","[3333 rows x 21 columns]"]},"metadata":{},"execution_count":29}]},{"cell_type":"markdown","metadata":{"id":"j2fEIU5ptU8Y"},"source":["Метод `apply` можно использовать и для того, чтобы применить функцию к каждой строке. Для этого нужно указать `axis=1`."]},{"cell_type":"markdown","metadata":{"id":"e-TxyhUttU8Y"},"source":["**Применение функции к каждой ячейке столбца**\n","\n","Допустим, по какой-то причине нас интересуют все люди из штатов, названия которых начинаются на 'W'. В данному случае это можно сделать по-разному, но наибольшую свободу дает связка `apply`-`lambda` – применение функции ко всем значениям в столбце."]},{"cell_type":"code","metadata":{"scrolled":false,"id":"-jnLxPnWtU8Z","colab":{"base_uri":"https://localhost:8080/","height":261},"executionInfo":{"status":"ok","timestamp":1633609638563,"user_tz":-300,"elapsed":65,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"bcab7df7-ed63-45c1-816b-ec70b6e4fa7b"},"source":["df[df[\"State\"].apply(lambda state: state[0] == \"W\")].head()"],"execution_count":30,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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StateAccount lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurnnew_Number_vmail_messages
9WV141415YesYes37258.68443.96222.011118.87326.49714.6911.253.0200148
26WY57408NoYes39213.011536.21191.111216.24182.71158.229.532.5700156
44WI64510NoNo0154.06726.18225.811819.19265.38611.943.530.95104
49WY97415NoYes24133.213522.64217.25818.4670.6793.1811.032.971096
54WY87415NoNo0151.08325.67219.711618.67203.91279.189.732.62514
\n","
"],"text/plain":[" State Account length Area code International plan Voice mail plan \\\n","9 WV 141 415 Yes Yes \n","26 WY 57 408 No Yes \n","44 WI 64 510 No No \n","49 WY 97 415 No Yes \n","54 WY 87 415 No No \n","\n"," Number vmail messages Total day minutes Total day calls \\\n","9 37 258.6 84 \n","26 39 213.0 115 \n","44 0 154.0 67 \n","49 24 133.2 135 \n","54 0 151.0 83 \n","\n"," Total day charge Total eve minutes Total eve calls Total eve charge \\\n","9 43.96 222.0 111 18.87 \n","26 36.21 191.1 112 16.24 \n","44 26.18 225.8 118 19.19 \n","49 22.64 217.2 58 18.46 \n","54 25.67 219.7 116 18.67 \n","\n"," Total night minutes Total night calls Total night charge \\\n","9 326.4 97 14.69 \n","26 182.7 115 8.22 \n","44 265.3 86 11.94 \n","49 70.6 79 3.18 \n","54 203.9 127 9.18 \n","\n"," Total intl minutes Total intl calls Total intl charge \\\n","9 11.2 5 3.02 \n","26 9.5 3 2.57 \n","44 3.5 3 0.95 \n","49 11.0 3 2.97 \n","54 9.7 3 2.62 \n","\n"," Customer service calls Churn new_Number_vmail_messages \n","9 0 0 148 \n","26 0 0 156 \n","44 1 0 4 \n","49 1 0 96 \n","54 5 1 4 "]},"metadata":{},"execution_count":30}]},{"cell_type":"markdown","metadata":{"id":"q6SkeDiJtU8Z"},"source":["Метод `map` можно использовать и для **замены значений в колонке**, передав ему в качестве аргумента словарь вида `{old_value: new_value}`:"]},{"cell_type":"code","metadata":{"id":"q3lbm6XXtU8a","colab":{"base_uri":"https://localhost:8080/","height":261},"executionInfo":{"status":"ok","timestamp":1633609638564,"user_tz":-300,"elapsed":63,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"10f505a4-3227-4ff5-b868-1efaadf4a181"},"source":["d = {\"No\": False, \"Yes\": True}\n","df[\"International plan\"] = df[\"International plan\"].map(d)\n","df.head()"],"execution_count":31,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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StateAccount lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurnnew_Number_vmail_messages
0KS128415FalseYes25265.111045.07197.49916.78244.79111.0110.032.7010100
1OH107415FalseYes26161.612327.47195.510316.62254.410311.4513.733.7010104
2NJ137415FalseNo0243.411441.38121.211010.30162.61047.3212.253.29004
3OH84408TrueNo0299.47150.9061.9885.26196.9898.866.671.78204
4OK75415TrueNo0166.711328.34148.312212.61186.91218.4110.132.73304
\n","
"],"text/plain":[" State Account length Area code International plan Voice mail plan \\\n","0 KS 128 415 False Yes \n","1 OH 107 415 False Yes \n","2 NJ 137 415 False No \n","3 OH 84 408 True No \n","4 OK 75 415 True No \n","\n"," Number vmail messages Total day minutes Total day calls \\\n","0 25 265.1 110 \n","1 26 161.6 123 \n","2 0 243.4 114 \n","3 0 299.4 71 \n","4 0 166.7 113 \n","\n"," Total day charge Total eve minutes Total eve calls Total eve charge \\\n","0 45.07 197.4 99 16.78 \n","1 27.47 195.5 103 16.62 \n","2 41.38 121.2 110 10.30 \n","3 50.90 61.9 88 5.26 \n","4 28.34 148.3 122 12.61 \n","\n"," Total night minutes Total night calls Total night charge \\\n","0 244.7 91 11.01 \n","1 254.4 103 11.45 \n","2 162.6 104 7.32 \n","3 196.9 89 8.86 \n","4 186.9 121 8.41 \n","\n"," Total intl minutes Total intl calls Total intl charge \\\n","0 10.0 3 2.70 \n","1 13.7 3 3.70 \n","2 12.2 5 3.29 \n","3 6.6 7 1.78 \n","4 10.1 3 2.73 \n","\n"," Customer service calls Churn new_Number_vmail_messages \n","0 1 0 100 \n","1 1 0 104 \n","2 0 0 4 \n","3 2 0 4 \n","4 3 0 4 "]},"metadata":{},"execution_count":31}]},{"cell_type":"markdown","metadata":{"id":"YkK8_gEBtU8b"},"source":["Аналогичную операцию можно провернуть с помощью метода `replace`:"]},{"cell_type":"code","metadata":{"id":"xop7OSmZtU8b","colab":{"base_uri":"https://localhost:8080/","height":261},"executionInfo":{"status":"ok","timestamp":1633609639156,"user_tz":-300,"elapsed":653,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"16d543f3-5373-45d1-cb55-b69355e6a5cb"},"source":["df = df.replace({\"Voice mail plan\": d})\n","df.head()"],"execution_count":32,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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StateAccount lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurnnew_Number_vmail_messages
0KS128415FalseTrue25265.111045.07197.49916.78244.79111.0110.032.7010100
1OH107415FalseTrue26161.612327.47195.510316.62254.410311.4513.733.7010104
2NJ137415FalseFalse0243.411441.38121.211010.30162.61047.3212.253.29004
3OH84408TrueFalse0299.47150.9061.9885.26196.9898.866.671.78204
4OK75415TrueFalse0166.711328.34148.312212.61186.91218.4110.132.73304
\n","
"],"text/plain":[" State Account length Area code International plan Voice mail plan \\\n","0 KS 128 415 False True \n","1 OH 107 415 False True \n","2 NJ 137 415 False False \n","3 OH 84 408 True False \n","4 OK 75 415 True False \n","\n"," Number vmail messages Total day minutes Total day calls \\\n","0 25 265.1 110 \n","1 26 161.6 123 \n","2 0 243.4 114 \n","3 0 299.4 71 \n","4 0 166.7 113 \n","\n"," Total day charge Total eve minutes Total eve calls Total eve charge \\\n","0 45.07 197.4 99 16.78 \n","1 27.47 195.5 103 16.62 \n","2 41.38 121.2 110 10.30 \n","3 50.90 61.9 88 5.26 \n","4 28.34 148.3 122 12.61 \n","\n"," Total night minutes Total night calls Total night charge \\\n","0 244.7 91 11.01 \n","1 254.4 103 11.45 \n","2 162.6 104 7.32 \n","3 196.9 89 8.86 \n","4 186.9 121 8.41 \n","\n"," Total intl minutes Total intl calls Total intl charge \\\n","0 10.0 3 2.70 \n","1 13.7 3 3.70 \n","2 12.2 5 3.29 \n","3 6.6 7 1.78 \n","4 10.1 3 2.73 \n","\n"," Customer service calls Churn new_Number_vmail_messages \n","0 1 0 100 \n","1 1 0 104 \n","2 0 0 4 \n","3 2 0 4 \n","4 3 0 4 "]},"metadata":{},"execution_count":32}]},{"cell_type":"markdown","metadata":{"id":"sJ9KC2CrtU8d"},"source":["### Группировка данных\n","\n","В общем случае группировка данных в Pandas выглядит следующим образом:\n","\n","```\n","df.groupby(by=grouping_columns)[columns_to_show].function()\n","```\n","\n","1. К датафрейму применяется метод **`groupby`**, который разделяет данные по `grouping_columns` – признаку или набору признаков.\n","3. Индексируем по нужным нам столбцам (`columns_to_show`). \n","2. К полученным группам применяется функция или несколько функций."]},{"cell_type":"markdown","metadata":{"id":"wiHvK8LFtU8d"},"source":["**Группирование данных в зависимости от значения признака `Churn` и вывод статистик по трём столбцам в каждой группе.**"]},{"cell_type":"code","metadata":{"id":"pXrstrQgtU8d","colab":{"base_uri":"https://localhost:8080/","height":175},"executionInfo":{"status":"ok","timestamp":1633609639159,"user_tz":-300,"elapsed":99,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"42ed5c1c-65c1-457f-cd03-6a26bb60da9f"},"source":["columns_to_show = [\"Total day minutes\", \"Total eve minutes\", \"Total night minutes\"]\n","\n","df.groupby([\"Churn\"])[columns_to_show].describe(percentiles=[])"],"execution_count":33,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
Total day minutesTotal eve minutesTotal night minutes
countmeanstdmin50%maxcountmeanstdmin50%maxcountmeanstdmin50%max
Churn
02850.0175.1850.180.0177.2315.62850.0199.0450.290.0199.6361.82850.0200.1351.1123.2200.25395.0
1483.0206.9169.000.0217.6350.8483.0212.4151.7370.9211.3363.7483.0205.2347.1347.4204.80354.9
\n","
"],"text/plain":[" Total day minutes Total eve minutes \\\n"," count mean std min 50% max count \n","Churn \n","0 2850.0 175.18 50.18 0.0 177.2 315.6 2850.0 \n","1 483.0 206.91 69.00 0.0 217.6 350.8 483.0 \n","\n"," Total night minutes \\\n"," mean std min 50% max count mean std \n","Churn \n","0 199.04 50.29 0.0 199.6 361.8 2850.0 200.13 51.11 \n","1 212.41 51.73 70.9 211.3 363.7 483.0 205.23 47.13 \n","\n"," \n"," min 50% max \n","Churn \n","0 23.2 200.25 395.0 \n","1 47.4 204.80 354.9 "]},"metadata":{},"execution_count":33}]},{"cell_type":"markdown","metadata":{"id":"_EZVRvNptU8d"},"source":["Сделаем то же самое, но немного по-другому, передав в `agg` список функций:"]},{"cell_type":"code","metadata":{"id":"9x5emqSwtU8e","colab":{"base_uri":"https://localhost:8080/","height":175},"executionInfo":{"status":"ok","timestamp":1633609639161,"user_tz":-300,"elapsed":97,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"9e81a4db-b89a-4e06-d928-70be8f26fdc5"},"source":["columns_to_show = [\"Total day minutes\", \"Total eve minutes\", \"Total night minutes\"]\n","\n","df.groupby([\"Churn\"])[columns_to_show].agg([np.mean, np.std, np.min, np.max])"],"execution_count":34,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
Total day minutesTotal eve minutesTotal night minutes
meanstdaminamaxmeanstdaminamaxmeanstdaminamax
Churn
0175.1850.180.0315.6199.0450.290.0361.8200.1351.1123.2395.0
1206.9169.000.0350.8212.4151.7370.9363.7205.2347.1347.4354.9
\n","
"],"text/plain":[" Total day minutes Total eve minutes \\\n"," mean std amin amax mean std amin \n","Churn \n","0 175.18 50.18 0.0 315.6 199.04 50.29 0.0 \n","1 206.91 69.00 0.0 350.8 212.41 51.73 70.9 \n","\n"," Total night minutes \n"," amax mean std amin amax \n","Churn \n","0 361.8 200.13 51.11 23.2 395.0 \n","1 363.7 205.23 47.13 47.4 354.9 "]},"metadata":{},"execution_count":34}]},{"cell_type":"markdown","metadata":{"id":"bMsnErVv_o77"},"source":["Сбрасываем индекс с группирующего поля"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"id":"8HFw5er5_DhM","executionInfo":{"status":"ok","timestamp":1633609639162,"user_tz":-300,"elapsed":93,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"bbdaaf9f-323e-42aa-e086-768f78599e65"},"source":["df.groupby('Churn', as_index=False)['State'].count()"],"execution_count":35,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
ChurnState
002850
11483
\n","
"],"text/plain":[" Churn State\n","0 0 2850\n","1 1 483"]},"metadata":{},"execution_count":35}]},{"cell_type":"markdown","metadata":{"id":"mwqgfLAVtU8e"},"source":["### Сводные таблицы"]},{"cell_type":"markdown","metadata":{"id":"gYnSr64ptU8e"},"source":["Допустим, мы хотим посмотреть, как наблюдения в нашей выборке распределены в контексте двух признаков — `Churn` и `Customer service calls`. Для этого мы можем построить **таблицу сопряженности**, воспользовавшись методом **`crosstab`**:"]},{"cell_type":"code","metadata":{"id":"yhgrYerutU8f","colab":{"base_uri":"https://localhost:8080/","height":143},"executionInfo":{"status":"ok","timestamp":1633609639163,"user_tz":-300,"elapsed":91,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"25d6f12a-34f1-4f8b-8f49-d8914548046d"},"source":["pd.crosstab(df[\"Churn\"], df[\"International plan\"])"],"execution_count":36,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
International planFalseTrue
Churn
02664186
1346137
\n","
"],"text/plain":["International plan False True \n","Churn \n","0 2664 186\n","1 346 137"]},"metadata":{},"execution_count":36}]},{"cell_type":"code","metadata":{"scrolled":true,"id":"cR0WankTtU8f","colab":{"base_uri":"https://localhost:8080/","height":143},"executionInfo":{"status":"ok","timestamp":1633609639165,"user_tz":-300,"elapsed":90,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"a949d3c8-88f3-4f4a-8a67-6208d2c44445"},"source":["pd.crosstab(df[\"Churn\"], df[\"Voice mail plan\"], normalize=True)"],"execution_count":37,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
Voice mail planFalseTrue
Churn
00.600.25
10.120.02
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"],"text/plain":["Voice mail plan False True \n","Churn \n","0 0.60 0.25\n","1 0.12 0.02"]},"metadata":{},"execution_count":37}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"P_eONQ24W0aU","executionInfo":{"status":"ok","timestamp":1633609639166,"user_tz":-300,"elapsed":87,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"525dd141-f2d7-47a6-c4ea-6bd3be3386a9"},"source":["df[\"Customer service calls\"].unique()"],"execution_count":38,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 0, 2, 3, 4, 5, 7, 9, 6, 8])"]},"metadata":{},"execution_count":38}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"abLbV3cKARwi","executionInfo":{"status":"ok","timestamp":1633609639167,"user_tz":-300,"elapsed":75,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"c5f227e8-5570-4c48-e7bf-48bb31227734"},"source":["df[\"Customer service calls\"].nunique()"],"execution_count":39,"outputs":[{"output_type":"execute_result","data":{"text/plain":["10"]},"metadata":{},"execution_count":39}]},{"cell_type":"markdown","metadata":{"id":"1F8uRUIwtU8h"},"source":["Мы видим, что большинство пользователей — лояльные и пользуются дополнительными услугами (международного роуминга / голосовой почты)."]},{"cell_type":"markdown","metadata":{"id":"reNYiSlJtU8h"},"source":["Продвинутые пользователи `Excel` наверняка вспомнят о такой фиче, как **сводные таблицы** (`pivot tables`). В `Pandas` за сводные таблицы отвечает метод **`pivot_table`**, который принимает в качестве параметров:\n","\n","* `values` – список переменных, по которым требуется рассчитать нужные статистики,\n","* `index` – список переменных, по которым нужно сгруппировать данные,\n","* `aggfunc` — то, что нам, собственно, нужно посчитать по группам — сумму, среднее, максимум, минимум или что-то ещё.\n","\n","Давайте посмотрим среднее число дневных, вечерних и ночных звонков для разных `Area code`:"]},{"cell_type":"code","metadata":{"scrolled":false,"id":"xabiD5fktU8h","colab":{"base_uri":"https://localhost:8080/","height":175},"executionInfo":{"status":"ok","timestamp":1633609639168,"user_tz":-300,"elapsed":68,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"a51980ac-bbb9-44b4-c453-1085d9014a7c"},"source":["df.pivot_table(\n"," [\"Total day calls\", \"Total eve calls\", \"Total night calls\"],\n"," [\"Area code\"],\n"," aggfunc=\"mean\",\n",").head(10)"],"execution_count":40,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
Total day callsTotal eve callsTotal night calls
Area code
408100.5099.7999.04
415100.58100.50100.40
510100.1099.67100.60
\n","
"],"text/plain":[" Total day calls Total eve calls Total night calls\n","Area code \n","408 100.50 99.79 99.04\n","415 100.58 100.50 100.40\n","510 100.10 99.67 100.60"]},"metadata":{},"execution_count":40}]},{"cell_type":"markdown","metadata":{"id":"0_haYJdjtU8h"},"source":["### Преобразование датафреймов\n","\n","Как и многие другие вещи, добавлять столбцы в `DataFrame` можно несколькими способами."]},{"cell_type":"markdown","metadata":{"id":"35zMtFv8tU8i"},"source":["Например, мы хотим посчитать общее количество звонков для всех пользователей. Создадим объект `total_calls` типа `Series` и вставим его в датафрейм:"]},{"cell_type":"code","metadata":{"id":"z1ktVfD0tU8i","colab":{"base_uri":"https://localhost:8080/","height":261},"executionInfo":{"status":"ok","timestamp":1633609639171,"user_tz":-300,"elapsed":67,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"a0c006bf-4504-4c46-af1d-8b8ab8167d79"},"source":["total_calls = (\n"," df[\"Total day calls\"]\n"," + df[\"Total eve calls\"]\n"," + df[\"Total night calls\"]\n"," + df[\"Total intl calls\"]\n",")\n","df.insert(loc=len(df.columns), column=\"Total calls\", value=total_calls)\n","# loc - номер столбца, после которого нужно вставить данный Series\n","# мы указали len(df.columns), чтобы вставить его в самом конце\n","df.head()"],"execution_count":41,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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StateAccount lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurnnew_Number_vmail_messagesTotal calls
0KS128415FalseTrue25265.111045.07197.49916.78244.79111.0110.032.7010100303
1OH107415FalseTrue26161.612327.47195.510316.62254.410311.4513.733.7010104332
2NJ137415FalseFalse0243.411441.38121.211010.30162.61047.3212.253.29004333
3OH84408TrueFalse0299.47150.9061.9885.26196.9898.866.671.78204255
4OK75415TrueFalse0166.711328.34148.312212.61186.91218.4110.132.73304359
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"],"text/plain":[" State Account length Area code International plan Voice mail plan \\\n","0 KS 128 415 False True \n","1 OH 107 415 False True \n","2 NJ 137 415 False False \n","3 OH 84 408 True False \n","4 OK 75 415 True False \n","\n"," Number vmail messages Total day minutes Total day calls \\\n","0 25 265.1 110 \n","1 26 161.6 123 \n","2 0 243.4 114 \n","3 0 299.4 71 \n","4 0 166.7 113 \n","\n"," Total day charge Total eve minutes Total eve calls Total eve charge \\\n","0 45.07 197.4 99 16.78 \n","1 27.47 195.5 103 16.62 \n","2 41.38 121.2 110 10.30 \n","3 50.90 61.9 88 5.26 \n","4 28.34 148.3 122 12.61 \n","\n"," Total night minutes Total night calls Total night charge \\\n","0 244.7 91 11.01 \n","1 254.4 103 11.45 \n","2 162.6 104 7.32 \n","3 196.9 89 8.86 \n","4 186.9 121 8.41 \n","\n"," Total intl minutes Total intl calls Total intl charge \\\n","0 10.0 3 2.70 \n","1 13.7 3 3.70 \n","2 12.2 5 3.29 \n","3 6.6 7 1.78 \n","4 10.1 3 2.73 \n","\n"," Customer service calls Churn new_Number_vmail_messages Total calls \n","0 1 0 100 303 \n","1 1 0 104 332 \n","2 0 0 4 333 \n","3 2 0 4 255 \n","4 3 0 4 359 "]},"metadata":{},"execution_count":41}]},{"cell_type":"markdown","metadata":{"id":"nB0mpCA1tU8j"},"source":["Добавить столбец из имеющихся можно и проще, не создавая промежуточных `Series`:"]},{"cell_type":"code","metadata":{"id":"ZVpdhf1etU8k","colab":{"base_uri":"https://localhost:8080/","height":261},"executionInfo":{"status":"ok","timestamp":1633609639173,"user_tz":-300,"elapsed":64,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"93b3fe31-2757-4cb7-afcc-7c10f765bb46"},"source":["df[\"Total charge\"] = (\n"," df[\"Total day charge\"]\n"," + df[\"Total eve charge\"]\n"," + df[\"Total night charge\"]\n"," + df[\"Total intl charge\"]\n",")\n","\n","df.head()"],"execution_count":42,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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StateAccount lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurnnew_Number_vmail_messagesTotal callsTotal charge
0KS128415FalseTrue25265.111045.07197.49916.78244.79111.0110.032.701010030375.56
1OH107415FalseTrue26161.612327.47195.510316.62254.410311.4513.733.701010433259.24
2NJ137415FalseFalse0243.411441.38121.211010.30162.61047.3212.253.2900433362.29
3OH84408TrueFalse0299.47150.9061.9885.26196.9898.866.671.7820425566.80
4OK75415TrueFalse0166.711328.34148.312212.61186.91218.4110.132.7330435952.09
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"],"text/plain":[" State Account length Area code International plan Voice mail plan \\\n","0 KS 128 415 False True \n","1 OH 107 415 False True \n","2 NJ 137 415 False False \n","3 OH 84 408 True False \n","4 OK 75 415 True False \n","\n"," Number vmail messages Total day minutes Total day calls \\\n","0 25 265.1 110 \n","1 26 161.6 123 \n","2 0 243.4 114 \n","3 0 299.4 71 \n","4 0 166.7 113 \n","\n"," Total day charge Total eve minutes Total eve calls Total eve charge \\\n","0 45.07 197.4 99 16.78 \n","1 27.47 195.5 103 16.62 \n","2 41.38 121.2 110 10.30 \n","3 50.90 61.9 88 5.26 \n","4 28.34 148.3 122 12.61 \n","\n"," Total night minutes Total night calls Total night charge \\\n","0 244.7 91 11.01 \n","1 254.4 103 11.45 \n","2 162.6 104 7.32 \n","3 196.9 89 8.86 \n","4 186.9 121 8.41 \n","\n"," Total intl minutes Total intl calls Total intl charge \\\n","0 10.0 3 2.70 \n","1 13.7 3 3.70 \n","2 12.2 5 3.29 \n","3 6.6 7 1.78 \n","4 10.1 3 2.73 \n","\n"," Customer service calls Churn new_Number_vmail_messages Total calls \\\n","0 1 0 100 303 \n","1 1 0 104 332 \n","2 0 0 4 333 \n","3 2 0 4 255 \n","4 3 0 4 359 \n","\n"," Total charge \n","0 75.56 \n","1 59.24 \n","2 62.29 \n","3 66.80 \n","4 52.09 "]},"metadata":{},"execution_count":42}]},{"cell_type":"markdown","metadata":{"id":"xrn0pZo1tU8l"},"source":["Чтобы удалить столбцы или строки, воспользуйтесь методом `drop`, передавая в качестве аргумента нужные индексы и требуемое значение параметра `axis` (`1`, если удаляете столбцы, и ничего или `0`, если удаляете строки):"]},{"cell_type":"code","metadata":{"scrolled":false,"id":"oSvOmNv-tU8l","colab":{"base_uri":"https://localhost:8080/","height":261},"executionInfo":{"status":"ok","timestamp":1633609639175,"user_tz":-300,"elapsed":62,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"d0304dcc-8e8a-42c9-8765-1822ae6f5c44"},"source":["# избавляемся от созданных только что столбцов\n","df = df.drop([\"Total charge\", \"Total calls\"], axis=1)\n","\n","df.drop([1, 2]).head() # а вот так можно удалить строчки"],"execution_count":43,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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StateAccount lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurnnew_Number_vmail_messages
0KS128415FalseTrue25265.111045.07197.49916.78244.79111.0110.032.7010100
3OH84408TrueFalse0299.47150.9061.9885.26196.9898.866.671.78204
4OK75415TrueFalse0166.711328.34148.312212.61186.91218.4110.132.73304
5AL118510TrueFalse0223.49837.98220.610118.75203.91189.186.361.70004
6MA121510FalseTrue24218.28837.09348.510829.62212.61189.577.572.033096
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"],"text/plain":[" State Account length Area code International plan Voice mail plan \\\n","0 KS 128 415 False True \n","3 OH 84 408 True False \n","4 OK 75 415 True False \n","5 AL 118 510 True False \n","6 MA 121 510 False True \n","\n"," Number vmail messages Total day minutes Total day calls \\\n","0 25 265.1 110 \n","3 0 299.4 71 \n","4 0 166.7 113 \n","5 0 223.4 98 \n","6 24 218.2 88 \n","\n"," Total day charge Total eve minutes Total eve calls Total eve charge \\\n","0 45.07 197.4 99 16.78 \n","3 50.90 61.9 88 5.26 \n","4 28.34 148.3 122 12.61 \n","5 37.98 220.6 101 18.75 \n","6 37.09 348.5 108 29.62 \n","\n"," Total night minutes Total night calls Total night charge \\\n","0 244.7 91 11.01 \n","3 196.9 89 8.86 \n","4 186.9 121 8.41 \n","5 203.9 118 9.18 \n","6 212.6 118 9.57 \n","\n"," Total intl minutes Total intl calls Total intl charge \\\n","0 10.0 3 2.70 \n","3 6.6 7 1.78 \n","4 10.1 3 2.73 \n","5 6.3 6 1.70 \n","6 7.5 7 2.03 \n","\n"," Customer service calls Churn new_Number_vmail_messages \n","0 1 0 100 \n","3 2 0 4 \n","4 3 0 4 \n","5 0 0 4 \n","6 3 0 96 "]},"metadata":{},"execution_count":43}]},{"cell_type":"markdown","metadata":{"id":"JLDUG5hNtU8l"},"source":["--------\n","\n","\n","\n","## Первые попытки прогнозирования оттока\n"]},{"cell_type":"markdown","metadata":{"id":"1sv6q4lNtU8m"},"source":["Посмотрим, как отток связан с признаком *\"Подключение международного роуминга\"* (`International plan`). Сделаем это с помощью сводной таблички `crosstab`, а также путем иллюстрации с `Seaborn` (как именно строить такие картинки и анализировать с их помощью графики – материал следующей статьи.)"]},{"cell_type":"code","metadata":{"collapsed":true,"id":"M7cBvVn-tU8m","executionInfo":{"status":"ok","timestamp":1633609639176,"user_tz":-300,"elapsed":57,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}}},"source":["# надо дополнительно установить (команда в терминале)\n","# чтоб картинки рисовались в тетрадке\n","# !conda install seaborn\n","%matplotlib inline\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","plt.rcParams[\"figure.figsize\"] = (8, 6)"],"execution_count":44,"outputs":[]},{"cell_type":"code","metadata":{"id":"8ZJBwL8NtU8m","colab":{"base_uri":"https://localhost:8080/","height":175},"executionInfo":{"status":"ok","timestamp":1633609639177,"user_tz":-300,"elapsed":56,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"334f814d-2c27-4f67-cabd-17159188ca2b"},"source":["pd.crosstab(df[\"Churn\"], df[\"International plan\"], margins=True)"],"execution_count":45,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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International planFalseTrueAll
Churn
026641862850
1346137483
All30103233333
\n","
"],"text/plain":["International plan False True All\n","Churn \n","0 2664 186 2850\n","1 346 137 483\n","All 3010 323 3333"]},"metadata":{},"execution_count":45}]},{"cell_type":"code","metadata":{"id":"BGwuNSretU8n","colab":{"base_uri":"https://localhost:8080/","height":388},"executionInfo":{"status":"ok","timestamp":1633609640633,"user_tz":-300,"elapsed":1509,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"0b7deac2-30bb-4ec9-e84b-ec60b64ceeff"},"source":["sns.countplot(x=\"International plan\", hue=\"Churn\", data=df)\n","plt.savefig(\"int_plan_and_churn.png\", dpi=300);"],"execution_count":46,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"j8CpA17FtU8o"},"source":["Видим, что когда роуминг подключен, доля оттока намного выше – интересное наблюдение! Возможно, большие и плохо контролируемые траты в роуминге очень конфликтогенны и приводят к недовольству клиентов телеком-оператора и, соответственно, к их оттоку. "]},{"cell_type":"markdown","metadata":{"id":"JDm9ePM4tU8o"},"source":["Далее посмотрим на еще один важный признак – *\"Число обращений в сервисный центр\"* (`Customer service calls`). Также построим сводную таблицу и картинку."]},{"cell_type":"code","metadata":{"id":"UKGrw7fbtU8p","colab":{"base_uri":"https://localhost:8080/","height":175},"executionInfo":{"status":"ok","timestamp":1633609640635,"user_tz":-300,"elapsed":25,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"7801c2d5-2a6d-4872-cde2-f847b851b3c5"},"source":["pd.crosstab(df[\"Churn\"], df[\"Customer service calls\"], margins=True)"],"execution_count":47,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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Customer service calls0123456789All
Churn
06051059672385902684102850
1921228744764014512483
All697118175942916666229223333
\n","
"],"text/plain":["Customer service calls 0 1 2 3 4 5 6 7 8 9 All\n","Churn \n","0 605 1059 672 385 90 26 8 4 1 0 2850\n","1 92 122 87 44 76 40 14 5 1 2 483\n","All 697 1181 759 429 166 66 22 9 2 2 3333"]},"metadata":{},"execution_count":47}]},{"cell_type":"code","metadata":{"id":"sMJh9m1VtU8p","colab":{"base_uri":"https://localhost:8080/","height":388},"executionInfo":{"status":"ok","timestamp":1633609642719,"user_tz":-300,"elapsed":2104,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"43183fcc-f324-4492-acd6-30a6cf0615b5"},"source":["sns.countplot(x=\"Customer service calls\", hue=\"Churn\", data=df)\n","plt.savefig(\"serv_calls__and_churn.png\", dpi=300);"],"execution_count":48,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"x2ZJPe-DtU8q"},"source":["Может быть, по сводной табличке это не так хорошо видно (или скучно ползать взглядом по строчкам с цифрами), а вот картинка красноречиво свидетельствует о том, что доля оттока сильно возрастает начиная с 4 звонков в сервисный центр. "]},{"cell_type":"markdown","metadata":{"id":"Dqj4LVe3tU8q"},"source":["Добавим теперь в наш DataFrame бинарный признак — результат сравнения `Customer service calls > 3`. И еще раз посмотрим, как он связан с оттоком. "]},{"cell_type":"code","metadata":{"scrolled":true,"id":"o9R6NM8ltU8q","colab":{"base_uri":"https://localhost:8080/","height":175},"executionInfo":{"status":"ok","timestamp":1633609642722,"user_tz":-300,"elapsed":14,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"ab3e7ee2-24f1-455b-c973-e994cf85a4c7"},"source":["df[\"Many_service_calls\"] = (df[\"Customer service calls\"] > 3).astype(\"int\")\n","\n","pd.crosstab(df[\"Many_service_calls\"], df[\"Churn\"], margins=True)"],"execution_count":49,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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Churn01All
Many_service_calls
027213453066
1129138267
All28504833333
\n","
"],"text/plain":["Churn 0 1 All\n","Many_service_calls \n","0 2721 345 3066\n","1 129 138 267\n","All 2850 483 3333"]},"metadata":{},"execution_count":49}]},{"cell_type":"code","metadata":{"id":"nUQk7G96tU8r","colab":{"base_uri":"https://localhost:8080/","height":389},"executionInfo":{"status":"ok","timestamp":1633609643487,"user_tz":-300,"elapsed":777,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"ed0370de-9813-45fa-f666-ee36ce5206a1"},"source":["sns.countplot(x=\"Many_service_calls\", hue=\"Churn\", data=df)\n","plt.savefig(\"many_serv_calls__and_churn.png\", dpi=300);"],"execution_count":50,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"XfYD5KqGtU8s"},"source":["Объединим рассмотренные выше условия и построим сводную табличку для этого объединения и оттока."]},{"cell_type":"code","metadata":{"id":"ZuaXCibrtU8s","colab":{"base_uri":"https://localhost:8080/","height":143},"executionInfo":{"status":"ok","timestamp":1633609643489,"user_tz":-300,"elapsed":36,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64","userId":"11145992452404092449"}},"outputId":"dbd40bfd-2dd2-41f1-ba50-cf159b12ddd7"},"source":["pd.crosstab(df[\"Many_service_calls\"] & df[\"International plan\"], df[\"Churn\"])"],"execution_count":51,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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Churn01
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True919
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"],"text/plain":["Churn 0 1\n","row_0 \n","False 2841 464\n","True 9 19"]},"metadata":{},"execution_count":51}]},{"cell_type":"markdown","metadata":{"id":"VyaMr43HtU8t"},"source":["Значит, прогнозируя отток клиента в случае, когда число звонков в сервисный центр больше 3 и подключен роуминг (и прогнозируя лояльность – в противном случае), можно ожидать около 85.8% правильных попаданий (ошибаемся всего 464 + 9 раз). Эти 85.8%, которые мы получили с помощью очень простых рассуждений – это неплохая отправная точка (*baseline*) для дальнейших моделей машинного обучения, которые мы будем строить. "]},{"cell_type":"markdown","metadata":{"id":"d6_n0ESntU8u"},"source":["В целом до появления машинного обучения процесс анализа данных выглядел примерно так. Прорезюмируем:\n"," \n","- Доля лояльных клиентов в выборке – 85.5%. Самая наивная модель, ответ которой \"Клиент всегда лоялен\" на подобных данных будет угадывать примерно в 85.5% случаев. То есть доли правильных ответов (*accuracy*) последующих моделей должны быть как минимум не меньше, а лучше, значительно выше этой цифры;\n","- С помощью простого прогноза , который условно можно выразить такой формулой: \"International plan = True & Customer Service calls > 3 => Churn = 1, else Churn = 0\", можно ожидать долю угадываний 85.8%, что еще чуть выше 85.5%\n","- Эти два бейзлайна мы получили без всякого машинного обучения, и они служат отправной точной для наших последующих моделей. Если окажется, что мы громадными усилиями увеличиваем долю правильных ответов всего, скажем, на 0.5%, то возможно, мы что-то делаем не так, и достаточно ограничиться простой моделью из двух условий. \n","- Перед обучением сложных моделей рекомендуется немного покрутить данные и проверить простые предположения. Более того, в бизнес-приложениях машинного обучения чаще всего начинают именно с простых решений, а потом экспериментируют с их усложнением. "]}]} \ No newline at end of file +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "FzQ_ch0ktU7n" + }, + "source": [ + "#
Первичный анализ данных с Pandas
" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true, + "executionInfo": { + "elapsed": 631, + "status": "ok", + "timestamp": 1633609636856, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "Parpx34utU7s", + "scrolled": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QxIKAzfCtU7u" + }, + "source": [ + "Данные, с которыми работают дата саентисты и аналитики, обычно хранятся в виде табличек — например, в форматах `.csv`, `.tsv` или `.xlsx`. Для того, чтобы считать нужные данные из такого файла, отлично подходит библиотека Pandas.\n", + "\n", + "Основными структурами данных в Pandas являются классы `Series` и `DataFrame`. Первый из них представляет собой одномерный индексированный массив данных некоторого фиксированного типа. Второй - это двухмерная структура данных, представляющая собой таблицу, каждый столбец которой содержит данные одного типа. Можно представлять её как словарь объектов типа `Series`. Структура `DataFrame` отлично подходит для представления реальных данных: строки соответствуют признаковым описаниям отдельных объектов, а столбцы соответствуют признакам." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "l_Ell72CtU7w" + }, + "source": [ + "---------\n", + "\n", + "## Демонстрация основных методов Pandas \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YMu_ER8WtU7y" + }, + "source": [ + "### Чтение из файла и первичный анализ" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "efGYx1kqtU7z" + }, + "source": [ + "Прочитаем данные и посмотрим на первые 5 строк с помощью метода `head`:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true, + "executionInfo": { + "elapsed": 597, + "status": "ok", + "timestamp": 1633609637892, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "ByXZK9MFtU71", + "scrolled": true + }, + "outputs": [], + "source": [ + "df = pd.read_csv(\"https://raw.githubusercontent.com/Yorko/mlcourse.ai/master/data/telecom_churn.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 241 + }, + "executionInfo": { + "elapsed": 77, + "status": "ok", + "timestamp": 1633609637895, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "hFaFpz2utU73", + "outputId": "cbd457e9-c2bd-4beb-a1fa-c7ba8a4c5b97", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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StateAccount lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurn
0KS128415NoYes25265.111045.07197.49916.78244.79111.0110.032.701False
1OH107415NoYes26161.612327.47195.510316.62254.410311.4513.733.701False
2NJ137415NoNo0243.411441.38121.211010.30162.61047.3212.253.290False
3OH84408YesNo0299.47150.9061.9885.26196.9898.866.671.782False
4OK75415YesNo0166.711328.34148.312212.61186.91218.4110.132.733False
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" + ], + "text/plain": [ + " State Account length ... Customer service calls Churn\n", + "0 KS 128 ... 1 False\n", + "1 OH 107 ... 1 False\n", + "2 NJ 137 ... 0 False\n", + "3 OH 84 ... 2 False\n", + "4 OK 75 ... 3 False\n", + "\n", + "[5 rows x 20 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CpV496POtU75" + }, + "source": [ + "В Jupyter-ноутбуках датафреймы `Pandas` выводятся в виде вот таких красивых табличек, и `print(df.head())` выглядит хуже.\n", + "\n", + "Кстати, по умолчанию `Pandas` выводит всего 20 столбцов и 60 строк, поэтому если ваш датафрейм больше, воспользуйтесь функцией `set_option`:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true, + "executionInfo": { + "elapsed": 68, + "status": "ok", + "timestamp": 1633609637897, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "CYFyCCGGtU77" + }, + "outputs": [], + "source": [ + "# задание проанализировать все опции и выбрать 3-5 самых полезных по личному мнению \n", + "# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.set_option.html\n", + "pd.set_option(\"display.max_columns\", 100)\n", + "pd.set_option(\"display.max_rows\", 100)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CbfNn4a9tU78" + }, + "source": [ + "А также укажем значение параметра `presicion` равным 2, чтобы отображать два знака после запятой (а не 6, как установлено по умолчанию." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true, + "executionInfo": { + "elapsed": 67, + "status": "ok", + "timestamp": 1633609637899, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "-0MCBxGItU78" + }, + "outputs": [], + "source": [ + "pd.set_option(\"precision\", 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Cu652IOYtU79" + }, + "source": [ + "**Посмотрим на размер данных, названия признаков и их типы**" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 66, + "status": "ok", + "timestamp": 1633609637901, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "LQw6THQytU79", + "outputId": "b2d6d2f1-a6d1-47c6-e4bb-5c5f33834c4a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3333, 20)\n" + ] + } + ], + "source": [ + "print(df.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LJEPKleBtU7-" + }, + "source": [ + "Видим, что в таблице 3333 строки и 20 столбцов. Выведем названия столбцов:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 57, + "status": "ok", + "timestamp": 1633609637903, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "CQArdzC8tU7_", + "outputId": "08e4c81f-5a94-4589-c4d3-c6792128de13" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['State', 'Account length', 'Area code', 'International plan',\n", + " 'Voice mail plan', 'Number vmail messages', 'Total day minutes',\n", + " 'Total day calls', 'Total day charge', 'Total eve minutes',\n", + " 'Total eve calls', 'Total eve charge', 'Total night minutes',\n", + " 'Total night calls', 'Total night charge', 'Total intl minutes',\n", + " 'Total intl calls', 'Total intl charge', 'Customer service calls',\n", + " 'Churn'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "print(df.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RoZn1MpBtU8A" + }, + "source": [ + "Чтобы посмотреть общую информацию по датафрейму и всем признакам, воспользуемся методом **`info`**:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 54, + "status": "ok", + "timestamp": 1633609637906, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "W_ZF3eM8tU8B", + "outputId": "b4d58f04-d867-458f-bb5e-7b91fbdc9cd9", + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 3333 entries, 0 to 3332\n", + "Data columns (total 20 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 State 3333 non-null object \n", + " 1 Account length 3333 non-null int64 \n", + " 2 Area code 3333 non-null int64 \n", + " 3 International plan 3333 non-null object \n", + " 4 Voice mail plan 3333 non-null object \n", + " 5 Number vmail messages 3333 non-null int64 \n", + " 6 Total day minutes 3333 non-null float64\n", + " 7 Total day calls 3333 non-null int64 \n", + " 8 Total day charge 3333 non-null float64\n", + " 9 Total eve minutes 3333 non-null float64\n", + " 10 Total eve calls 3333 non-null int64 \n", + " 11 Total eve charge 3333 non-null float64\n", + " 12 Total night minutes 3333 non-null float64\n", + " 13 Total night calls 3333 non-null int64 \n", + " 14 Total night charge 3333 non-null float64\n", + " 15 Total intl minutes 3333 non-null float64\n", + " 16 Total intl calls 3333 non-null int64 \n", + " 17 Total intl charge 3333 non-null float64\n", + " 18 Customer service calls 3333 non-null int64 \n", + " 19 Churn 3333 non-null bool \n", + "dtypes: bool(1), float64(8), int64(8), object(3)\n", + "memory usage: 498.1+ KB\n", + "None\n" + ] + } + ], + "source": [ + "print(df.info())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FYDNyB6CtU8C" + }, + "source": [ + "`bool`, `int64`, `float64` и `object` — это типы признаков. Видим, что 1 признак — логический (`bool`), 3 признака имеют тип `object` и 16 признаков — числовые.\n", + "\n", + "**Изменить тип колонки** можно с помощью метода `astype`. Применим этот метод к признаку `Churn` и переведём его в `int64`:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true, + "executionInfo": { + "elapsed": 48, + "status": "ok", + "timestamp": 1633609637909, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "byRJQVM5tU8D" + }, + "outputs": [], + "source": [ + "df[\"Churn\"] = df[\"Churn\"].astype(\"int64\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sBTm0lLYtU8D" + }, + "source": [ + "Метод **`describe`** показывает основные статистические характеристики данных по каждому числовому признаку (типы `int64` и `float64`): число непропущенных значений, среднее, стандартное отклонение, диапазон, медиану, 0.25 и 0.75 квартили." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 335 + }, + "executionInfo": { + "elapsed": 48, + "status": "ok", + "timestamp": 1633609637911, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "bAsmrRI6tU8D", + "outputId": "32a7192a-b49b-4be7-9b6e-9b7f08f57731" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Account lengthArea codeNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurn
count3333.003333.003333.003333.003333.003333.003333.003333.003333.003333.003333.003333.003333.003333.003333.003333.003333.00
mean101.06437.188.10179.78100.4430.56200.98100.1117.08200.87100.119.0410.244.482.761.560.14
std39.8242.3713.6954.4720.079.2650.7119.924.3150.5719.572.282.792.460.751.320.35
min1.00408.000.000.000.000.000.000.000.0023.2033.001.040.000.000.000.000.00
25%74.00408.000.00143.7087.0024.43166.6087.0014.16167.0087.007.528.503.002.301.000.00
50%101.00415.000.00179.40101.0030.50201.40100.0017.12201.20100.009.0510.304.002.781.000.00
75%127.00510.0020.00216.40114.0036.79235.30114.0020.00235.30113.0010.5912.106.003.272.000.00
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" + ], + "text/plain": [ + " State International plan Voice mail plan\n", + "0 KS No Yes\n", + "1 OH No Yes\n", + "2 NJ No No\n", + "3 OH Yes No\n", + "4 OK Yes No\n", + "... ... ... ...\n", + "3328 AZ No Yes\n", + "3329 WV No No\n", + "3330 RI No No\n", + "3331 CT Yes No\n", + "3332 TN No Yes\n", + "\n", + "[3333 rows x 3 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.select_dtypes(include=['object', 'bool']) # exclude" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ge-uZsFvtU8G" + }, + "source": [ + "Для категориальных (тип `object`) и булевых (тип `bool`) признаков можно воспользоваться методом **`value_counts`**. Посмотрим на распределение нашей целевой переменной — `Churn`:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 115, + "status": "ok", + "timestamp": 1633609638540, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "eeDu-JiYtU8G", + "outputId": "19761b7d-d89b-49eb-e4bd-371bd68907d7" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 2850\n", + "1 483\n", + "Name: Churn, dtype: int64" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"Churn\"].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KANMt5q2tU8I" + }, + "source": [ + "2850 пользователей из 3333 — лояльные, значение переменной `Churn` у них — `0`.\n", + "\n", + "Посмотрим на распределение пользователей по переменной `Area code`. Укажем значение параметра `normalize=True`, чтобы посмотреть не абсолютные частоты, а относительные." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 109, + "status": "ok", + "timestamp": 1633609638542, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "pMenDSyHtU8I", + "outputId": "a99c176c-d2b0-45b9-e54f-653c1f060dd0" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "415 0.50\n", + "510 0.25\n", + "408 0.25\n", + "Name: Area code, dtype: float64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"Area code\"].value_counts(normalize=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "l4ikQZaptU8I" + }, + "source": [ + "### Сортировка\n", + "\n", + "`DataFrame` можно отсортировать по значению какого-нибудь из признаков. В нашем случае, например, по `Total day charge` (`ascending=False` для сортировки по убыванию):" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 241 + }, + "executionInfo": { + "elapsed": 102, + "status": "ok", + "timestamp": 1633609638544, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "GrbzIXBQtU8J", + "outputId": "7cf76892-8c0d-42fa-fa98-aa49f8c2ab6e" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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StateAccount lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurn
365CO154415NoNo0350.87559.64216.59418.40253.910011.4310.192.7311
985NY64415YesNo0346.85558.96249.57921.21275.410212.3913.393.5911
2594OH115510YesNo0345.38158.70203.410617.29217.51079.7911.883.1911
156OH83415NoNo0337.412057.36227.411619.33153.91146.9315.874.2701
605MO112415NoNo0335.57757.04212.510918.06265.013211.9312.783.4321
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" + ], + "text/plain": [ + " State Account length Area code International plan Voice mail plan \\\n", + "365 CO 154 415 No No \n", + "985 NY 64 415 Yes No \n", + "2594 OH 115 510 Yes No \n", + "156 OH 83 415 No No \n", + "605 MO 112 415 No No \n", + "\n", + " Number vmail messages Total day minutes Total day calls \\\n", + "365 0 350.8 75 \n", + "985 0 346.8 55 \n", + "2594 0 345.3 81 \n", + "156 0 337.4 120 \n", + "605 0 335.5 77 \n", + "\n", + " Total day charge Total eve minutes Total eve calls Total eve charge \\\n", + "365 59.64 216.5 94 18.40 \n", + "985 58.96 249.5 79 21.21 \n", + "2594 58.70 203.4 106 17.29 \n", + "156 57.36 227.4 116 19.33 \n", + "605 57.04 212.5 109 18.06 \n", + "\n", + " Total night minutes Total night calls Total night charge \\\n", + "365 253.9 100 11.43 \n", + "985 275.4 102 12.39 \n", + "2594 217.5 107 9.79 \n", + "156 153.9 114 6.93 \n", + "605 265.0 132 11.93 \n", + "\n", + " Total intl minutes Total intl calls Total intl charge \\\n", + "365 10.1 9 2.73 \n", + "985 13.3 9 3.59 \n", + "2594 11.8 8 3.19 \n", + "156 15.8 7 4.27 \n", + "605 12.7 8 3.43 \n", + "\n", + " Customer service calls Churn \n", + "365 1 1 \n", + "985 1 1 \n", + "2594 1 1 \n", + "156 0 1 \n", + "605 2 1 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sort_values(by=\"Total day charge\", ascending=False).head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "apUOhvc_tU8J" + }, + "source": [ + "Сортировать можно и по группе столбцов:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 241 + }, + "executionInfo": { + "elapsed": 100, + "status": "ok", + "timestamp": 1633609638545, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "KUU1Xp63tU8K", + "outputId": "0bbacb6a-bbf7-4697-b720-20033f341ff3" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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StateAccount lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurn
688MN13510NoYes21315.610553.65208.97117.76260.112311.7012.133.2730
2259NC210415NoYes31313.88753.35147.710312.55192.7978.6710.172.7330
534LA67510NoNo0310.49752.7766.51235.65246.59911.099.2102.4840
575SD114415NoYes36309.99052.68200.38917.03183.51058.2614.223.8310
2858AL141510NoYes28308.012352.36247.812821.06152.91036.887.432.0010
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" + ], + "text/plain": [ + " State Account length Area code International plan Voice mail plan \\\n", + "688 MN 13 510 No Yes \n", + "2259 NC 210 415 No Yes \n", + "534 LA 67 510 No No \n", + "575 SD 114 415 No Yes \n", + "2858 AL 141 510 No Yes \n", + "\n", + " Number vmail messages Total day minutes Total day calls \\\n", + "688 21 315.6 105 \n", + "2259 31 313.8 87 \n", + "534 0 310.4 97 \n", + "575 36 309.9 90 \n", + "2858 28 308.0 123 \n", + "\n", + " Total day charge Total eve minutes Total eve calls Total eve charge \\\n", + "688 53.65 208.9 71 17.76 \n", + "2259 53.35 147.7 103 12.55 \n", + "534 52.77 66.5 123 5.65 \n", + "575 52.68 200.3 89 17.03 \n", + "2858 52.36 247.8 128 21.06 \n", + "\n", + " Total night minutes Total night calls Total night charge \\\n", + "688 260.1 123 11.70 \n", + "2259 192.7 97 8.67 \n", + "534 246.5 99 11.09 \n", + "575 183.5 105 8.26 \n", + "2858 152.9 103 6.88 \n", + "\n", + " Total intl minutes Total intl calls Total intl charge \\\n", + "688 12.1 3 3.27 \n", + "2259 10.1 7 2.73 \n", + "534 9.2 10 2.48 \n", + "575 14.2 2 3.83 \n", + "2858 7.4 3 2.00 \n", + "\n", + " Customer service calls Churn \n", + "688 3 0 \n", + "2259 3 0 \n", + "534 4 0 \n", + "575 1 0 \n", + "2858 1 0 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sort_values(by=[\"Churn\", \"Total day charge\"], ascending=[True, False]).head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VCTKeJUYtU8L" + }, + "source": [ + "### Индексация и извлечение данных" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lveNXBbztU8L" + }, + "source": [ + "`DataFrame` можно индексировать по-разному. В связи с этим рассмотрим различные способы индексации и извлечения нужных нам данных из датафрейма на примере простых вопросов.\n", + "\n", + "Для извлечения отдельного столбца можно использовать конструкцию вида `DataFrame['Name']`. Воспользуемся этим для ответа на вопрос: **какова доля нелояльных пользователей в нашем датафрейме?**" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 98, + "status": "ok", + "timestamp": 1633609638547, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "FLaA5u1ztU8L", + "outputId": "d1b61bde-7b0c-45d0-c2e9-32d9bb9539c0" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.14491449144914492" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"Churn\"].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QiJUnpEJtU8M" + }, + "source": [ + "14,5% — довольно плохой показатель для компании, с таким процентом оттока можно и разориться." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2v6CRyJ3tU8M" + }, + "source": [ + "Очень удобной является логическая индексация `DataFrame` по одному столбцу. Выглядит она следующим образом: `df[P(df['Name'])]`, где `P` - это некоторое логическое условие, проверяемое для каждого элемента столбца `Name`. Итогом такой индексации является `DataFrame`, состоящий только из строк, удовлетворяющих условию `P` по столбцу `Name`. \n", + "\n", + "Воспользуемся этим для ответа на вопрос: **каковы средние значения числовых признаков среди нелояльных пользователей?**" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 90, + "status": "ok", + "timestamp": 1633609638548, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "0G0_4zPytU8O", + "outputId": "79d763ca-3a4e-4408-f218-e5996dbd68bb", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Account length 102.66\n", + "Area code 437.82\n", + "Number vmail messages 5.12\n", + "Total day minutes 206.91\n", + "Total day calls 101.34\n", + "Total day charge 35.18\n", + "Total eve minutes 212.41\n", + "Total eve calls 100.56\n", + "Total eve charge 18.05\n", + "Total night minutes 205.23\n", + "Total night calls 100.40\n", + "Total night charge 9.24\n", + "Total intl minutes 10.70\n", + "Total intl calls 4.16\n", + "Total intl charge 2.89\n", + "Customer service calls 2.23\n", + "Churn 1.00\n", + "dtype: float64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df[\"Churn\"] == 1].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vX7Kv82ztU8O" + }, + "source": [ + "Скомбинировав предыдущие два вида индексации, ответим на вопрос: **сколько в среднем в течение дня разговаривают по телефону нелояльные пользователи**?" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 87, + "status": "ok", + "timestamp": 1633609638551, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "ZmpzMz9LtU8O", + "outputId": "f4ef2f49-5d18-4228-b513-96402e23b1b4" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "206.91407867494814" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df[\"Churn\"] == 1][\"Total day minutes\"].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rME2EKe8tU8P" + }, + "source": [ + "**Какова максимальная длина международных звонков среди лояльных пользователей (`Churn == 0`), не пользующихся услугой международного роуминга (`'International plan' == 'No'`)?**" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 82, + "status": "ok", + "timestamp": 1633609638552, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "DQ0H-bJttU8Q", + "outputId": "3c8a6304-7ede-495b-f2cc-dcf70beb252f" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "18.9" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[(df[\"Churn\"] == 0) & (df[\"International plan\"] == \"No\")][\"Total intl minutes\"].max()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f6IelrO4tU8Q" + }, + "source": [ + "Датафреймы можно индексировать как по названию столбца или строки, так и по порядковому номеру. Для индексации **по названию** используется метод **`loc`**, **по номеру** — **`iloc`**.\n", + "\n", + "В первом случае мы говорим _«передай нам значения для id строк от 0 до 5 и для столбцов от State до Area code»_, а во втором — _«передай нам значения первых пяти строк в первых трёх столбцах»_. \n", + "\n", + "В случае `iloc` срез работает как обычно, однако в случае `loc` учитываются и начало, и конец среза. Да, неудобно, да, вызывает путаницу." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "executionInfo": { + "elapsed": 78, + "status": "ok", + "timestamp": 1633609638554, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "Pp82lj7ktU8R", + "outputId": "8e2a9392-b3f0-44ee-e383-b19a46f8d708", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Account lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurn
0128415NoYes25265.111045.07197.49916.78244.79111.0110.032.7010
1107415NoYes26161.612327.47195.510316.62254.410311.4513.733.7010
2137415NoNo0243.411441.38121.211010.30162.61047.3212.253.2900
375415YesNo0166.711328.34148.312212.61186.91218.4110.132.7330
4118510YesNo0223.49837.98220.610118.75203.91189.186.361.7000
5121510NoYes24218.28837.09348.510829.62212.61189.577.572.0330
6147415YesNo0157.07926.69103.1948.76211.8969.537.161.9200
7117408NoNo0184.59731.37351.68029.89215.8909.718.742.3510
8141415YesYes37258.68443.96222.011118.87326.49714.6911.253.0200
965415NoNo0129.113721.95228.58319.42208.81119.4012.763.4341
1074415NoNo0187.712731.91163.414813.89196.0948.829.152.4600
11168408NoNo0128.89621.90104.9718.92141.11286.3511.223.0210
1295510NoNo0156.68826.62247.67521.05192.31158.6512.353.3230
13161415NoNo0332.96756.59317.89727.01160.61287.235.491.4641
1485408NoYes27196.413933.39280.99023.8889.3754.0213.843.7310
1593510NoNo0190.711432.42218.211118.55129.61215.838.132.1930
1676510NoYes33189.76632.25212.86518.09165.71087.4610.052.7010
1773415NoNo0224.49038.15159.58813.56192.8748.6813.023.5110
18147415NoNo0155.111726.37239.79320.37208.81339.4010.642.8600
1977408NoNo062.48910.61169.912114.44209.6649.435.761.5451
20130415NoNo0183.011231.1172.9996.20181.8788.189.5192.5700
21111415NoNo0110.410318.77137.310211.67189.61058.537.762.0820
22174415NoNo0124.37621.13277.111223.55250.711511.2815.554.1930
2357408NoYes39213.011536.21191.111216.24182.71158.229.532.5700
2449510NoNo0119.311720.28215.110918.28178.7908.0411.113.0010
25142415NoNo084.89514.42136.76311.62250.514811.2714.263.8320
2675510NoNo0226.110538.44201.510717.13246.29811.0810.352.7810
2772415NoYes37220.08037.40217.310218.47152.8716.8814.763.9730
2836408NoYes30146.312824.87162.58013.81129.31095.8214.563.9200
29135408YesYes41173.18529.43203.910717.33122.2785.5014.6153.9401
3034510NoNo0124.88221.22282.29823.99311.57814.0210.042.7020
3164510NoNo0154.06726.18225.811819.19265.38611.943.530.9510
3259408NoYes28120.99720.55213.09218.11163.11167.348.552.3020
3365415NoNo0211.312035.92162.612213.82134.71186.0613.253.5630
34142408NoNo0187.013331.79134.67411.44242.212710.907.452.0020
3596415NoNo0160.211727.23267.56722.74228.56810.289.352.5120
36116415NoYes34268.68345.66178.214215.15166.31067.4811.633.1320
3774510NoYes33193.79132.93246.19620.92138.0926.2114.633.9420
38149408NoYes28180.79230.72187.86415.96265.55311.9512.633.4030
3938408NoNo0131.29822.30162.99713.85159.01067.158.262.2120
4040415NoYes41148.17425.18169.58814.41214.11029.636.251.6720
41147510NoNo0248.68342.26148.98512.66172.51097.768.042.1630
4290415NoNo0203.414634.58226.711719.27152.41056.867.341.9710
4382415NoNo0300.310951.05181.010015.39270.17312.1511.743.1601
4474415NoYes35154.110426.20123.48410.49202.1579.0910.992.9420
4578415NoNo0252.99342.99178.411215.16263.910511.889.572.5730
46120408NoNo0212.113136.06209.410417.80167.2967.525.351.4311
4778415NoNo0149.711925.45182.211515.49261.512611.779.782.6200
4882415NoYes24155.213126.38244.510620.78122.4685.5110.732.8910
49199415NoYes34230.612139.20219.49918.65299.39413.478.022.1600
5079408NoNo0205.712334.97214.510818.23226.110610.176.7181.8110
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" + ], + "text/plain": [ + " Account length Area code International plan Voice mail plan \\\n", + "0 128 415 No Yes \n", + "1 107 415 No Yes \n", + "2 137 415 No No \n", + "3 75 415 Yes No \n", + "4 118 510 Yes No \n", + "5 121 510 No Yes \n", + "6 147 415 Yes No \n", + "7 117 408 No No \n", + "8 141 415 Yes Yes \n", + "9 65 415 No No \n", + "10 74 415 No No \n", + "11 168 408 No No \n", + "12 95 510 No No \n", + "13 161 415 No No \n", + "14 85 408 No Yes \n", + "15 93 510 No No \n", + "16 76 510 No Yes \n", + "17 73 415 No No \n", + "18 147 415 No No \n", + "19 77 408 No No \n", + "20 130 415 No No \n", + "21 111 415 No No \n", + "22 174 415 No No \n", + "23 57 408 No Yes \n", + "24 49 510 No No \n", + "25 142 415 No No \n", + "26 75 510 No No \n", + "27 72 415 No Yes \n", + "28 36 408 No Yes \n", + "29 135 408 Yes Yes \n", + "30 34 510 No No \n", + "31 64 510 No No \n", + "32 59 408 No Yes \n", + "33 65 415 No No \n", + "34 142 408 No No \n", + "35 96 415 No No \n", + "36 116 415 No Yes \n", + "37 74 510 No Yes \n", + "38 149 408 No Yes \n", + "39 38 408 No No \n", + "40 40 415 No Yes \n", + "41 147 510 No No \n", + "42 90 415 No No \n", + "43 82 415 No No \n", + "44 74 415 No Yes \n", + "45 78 415 No No \n", + "46 120 408 No No \n", + "47 78 415 No No \n", + "48 82 415 No Yes \n", + "49 199 415 No Yes \n", + "50 79 408 No No \n", + "\n", + " Number vmail messages Total day minutes Total day calls \\\n", + "0 25 265.1 110 \n", + "1 26 161.6 123 \n", + "2 0 243.4 114 \n", + "3 0 166.7 113 \n", + "4 0 223.4 98 \n", + "5 24 218.2 88 \n", + "6 0 157.0 79 \n", + "7 0 184.5 97 \n", + "8 37 258.6 84 \n", + "9 0 129.1 137 \n", + "10 0 187.7 127 \n", + "11 0 128.8 96 \n", + "12 0 156.6 88 \n", + "13 0 332.9 67 \n", + "14 27 196.4 139 \n", + "15 0 190.7 114 \n", + "16 33 189.7 66 \n", + "17 0 224.4 90 \n", + "18 0 155.1 117 \n", + "19 0 62.4 89 \n", + "20 0 183.0 112 \n", + "21 0 110.4 103 \n", + "22 0 124.3 76 \n", + "23 39 213.0 115 \n", + "24 0 119.3 117 \n", + "25 0 84.8 95 \n", + "26 0 226.1 105 \n", + "27 37 220.0 80 \n", + "28 30 146.3 128 \n", + "29 41 173.1 85 \n", + "30 0 124.8 82 \n", + "31 0 154.0 67 \n", + "32 28 120.9 97 \n", + "33 0 211.3 120 \n", + "34 0 187.0 133 \n", + "35 0 160.2 117 \n", + "36 34 268.6 83 \n", + "37 33 193.7 91 \n", + "38 28 180.7 92 \n", + "39 0 131.2 98 \n", + "40 41 148.1 74 \n", + "41 0 248.6 83 \n", + "42 0 203.4 146 \n", + "43 0 300.3 109 \n", + "44 35 154.1 104 \n", + "45 0 252.9 93 \n", + "46 0 212.1 131 \n", + "47 0 149.7 119 \n", + "48 24 155.2 131 \n", + "49 34 230.6 121 \n", + "50 0 205.7 123 \n", + "\n", + " Total day charge Total eve minutes Total eve calls Total eve charge \\\n", + "0 45.07 197.4 99 16.78 \n", + "1 27.47 195.5 103 16.62 \n", + "2 41.38 121.2 110 10.30 \n", + "3 28.34 148.3 122 12.61 \n", + "4 37.98 220.6 101 18.75 \n", + "5 37.09 348.5 108 29.62 \n", + "6 26.69 103.1 94 8.76 \n", + "7 31.37 351.6 80 29.89 \n", + "8 43.96 222.0 111 18.87 \n", + "9 21.95 228.5 83 19.42 \n", + "10 31.91 163.4 148 13.89 \n", + "11 21.90 104.9 71 8.92 \n", + "12 26.62 247.6 75 21.05 \n", + "13 56.59 317.8 97 27.01 \n", + "14 33.39 280.9 90 23.88 \n", + "15 32.42 218.2 111 18.55 \n", + "16 32.25 212.8 65 18.09 \n", + "17 38.15 159.5 88 13.56 \n", + "18 26.37 239.7 93 20.37 \n", + "19 10.61 169.9 121 14.44 \n", + "20 31.11 72.9 99 6.20 \n", + "21 18.77 137.3 102 11.67 \n", + "22 21.13 277.1 112 23.55 \n", + "23 36.21 191.1 112 16.24 \n", + "24 20.28 215.1 109 18.28 \n", + "25 14.42 136.7 63 11.62 \n", + "26 38.44 201.5 107 17.13 \n", + "27 37.40 217.3 102 18.47 \n", + "28 24.87 162.5 80 13.81 \n", + "29 29.43 203.9 107 17.33 \n", + "30 21.22 282.2 98 23.99 \n", + "31 26.18 225.8 118 19.19 \n", + "32 20.55 213.0 92 18.11 \n", + "33 35.92 162.6 122 13.82 \n", + "34 31.79 134.6 74 11.44 \n", + "35 27.23 267.5 67 22.74 \n", + "36 45.66 178.2 142 15.15 \n", + "37 32.93 246.1 96 20.92 \n", + "38 30.72 187.8 64 15.96 \n", + "39 22.30 162.9 97 13.85 \n", + "40 25.18 169.5 88 14.41 \n", + "41 42.26 148.9 85 12.66 \n", + "42 34.58 226.7 117 19.27 \n", + "43 51.05 181.0 100 15.39 \n", + "44 26.20 123.4 84 10.49 \n", + "45 42.99 178.4 112 15.16 \n", + "46 36.06 209.4 104 17.80 \n", + "47 25.45 182.2 115 15.49 \n", + "48 26.38 244.5 106 20.78 \n", + "49 39.20 219.4 99 18.65 \n", + "50 34.97 214.5 108 18.23 \n", + "\n", + " Total night minutes Total night calls Total night charge \\\n", + "0 244.7 91 11.01 \n", + "1 254.4 103 11.45 \n", + "2 162.6 104 7.32 \n", + "3 186.9 121 8.41 \n", + "4 203.9 118 9.18 \n", + "5 212.6 118 9.57 \n", + "6 211.8 96 9.53 \n", + "7 215.8 90 9.71 \n", + "8 326.4 97 14.69 \n", + "9 208.8 111 9.40 \n", + "10 196.0 94 8.82 \n", + "11 141.1 128 6.35 \n", + "12 192.3 115 8.65 \n", + "13 160.6 128 7.23 \n", + "14 89.3 75 4.02 \n", + "15 129.6 121 5.83 \n", + "16 165.7 108 7.46 \n", + "17 192.8 74 8.68 \n", + "18 208.8 133 9.40 \n", + "19 209.6 64 9.43 \n", + "20 181.8 78 8.18 \n", + "21 189.6 105 8.53 \n", + "22 250.7 115 11.28 \n", + "23 182.7 115 8.22 \n", + "24 178.7 90 8.04 \n", + "25 250.5 148 11.27 \n", + "26 246.2 98 11.08 \n", + "27 152.8 71 6.88 \n", + "28 129.3 109 5.82 \n", + "29 122.2 78 5.50 \n", + "30 311.5 78 14.02 \n", + "31 265.3 86 11.94 \n", + "32 163.1 116 7.34 \n", + "33 134.7 118 6.06 \n", + "34 242.2 127 10.90 \n", + "35 228.5 68 10.28 \n", + "36 166.3 106 7.48 \n", + "37 138.0 92 6.21 \n", + "38 265.5 53 11.95 \n", + "39 159.0 106 7.15 \n", + "40 214.1 102 9.63 \n", + "41 172.5 109 7.76 \n", + "42 152.4 105 6.86 \n", + "43 270.1 73 12.15 \n", + "44 202.1 57 9.09 \n", + "45 263.9 105 11.88 \n", + "46 167.2 96 7.52 \n", + "47 261.5 126 11.77 \n", + "48 122.4 68 5.51 \n", + "49 299.3 94 13.47 \n", + "50 226.1 106 10.17 \n", + "\n", + " Total intl minutes Total intl calls Total intl charge \\\n", + "0 10.0 3 2.70 \n", + "1 13.7 3 3.70 \n", + "2 12.2 5 3.29 \n", + "3 10.1 3 2.73 \n", + "4 6.3 6 1.70 \n", + "5 7.5 7 2.03 \n", + "6 7.1 6 1.92 \n", + "7 8.7 4 2.35 \n", + "8 11.2 5 3.02 \n", + "9 12.7 6 3.43 \n", + "10 9.1 5 2.46 \n", + "11 11.2 2 3.02 \n", + "12 12.3 5 3.32 \n", + "13 5.4 9 1.46 \n", + "14 13.8 4 3.73 \n", + "15 8.1 3 2.19 \n", + "16 10.0 5 2.70 \n", + "17 13.0 2 3.51 \n", + "18 10.6 4 2.86 \n", + "19 5.7 6 1.54 \n", + "20 9.5 19 2.57 \n", + "21 7.7 6 2.08 \n", + "22 15.5 5 4.19 \n", + "23 9.5 3 2.57 \n", + "24 11.1 1 3.00 \n", + "25 14.2 6 3.83 \n", + "26 10.3 5 2.78 \n", + "27 14.7 6 3.97 \n", + "28 14.5 6 3.92 \n", + "29 14.6 15 3.94 \n", + "30 10.0 4 2.70 \n", + "31 3.5 3 0.95 \n", + "32 8.5 5 2.30 \n", + "33 13.2 5 3.56 \n", + "34 7.4 5 2.00 \n", + "35 9.3 5 2.51 \n", + "36 11.6 3 3.13 \n", + "37 14.6 3 3.94 \n", + "38 12.6 3 3.40 \n", + "39 8.2 6 2.21 \n", + "40 6.2 5 1.67 \n", + "41 8.0 4 2.16 \n", + "42 7.3 4 1.97 \n", + "43 11.7 4 3.16 \n", + "44 10.9 9 2.94 \n", + "45 9.5 7 2.57 \n", + "46 5.3 5 1.43 \n", + "47 9.7 8 2.62 \n", + "48 10.7 3 2.89 \n", + "49 8.0 2 2.16 \n", + "50 6.7 18 1.81 \n", + "\n", + " Customer service calls Churn \n", + "0 1 0 \n", + "1 1 0 \n", + "2 0 0 \n", + "3 3 0 \n", + "4 0 0 \n", + "5 3 0 \n", + "6 0 0 \n", + "7 1 0 \n", + "8 0 0 \n", + "9 4 1 \n", + "10 0 0 \n", + "11 1 0 \n", + "12 3 0 \n", + "13 4 1 \n", + "14 1 0 \n", + "15 3 0 \n", + "16 1 0 \n", + "17 1 0 \n", + "18 0 0 \n", + "19 5 1 \n", + "20 0 0 \n", + "21 2 0 \n", + "22 3 0 \n", + "23 0 0 \n", + "24 1 0 \n", + "25 2 0 \n", + "26 1 0 \n", + "27 3 0 \n", + "28 0 0 \n", + "29 0 1 \n", + "30 2 0 \n", + "31 1 0 \n", + "32 2 0 \n", + "33 3 0 \n", + "34 2 0 \n", + "35 2 0 \n", + "36 2 0 \n", + "37 2 0 \n", + "38 3 0 \n", + "39 2 0 \n", + "40 2 0 \n", + "41 3 0 \n", + "42 1 0 \n", + "43 0 1 \n", + "44 2 0 \n", + "45 3 0 \n", + "46 1 1 \n", + "47 0 0 \n", + "48 1 0 \n", + "49 0 0 \n", + "50 1 0 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d = df.copy()\n", + "d = d.drop_duplicates('State')\n", + "d = d.set_index('State')\n", + "# d = d.reset_index() # сбрасываем столбец-индекс не удаляя его\n", + "d = d.reset_index(drop=True) # сбрасываем столбец-индекс удаляя его\n", + "d\n", + "# d.loc['KS':'OK','Area code':'Total day minutes']" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "executionInfo": { + "elapsed": 75, + "status": "ok", + "timestamp": 1633609638556, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "qGN5gaALtU8R", + "outputId": "1f803cde-6306-4ebe-cd3a-1cb7ad85010e", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " State Account length Area code\n", + "0 KS 128 415\n", + "1 OH 107 415\n", + "2 NJ 137 415\n", + "3 OH 84 408\n", + "4 OK 75 415" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.iloc[0:5, 0:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UCMKdcx9tU8S" + }, + "source": [ + "Метод `ix` индексирует и по названию, и по номеру, но он вызывает путаницу, и поэтому был объявлен устаревшим (deprecated)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HnMAXWTAtU8S" + }, + "source": [ + "Если нам нужна первая или последняя строчка датафрейма, пользуемся конструкцией `df[:1]` или `df[-1:]`:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 115 + }, + "executionInfo": { + "elapsed": 74, + "status": "ok", + "timestamp": 1633609638558, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "OrwoqAGPtU8U", + "outputId": "53bc6332-8c03-4b98-9335-295812d859cd", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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1OH107415NoYes26161.612327.47195.510316.62254.410311.4513.733.7010104
2NJ137415NoNo0243.411441.38121.211010.30162.61047.3212.253.29004
3OH84408YesNo0299.47150.9061.9885.26196.9898.866.671.78204
4OK75415YesNo0166.711328.34148.312212.61186.91218.4110.132.73304
..................................................................
3328AZ192415NoYes36156.27726.55215.512618.32279.18312.569.962.6720144
3329WV68415NoNo0231.15739.29153.45513.04191.31238.619.642.59304
3330RI28510NoNo0180.810930.74288.85824.55191.9918.6414.163.81204
3331CT184510YesNo0213.810536.35159.68413.57139.21376.265.0101.35204
3332TN74415NoYes25234.411339.85265.98222.60241.47710.8613.743.7000100
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" + ], + "text/plain": [ + " State Account length Area code International plan Voice mail plan \\\n", + "0 KS 128 415 No Yes \n", + "1 OH 107 415 No Yes \n", + "2 NJ 137 415 No No \n", + "3 OH 84 408 Yes No \n", + "4 OK 75 415 Yes No \n", + "... ... ... ... ... ... \n", + "3328 AZ 192 415 No Yes \n", + "3329 WV 68 415 No No \n", + "3330 RI 28 510 No No \n", + "3331 CT 184 510 Yes No \n", + "3332 TN 74 415 No Yes \n", + "\n", + " Number vmail messages Total day minutes Total day calls \\\n", + "0 25 265.1 110 \n", + "1 26 161.6 123 \n", + "2 0 243.4 114 \n", + "3 0 299.4 71 \n", + "4 0 166.7 113 \n", + "... ... ... ... \n", + "3328 36 156.2 77 \n", + "3329 0 231.1 57 \n", + "3330 0 180.8 109 \n", + "3331 0 213.8 105 \n", + "3332 25 234.4 113 \n", + "\n", + " Total day charge Total eve minutes Total eve calls Total eve charge \\\n", + "0 45.07 197.4 99 16.78 \n", + "1 27.47 195.5 103 16.62 \n", + "2 41.38 121.2 110 10.30 \n", + "3 50.90 61.9 88 5.26 \n", + "4 28.34 148.3 122 12.61 \n", + "... ... ... ... ... \n", + "3328 26.55 215.5 126 18.32 \n", + "3329 39.29 153.4 55 13.04 \n", + "3330 30.74 288.8 58 24.55 \n", + "3331 36.35 159.6 84 13.57 \n", + "3332 39.85 265.9 82 22.60 \n", + "\n", + " Total night minutes Total night calls Total night charge \\\n", + "0 244.7 91 11.01 \n", + "1 254.4 103 11.45 \n", + "2 162.6 104 7.32 \n", + "3 196.9 89 8.86 \n", + "4 186.9 121 8.41 \n", + "... ... ... ... \n", + "3328 279.1 83 12.56 \n", + "3329 191.3 123 8.61 \n", + "3330 191.9 91 8.64 \n", + "3331 139.2 137 6.26 \n", + "3332 241.4 77 10.86 \n", + "\n", + " Total intl minutes Total intl calls Total intl charge \\\n", + "0 10.0 3 2.70 \n", + "1 13.7 3 3.70 \n", + "2 12.2 5 3.29 \n", + "3 6.6 7 1.78 \n", + "4 10.1 3 2.73 \n", + "... ... ... ... \n", + "3328 9.9 6 2.67 \n", + "3329 9.6 4 2.59 \n", + "3330 14.1 6 3.81 \n", + "3331 5.0 10 1.35 \n", + "3332 13.7 4 3.70 \n", + "\n", + " Customer service calls Churn new_Number_vmail_messages \n", + "0 1 0 100 \n", + "1 1 0 104 \n", + "2 0 0 4 \n", + "3 2 0 4 \n", + "4 3 0 4 \n", + "... ... ... ... \n", + "3328 2 0 144 \n", + "3329 3 0 4 \n", + "3330 2 0 4 \n", + "3331 2 0 4 \n", + "3332 0 0 100 \n", + "\n", + "[3333 rows x 21 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def make_feature(row):\n", + " if row['Voice mail plan'] == 'Yes':\n", + " return row['Number vmail messages'] * 4\n", + " return row['Number vmail messages'] + 4\n", + "df['new_Number_vmail_messages'] = df.apply(make_feature, axis=1)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j2fEIU5ptU8Y" + }, + "source": [ + "Метод `apply` можно использовать и для того, чтобы применить функцию к каждой строке. Для этого нужно указать `axis=1`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e-TxyhUttU8Y" + }, + "source": [ + "**Применение функции к каждой ячейке столбца**\n", + "\n", + "Допустим, по какой-то причине нас интересуют все люди из штатов, названия которых начинаются на 'W'. В данному случае это можно сделать по-разному, но наибольшую свободу дает связка `apply`-`lambda` – применение функции ко всем значениям в столбце." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 261 + }, + "executionInfo": { + "elapsed": 65, + "status": "ok", + "timestamp": 1633609638563, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "-jnLxPnWtU8Z", + "outputId": "bcab7df7-ed63-45c1-816b-ec70b6e4fa7b", + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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StateAccount lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurnnew_Number_vmail_messages
9WV141415YesYes37258.68443.96222.011118.87326.49714.6911.253.0200148
26WY57408NoYes39213.011536.21191.111216.24182.71158.229.532.5700156
44WI64510NoNo0154.06726.18225.811819.19265.38611.943.530.95104
49WY97415NoYes24133.213522.64217.25818.4670.6793.1811.032.971096
54WY87415NoNo0151.08325.67219.711618.67203.91279.189.732.62514
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" + ], + "text/plain": [ + " State Account length Area code International plan Voice mail plan \\\n", + "0 KS 128 415 False Yes \n", + "1 OH 107 415 False Yes \n", + "2 NJ 137 415 False No \n", + "3 OH 84 408 True No \n", + "4 OK 75 415 True No \n", + "\n", + " Number vmail messages Total day minutes Total day calls \\\n", + "0 25 265.1 110 \n", + "1 26 161.6 123 \n", + "2 0 243.4 114 \n", + "3 0 299.4 71 \n", + "4 0 166.7 113 \n", + "\n", + " Total day charge Total eve minutes Total eve calls Total eve charge \\\n", + "0 45.07 197.4 99 16.78 \n", + "1 27.47 195.5 103 16.62 \n", + "2 41.38 121.2 110 10.30 \n", + "3 50.90 61.9 88 5.26 \n", + "4 28.34 148.3 122 12.61 \n", + "\n", + " Total night minutes Total night calls Total night charge \\\n", + "0 244.7 91 11.01 \n", + "1 254.4 103 11.45 \n", + "2 162.6 104 7.32 \n", + "3 196.9 89 8.86 \n", + "4 186.9 121 8.41 \n", + "\n", + " Total intl minutes Total intl calls Total intl charge \\\n", + "0 10.0 3 2.70 \n", + "1 13.7 3 3.70 \n", + "2 12.2 5 3.29 \n", + "3 6.6 7 1.78 \n", + "4 10.1 3 2.73 \n", + "\n", + " Customer service calls Churn new_Number_vmail_messages \n", + "0 1 0 100 \n", + "1 1 0 104 \n", + "2 0 0 4 \n", + "3 2 0 4 \n", + "4 3 0 4 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d = {\"No\": False, \"Yes\": True}\n", + "df[\"International plan\"] = df[\"International plan\"].map(d)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YkK8_gEBtU8b" + }, + "source": [ + "Аналогичную операцию можно провернуть с помощью метода `replace`:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 261 + }, + "executionInfo": { + "elapsed": 653, + "status": "ok", + "timestamp": 1633609639156, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "xop7OSmZtU8b", + "outputId": "16d543f3-5373-45d1-cb55-b69355e6a5cb" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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StateAccount lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurnnew_Number_vmail_messages
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" + ], + "text/plain": [ + " Total day minutes Total eve minutes \\\n", + " count mean std min 50% max count \n", + "Churn \n", + "0 2850.0 175.18 50.18 0.0 177.2 315.6 2850.0 \n", + "1 483.0 206.91 69.00 0.0 217.6 350.8 483.0 \n", + "\n", + " Total night minutes \\\n", + " mean std min 50% max count mean std \n", + "Churn \n", + "0 199.04 50.29 0.0 199.6 361.8 2850.0 200.13 51.11 \n", + "1 212.41 51.73 70.9 211.3 363.7 483.0 205.23 47.13 \n", + "\n", + " \n", + " min 50% max \n", + "Churn \n", + "0 23.2 200.25 395.0 \n", + "1 47.4 204.80 354.9 " + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "columns_to_show = [\"Total day minutes\", \"Total eve minutes\", \"Total night minutes\"]\n", + "\n", + "df.groupby([\"Churn\"])[columns_to_show].describe(percentiles=[])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_EZVRvNptU8d" + }, + "source": [ + "Сделаем то же самое, но немного по-другому, передав в `agg` список функций:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 175 + }, + "executionInfo": { + "elapsed": 97, + "status": "ok", + "timestamp": 1633609639161, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "9x5emqSwtU8e", + "outputId": "9e81a4db-b89a-4e06-d928-70be8f26fdc5" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Total day minutes Total eve minutes \\\n", + " mean std amin amax mean std amin \n", + "Churn \n", + "0 175.18 50.18 0.0 315.6 199.04 50.29 0.0 \n", + "1 206.91 69.00 0.0 350.8 212.41 51.73 70.9 \n", + "\n", + " Total night minutes \n", + " amax mean std amin amax \n", + "Churn \n", + "0 361.8 200.13 51.11 23.2 395.0 \n", + "1 363.7 205.23 47.13 47.4 354.9 " + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "columns_to_show = [\"Total day minutes\", \"Total eve minutes\", \"Total night minutes\"]\n", + "\n", + "df.groupby([\"Churn\"])[columns_to_show].agg([np.mean, np.std, np.min, np.max])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bMsnErVv_o77" + }, + "source": [ + "Сбрасываем индекс с группирующего поля" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 112 + }, + "executionInfo": { + "elapsed": 93, + "status": "ok", + "timestamp": 1633609639162, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "8HFw5er5_DhM", + "outputId": "bbdaaf9f-323e-42aa-e086-768f78599e65" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Churn State\n", + "0 0 2850\n", + "1 1 483" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby('Churn', as_index=False)['State'].count()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mwqgfLAVtU8e" + }, + "source": [ + "### Сводные таблицы" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gYnSr64ptU8e" + }, + "source": [ + "Допустим, мы хотим посмотреть, как наблюдения в нашей выборке распределены в контексте двух признаков — `Churn` и `Customer service calls`. Для этого мы можем построить **таблицу сопряженности**, воспользовавшись методом **`crosstab`**:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + }, + "executionInfo": { + "elapsed": 91, + "status": "ok", + "timestamp": 1633609639163, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "yhgrYerutU8f", + "outputId": "25d6f12a-34f1-4f8b-8f49-d8914548046d" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Total day calls Total eve calls Total night calls\n", + "Area code \n", + "408 100.50 99.79 99.04\n", + "415 100.58 100.50 100.40\n", + "510 100.10 99.67 100.60" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.pivot_table(\n", + " [\"Total day calls\", \"Total eve calls\", \"Total night calls\"],\n", + " [\"Area code\"],\n", + " aggfunc=\"mean\",\n", + ").head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0_haYJdjtU8h" + }, + "source": [ + "### Преобразование датафреймов\n", + "\n", + "Как и многие другие вещи, добавлять столбцы в `DataFrame` можно несколькими способами." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "35zMtFv8tU8i" + }, + "source": [ + "Например, мы хотим посчитать общее количество звонков для всех пользователей. Создадим объект `total_calls` типа `Series` и вставим его в датафрейм:" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 261 + }, + "executionInfo": { + "elapsed": 67, + "status": "ok", + "timestamp": 1633609639171, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "z1ktVfD0tU8i", + "outputId": "a0c006bf-4504-4c46-af1d-8b8ab8167d79" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " State Account length Area code International plan Voice mail plan \\\n", + "0 KS 128 415 False True \n", + "1 OH 107 415 False True \n", + "2 NJ 137 415 False False \n", + "3 OH 84 408 True False \n", + "4 OK 75 415 True False \n", + "\n", + " Number vmail messages Total day minutes Total day calls \\\n", + "0 25 265.1 110 \n", + "1 26 161.6 123 \n", + "2 0 243.4 114 \n", + "3 0 299.4 71 \n", + "4 0 166.7 113 \n", + "\n", + " Total day charge Total eve minutes Total eve calls Total eve charge \\\n", + "0 45.07 197.4 99 16.78 \n", + "1 27.47 195.5 103 16.62 \n", + "2 41.38 121.2 110 10.30 \n", + "3 50.90 61.9 88 5.26 \n", + "4 28.34 148.3 122 12.61 \n", + "\n", + " Total night minutes Total night calls Total night charge \\\n", + "0 244.7 91 11.01 \n", + "1 254.4 103 11.45 \n", + "2 162.6 104 7.32 \n", + "3 196.9 89 8.86 \n", + "4 186.9 121 8.41 \n", + "\n", + " Total intl minutes Total intl calls Total intl charge \\\n", + "0 10.0 3 2.70 \n", + "1 13.7 3 3.70 \n", + "2 12.2 5 3.29 \n", + "3 6.6 7 1.78 \n", + "4 10.1 3 2.73 \n", + "\n", + " Customer service calls Churn new_Number_vmail_messages Total calls \n", + "0 1 0 100 303 \n", + "1 1 0 104 332 \n", + "2 0 0 4 333 \n", + "3 2 0 4 255 \n", + "4 3 0 4 359 " + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_calls = (\n", + " df[\"Total day calls\"]\n", + " + df[\"Total eve calls\"]\n", + " + df[\"Total night calls\"]\n", + " + df[\"Total intl calls\"]\n", + ")\n", + "df.insert(loc=len(df.columns), column=\"Total calls\", value=total_calls)\n", + "# loc - номер столбца, после которого нужно вставить данный Series\n", + "# мы указали len(df.columns), чтобы вставить его в самом конце\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nB0mpCA1tU8j" + }, + "source": [ + "Добавить столбец из имеющихся можно и проще, не создавая промежуточных `Series`:" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 261 + }, + "executionInfo": { + "elapsed": 64, + "status": "ok", + "timestamp": 1633609639173, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "ZVpdhf1etU8k", + "outputId": "93b3fe31-2757-4cb7-afcc-7c10f765bb46" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " State Account length Area code International plan Voice mail plan \\\n", + "0 KS 128 415 False True \n", + "1 OH 107 415 False True \n", + "2 NJ 137 415 False False \n", + "3 OH 84 408 True False \n", + "4 OK 75 415 True False \n", + "\n", + " Number vmail messages Total day minutes Total day calls \\\n", + "0 25 265.1 110 \n", + "1 26 161.6 123 \n", + "2 0 243.4 114 \n", + "3 0 299.4 71 \n", + "4 0 166.7 113 \n", + "\n", + " Total day charge Total eve minutes Total eve calls Total eve charge \\\n", + "0 45.07 197.4 99 16.78 \n", + "1 27.47 195.5 103 16.62 \n", + "2 41.38 121.2 110 10.30 \n", + "3 50.90 61.9 88 5.26 \n", + "4 28.34 148.3 122 12.61 \n", + "\n", + " Total night minutes Total night calls Total night charge \\\n", + "0 244.7 91 11.01 \n", + "1 254.4 103 11.45 \n", + "2 162.6 104 7.32 \n", + "3 196.9 89 8.86 \n", + "4 186.9 121 8.41 \n", + "\n", + " Total intl minutes Total intl calls Total intl charge \\\n", + "0 10.0 3 2.70 \n", + "1 13.7 3 3.70 \n", + "2 12.2 5 3.29 \n", + "3 6.6 7 1.78 \n", + "4 10.1 3 2.73 \n", + "\n", + " Customer service calls Churn new_Number_vmail_messages Total calls \\\n", + "0 1 0 100 303 \n", + "1 1 0 104 332 \n", + "2 0 0 4 333 \n", + "3 2 0 4 255 \n", + "4 3 0 4 359 \n", + "\n", + " Total charge \n", + "0 75.56 \n", + "1 59.24 \n", + "2 62.29 \n", + "3 66.80 \n", + "4 52.09 " + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"Total charge\"] = (\n", + " df[\"Total day charge\"]\n", + " + df[\"Total eve charge\"]\n", + " + df[\"Total night charge\"]\n", + " + df[\"Total intl charge\"]\n", + ")\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xrn0pZo1tU8l" + }, + "source": [ + "Чтобы удалить столбцы или строки, воспользуйтесь методом `drop`, передавая в качестве аргумента нужные индексы и требуемое значение параметра `axis` (`1`, если удаляете столбцы, и ничего или `0`, если удаляете строки):" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 261 + }, + "executionInfo": { + "elapsed": 62, + "status": "ok", + "timestamp": 1633609639175, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "oSvOmNv-tU8l", + "outputId": "d0304dcc-8e8a-42c9-8765-1822ae6f5c44", + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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3OH84408TrueFalse0299.47150.9061.9885.26196.9898.866.671.78204
4OK75415TrueFalse0166.711328.34148.312212.61186.91218.4110.132.73304
5AL118510TrueFalse0223.49837.98220.610118.75203.91189.186.361.70004
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" + ], + "text/plain": [ + " State Account length Area code International plan Voice mail plan \\\n", + "0 KS 128 415 False True \n", + "3 OH 84 408 True False \n", + "4 OK 75 415 True False \n", + "5 AL 118 510 True False \n", + "6 MA 121 510 False True \n", + "\n", + " Number vmail messages Total day minutes Total day calls \\\n", + "0 25 265.1 110 \n", + "3 0 299.4 71 \n", + "4 0 166.7 113 \n", + "5 0 223.4 98 \n", + "6 24 218.2 88 \n", + "\n", + " Total day charge Total eve minutes Total eve calls Total eve charge \\\n", + "0 45.07 197.4 99 16.78 \n", + "3 50.90 61.9 88 5.26 \n", + "4 28.34 148.3 122 12.61 \n", + "5 37.98 220.6 101 18.75 \n", + "6 37.09 348.5 108 29.62 \n", + "\n", + " Total night minutes Total night calls Total night charge \\\n", + "0 244.7 91 11.01 \n", + "3 196.9 89 8.86 \n", + "4 186.9 121 8.41 \n", + "5 203.9 118 9.18 \n", + "6 212.6 118 9.57 \n", + "\n", + " Total intl minutes Total intl calls Total intl charge \\\n", + "0 10.0 3 2.70 \n", + "3 6.6 7 1.78 \n", + "4 10.1 3 2.73 \n", + "5 6.3 6 1.70 \n", + "6 7.5 7 2.03 \n", + "\n", + " Customer service calls Churn new_Number_vmail_messages \n", + "0 1 0 100 \n", + "3 2 0 4 \n", + "4 3 0 4 \n", + "5 0 0 4 \n", + "6 3 0 96 " + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# избавляемся от созданных только что столбцов\n", + "df = df.drop([\"Total charge\", \"Total calls\"], axis=1)\n", + "\n", + "df.drop([1, 2]).head() # а вот так можно удалить строчки" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JLDUG5hNtU8l" + }, + "source": [ + "--------\n", + "\n", + "\n", + "\n", + "## Первые попытки прогнозирования оттока\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1sv6q4lNtU8m" + }, + "source": [ + "Посмотрим, как отток связан с признаком *\"Подключение международного роуминга\"* (`International plan`). Сделаем это с помощью сводной таблички `crosstab`, а также путем иллюстрации с `Seaborn` (как именно строить такие картинки и анализировать с их помощью графики – материал следующей статьи.)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": true, + "executionInfo": { + "elapsed": 57, + "status": "ok", + "timestamp": 1633609639176, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "M7cBvVn-tU8m" + }, + "outputs": [], + "source": [ + "# надо дополнительно установить (команда в терминале)\n", + "# чтоб картинки рисовались в тетрадке\n", + "# !conda install seaborn\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "plt.rcParams[\"figure.figsize\"] = (8, 6)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 175 + }, + "executionInfo": { + "elapsed": 56, + "status": "ok", + "timestamp": 1633609639177, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "8ZJBwL8NtU8m", + "outputId": "334f814d-2c27-4f67-cabd-17159188ca2b" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "Customer service calls 0 1 2 3 4 5 6 7 8 9 All\n", + "Churn \n", + "0 605 1059 672 385 90 26 8 4 1 0 2850\n", + "1 92 122 87 44 76 40 14 5 1 2 483\n", + "All 697 1181 759 429 166 66 22 9 2 2 3333" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.crosstab(df[\"Churn\"], df[\"Customer service calls\"], margins=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 388 + }, + "executionInfo": { + "elapsed": 2104, + "status": "ok", + "timestamp": 1633609642719, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "sMJh9m1VtU8p", + "outputId": "43183fcc-f324-4492-acd6-30a6cf0615b5" + }, + "outputs": [ + { + "data": { + "image/png": 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" + ], + "text/plain": [ + "Churn 0 1\n", + "row_0 \n", + "False 2841 464\n", + "True 9 19" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.crosstab(df[\"Many_service_calls\"] & df[\"International plan\"], df[\"Churn\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VyaMr43HtU8t" + }, + "source": [ + "Значит, прогнозируя отток клиента в случае, когда число звонков в сервисный центр больше 3 и подключен роуминг (и прогнозируя лояльность – в противном случае), можно ожидать около 85.8% правильных попаданий (ошибаемся всего 464 + 9 раз). Эти 85.8%, которые мы получили с помощью очень простых рассуждений – это неплохая отправная точка (*baseline*) для дальнейших моделей машинного обучения, которые мы будем строить. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "d6_n0ESntU8u" + }, + "source": [ + "В целом до появления машинного обучения процесс анализа данных выглядел примерно так. Прорезюмируем:\n", + " \n", + "- Доля лояльных клиентов в выборке – 85.5%. Самая наивная модель, ответ которой \"Клиент всегда лоялен\" на подобных данных будет угадывать примерно в 85.5% случаев. То есть доли правильных ответов (*accuracy*) последующих моделей должны быть как минимум не меньше, а лучше, значительно выше этой цифры;\n", + "- С помощью простого прогноза , который условно можно выразить такой формулой: \"International plan = True & Customer Service calls > 3 => Churn = 1, else Churn = 0\", можно ожидать долю угадываний 85.8%, что еще чуть выше 85.5%\n", + "- Эти два бейзлайна мы получили без всякого машинного обучения, и они служат отправной точной для наших последующих моделей. Если окажется, что мы громадными усилиями увеличиваем долю правильных ответов всего, скажем, на 0.5%, то возможно, мы что-то делаем не так, и достаточно ограничиться простой моделью из двух условий. \n", + "- Перед обучением сложных моделей рекомендуется немного покрутить данные и проверить простые предположения. Более того, в бизнес-приложениях машинного обучения чаще всего начинают именно с простых решений, а потом экспериментируют с их усложнением. " + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "colab": { + "collapsed_sections": [], + "name": "02_Pandas.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + }, + "name": "seminar02_part2_pandas.ipynb" + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Pandas (06.03)/Pandas. Task. Part 2.ipynb b/Pandas (06.03)/Pandas. Task. Part 2.ipynb index bb60a1c..ceb2106 100644 --- a/Pandas (06.03)/Pandas. Task. Part 2.ipynb +++ b/Pandas (06.03)/Pandas. Task. Part 2.ipynb @@ -1 +1,671 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.8"},"colab":{"name":"02_pandas_task.ipynb","provenance":[],"collapsed_sections":[]}},"cells":[{"cell_type":"markdown","metadata":{"id":"EmV0s8YY05p7"},"source":["- __ID__ - Unique number for each athlete\n","- __Name__ - Athlete's name\n","- __Sex__ - M or F\n","- __Age__ - Integer\n","- __Height__ - In centimeters\n","- __Weight__ - In kilograms\n","- __Team__ - Team name\n","- __NOC__ - National Olympic Committee 3-letter code\n","- __Games__ - Year and season\n","- __Year__ - Integer\n","- __Season__ - Summer or Winter\n","- __City__ - Host city\n","- __Sport__ - Sport\n","- __Event__ - Event\n","- __Medal__ - Gold, Silver, Bronze, or NA"]},{"cell_type":"code","metadata":{"id":"rVCrMDMh05p_"},"source":["import pandas as pd"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"D5Q4Z-JW05qC"},"source":["# не меняем путь!\n","PATH = 'https://github.com/aksenov7/Kaggle_competition_group/blob/master/athlete_events.csv.zip?raw=true'"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"mI0LtqkY4Kp-"},"source":["__0. Откройте файл используя необходимые параметры и не меняя переменную PATH__"]},{"cell_type":"code","metadata":{"id":"h5SQwBLr05qG","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1615627554682,"user_tz":-300,"elapsed":2477,"user":{"displayName":"Александр Аксёнов","photoUrl":"https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg","userId":"11145992452404092449"}},"outputId":"882f9e83-5fd7-4c3b-b005-56917b15a0fd"},"source":["data = \n","data.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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IDNameSexAgeHeightWeightTeamNOCGamesYearSeasonCitySportEventMedal
01A DijiangM24.0180.080.0ChinaCHN1992 Summer1992SummerBarcelonaBasketballBasketball Men's BasketballNaN
12A LamusiM23.0170.060.0ChinaCHN2012 Summer2012SummerLondonJudoJudo Men's Extra-LightweightNaN
23Gunnar Nielsen AabyM24.0NaNNaNDenmarkDEN1920 Summer1920SummerAntwerpenFootballFootball Men's FootballNaN
34Edgar Lindenau AabyeM34.0NaNNaNDenmark/SwedenDEN1900 Summer1900SummerParisTug-Of-WarTug-Of-War Men's Tug-Of-WarGold
45Christine Jacoba AaftinkF21.0185.082.0NetherlandsNED1988 Winter1988WinterCalgarySpeed SkatingSpeed Skating Women's 500 metresNaN
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"],"text/plain":[" ID Name ... Event Medal\n","0 1 A Dijiang ... Basketball Men's Basketball NaN\n","1 2 A Lamusi ... Judo Men's Extra-Lightweight NaN\n","2 3 Gunnar Nielsen Aaby ... Football Men's Football NaN\n","3 4 Edgar Lindenau Aabye ... Tug-Of-War Men's Tug-Of-War Gold\n","4 5 Christine Jacoba Aaftink ... Speed Skating Women's 500 metres NaN\n","\n","[5 rows x 15 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"stYR4EbV05qP"},"source":["__1. Сколько лет было самым молодым мужчинам и женщинам-участникам Олимпийских игр 1992 года ?__\n","- 16 и 15\n","- 14 и 13 \n","- 13 и 11\n","- 11 и 12"]},{"cell_type":"code","metadata":{"id":"HgiqBXtb05qR"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"GQ290dsi05qc"},"source":["__2. Каков был процент баскетболистов-мужчин среди всех мужчин-участников Олимпийских игр 2012 года? Округлите ответ до первого десятичного знака.__\n","\n","Здесь и далее при необходимости отбрасывайте дублированных спортсменов, чтобы считать только уникальных . \n","- 0.2\n","- 1.5 \n","- 2.5\n","- 7.7"]},{"cell_type":"code","metadata":{"id":"-fI5MqWP05qi"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"u5WrTgIC05qv"},"source":["__3. Каковы среднее и стандартное отклонение роста теннисисток, участвовавших в Олимпийских играх 2000 года? Округлите ответ до первого десятичного знака.__\n","\n","- 171.8 и 6.5\n","- 179.4 и 10\n","- 180.7 и 6.7\n","- 182.4 и 9.1 "]},{"cell_type":"code","metadata":{"id":"vsKTqn6405qw"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"xOOEzhNQ05qy"},"source":["__4. Найдите спортсмена, который участвовал в Олимпийских играх 2006 года, с наибольшим весом среди других участников той же Олимпиады. Каким спортом он или она занимался?__\n","\n","- Judo\n","- Bobsleigh \n","- Skeleton\n","- Boxing"]},{"cell_type":"code","metadata":{"id":"EkWD1Tnb05qz"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"UQzxZ3HT05q0"},"source":["__5. Сколько раз John Aalberg участвовал в Олимпийских играх в разные годы?__\n","\n","Один год - это один раз. Неважно сколько участий внутри одного года\n","- 0\n","- 1 \n","- 2\n","- 3 "]},{"cell_type":"code","metadata":{"id":"ZSfkdjPO05q0"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"8EnLcNrk05q3"},"source":["__6. Сколько золотых медалей по теннису выиграли спортсмены сборной Switzerland на Олимпиаде-2008? Считайте каждую медаль от каждого спортсмена.__\n","\n","- 0\n","- 1 \n","- 2\n","- 3 "]},{"cell_type":"code","metadata":{"id":"Y754OGI-05q3"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"v3h5sQF805q5"},"source":["__7. Правда ли, что на Олимпийских играх 2016 Spain выиграла меньше медалей, чем Италия?__ \n","\n","- Да\n","- Нет"]},{"cell_type":"code","metadata":{"id":"gqJqDi2605q7"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"kkSYL5mK05q-"},"source":["__8. К какой возрастной категории принадлежало наименьшее и наибольшее количество участников Олимпиады-2008?__\n","\n","- [45-55] и [25-35) соответственно\n","- [45-55] и [15-25) соответственно\n","- [35-45) и [25-35) соответственно\n","- [45-55] и [35-45) соответственно"]},{"cell_type":"code","metadata":{"id":"pMAQtW7i05q_"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"JQmJPiXv05rB"},"source":["__9. Правда ли, что в Atlanta проводились летние Олимпийские игры? Правда ли, что в Squaw Valley проводились зимние Олимпийские игры? ?__\n","\n","- Да, Да\n","- Да, Нет\n","- Нет, Да \n","- Нет, Нет "]},{"cell_type":"code","metadata":{"id":"UU66wRHC05rB"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"4hxR5D-t05rF"},"source":["__10. Какова абсолютная разница между количеством уникальных видов спорта на Олимпиаде 1986 года и Олимпиаде 2002 года?__\n","\n","- 3 \n","- 10\n","- 15\n","- 27 "]},{"cell_type":"code","metadata":{"id":"WKIr-TR105rF"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "EmV0s8YY05p7" + }, + "source": [ + "- __ID__ - Unique number for each athlete\n", + "- __Name__ - Athlete's name\n", + "- __Sex__ - M or F\n", + "- __Age__ - Integer\n", + "- __Height__ - In centimeters\n", + "- __Weight__ - In kilograms\n", + "- __Team__ - Team name\n", + "- __NOC__ - National Olympic Committee 3-letter code\n", + "- __Games__ - Year and season\n", + "- __Year__ - Integer\n", + "- __Season__ - Summer or Winter\n", + "- __City__ - Host city\n", + "- __Sport__ - Sport\n", + "- __Event__ - Event\n", + "- __Medal__ - Gold, Silver, Bronze, or NA" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "rVCrMDMh05p_" + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "D5Q4Z-JW05qC" + }, + "outputs": [], + "source": [ + "# не меняем путь!\n", + "PATH = 'https://github.com/aksenov7/Kaggle_competition_group/blob/master/athlete_events.csv.zip?raw=true'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mI0LtqkY4Kp-" + }, + "source": [ + "__0. Откройте файл используя необходимые параметры и не меняя переменную PATH__" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "executionInfo": { + "elapsed": 2477, + "status": "ok", + "timestamp": 1615627554682, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh5.googleusercontent.com/-jOf_oDVHsg8/AAAAAAAAAAI/AAAAAAAAAFM/qwdbG0GW_To/s64/photo.jpg", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "h5SQwBLr05qG", + "outputId": "882f9e83-5fd7-4c3b-b005-56917b15a0fd" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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IDNameSexAgeHeightWeightTeamNOCGamesYearSeasonCitySportEventMedal
01A DijiangM24.0180.080.0ChinaCHN1992 Summer1992SummerBarcelonaBasketballBasketball Men's BasketballNaN
12A LamusiM23.0170.060.0ChinaCHN2012 Summer2012SummerLondonJudoJudo Men's Extra-LightweightNaN
23Gunnar Nielsen AabyM24.0NaNNaNDenmarkDEN1920 Summer1920SummerAntwerpenFootballFootball Men's FootballNaN
34Edgar Lindenau AabyeM34.0NaNNaNDenmark/SwedenDEN1900 Summer1900SummerParisTug-Of-WarTug-Of-War Men's Tug-Of-WarGold
45Christine Jacoba AaftinkF21.0185.082.0NetherlandsNED1988 Winter1988WinterCalgarySpeed SkatingSpeed Skating Women's 500 metresNaN
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" + ], + "text/plain": [ + " ID Name Sex Age Height Weight Team \\\n", + "0 1 A Dijiang M 24.0 180.0 80.0 China \n", + "1 2 A Lamusi M 23.0 170.0 60.0 China \n", + "2 3 Gunnar Nielsen Aaby M 24.0 NaN NaN Denmark \n", + "3 4 Edgar Lindenau Aabye M 34.0 NaN NaN Denmark/Sweden \n", + "4 5 Christine Jacoba Aaftink F 21.0 185.0 82.0 Netherlands \n", + "\n", + " NOC Games Year Season City Sport \\\n", + "0 CHN 1992 Summer 1992 Summer Barcelona Basketball \n", + "1 CHN 2012 Summer 2012 Summer London Judo \n", + "2 DEN 1920 Summer 1920 Summer Antwerpen Football \n", + "3 DEN 1900 Summer 1900 Summer Paris Tug-Of-War \n", + "4 NED 1988 Winter 1988 Winter Calgary Speed Skating \n", + "\n", + " Event Medal \n", + "0 Basketball Men's Basketball NaN \n", + "1 Judo Men's Extra-Lightweight NaN \n", + "2 Football Men's Football NaN \n", + "3 Tug-Of-War Men's Tug-Of-War Gold \n", + "4 Speed Skating Women's 500 metres NaN " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(PATH, compression='zip')\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "stYR4EbV05qP" + }, + "source": [ + "__1. Сколько лет было самым молодым мужчинам и женщинам-участникам Олимпийских игр 1992 года ?__\n", + "- 16 и 15\n", + "- 14 и 13 \n", + "- 13 и 11\n", + "- 11 и 12" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "id": "HgiqBXtb05qR" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Мужчина: 11\n", + "Женщина: 12\n" + ] + } + ], + "source": [ + "people = data[data['Year'] == 1992][['Sex', 'Age']]\n", + "\n", + "print('Мужчина: ', int(people[people['Sex'] == 'M']['Age'].min()))\n", + "print('Женщина: ', int(people[people['Sex'] == 'F']['Age'].min()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GQ290dsi05qc" + }, + "source": [ + "__2. Каков был процент баскетболистов-мужчин среди всех мужчин-участников Олимпийских игр 2012 года? Округлите ответ до первого десятичного знака.__\n", + "\n", + "Здесь и далее при необходимости отбрасывайте дублированных спортсменов, чтобы считать только уникальных . \n", + "- 0.2\n", + "- 1.5 \n", + "- 2.5\n", + "- 7.7" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "-fI5MqWP05qi" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.0\n" + ] + } + ], + "source": [ + "people = data[(data['Sex'] == 'M') & (data['Year'] == 2012)].drop_duplicates()\n", + "basket = people[people['Sport'] == 'Basketball']\n", + "print(round(len(basket) / len(people) * 100, 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "u5WrTgIC05qv" + }, + "source": [ + "__3. Каковы среднее и стандартное отклонение роста теннисисток, участвовавших в Олимпийских играх 2000 года? Округлите ответ до первого десятичного знака.__\n", + "\n", + "- 171.8 и 6.5\n", + "- 179.4 и 10\n", + "- 180.7 и 6.7\n", + "- 182.4 и 9.1 " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "vsKTqn6405qw" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "171.8\n", + "6.5\n" + ] + } + ], + "source": [ + "female = data[(data['Sex'] == 'F') & (data['Year'] == 2000) & (data['Sport'] == 'Tennis')][['Height']]\n", + "print(round(female.mean()['Height'], 1))\n", + "print(round(female.std()['Height'], 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xOOEzhNQ05qy" + }, + "source": [ + "__4. Найдите спортсмена, который участвовал в Олимпийских играх 2006 года, с наибольшим весом среди других участников той же Олимпиады. Каким спортом он или она занимался?__\n", + "\n", + "- Judo\n", + "- Bobsleigh \n", + "- Skeleton\n", + "- Boxing" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "EkWD1Tnb05qz" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
WeightSexSport
8102127.0MSkeleton
\n", + "
" + ], + "text/plain": [ + " Weight Sex Sport\n", + "8102 127.0 M Skeleton" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "people = data[data['Year'] == 2006][['Weight', 'Sex', 'Sport']]\n", + "max_w = people['Weight'].max()\n", + "people[people['Weight'] == max_w]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UQzxZ3HT05q0" + }, + "source": [ + "__5. Сколько раз John Aalberg участвовал в Олимпийских играх в разные годы?__\n", + "\n", + "Один год - это один раз. Неважно сколько участий внутри одного года\n", + "- 0\n", + "- 1 \n", + "- 2\n", + "- 3 " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "ZSfkdjPO05q0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n" + ] + } + ], + "source": [ + "print(data[data['Name'] == 'John Aalberg']['Year'].drop_duplicates().count())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8EnLcNrk05q3" + }, + "source": [ + "__6. Сколько золотых медалей по теннису выиграли спортсмены сборной Switzerland на Олимпиаде-2008? Считайте каждую медаль от каждого спортсмена.__\n", + "\n", + "- 0\n", + "- 1 \n", + "- 2\n", + "- 3 " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "id": "Y754OGI-05q3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n" + ] + } + ], + "source": [ + "people = data[(data['Year'] == 2008) & (data['Team'] == 'Switzerland') & (data['Medal'] == 'Gold') \n", + " & (data['Sport'] == 'Tennis')]\n", + "print(len(people))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "v3h5sQF805q5" + }, + "source": [ + "__7. Правда ли, что на Олимпийских играх 2016 Spain выиграла меньше медалей, чем Италия?__ \n", + "\n", + "- Да\n", + "- Нет" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "gqJqDi2605q7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "people = data[(data['Year'] == 2016) & (data['Medal'])][['Team', 'Medal']]\n", + "italy_count = people[people['Team'] == 'Italy']['Medal'].count()\n", + "spain_count = people[people['Team'] == 'Spain']['Medal'].count()\n", + "print(italy_count > spain_count)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kkSYL5mK05q-" + }, + "source": [ + "__8. К какой возрастной категории принадлежало наименьшее и наибольшее количество участников Олимпиады-2008?__\n", + "\n", + "- [45-55] и [25-35) соответственно\n", + "- [45-55] и [15-25) соответственно\n", + "- [35-45) и [25-35) соответственно\n", + "- [45-55] и [35-45) соответственно" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "id": "pMAQtW7i05q_" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[45-55] и [15-25) соответственно\n" + ] + } + ], + "source": [ + "people = data[(data['Year'] == 2008)][['Age']]\n", + "categories = {\n", + " \"[45-55]\" : len(people[(people['Age'] >= 45) & (people['Age'] <= 55)]),\n", + " \"[25-35)\" : len(people[(people['Age'] >= 25) & (people['Age'] < 35)]),\n", + " \"[15-25)\" : len(people[(people['Age'] >= 15) & (people['Age'] <= 25)]),\n", + " \"[35-45]\" : len(people[(people['Age'] >= 35) & (people['Age'] < 45)])\n", + "}\n", + "\n", + "print(f'{max(categories)} и {min(categories)} соответственно')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JQmJPiXv05rB" + }, + "source": [ + "__9. Правда ли, что в Atlanta проводились летние Олимпийские игры? Правда ли, что в Squaw Valley проводились зимние Олимпийские игры? ?__\n", + "\n", + "- Да, Да\n", + "- Да, Нет\n", + "- Нет, Да \n", + "- Нет, Нет " + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "id": "UU66wRHC05rB" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Да, Да\n" + ] + } + ], + "source": [ + "atlanta = len(data[(data['Season'] == 'Summer') & (data['City'] == 'Atlanta')])\n", + "squaw_valley = len(data[(data['Season'] == 'Winter') & (data['City'] == 'Squaw Valley')])\n", + "\n", + "print('Да,' if atlanta > 0 else 'Нет,', 'Да' if squaw_valley > 0 else 'Нет')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4hxR5D-t05rF" + }, + "source": [ + "__10. Какова абсолютная разница между количеством уникальных видов спорта на Олимпиаде 1986 года и Олимпиаде 2002 года?__\n", + "\n", + "- 3 \n", + "- 10\n", + "- 15\n", + "- 27 " + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "id": "WKIr-TR105rF" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "15\n" + ] + } + ], + "source": [ + "a = len(data[data[\"Year\"]==2002].drop_duplicates(subset=['Sport']))\n", + "b = len(data[data[\"Year\"]==1986].drop_duplicates(subset=['Sport']))\n", + "print(a - b)" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "02_pandas_task.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 89dc2ebee266731e6a2a6569474efdb5fcf9bea5 Mon Sep 17 00:00:00 2001 From: ooonush Date: Wed, 12 Oct 2022 19:50:17 +0500 Subject: [PATCH 5/5] r --- .idea/.gitignore | 8 + .../inspectionProfiles/profiles_settings.xml | 6 + .idea/misc.xml | 4 + .idea/modules.xml | 8 + .idea/ooonush_data_analysis_in_python.iml | 8 + .idea/vcs.xml | 6 + ...267\320\260\321\206\320\270\321\217.ipynb" | 105 +- ...206\320\270\321\217 \320\224\320\227.html" | 182 ++++ ...265\321\201\321\201\320\270\321\217.ipynb" | 732 +++++++++++++ ...06\320\270\321\217 \320\224\320\227.ipynb" | 999 ++++++++++++++++++ ...267\320\260\321\206\320\270\321\217.ipynb" | 125 +-- 11 files changed, 2013 insertions(+), 170 deletions(-) create mode 100644 .idea/.gitignore create mode 100644 .idea/inspectionProfiles/profiles_settings.xml create mode 100644 .idea/misc.xml create mode 100644 .idea/modules.xml create mode 100644 .idea/ooonush_data_analysis_in_python.iml create mode 100644 .idea/vcs.xml create mode 100644 "exportToHTML/\320\222\320\270\320\267\321\203\320\260\320\273\320\270\320\267\320\260\321\206\320\270\321\217 \320\224\320\227.html" create mode 100644 "my/\320\273\320\270\320\275\320\265\320\271\320\275\320\260\321\217 \321\200\320\265\320\263\321\200\320\265\321\201\321\201\320\270\321\217.ipynb" create mode 100644 "\320\222\320\270\320\267\321\203\320\260\320\273\320\270\320\267\320\260\321\206\320\270\321\217 \320\224\320\227.ipynb" diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..13566b8 --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,8 @@ +# Default ignored files +/shelf/ +/workspace.xml +# Editor-based HTTP Client requests +/httpRequests/ +# Datasource local storage ignored files +/dataSources/ +/dataSources.local.xml diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 0000000..dc9ea49 --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 0000000..d961b75 --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/ooonush_data_analysis_in_python.iml b/.idea/ooonush_data_analysis_in_python.iml new file mode 100644 index 0000000..d0876a7 --- /dev/null +++ b/.idea/ooonush_data_analysis_in_python.iml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 0000000..94a25f7 --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git "a/Pandas and EDA (12.03)/\320\237\320\265\321\200\320\262\320\270\321\207\320\275\321\213\320\271 \320\260\320\275\320\260\320\273\320\270\320\267 \320\264\320\260\320\275\320\275\321\213\321\205 (EDA). \320\222\320\270\320\267\321\203\320\260\320\273\320\270\320\267\320\260\321\206\320\270\321\217.ipynb" "b/Pandas and EDA (12.03)/\320\237\320\265\321\200\320\262\320\270\321\207\320\275\321\213\320\271 \320\260\320\275\320\260\320\273\320\270\320\267 \320\264\320\260\320\275\320\275\321\213\321\205 (EDA). \320\222\320\270\320\267\321\203\320\260\320\273\320\270\320\267\320\260\321\206\320\270\321\217.ipynb" index 12ac412..c649924 100644 --- "a/Pandas and EDA (12.03)/\320\237\320\265\321\200\320\262\320\270\321\207\320\275\321\213\320\271 \320\260\320\275\320\260\320\273\320\270\320\267 \320\264\320\260\320\275\320\275\321\213\321\205 (EDA). \320\222\320\270\320\267\321\203\320\260\320\273\320\270\320\267\320\260\321\206\320\270\321\217.ipynb" +++ "b/Pandas and EDA (12.03)/\320\237\320\265\321\200\320\262\320\270\321\207\320\275\321\213\320\271 \320\260\320\275\320\260\320\273\320\270\320\267 \320\264\320\260\320\275\320\275\321\213\321\205 (EDA). \320\222\320\270\320\267\321\203\320\260\320\273\320\270\320\267\320\260\321\206\320\270\321\217.ipynb" @@ -532,57 +532,6 @@ " plt.show()" ] }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 284 - }, - "executionInfo": { - "elapsed": 507, - "status": "ok", - "timestamp": 1633614688173, - "user": { - "displayName": "Александр Аксёнов", - "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhmPE3kg2vafh4QNEoLX_DeI08tDxoR8I8MoJZP=s64", - "userId": "11145992452404092449" - }, - "user_tz": -300 - }, - "id": "G_yJAqux4pqE", - "outputId": "28193c74-e4b7-446f-e81f-04b85ae4e477" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Примеры специальных функций. Гистограмма\n", - "df['Number vmail messages'].hist(bins=50, alpha=0.8)" - ] - }, { "cell_type": "code", "execution_count": 9, @@ -639,7 +588,7 @@ "id": "SqpJpNfy4pqI" }, "source": [ - "Диаграмма рассеяния позволяет анализировать **пары** параметров и выявлять их взаимосвязь " + "Диаграмма рассеяния позволяет анализировать **пары** параметров и выявлять их взаимосвязь" ] }, { @@ -1099,10 +1048,6 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "id": "PGZfPcWz4pqJ", - "outputId": "60091379-8ac7-479c-a43e-c5f5c6026241" - }, "outputs": [ { "data": { @@ -1133,7 +1078,51 @@ "# Диаграмма рассеяния в seaborn\n", "plt.figure(figsize=(15,10))\n", "sns.relplot(x=\"Total day calls\", y=\"Total intl calls\", hue=\"Churn\", size=\"International plan\", sizes=(40, 10), data=df);" - ] + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Примеры специальных функций. Гистограмма\n", + "df['Number vmail messages'].hist(bins=50, alpha=0.8)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } }, { "cell_type": "markdown", @@ -3613,4 +3602,4 @@ }, "nbformat": 4, "nbformat_minor": 1 -} +} \ No newline at end of file diff --git "a/exportToHTML/\320\222\320\270\320\267\321\203\320\260\320\273\320\270\320\267\320\260\321\206\320\270\321\217 \320\224\320\227.html" "b/exportToHTML/\320\222\320\270\320\267\321\203\320\260\320\273\320\270\320\267\320\260\321\206\320\270\321\217 \320\224\320\227.html" new file mode 100644 index 0000000..11f0142 --- /dev/null +++ "b/exportToHTML/\320\222\320\270\320\267\321\203\320\260\320\273\320\270\320\267\320\260\321\206\320\270\321\217 \320\224\320\227.html" @@ -0,0 +1,182 @@ + + +Визуализация ДЗ.ipynb + + + + + +
+ +Визуализация ДЗ.ipynb +
+
#%% md 
+**Импорт всех необходимых библиотек** 
+#%% 
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+pd.set_option("display.max_rows", 20)
+pd.set_option("display.max_columns", 20)
+pd.set_option("display.precision", 4)
+pd.set_option("plotting.backend", "matplotlib")
+
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, RobustScaler
+#%% md 
+# 1. Исследовательский анализ данных (exploratory data analysis - EDA) 
+#%% md 
+## 1.1 Словесное описание признаков 
+#%% md 
+- __ID__ - Уникальный номер спортсмена 
+- __Name__ - ФИО спортсмена 
+- __Sex__ - Пол: M or F - Мужчина или женщина 
+- __Age__ - Возраст спортсмена 
+- __Height__ - Рост в см. 
+- __Weight__ - Вес в кг. 
+- __Team__ - Название команды 
+- __NOC__ - 3-буквенный код Национального олимпийского комитета 
+- __Games__ - Год и сезон проведения олимпиады, напр: Summer 2000 или Winter 2000 
+- __Year__ - Год проведения олимпиады 
+- __Season__ - Сезон проведения олимпиады: Summer, Winter 
+- __City__ - Город проведения олимпиады 
+- __Sport__ - Название вида спорта 
+- __Event__ - Название мероприятия 
+- __Medal__ - Медаль: Gold, Silver, Bronze, or NA 
+#%% md 
+## 1.2 Загрузка данных общее описание набора данных 
+#%% 
+PATH = 'https://github.com/aksenov7/Kaggle_competition_group/blob/master/athlete_events.csv.zip?raw=true'
+df = pd.read_csv(PATH, compression='zip')
+df.head()
+#%% 
+df.shape
+#%% 
+df.info()
+#%% md 
+- Видим большое кол-во пропусков Medal, но будем считать, что это люди, которые не получили медаль. 
+- Что более существенно, так это пропуски по Weight, Height, Age 
+ 
+#%% 
+df.Sex.value_counts()
+#%% 
+df.Sex.value_counts(normalize=True)
+#%% md 
+Видим преобладание мужского пола 
+#%% 
+df.describe()
+#%% md 
+**Первичные выводы по числовым данным** 
+* Возраст людей в выборке от 10 до 97 лет. В среднем возраст варьируется от 19 до 32 лет. Младшие 21 покрывают 25%, 21-28 летние 50%. Остальные 25% это люди от 28 до 97. 
+* Рост от 127 до 226 см. В среднем 175. СКО = 10. 
+* Вес от 25 до 214 кг. В среднем 70.7 кг. 
+* Год проведения лучше рассмотреть отдельно. Но можно сказать, что данные приведены с 1896-2016г. 
+#%% 
+df.describe(include=object)
+#%% md 
+**Первичные выводы по строковым данным** 
+* 1184 уникальных команд. Самыми активными являются United States - они поучаствовали в 17847 соревнованиях. 
+* Самым же активным спортсменом является Robert Tail McKenzie - 58 участий. 
+* Самой популярной игрой является 2000 Summer - 13821 участников. 
+* В выборке преобладают мужчины - 72.5%. 
+* Как уже говорилось. Больше всего игр проводятся летом - 82%. 
+* Самым популярным видом спорта является Athletics - 38624 человека. 
+* Самый популярный Event - Football Men's Football. Всего различных ивентов - 765. 
+#%% md 
+### Медали: 
+Теперь посмотрим на медалистов 
+#%% 
+golds = df[(df['Medal'] == 'Gold')]
+golds.Team.describe(include=object)
+#%% md 
+* United States выигрывали золото чаще других. 
+#%% 
+df.groupby('Medal').describe(include=object)['Name']
+#%% md 
+* Больше всего Gold получал Michael Fred Phelps, II - 23 раза. 
+* Silver - Mikhail Yakovlevich Voronin - 6 раз. 
+* Bronze - Heikki Ilmari Savolainen - 6 раз. 
+#%% 
+df.groupby('Medal').describe(include=object)['Team']
+#%% md 
+* United States получали медали чаще остальных. И почти половина из них Gold 
+#%% 
+no_medal = df[df['Medal'].isna()]
+no_medal.describe(include=object)
+#%% md 
+* United States не получали медали чаще остальных. 
+* Robert Tait McKenzie не получал медали чаще остальных - 57 раз. 
+#%% md 
+
+#%% md 
+## 1.3 Визуальный и статистический анализ данных 
+#%% 
+#Кол-во мужчин и женщин по возрасту
+plt.figure(figsize=(18,8))
+sns.histplot(data=df, x='Age', hue='Sex')
+#%% md 
+* Распределение нормальное, что и можно было ожидать. 
+* Видно, что более молодых женщин (до 18 лет) больше, чем мужчин. 
+#%% md 
+### По годам 
+#%% 
+#Средний возраст по годам
+gr = df.groupby('Year').mean()
+plt.figure(figsize=(18,8))
+gr['Age'].plot()
+#%% md 
+Из графика видно, что ранее 195-ых в основном преобладали люди с возрастом ~28, к 1980 году средний возраст упал до 24, а потом опять начал расти. 
+#%% 
+#Кол-во мужчин и женщин по годам
+silver = df
+plt.figure(figsize=(18,8))
+sns.histplot(x="Year", hue="Sex", data=df)
+#%% md 
+### По времени года 
+#%% 
+#Кол-во мужчин и женщин с Gold по возрасту
+plt.figure(figsize=(18, 8))
+sns.countplot(data=df, x='Year', hue='Season')
+#%% md 
+Зимние виды спорта проводятся реже. А до 1924 они вообще не проводились. 
+#%% md 
+### Корреляции 
+#%% 
+df.corr()
+#%% 
+# Посмотрим на тепловую карту
+plt.figure(figsize=(8,8))
+sns.heatmap(df.corr(), annot=True, cmap="YlGnBu", cbar=False);
+#%% md 
+* Коэф. корреляции между Height и Weight равен 0.8, что ожидаемо 
+* Также немного коррелируют между собой Age и Height или Weight 
+* Остальные данные вообще не коррелируют между собой 
+#%% 
+df.plot.scatter(x='Height', y='Weight')
+#%% 
+# Количество медалей по командам
+plt.figure(figsize=(18,9))
+medals = df.groupby('Team')['Medal'].describe(include=object)['count'].sort_values(ascending=False)[:50]
+medals.plot()
+medals
+#%% md 
+Количество полученных медалей 50 лучших команд. 
+Видно, что United States получили больше всех - 5219, а второе место аж в два раза меньше - 2451. 
+#%% md 
+## Интересные факты 
+* Возраст самого старого спортсмена 97 лет 
+* Возраст самого молодого - 10 лет 
+* Наименьший вес спортсмена - 25 кг 
+* Раньше спортсменок почти не было, но к настоящему моменту наблюдается тенденция равного кол-ва мужчин и женщин 
+* До 1992 года олимпиады проводились каждые 4 года, но начиная с 1992 стали проводить их каждые 2 года, причем каждая вторая олимпиада была менее "масштабной". 
+* United States выигрывали медали 5219 раз. Это более чем в два раза больше следующей по счету команды.
+ + \ No newline at end of file diff --git "a/my/\320\273\320\270\320\275\320\265\320\271\320\275\320\260\321\217 \321\200\320\265\320\263\321\200\320\265\321\201\321\201\320\270\321\217.ipynb" "b/my/\320\273\320\270\320\275\320\265\320\271\320\275\320\260\321\217 \321\200\320\265\320\263\321\200\320\265\321\201\321\201\320\270\321\217.ipynb" new file mode 100644 index 0000000..847a5c7 --- /dev/null +++ "b/my/\320\273\320\270\320\275\320\265\320\271\320\275\320\260\321\217 \321\200\320\265\320\263\321\200\320\265\321\201\321\201\320\270\321\217.ipynb" @@ -0,0 +1,732 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 90, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.datasets import load_boston\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import r2_score\n", + "from sklearn import metrics\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ooonu\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n", + "\n", + " The Boston housing prices dataset has an ethical problem. You can refer to\n", + " the documentation of this function for further details.\n", + "\n", + " The scikit-learn maintainers therefore strongly discourage the use of this\n", + " dataset unless the purpose of the code is to study and educate about\n", + " ethical issues in data science and machine learning.\n", + "\n", + " In this special case, you can fetch the dataset from the original\n", + " source::\n", + "\n", + " import pandas as pd\n", + " import numpy as np\n", + "\n", + "\n", + " data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n", + " raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n", + " data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n", + " target = raw_df.values[1::2, 2]\n", + "\n", + " Alternative datasets include the California housing dataset (i.e.\n", + " :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n", + " dataset. You can load the datasets as follows::\n", + "\n", + " from sklearn.datasets import fetch_california_housing\n", + " housing = fetch_california_housing()\n", + "\n", + " for the California housing dataset and::\n", + "\n", + " from sklearn.datasets import fetch_openml\n", + " housing = fetch_openml(name=\"house_prices\", as_frame=True)\n", + "\n", + " for the Ames housing dataset.\n", + " \n", + " warnings.warn(msg, category=FutureWarning)\n" + ] + } + ], + "source": [ + "boston = load_boston()\n", + "features = boston.data\n", + "target = boston.target\n", + "features_name = boston.feature_names" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 282, + "outputs": [], + "source": [ + "def test_linear_regression(X, y):\n", + " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + " l_reg = LinearRegression()\n", + " l_reg.fit(X_train, y_train)\n", + " l_reg.score(X_test, y_test)\n", + "\n", + " l_reg = LinearRegression()\n", + " l_reg.fit(X_train, y_train)\n", + " l_reg.score(X_test, y_test)\n", + "\n", + " #print('r2_score: ', l_reg.score(X_test, y_test))\n", + " return (l_reg, l_reg.score(X_test, y_test))" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 94, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "r2_score: 0.6687594935356278\n" + ] + } + ], + "source": [ + "test_linear_regression(features, target)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "# Нормализуем значения" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 97, + "outputs": [], + "source": [ + "from sklearn import preprocessing" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "# Проведем EDA" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 103, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 303, + "outputs": [ + { + "data": { + "text/plain": " CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX \\\n0 0.00632 18.0 2.31 0.0 0.538 6.575 65.2 4.0900 1.0 296.0 \n1 0.02731 0.0 7.07 0.0 0.469 6.421 78.9 4.9671 2.0 242.0 \n2 0.02729 0.0 7.07 0.0 0.469 7.185 61.1 4.9671 2.0 242.0 \n3 0.03237 0.0 2.18 0.0 0.458 6.998 45.8 6.0622 3.0 222.0 \n4 0.06905 0.0 2.18 0.0 0.458 7.147 54.2 6.0622 3.0 222.0 \n\n PTRATIO B LSTAT TARGET \n0 15.3 396.90 4.98 24.0 \n1 17.8 396.90 9.14 21.6 \n2 17.8 392.83 4.03 34.7 \n3 18.7 394.63 2.94 33.4 \n4 18.7 396.90 5.33 36.2 ", + "text/html": "
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\n" 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Посмотрим на тепловую карту\n", + "plt.figure(figsize=(8,8))\n", + "sns.heatmap(df.corr(), annot=True, cmap=\"YlGnBu\", cbar=False);" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 117, + "outputs": [], + "source": [ + "def search_outliers(feature):\n", + " \"\"\"Функция принимает набор значений 1-го признака и\n", + " возвращает массив индексов тех значений, которые являются выбросами\"\"\"\n", + " q1, q3 = np.percentile(feature, [25, 75])\n", + "\n", + " iqr = q3 - q1\n", + " lower_bound = q1 - 1.5 * iqr\n", + " upper_bound = q3 + 1.5 * iqr\n", + " return np.where((feature < lower_bound) | (feature > upper_bound))[0]" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 128, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Признак CRIM содержит 66 выбросов из 506 наблюдений\n", + "Признак ZN содержит 68 выбросов из 506 наблюдений\n", + "Признак INDUS содержит 0 выбросов из 506 наблюдений\n", + "Признак CHAS содержит 35 выбросов из 506 наблюдений\n", + "Признак NOX содержит 0 выбросов из 506 наблюдений\n", + "Признак RM содержит 30 выбросов из 506 наблюдений\n", + "Признак AGE содержит 0 выбросов из 506 наблюдений\n", + "Признак DIS содержит 5 выбросов из 506 наблюдений\n", + "Признак RAD содержит 0 выбросов из 506 наблюдений\n", + "Признак TAX содержит 0 выбросов из 506 наблюдений\n", + "Признак PTRATIO содержит 15 выбросов из 506 наблюдений\n", + "Признак B содержит 77 выбросов из 506 наблюдений\n", + "Признак LSTAT содержит 7 выбросов из 506 наблюдений\n", + "Признак TARGET содержит 40 выбросов из 506 наблюдений\n" + ] + } + ], + "source": [ + "for feature in df.columns:\n", + " sum_outliers = len(search_outliers(df[feature]))\n", + " print(f\"Признак {feature} содержит {sum_outliers} выбросов из {df[feature].shape[0]} наблюдений\")" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 307, + "outputs": [], + "source": [ + "def drop_outliers(data, inplace=False):\n", + " drop_index = np.array([])\n", + " for feature in df.columns:\n", + " drop_index = np.hstack((drop_index, search_outliers(df[feature])))\n", + " return data.drop(drop_index, inplace=inplace)\n", + "\n", + "def search_outliers_new(old_feature, new_feature):\n", + " \"\"\"Функция принимает набор значений 1-го признака каким он был до удаления выбросов,\n", + " чтобы корректно расчитать границы выбросов\n", + " И набор значений того же признака после удаления выбросов\n", + " Возвращает массив индексов тех значений, которые являются выбросами\"\"\"\n", + " q1, q3 = np.percentile(old_feature, [25, 75])\n", + " iqr = q3 - q1\n", + " lower_bound = q1 - 1.5 * iqr\n", + " upper_bound = q3 + 1.5 * iqr\n", + " return np.where((new_feature < lower_bound) | (new_feature > upper_bound))[0]\n", + "\n", + "def find_best_features(reg, data):\n", + " coef = pd.DataFrame(reg.coef_, index=features_name, columns=['coef'])\n", + " sorted_coef = round(abs(coef).sort_values('coef', ascending=False))\n", + "\n", + " best_score = test_linear_regression(data.drop('TARGET', axis=1), data['TARGET'])[1]\n", + " best_features = list(data.columns.values)\n", + " for i in range(1, data.shape[1] - 1):\n", + " X_best = data[list(sorted_coef[:i].index)]\n", + " y = data.TARGET\n", + " new_score = test_linear_regression(X_best, y)[1]\n", + " if new_score > best_score:\n", + " best_score = new_score\n", + " best_features = X_best\n", + " print('best score:', best_score)\n", + " return best_features" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 256, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Признак CRIM содержит 0 выбросов из 268 наблюдений\n", + "Признак ZN содержит 0 выбросов из 268 наблюдений\n", + "Признак INDUS содержит 0 выбросов из 268 наблюдений\n", + "Признак CHAS содержит 0 выбросов из 268 наблюдений\n", + "Признак NOX содержит 0 выбросов из 268 наблюдений\n", + "Признак RM содержит 0 выбросов из 268 наблюдений\n", + "Признак AGE содержит 0 выбросов из 268 наблюдений\n", + "Признак DIS содержит 0 выбросов из 268 наблюдений\n", + "Признак RAD содержит 0 выбросов из 268 наблюдений\n", + "Признак TAX содержит 0 выбросов из 268 наблюдений\n", + "Признак PTRATIO содержит 0 выбросов из 268 наблюдений\n", + "Признак B содержит 0 выбросов из 268 наблюдений\n", + "Признак LSTAT содержит 0 выбросов из 268 наблюдений\n", + "Признак TARGET содержит 0 выбросов из 268 наблюдений\n" + ] + } + ], + "source": [ + "d = drop_outliers(df)\n", + "for feature in df.columns:\n", + " sum_outliers = len(search_outliers_new(df[feature], d[feature]))\n", + " print(f\"Признак {feature} содержит {sum_outliers} выбросов из {d[feature].shape[0]} наблюдений\")" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 290, + "outputs": [ + { + "data": { + "text/plain": "[-1.053401914044275,\n -0.7109720834359541,\n -0.6569429298489212,\n -0.2992475805890461,\n -0.17398391375182257,\n -0.0441895094140321,\n -0.03386775527943525,\n -0.014740240787574542,\n -0.010817480966202496,\n 5.440092820663267e-15,\n 0.01476202413443139,\n 0.45936692695078507,\n 4.8874632022255335]" + }, + "execution_count": 290, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reg = test_linear_regression(d.drop('TARGET', axis=1), d.TARGET)[0]\n", + "sorted(reg.coef_)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Видим, что большинство признаков не очень информативные, попробуем удалить" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 308, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "best score: 0.6754022231974177\n" + ] + }, + { + "data": { + "text/plain": " NOX RM CHAS DIS PTRATIO LSTAT RAD CRIM INDUS ZN\n0 0.538 6.575 0.0 4.0900 15.3 4.98 1.0 0.00632 2.31 18.0\n1 0.469 6.421 0.0 4.9671 17.8 9.14 2.0 0.02731 7.07 0.0\n2 0.469 7.185 0.0 4.9671 17.8 4.03 2.0 0.02729 7.07 0.0\n3 0.458 6.998 0.0 6.0622 18.7 2.94 3.0 0.03237 2.18 0.0\n4 0.458 7.147 0.0 6.0622 18.7 5.33 3.0 0.06905 2.18 0.0\n.. ... ... ... ... ... ... ... ... ... ...\n501 0.573 6.593 0.0 2.4786 21.0 9.67 1.0 0.06263 11.93 0.0\n502 0.573 6.120 0.0 2.2875 21.0 9.08 1.0 0.04527 11.93 0.0\n503 0.573 6.976 0.0 2.1675 21.0 5.64 1.0 0.06076 11.93 0.0\n504 0.573 6.794 0.0 2.3889 21.0 6.48 1.0 0.10959 11.93 0.0\n505 0.573 6.030 0.0 2.5050 21.0 7.88 1.0 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" + }, + "execution_count": 308, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reg = test_linear_regression(df.drop('TARGET', axis=1), df.TARGET)[0]\n", + "find_best_features(reg, df)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 309, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "best score: 0.808542297158312\n" + ] + }, + { + "data": { + "text/plain": " RM CRIM PTRATIO DIS RAD NOX LSTAT AGE ZN\n0 6.575 0.00632 15.3 4.0900 1.0 0.538 4.98 65.2 18.0\n1 6.421 0.02731 17.8 4.9671 2.0 0.469 9.14 78.9 0.0\n2 7.185 0.02729 17.8 4.9671 2.0 0.469 4.03 61.1 0.0\n3 6.998 0.03237 18.7 6.0622 3.0 0.458 2.94 45.8 0.0\n4 7.147 0.06905 18.7 6.0622 3.0 0.458 5.33 54.2 0.0\n.. ... ... ... ... ... ... ... ... ...\n501 6.593 0.06263 21.0 2.4786 1.0 0.573 9.67 69.1 0.0\n502 6.120 0.04527 21.0 2.2875 1.0 0.573 9.08 76.7 0.0\n503 6.976 0.06076 21.0 2.1675 1.0 0.573 5.64 91.0 0.0\n504 6.794 0.10959 21.0 2.3889 1.0 0.573 6.48 89.3 0.0\n505 6.030 0.04741 21.0 2.5050 1.0 0.573 7.88 80.8 0.0\n\n[268 rows x 9 columns]", + "text/html": "
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" + }, + "execution_count": 309, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reg = test_linear_regression(d.drop('TARGET', axis=1), d.TARGET)[0]\n", + "find_best_features(reg, d)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Видим, что для данных без выбросов лучше брать 9 фич, а для исходных - все 13" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "# Нормализация" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 305, + "outputs": [], + "source": [ + "min_max_scaler = preprocessing.MinMaxScaler()\n", + "norm_features = min_max_scaler.fit_transform(features)\n", + "nd = pd.DataFrame(np.column_stack((norm_features, target)), columns=np.hstack((features_name, ['TARGET'])))\n", + "reg_and_score = test_linear_regression(nd.drop('TARGET', axis=1), nd.TARGET)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Качество не улучшилось, попробуем оставить только лучшие фичи" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 310, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "best score: 0.6715231799608913\n" + ] + }, + { + "data": { + "text/plain": " RM LSTAT DIS CRIM PTRATIO NOX RAD \\\n0 0.577505 0.089680 0.269203 0.000000 0.287234 0.314815 0.000000 \n1 0.547998 0.204470 0.348962 0.000236 0.553191 0.172840 0.043478 \n2 0.694386 0.063466 0.348962 0.000236 0.553191 0.172840 0.043478 \n3 0.658555 0.033389 0.448545 0.000293 0.648936 0.150206 0.086957 \n4 0.687105 0.099338 0.448545 0.000705 0.648936 0.150206 0.086957 \n.. ... ... ... ... ... ... ... \n501 0.580954 0.219095 0.122671 0.000633 0.893617 0.386831 0.000000 \n502 0.490324 0.202815 0.105293 0.000438 0.893617 0.386831 0.000000 \n503 0.654340 0.107892 0.094381 0.000612 0.893617 0.386831 0.000000 \n504 0.619467 0.131071 0.114514 0.001161 0.893617 0.386831 0.000000 \n505 0.473079 0.169702 0.125072 0.000462 0.893617 0.386831 0.000000 \n\n TAX B ZN CHAS \n0 0.208015 1.000000 0.18 0.0 \n1 0.104962 1.000000 0.00 0.0 \n2 0.104962 0.989737 0.00 0.0 \n3 0.066794 0.994276 0.00 0.0 \n4 0.066794 1.000000 0.00 0.0 \n.. ... ... ... ... \n501 0.164122 0.987619 0.00 0.0 \n502 0.164122 1.000000 0.00 0.0 \n503 0.164122 1.000000 0.00 0.0 \n504 0.164122 0.991301 0.00 0.0 \n505 0.164122 1.000000 0.00 0.0 \n\n[506 rows x 11 columns]", + "text/html": "
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" + }, + "execution_count": 320, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "standart_scaler = preprocessing.StandardScaler()\n", + "standart_feature = standart_scaler.fit_transform(df.drop('TARGET', axis=1))\n", + "sd = pd.DataFrame(np.column_stack((standart_feature, target)), columns=np.hstack((features_name, ['TARGET'])))\n", + "sd" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 321, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "best score: 0.6726543801851019\n" + ] + }, + { + "data": { + "text/plain": " LSTAT RM DIS RAD NOX PTRATIO TAX\n0 -1.075562 0.413672 0.140214 -0.982843 -0.144217 -1.459000 -0.666608\n1 -0.492439 0.194274 0.557160 -0.867883 -0.740262 -0.303094 -0.987329\n2 -1.208727 1.282714 0.557160 -0.867883 -0.740262 -0.303094 -0.987329\n3 -1.361517 1.016303 1.077737 -0.752922 -0.835284 0.113032 -1.106115\n4 -1.026501 1.228577 1.077737 -0.752922 -0.835284 0.113032 -1.106115\n.. ... ... ... ... ... ... ...\n501 -0.418147 0.439316 -0.625796 -0.982843 0.158124 1.176466 -0.803212\n502 -0.500850 -0.234548 -0.716639 -0.982843 0.158124 1.176466 -0.803212\n503 -0.983048 0.984960 -0.773684 -0.982843 0.158124 1.176466 -0.803212\n504 -0.865302 0.725672 -0.668437 -0.982843 0.158124 1.176466 -0.803212\n505 -0.669058 -0.362767 -0.613246 -0.982843 0.158124 1.176466 -0.803212\n\n[506 rows x 7 columns]", + "text/html": "
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506 rows × 7 columns

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" + }, + "execution_count": 321, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reg = test_linear_regression(sd.drop('TARGET', axis=1), sd.TARGET)[0]\n", + "find_best_features(reg, sd)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git "a/\320\222\320\270\320\267\321\203\320\260\320\273\320\270\320\267\320\260\321\206\320\270\321\217 \320\224\320\227.ipynb" "b/\320\222\320\270\320\267\321\203\320\260\320\273\320\270\320\267\320\260\321\206\320\270\321\217 \320\224\320\227.ipynb" new file mode 100644 index 0000000..4058c2c --- /dev/null +++ "b/\320\222\320\270\320\267\321\203\320\260\320\273\320\270\320\267\320\260\321\206\320\270\321\217 \320\224\320\227.ipynb" @@ -0,0 +1,999 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "CXsKzXe_x-t4" + }, + "source": [ + "**Импорт всех необходимых библиотек**" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "RrSo2OJzx-Pw" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import missingno as msno\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "pd.set_option(\"display.max_rows\", 20)\n", + "pd.set_option(\"display.max_columns\", 20)\n", + "pd.set_option(\"display.precision\", 4)\n", + "pd.set_option(\"plotting.backend\", \"matplotlib\")\n", + "\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler, RobustScaler" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UTKVH3sMutTM" + }, + "source": [ + "# 1. Исследовательский анализ данных (exploratory data analysis - EDA)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tiFgQjEcxnu2" + }, + "source": [ + "## 1.1 Словесное описание признаков" + ] + }, + { + "cell_type": "markdown", + "source": [ + "- __ID__ - Уникальный номер спортсмена\n", + "- __Name__ - ФИО спортсмена\n", + "- __Sex__ - Пол: M or F - Мужчина или женщина\n", + "- __Age__ - Возраст спортсмена\n", + "- __Height__ - Рост в см.\n", + "- __Weight__ - Вес в кг.\n", + "- __Team__ - Название команды\n", + "- __NOC__ - 3-буквенный код Национального олимпийского комитета\n", + "- __Games__ - Год и сезон проведения олимпиады, напр: Summer 2000 или Winter 2000\n", + "- __Year__ - Год проведения олимпиады\n", + "- __Season__ - Сезон проведения олимпиады: Summer, Winter\n", + "- __City__ - Город проведения олимпиады\n", + "- __Sport__ - Название вида спорта\n", + "- __Event__ - Название мероприятия\n", + "- __Medal__ - Медаль: Gold, Silver, Bronze, or NA" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qy4yj--r07RL" + }, + "source": [ + "## 1.2 Загрузка данных общее описание набора данных" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "executionInfo": { + "elapsed": 657, + "status": "ok", + "timestamp": 1636625260411, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgpkPDdBChJz5khG7PXMg_P3ziSIZzWUDpDAjL7KA=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "SJ3LbaoiutTT", + "outputId": "c92018d9-5be4-4b69-8b9a-8abdae205a6f" + }, + "outputs": [ + { + "data": { + "text/plain": " ID Name Sex Age Height Weight Team \\\n0 1 A Dijiang M 24.0 180.0 80.0 China \n1 2 A Lamusi M 23.0 170.0 60.0 China \n2 3 Gunnar Nielsen Aaby M 24.0 NaN NaN Denmark \n3 4 Edgar Lindenau Aabye M 34.0 NaN NaN Denmark/Sweden \n4 5 Christine Jacoba Aaftink F 21.0 185.0 82.0 Netherlands \n\n NOC Games Year Season City Sport \\\n0 CHN 1992 Summer 1992 Summer Barcelona Basketball \n1 CHN 2012 Summer 2012 Summer London Judo \n2 DEN 1920 Summer 1920 Summer Antwerpen Football \n3 DEN 1900 Summer 1900 Summer Paris Tug-Of-War \n4 NED 1988 Winter 1988 Winter Calgary Speed Skating \n\n Event Medal \n0 Basketball Men's Basketball NaN \n1 Judo Men's Extra-Lightweight NaN \n2 Football Men's Football NaN \n3 Tug-Of-War Men's Tug-Of-War Gold \n4 Speed Skating Women's 500 metres NaN ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
IDNameSexAgeHeightWeightTeamNOCGamesYearSeasonCitySportEventMedal
01A DijiangM24.0180.080.0ChinaCHN1992 Summer1992SummerBarcelonaBasketballBasketball Men's BasketballNaN
12A LamusiM23.0170.060.0ChinaCHN2012 Summer2012SummerLondonJudoJudo Men's Extra-LightweightNaN
23Gunnar Nielsen AabyM24.0NaNNaNDenmarkDEN1920 Summer1920SummerAntwerpenFootballFootball Men's FootballNaN
34Edgar Lindenau AabyeM34.0NaNNaNDenmark/SwedenDEN1900 Summer1900SummerParisTug-Of-WarTug-Of-War Men's Tug-Of-WarGold
45Christine Jacoba AaftinkF21.0185.082.0NetherlandsNED1988 Winter1988WinterCalgarySpeed SkatingSpeed Skating Women's 500 metresNaN
\n
" + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "PATH = 'https://github.com/aksenov7/Kaggle_competition_group/blob/master/athlete_events.csv.zip?raw=true'\n", + "df = pd.read_csv(PATH, compression='zip')\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 291, + "status": "ok", + "timestamp": 1636625261045, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgpkPDdBChJz5khG7PXMg_P3ziSIZzWUDpDAjL7KA=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "8YtRZ5qi0epJ", + "outputId": "3f18857b-f4d0-42c4-bee1-d9252110857d" + }, + "outputs": [ + { + "data": { + "text/plain": "(271116, 15)" + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 271116 entries, 0 to 271115\n", + "Data columns (total 15 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 ID 271116 non-null int64 \n", + " 1 Name 271116 non-null object \n", + " 2 Sex 271116 non-null object \n", + " 3 Age 261642 non-null float64\n", + " 4 Height 210945 non-null float64\n", + " 5 Weight 208241 non-null float64\n", + " 6 Team 271116 non-null object \n", + " 7 NOC 271116 non-null object \n", + " 8 Games 271116 non-null object \n", + " 9 Year 271116 non-null int64 \n", + " 10 Season 271116 non-null object \n", + " 11 City 271116 non-null object \n", + " 12 Sport 271116 non-null object \n", + " 13 Event 271116 non-null object \n", + " 14 Medal 39783 non-null object \n", + "dtypes: float64(3), int64(2), object(10)\n", + "memory usage: 31.0+ MB\n" + ] + } + ], + "source": [ + "df.info()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "- Видим большое кол-во пропусков Medal, но будем считать, что это люди, которые не получили медаль.\n", + "- Что более существенно, так это пропуски по Weight, Height, Age\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 4, + "status": "ok", + "timestamp": 1636625263002, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgpkPDdBChJz5khG7PXMg_P3ziSIZzWUDpDAjL7KA=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "asswEHNi0emi", + "outputId": "7839e073-a7a6-4611-b355-943739b277fa" + }, + "outputs": [ + { + "data": { + "text/plain": "M 196594\nF 74522\nName: Sex, dtype: int64" + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.Sex.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "data": { + "text/plain": "M 0.7251\nF 0.2749\nName: Sex, dtype: float64" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.Sex.value_counts(normalize=True)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Видим преобладание мужского пола" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 + }, + "executionInfo": { + "elapsed": 309, + "status": "ok", + "timestamp": 1636625267072, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgpkPDdBChJz5khG7PXMg_P3ziSIZzWUDpDAjL7KA=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "EpQFv8t1ds05", + "outputId": "58426d5e-4b8f-45e4-82ff-6278d2db26af" + }, + "outputs": [ + { + "data": { + "text/plain": " ID Age Height Weight Year\ncount 271116.0000 261642.0000 210945.0000 208241.0000 271116.0000\nmean 68248.9544 25.5569 175.3390 70.7024 1978.3785\nstd 39022.2863 6.3936 10.5185 14.3480 29.8776\nmin 1.0000 10.0000 127.0000 25.0000 1896.0000\n25% 34643.0000 21.0000 168.0000 60.0000 1960.0000\n50% 68205.0000 24.0000 175.0000 70.0000 1988.0000\n75% 102097.2500 28.0000 183.0000 79.0000 2002.0000\nmax 135571.0000 97.0000 226.0000 214.0000 2016.0000", + "text/html": "
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" + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZGB2K6975SrE" + }, + "source": [ + "**Первичные выводы по числовым данным**\n", + "* Возраст людей в выборке от 10 до 97 лет. В среднем возраст варьируется от 19 до 32 лет. Младшие 21 покрывают 25%, 21-28 летние 50%. Остальные 25% это люди от 28 до 97.\n", + "* Рост от 127 до 226 см. В среднем 175. СКО = 10.\n", + "* Вес от 25 до 214 кг. В среднем 70.7 кг.\n", + "* Год проведения лучше рассмотреть отдельно. Но можно сказать, что данные приведены с 1896-2016г." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 175 + }, + "executionInfo": { + "elapsed": 6, + "status": "ok", + "timestamp": 1636625269818, + "user": { + "displayName": "Александр Аксёнов", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgpkPDdBChJz5khG7PXMg_P3ziSIZzWUDpDAjL7KA=s64", + "userId": "11145992452404092449" + }, + "user_tz": -300 + }, + "id": "LMdTGO9C4187", + "outputId": "10bdbd48-35be-4e98-e64a-9daa9db723ef" + }, + "outputs": [ + { + "data": { + "text/plain": " Name Sex Team NOC Games \\\ncount 271116 271116 271116 271116 271116 \nunique 134732 2 1184 230 51 \ntop Robert Tait McKenzie M United States USA 2000 Summer \nfreq 58 196594 17847 18853 13821 \n\n Season City Sport Event Medal \ncount 271116 271116 271116 271116 39783 \nunique 2 42 66 765 3 \ntop Summer London Athletics Football Men's Football Gold \nfreq 222552 22426 38624 5733 13372 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
NameSexTeamNOCGamesSeasonCitySportEventMedal
count27111627111627111627111627111627111627111627111627111639783
unique1347322118423051242667653
topRobert Tait McKenzieMUnited StatesUSA2000 SummerSummerLondonAthleticsFootball Men's FootballGold
freq581965941784718853138212225522242638624573313372
\n
" + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe(include=object)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "70kXb6Vk9zYK" + }, + "source": [ + "**Первичные выводы по строковым данным**\n", + "* 1184 уникальных команд. Самыми активными являются United States - они поучаствовали в 17847 соревнованиях.\n", + "* Самым же активным спортсменом является Robert Tail McKenzie - 58 участий.\n", + "* Самой популярной игрой является 2000 Summer - 13821 участников.\n", + "* В выборке преобладают мужчины - 72.5%.\n", + "* Как уже говорилось. Больше всего игр проводятся летом - 82%.\n", + "* Самым популярным видом спорта является Athletics - 38624 человека.\n", + "* Самый популярный Event - Football Men's Football. Всего различных ивентов - 765." + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Медали:\n", + "Теперь посмотрим на медалистов" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [ + { + "data": { + "text/plain": "count 13372\nunique 242\ntop United States\nfreq 2474\nName: Team, dtype: object" + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "golds = df[(df['Medal'] == 'Gold')]\n", + "golds.Team.describe(include=object)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "* United States выигрывали золото чаще других." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [ + { + "data": { + "text/plain": " count unique top freq\nMedal \nBronze 13295 11867 Heikki Ilmari Savolainen 6\nGold 13372 10413 Michael Fred Phelps, II 23\nSilver 13116 11430 Mikhail Yakovlevich Voronin 6", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countuniquetopfreq
Medal
Bronze1329511867Heikki Ilmari Savolainen6
Gold1337210413Michael Fred Phelps, II23
Silver1311611430Mikhail Yakovlevich Voronin6
\n
" + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby('Medal').describe(include=object)['Name']" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "* Больше всего Gold получал Michael Fred Phelps, II - 23 раза.\n", + "* Silver - Mikhail Yakovlevich Voronin - 6 раз.\n", + "* Bronze - Heikki Ilmari Savolainen - 6 раз." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "data": { + "text/plain": " count unique top freq\nMedal \nBronze 13295 268 United States 1233\nGold 13372 242 United States 2474\nSilver 13116 273 United States 1512", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countuniquetopfreq
Medal
Bronze13295268United States1233
Gold13372242United States2474
Silver13116273United States1512
\n
" + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby('Medal').describe(include=object)['Team']" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "* United States получали медали чаще остальных. И почти половина из них Gold" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [ + { + "data": { + "text/plain": " Name Sex Team NOC Games \\\ncount 231333 231333 231333 231333 231333 \nunique 120401 2 932 230 51 \ntop Robert Tait McKenzie M United States USA 1996 Summer \nfreq 57 168064 12628 13216 11938 \n\n Season City Sport Event Medal \ncount 231333 231333 231333 231333 0 \nunique 2 42 60 723 0 \ntop Summer London Athletics Football Men's Football NaN \nfreq 188464 18802 34655 4464 NaN ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
NameSexTeamNOCGamesSeasonCitySportEventMedal
count2313332313332313332313332313332313332313332313332313330
unique120401293223051242607230
topRobert Tait McKenzieMUnited StatesUSA1996 SummerSummerLondonAthleticsFootball Men's FootballNaN
freq5716806412628132161193818846418802346554464NaN
\n
" + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "no_medal = df[df['Medal'].isna()]\n", + "no_medal.describe(include=object)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "* United States не получали медали чаще остальных.\n", + "* Robert Tait McKenzie не получал медали чаще остальных - 57 раз." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "USQjKMAIETO8" + }, + "source": [ + "## 1.3 Визуальный и статистический анализ данных" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Кол-во мужчин и женщин по возрасту\n", + "plt.figure(figsize=(18,8))\n", + "sns.histplot(data=df, x='Age', hue='Sex')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "* Распределение нормальное, что и можно было ожидать.\n", + "* Видно, что более молодых женщин (до 18 лет) больше, чем мужчин." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### По годам" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Средний возраст по годам\n", + "gr = df.groupby('Year').mean()\n", + "plt.figure(figsize=(18,8))\n", + "gr['Age'].plot()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Из графика видно, что ранее 195-ых в основном преобладали люди с возрастом ~28, к 1980 году средний возраст упал до 24, а потом опять начал расти." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 251, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 251, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Кол-во мужчин и женщин по годам\n", + "silver = df\n", + "plt.figure(figsize=(18,8))\n", + "sns.histplot(x=\"Year\", hue=\"Sex\", data=df)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### По времени года" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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+ }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Кол-во мужчин и женщин с Gold по возрасту\n", + "plt.figure(figsize=(18, 8))\n", + "sns.countplot(data=df, x='Year', hue='Season')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Зимние виды спорта проводятся реже. А до 1924 они вообще не проводились." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### Корреляции" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [ + { + "data": { + "text/plain": " ID Age Height Weight Year\nID 1.0000 -0.0036 -0.0111 -0.0092 0.0119\nAge -0.0036 1.0000 0.1382 0.2121 -0.1151\nHeight -0.0111 0.1382 1.0000 0.7962 0.0476\nWeight -0.0092 0.2121 0.7962 1.0000 0.0191\nYear 0.0119 -0.1151 0.0476 0.0191 1.0000", + "text/html": "
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IDAgeHeightWeightYear
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\n
" + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.corr()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [ + { + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Посмотрим на тепловую карту\n", + "plt.figure(figsize=(8,8))\n", + "sns.heatmap(df.corr(), annot=True, cmap=\"YlGnBu\", cbar=False);" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "* Коэф. корреляции между Height и Weight равен 0.8, что ожидаемо\n", + "* Также немного коррелируют между собой Age и Height или Weight\n", + "* Остальные данные вообще не коррелируют между собой" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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b9FSaW4mOpBoFERlpjNnlffpFwJeZtBj4fyLyEJ5A81jgn8mcm6LEyoyxxTyyLPQCPmNsccBzp4vd/JoGPF4Wz632HQvqIq4QfPzuHx9FbRAAOru7EfFkIjld8H39F+ZOK2Pu1PKIrzlv5Xb+MNd+lZKT6WLooCzgqFEYXpAD9E5eO17S3IozCTMKIvI08HmgWEQagR8DnxeRKjzfgm3ADQDGmA0iMh/YCHQC39bMIyXdqa4oCtEfml5ZZKuLFBxYtXM9RWsQAFZ8GNq6Mhy+rKTbn1/Lk984Ley+81ZuZ0qUCrAN+w7jdgldQT6k2+bXciToG/xGfbMnDdYhwP5m/d6wF/x4ZCgpkUlk9tHVNsN/CLP//wD/k6j5KEoieOK6qWGzj5xYWLuzV+87ujCXTUHZPdGQ6XKRmeFm7rSygHTTYJyC0cEU52fbpsYGGwQfSzY22QbYN+xsiXjB1wyl5KAVzYrSS6orirjlghNiUk4dktu7+7FD7fZX3YwI32h/Rk+ERYlTMNrK3GllTDu+KKbU2AvGl9gG2EFs9Yt81ddLN+7m/761hcPtnSHHhhPKC1cYqNij2keKkgSCVxNTygpjfg23gE84dOWWfbb7/GT2ydz9wsaQFNXcTBfdxlNVvO9ge9h4xNxpZVw2ZQzPr9kR4hq78byxIasiXyDdJeJorHzHnzd+hG3gfcKowY7ZWMEtQgEKsjPCSmKnMvbQk5VjOr2H9mhWlCjp6RcxuJfy9MoiZleN5rYFa0P2/cYZx3KwvYs9rUdY/sHR7KbZk0cyujCX//P6Fsf3mVNdyldPP5b1O1r47xc2AoZuA8YYsjM82UdXnjKGd7fv4/1doa0tv1xdyjenHxcgkW095x0tRwIutD+6dDwnjxpCaWEutdv38fQ7Dby+aQ/BoZG8LBcXTyjhZ1+eYvu6vt+lXX/o2y88MSDjyccPLjmRL00ptTUIzQfaOPP+ZRyx1ErkZLp4645zKcrPZunG3SEFefHC7m9trVtJl/cI16NZVwqKEgWRitSccKpluHyK/V3rV04/lsqSAhbV7uCt+r0ILjq7u3lp3S6EUK0jK/NrGnl53W4OtneG1A74JLWfXO28QrjylFLHngkthztCfP4/+Ot68rLcHAyzOgA42N7Ngvd2saj2JTb/9AsBqbSPLKtn7rQyZk0aZVsB/ZMXN9i+Zv2eAxGltO1iD1c/vtJR3qO3hKtbidfdfDLeQ42CokSgN19Ep1qGDTZ36gCFeVn+LBvPtdZ7YTP+f8ISHPCNhd/8o54HhuX7L7bBd6RuG5sUySBY6TDww7/U8eQ/GwPG563czjqHNNltnx6xHQ/XItSpYHDrJ/Yy5MHyHj0lmrqVvvAeGmhWlAiE+yJGIrhmwYfTPf+SDbtp3HeYjihlK+LJ0k17/eqkdoawKw6e5pc3NNmOf+wg5+3EsUWeRkN2wWSn6uiVWz61fa0lG+3nFIng93b6WzuN1ze1sqCmgfom+xuEWF7Labwn6EpB6Rcksso1UpGa1T8OBPjKnWoZdu23vwi+Xb+X4QXZYS/AmS4454ThLHk/tlqFaPDJYH9tamJ0xc6oKOSF9aHzvrxqFI+/9XHUr1PbsJ8Nuz5zDCbbFQzmZbl5tqYx5LXCrTqccApkR1u3ElyN7tOVikQstTE9RY2C0udJdKZJuC9icKzBxyPL6jm9opDvXzKeX1z1OWq37/MHN6vKCrntuTrb98rKdFPX2GK7LdMluN3CA5dP4szKYpbc+1rIPi4hJJ4QK11d3ezab++26S1nnziCwoKcEEnuH/zLyTy5envUBXzlRYO45o//DFvXEFwweN74EWGlN6zUN7VS27Df38/C97iypCBsEV24uhXf+HHFg2x1paxd7cLR09qYaFGjoPRpklXlavdFtHOxWFm9dR9XP7aK9q4uRIScDDfPr2lEJDQn38fVp3okwOxWJtPHFvHV04/lvPEjeLHOvvjNZxCKBmUwLD+bTXtiL3Dr6I6+eC1WyosGcUX1GOZOLQ+40AK8f+8l/G75Zhau3cXhtk62fWq/mpo7rYzMDHePCtlevfnzEbOPwmlKzZ1WxuVTxthu8723b4VoJTg+Y0dtw/6ojAJg+x7xQo2C0qdJZpVr8BcxmpiCL+sHDB1dnZbHoQFawSN0V5SfHbIyAVj2wV6WfbCX0ysKGRNBUrr5UGeP+i0kmswMN+BpHmR3AbzhnLHccM5YINAtN3RQVsjdeiSpbSeX4nnjRzgGlu3UYa3MW7mds8cOC0h3BY/bLS/LbXtMpJsHH05d9pKNBpqVPk0qdfjjGdwDyM/O8FfwPnHdVBbcMJUrpowK2W/11n38ZU1kmYzPjqSfUdjbGr1bylop/nHzQd7Z9ikfN3tWPpGktnva2jOanth1jS1kB6ViZbvFMRMrmpsHX1e7dCCqlYKI3G+MuSPSmKIkGydp6kRr4dQ3tbJ+52eIQLzqP4ONWXVFkeMFpTeX+8LcDPYd7nnqam9YtfXTmNM/nVqHOqnP9salGM3d+oyxxTz2xpaAdCxxieONiFOiwi/mTKKzmwAXWjoQrfvofCDYAFxsM6YoSWdW1WjGjxwc4qOOB3b+53A+Z5d4isDe/mgvDfuO3hULkJvlpq2jC5fLE1840tlFh+XC8sDlkwCoa9hPaWEudy6o5fUPI99lxkqqDALgd7Fc9qt/sHbHASaNzucP/zrV1s1Ts7WZP7+9zba24J7F6/2FflZj0LjvMC2HO8K6FK1B5ODPSmFelq3qq4+508qoriiK6UbEKVHhMofYRKoJK3MhIt8C/gM4DvjIsqkAeMsYc01ipxcelblQIHHZR3ayC49+5RRmPrwi7HF3XjiOn776Ych4thsQ4a5LJ3Dy6CHMfvStkH1yMl1kuly9KkJLZ+7+l5O4+4X3Q8atWkazqkZHFZiFo6mc1s9Ae1c3Xd3dWEs9fDIXv3ztw7CpoHUN+5nz27dpsxjrTIFvnVPJrMmjAoxIrGnQydBEipZwMheRjMIQoBD4KfA9y6ZWY4x9JUgSUaOgRNK56SlLN+621dz5xhnH8se3w+fTlw7JobHF2XeenSEcVzSI93sgfd3XGZQBhyLYu5L8LJoOtEf9mgtumOpPT/WR6RZcAllut9/YjB852Nagv3bzDP/Fvr6pNeI+/YEeax8ZY1qAFuBqEXEDJd5j8kUk3xgTfesnRUkAico+cqpy3RnmYu+jrTO89ENbpxmQBgEiGwQgJoMAnkBu8GcgJ8PNo1/9HENys/x38gtqGmyPt6aCHmzvIifTFXKTEYucR18nquwjEfkO0AT8HXjJ+/Ni2IMUJQnEK/soWHLAqcr1ylNKmTstfLXvxSePjOm9BxLZ4TX9esRxxYNsPwMTRg1h8pih/puDwkGZtsdbx+0+N8Z4BAEHSl+GaFNSbwJOMMZMMMZM9P5MCneAiPxRRPaIyHrL2IMisklE1orIX0VkqHe8XEQOi0it9+e3PT0hZWBhl5p4y8xxLN+0J2pNmbsWrmPmwyu4bcFaZj68grsWrfNXv1opL8pl36EO5k4tZ8ENU/nm9HLb17vx/HFMr0ytzzhdmXRs7H0kIrFyy6fcMnNcSHoqEKBNtO9Qh+3xdY0t/n2CP0+ZbqGru5tvP7UmptTWvkxU/RREZDlwvjEm6uiXiMwADgDzjDEne8cuAJYZYzpF5H4AY8wdIlIOvOjbL1o0pqD48AX9nlr9MfMt+jbBgcTgzJNwPuQNuz7jtvl1GAxd3YEapYKnriA4IDxycBYrv39+2AylgUx2hiukAVAkhGj0YY/2kygtzLXt9zxqSA5X/G5VyHG5GYIRCUhQaD7QxoadLXxzXg1tFumNeMSr0oEexxRE5Bbvwy3A6yLyEuBfQxljHnI61hizwnuxt44tsTxdBVwRfuqKEh1F+dnsO9geYBAgUFPGToTsuOK84JcC4NUNu/nV8no6HFITDfYy1bs+a+fJt7eqQXAgVoMA0RkE8PSTuH76cQC2dQp3Xzre9jiP3pIJqGUoys9mSG4WWW43bZ1H/84DoSd0JPdRgfdnO554QpZlrLeh+G8AL1ueV4jIeyLyDxGZ7nSQiFwvIjUiUvPJJ5/0cgpKf8KpGrW2Yb+tfMG8ldsD6gSCcdInioSdEqeSHGob9vuTD6xkulys2/lZ2GOtPaGffHsrdy9ez6Egw5+Mavmarc08tOQDarZGTslNBJGyj/47EW8qIj8AOoGnvEO7gDJjTLOInAIsFJEJxpiQv6Ix5jHgMfC4jxIxP6Vv4lSNWjVmqKPByHRLiHtCgAsnjOBXy0OrUKNh+OAsiKBCkZclHGzXj2+8qRozlMK8LNvA88RRg8Mee6Szi9LCXCbf/QotRwKzjSL1hI4XPe3wF0+izT56QUQWB/08ISI3ikhOLG8oIl8HLgW+arwBDWNMmzGm2fv4XTyFcuNiOhNlwFNZUhCSGeTTlHEyGGdVDuOXV1WR5YZst4ssN/zyqioqSwr8AcecDPuUmUyHTJopZcdEnOtJI4dE3Kc/EuvaKy/LHaEJ6VEET0Wyky5SdXn4v4sxhgXvbA8xCABfOW0Mb91xblwl2YMJ1+EvmUQrc7EFGAY87X3+ZaAVz4X7ceBr0byIiFwE3A6cbYw5ZBkfBnxqjOkSkeOAsd73VJSYuGf2RFtZZp/BCNbx96l12slk+OQzfr18Mwtrd4W819fPqqDlcEdAHOOs44/hjSgE0Fxif6m788JxHOzoRoBHl9fTbXqnc5ROuICzTyhm+QfOvx+3eFZtbhHOqjyGTU0H+MqppUw9vpglG5sYnJPJE6s/xoVYFGg95Ga5+fPb25g1eRSzqkazrmEfL67fzaUnjyYvy819r2wi0+WRBrcjNzODhWtD/84Aaxr2R5TI6C3JaLUZDdFmH71jjDnVbkxENhhjJtgc8zTweaAYT43Dj4E7gWzAZ/pWGWP+XUQuB+4BOvB8B35sjHkh0rw0+0iJFbsvtZNMhm/cBRyyuZK8dvMM7n5hQ1RyDNGy7b4vANi6MPoDgzJdtr/LWJhTXcqlk0Z5M4PiazJvPOc4frk89H70zgvHsaPlSI+6pUVLzdZm2+yoBTdMjbtR6HH2kYV8ESnzVTCLSBmQ791mW35ojLnaZvgPDvs+Dzwf5VyUfkoiW2r6CNbxd1LUHD9ysH/ciQde2RRXgwAeobgrTinrlwYB7I1rrMyvaWTG2GHcdel4/vuFjRhM2IQBO5zSXD91KLluam3rVbe0aEhGq81oiNYo3Aq8KSIf4e0FAvyHiOQBf07U5JSBw6LaHdy+oA63uOgy3Tx4xeSE+m99NO47bNswpbZhf4h0QjCJ6JFct+MAbd32cgzKUW57ro72ru4etx51Omz1Nnsj76R3FUu3tGhIdKvNaIjKKBhj/iYiY4ETvUMfGGN8IjC/SMTElIFD84E2bp1f61W19Nwh3zK/Nu4tNe3Y+ol91fOR9tR0LjPAlj3RVWIPZI7E2W3k4+LxJXzYFH0400k6ozckstVmNIRNBhCRc73/fwn4AnC89+cS75ii9JoNOz8j+Dve2e0ZB4/RsMoV2BHNPna8ssFe+O7Nj5r9GSyDHNosJoq2/uk56hPEKsbnk8iw1hYs3bibOxbUsXRjYvpcJ5pIK4WzgWXAv9hsM8Bf4j4jZQDitJg3UfVK6E0/hdxM+/ui3EyXP/vozfq93PvSxhDD5URZYTbb9w0M8bT+xrPvxFZ4+Ic3twR0VbM+tnaJsyMZMbSeEKl47cfe//81OdNRBiITRg0h0y0BwcJMtzBqSC7XP/Fu2LaKvWm9CFA5vABP7WTouM/YZLgkaoMA8I2zjufuFzZGf4CSNsQaojjYHv6D8UHTQR5f8RFfmlIa8HlMVGOoeBBt8VqJiPxBRF72Ph8vIv+W2KkpA4Wi/Gx+fuVksjM8rprsDBc/v3IyB9u7bOUKfFIEgK2kgRjDA69siqro5/QK+4KmE0cU+I3NgRj9OfNWbo1pf6V/c98rmzjz/mU8tfpj6rySK77PVmtbJ0c6urn9+bVpI80dbfbRn4D/C/zA+/xD4FkcUkwVJVbsmrA3H2iL2CvBrp/C4U7DszWNPFvTGCITELxkz8xw45aAHuy4xSOzHCn7yIktew9H3kkZMHR1Q1d3Nz/463rystx0dBskqD4snYT2oq06LzbGzMdbXOmV0NZwmBJXivKzA5qiOMkV+LbXN7WyfNMev5Z+ro0chVUmYFHtDk7/n9f40v95i9P/5zUW1+6gtDCXzIyg1UiGi6oxQ1OSfaT0bw62d9He2R3QAxqSI7QXLdGuFA6KSBFel5uITMXTplNREordCgIIkcGeNXkkh9q7eM2mdmDF5r1UDMvnxmdqA8b/65la3v3hTB64fBLftdRIPHD5JL/20XcXrKXbxF4cpQw8glecsTCnujQtVgkQuZ/CTcDbePSKFgHHichbeHSQrkz47BQF/Pr2PuxksBfX7SLHIXN0xthinli5zXbbEyu3UTEsHxC6jQGL/FrNtk/jLqOg9F+cDEKWCyLEo5lf08iN541LC8MQyX1Uiqc47RXvvn8HngHOMMbUJXZqimKPkwy2nTKETybgPYdjVm/9lNueq6Ots5sjnd20dXZz63N11Gxt1kY5Slw4PoqK5+AEilQS1igYY24zxpwBjABuA1bjEblbKyKac6ekBCcZbB+5GcKXq0tZcMNUf5D5i1WjbPedNHpIiGuoo8uwsDZCQwRFiZKvnjom4j7pFFOINtCcCwwGhnh/duIxEIqSdOz6JljpRvjCpFFet5An46hiWAElBYGSBCMHZ3Hm2GG2r5HpjlbFX1GcOaEkj2vOqAj5vE6vLHJMoEg1kWIKjwET8PROWI0nvvCQMWZfEuamKI5s+eRgwHOXQF5WBkc6u+jq7ubbT62ho7ubOdWlPPtOI26XcCRIf//8CSOY4NCNa1yctfKVgcW5Jwzjq6eXUVVWSF3Dfm48b1xIn48+WdEMlOHpf7AZ2AE0AvsTPCdlgBLtl8SuQ1W3gStPGc2Tqz+moxtavb11rU11gpm3cjuFOfZfgdrtet+j9JyTRw/hQHsXZ96/LKBq+Yrqo66k4ASKdCGSzMVFIiJ4Vgtn4JHQPllEPgVW+mQwFKW3xFL279Sh6k8rP45ZSvmZmh3271EfuXuaojgxuXQI3376vR7Jr6R6BRExpmA8rAf+BrwMvIVHKfXGBM9NGSBY9YuiKfufMbbYdrwn2vpDsh3yWHsq1K8MeIbkuCkuyIko0WLHotodnHn/Mq75/WrOvH8Zi2vtb1oSSSTp7P8SkWdEZDvwD+BSYBPwJSBid3IR+aOI7BGR9ZaxY0Tk7yKy2ft/oXdcROQREakXkbUiMqVXZ6bElZ5KU0eDnX5RuC+Qr0NVOLIzXGRnuMI2fXcJ5OfaL5Zb27VgXwmPgG0VfcuRLva2Hoko0RJMrDdHiSLSSqEceA443RhzvDHma8aY3xhj6owx0VT1/Am4KGjse8BSY8xYYKn3OcDFwFjvz/XAb6I7BSXRJPrupbQwlyOdgRfhI51dYb9AT1w3lQU3TGXc8Dzb7VMrjuHt753L16Y6ZykJUJyfY7stVhE8pX9jl41mgLNPGG67f11jS1iJFjtivTlKFJFiCrf05sWNMStEpDxoeDaeWgfwtPJ8HbjDOz7PGGOAVSIyVERGGmNCdY2VpNFbaepoMUECYcHP7aiuKHLsh7D/kOfuav67zvr4XQZ27T8UwyyVgYqTzMnxw+xvSnytNO0kWpywE3dMRf1CtHUK8aTEcqHfDZR4H48GrM1pG71jAYjI9SJSIyI1n3zySWJnqiTl7qVx32FyMwPvT3IzM/zvEc51ddKoIbavedKoIbZzD+b93Qd6OGtFgS9+rjTElemroodQkcdwRBKATBbRCuIlBGOMEZGYInrGmMeAxwCqq6s1GphgknH3Eu49ImUlXXfWcTxj0y3rurOOozAvK6LSaad+gpQeMr2yiMqSAp64bio1W5tZsXmvf4XQU5wEIJNJKlYKTSIyEsD7v0/WcgdgrQcv9Y4pKSQZdy9O7wFEDLwV5mXhCnL3usQzXpSfTfWxhXGbp6JYeefjff7PYnVFEbdccEKvDIKPWFYXiSAVK4XFwLXAfd7/F1nGvyMizwCnAy0aT0gPYrl7efLtrSxau4vZk0ZyzRkVMb3HqCE5AXdbdTYidsbA8k17OOfE4RTlZ3tdRBKgT5/pEhr3HWbfwfaQIjdFiRfWxjipri2IJwk1CiLyNJ6gcrGINAI/xmMM5nvbeX4MzPHu/jfgEqAeOARoX+g0Iprqy8l3v0KLV6r0nW37eHDJB9TdHZx8Zo+1P8Ijy+qZO62MuVPLOdIR6P5p6+zmx4vX88NF8MDlkxg/cnBIw5K2LkNelps3tQBNSSDRujj7Ggk1CsaYqx02nWezrwG+ncj5KInjybe3+g2Cj5YjXTz59taIKwa7/gjzVm7ntPJjyHZLyEXf1yz91vl1lBcPIsMldFqKzXIyXRxs72Ln/vSQIlb6JoMyXbR3dSMi5GS4OdLZhTGG3MwM/8UfCMnO++6CtXx6sJ2zKoup7IMaWikNNCv9h0Vr7T19i9buimgUnO7oP24+hLic21l1dBs27zkYMt7Z5bmDe3/nZxFmrSj2ZLmFe2afzDkneuoQfK4h6+Oi/GzqGvaH9PJu6+zm7hc8nQXmTivjntkTk38CvSAVgWalDxKponn2pJExjVspzs+yHT+2aBBzqkujn6QX461jdgdHoBUlSlwu8cetrIHf4CCwXeaclXkrt1Pf1JqsaccFNQpKRBbV7uC0e1/ji4++xWn3vmZb0XzNGRUMCeqHOSTHHVWwedrxxbYZRCeOGMz8GufiMye6ug0bdrYwYbS9LLaiOJHtdpGT6eKWmeNYvmlPyAU9+ObImjmXnWF/OXXqFJiuqPtICUvzgTbbhvd2Fc0TS4cGZPtMKh0a1XsU5Wfziy9XcdtzdYgIxhh+duVkDrZ3hSzNo2XHvsNMKdN0VCU2fj5nMis2f8L/vrzJP+ZzATkFlH3ZeSs/auY7T78X8pqROgWmG2oUlLCEa3h/0/kn+J/b9Th4o76Zmq3NUeVu26W9Nh9oi1h85sTmplbWNu7v0bHKwGTutDJOHFEQcmGft3I7syaNCiv3UpSfzaWTR/HPbc0BPTzmTivrc8FmNQpKWN5rsG82Ezzu1ONgxea9PS7o8RWfWY2N4BEii8S6xhY+OXikR++r9E+cPjszTxrO9y46kcqSAhbUNNjs4fkcB69arXUKPu6ZPTGkw1pfQ2MKSli+6JBvHTzu1ONg5JCcqKR/F9XuYNpPlzLndyuZ9tOlLK7dQX1Ta8jqI1pVih0thzl0RJVOlaNcfLK9ounVp47xX7ydXD0zxhZHLfdSWVLAFdVj+qRBADUKSgQumzKGkYMDs4NGDs7isiljAsacehz87982hZXbrm9q5U9vbeHGZ2pp7zK0dXbT3mW46dla3qzvueDhzpY29hzs6PHxSv/jtIrikP4aAhQXHJVPrywpYO60QLn1udPKqK4oSguxumSg7iMlIiu/fz4L1zTw4rrdXDpxBNPHDaeuYX9ISX9FcR5vBN3Z+3ol28ltW6uYg+k2znLFitITTh41mOxMV0CVfHamK+Ru38kFlA5idclAjYISFdPHDadiWAHrd7SENCOfVTXatirZSrD/NdL+ABt3ttiOu13gFuH44kG83xRavKYoweRkusjMcPPA5ZO4PSiDyO7iXllSYOv+iUbupa+jRkGJiC8VL8Ml/o5kwRkYkXKxrf7XpRt389sVWyK+7/ZP7RvgdHVDF0YNghITpYW5TB4zdEDc7fcGNQpKWKyd1+zwrQDKiwaFfZ051aUU5WdzwcOv82GUF/PSobm8u91+taAokchyC9kZ7pAVwUC42+8NahSUsPi6lzkVkPlWAI37DpPpFsc4wPyaRqaMGRq1QQB4r49VgirpQ6Zb+P211QzJzdIVQYyoUVDC4qTtkpflpsuYgDswt8vZKGS6XDy56uOY3nv7Pq0zUELJFBh9zCAKB2XwXoO96KHbJUwYNUSNQQ9Qo6CExaftYg3O/ejS8Zw8akjAHZhvv+8uWItL4HCQu+lQeyfuCP2SFSUa3Bkunv/WGRTlZ1Pf1Eptw34+PdjOQ699GDGArERGjYISkWhT8Yz3X2NC1Um7DKzdofEBpecInhRS6wXfmiV0+SmlGkCOA2oUlKiwBufsWg/6AtJtnQanuuPg1YOixMLMk4Zx3+WTHS/4wQHkpRt3s2RjExeML+G88SOSNc0+T9KNgoicADxrGToOuAsYCnwT8JWxft8Y87fkzk6JhJ1S5PiRg1lctxMTQ63ZzJOG8/oHnwR0TFOUcNxx0UlRrwCsWW7P1jRyQkker978+cRNrh+RdKNgjPkAqAIQETewA/grnp7MDxtjfpbsOSmRaT7Qxoadn3H7grW0dR5Virx5fh1dPbiwv/HhHnoogKoMQOZUl3KwvYvmA20RDcPSjbtDstw+aDrI0o27dcUQBal2H50HfGSM+VgkdV2y7NwhylEW1e7g9gVrAU+rQSs9MQgAbapVp0TBl6tLOX5YPg+99iEvr9sdUEXvxJKNTY7jahQik+p0kKuApy3PvyMia0XkjyKSlA4pi2p3cOb9y7jm96vDCrcNVJoPtHHbc3W0dXaHGARFSTTTjjuGh177kCMd3bS2dXKko5vbn18bVnn3gvElMY3bEan9bH8mZUZBRLKAWcBz3qHfAMfjcS3tAn7ucNz1IlIjIjWffNJzFU0IrNaN9gPX31m6cTd3LKhj6cbdAGzY2WJbe5Abpv2gosSLLXsPkRmUyizG8MArm6jZGii+WLO1mYeWfMCQ3ExOKMkL2HZCSV7Uq4SBfqOYSvfRxcAaY0wTgO9/ABF5HHjR7iBjzGPAYwDV1dW9ilLaVevaNc4YKNgF5/7z3HG2+37nnEquOq2MG595L6TngaLEixlji3nsjUCdrMOdhmdrGnm2ppHplUU8cd1Urvn9Kv/n8JFl9UyvLOL2C0+MOfvIeqNo12FtIJDKW72rsbiORGSkZdsXgfWJnoBdta5T44xkk+zlq1NwzrmlpWHfwXZWb/004XNTBibBfQxyM0Ljjm/UN/Pk21ttW8EOyc3k/ismxxRH8N0oWvHdKA4UUrJSEJE84HzgBsvwAyJShSfJfVvQtoRgV62bDpWQTg3CE4lTcG6dQ8HZQ3/fzM+XbHZQRFKUyGS5XbR3hX6CvlxdyjenHxfSx+CBVzbxbE1jyP6L1u6yff2etIItLczlcEdnwNjhjs60uFFMFikxCsaYg0BR0NjXUjGXdGuckarl68RRgwOKR3yUDrX/Mmj/G6W3zKku5cnVoT01rAbBR1F+NleeUmprFGZPGsk720J7iTu1iI2EJxPSBD0fOGikEM8HbvKYoSEX3US7cHyBMWvALFXL15ws+/uD7Ex3Qt9XGbh8/Yxy29aXds1tmg+0kZnh5vSKwKTE6ZVFXHNGRUgr2OmVRTGvEsDz/cvJCPzM52S41X2khHfhxKOuwS4w9sR1U1MW5ygclGk73qkVZkoC8F38nVpfWgn+Lt50XiXdxrMS8F34n7huKjVbm1mxeW/AeKykc5wxWahRsCGcC+fN+r299vfXbG22DYzVbG32B9aSHefYd8i+yf2Cd0KX64oSiYLsDA60dQaoYE0pG8JXTjs25OLv1PoS7L+Lv12xhbfuODfkO1Fd0bPVgZV0jTMmEzUKNjilqm7Y2RIXf/+r3hoAu/HqiqKUxDmcOqd12o4qSignleRxw9nHM33ccDbsbOGb82q8AokeNu5q5ZwTh8f0eU5F2ni6xRmTjRoFG5yWkCBx+YAeV5wfcTzZLQPr9xxI2nsp/ZPbLjzRn/45JDeLLLebts6jtxWxflfqm1p5b/u+kAylZLhzBnLLzgEbaA4XRPYtIXMyXRRkZ5Dj1XCfMGpwXPyNF0ywz5u2jie7TqHm49DsDUUJRgC3w1WjrvFo+nJvffN3LVzHzIdXcPcLG2nr7MbtkoDv4kC9YCeDAblSiKYOwGkJGQ9/Y1F+No9cVcWt82vxfM0MP59T5X+dVNQpdDkElF2gtQiKn8JBGXz3whO586+htaXWFNDe+Obrm1qZtyowVbWr2/DTL01k2vFFSTEI8Qha91UGnFGIpQ7AbgkZL3+j0+vEo07B16LQKZvDjn0H7QPNQ3Ld7DuskqaKhxmVxVwwYYStUagYFugW7el35dUN9jG3j5sPcunkUbFPOkacMgMHCgPOKMQjcBUvf6Pd6/R2fnctXBdwlzV3Whn3zJ4Y9pilG3fzQZN9A3Q1CAOTm86r5BdL60PGfzTrZBr3HaYgO4PWtqPxgkFZbv789jZmTR4VcCPS13zzkTIDBwIDLqYQjzzkRPr7Y5lf8Dzslt3zVm6nvqnV8f0uePh1/m3eu+z6rD0Os1f6A26Bc04s4ZGrqnC7xBtHEB65yuPitJOCONTexSPL6pn58AruWrSuV+9/oUPMzWk8nqzYvDem8f7IgFsp9DYPOdH+/kjz8xXOrd/Rwj0vbsTtErq6DQ9eMYl2h34Hj7+xJUQ6YOGaBv741rYQETxFycxwUVqYy+QxQx3dP4ZAKQgr81ZuZ+7U8qhdl8FUlhQwd1oZ81YGrnh7+nqxMGNsMY8sC10h9VQyoy8y4IwC9NzXmSxdollVoxk1JCck0OUzSG4RDrYHunVufa6Op6873fb1fDLDF04Yzgklg3ly1VY+PaRuoYHK0EFu9of5+8+pLvV/nu3cPxt2tkTsuFfbsL9XF/FoKp0TQXVFEdMri3jD4kLqqWRGX2VAGgXoma8zWYU01rjAI8vqmTutjBvPG+c3SHZ0dBkOdXQxriTP8e7/1Q17eHXDnrjNU+mbfPW0Y2lt6wy4E7cyv6aRG88bF+YzHVkgrmrM0J5P0Eu4SudEEi/JjL7KgDUKPSEZuihOcYHTyotCDFIwq7Y0qztIichJI4dw6eRRjBuWz1PvNLClqZU2y8cq0o3OhFGDyXCBU3fWZLl6Ekk8JDP6KmoUYqCn8QgnAT278dqG/bavsfdAW4hBCuYPb26L6XyUgYcA044vCuiyF0x7VzcthztoPtBm+9kuys/moTlVfHdBHW5x0WW6ufX8EzgmLyvE1dOT9GgltahRiJFY4xFOgWmncadl91mVxWS7hSP25QQAtDnduikDnmy3C0M3P7uyitrt+2wNQk6G0GU8hYzffmpN2ESKaL4HPUmPVlLPgEtJjQdO/ReCsQamW9s6OdLRzXcXrOXRZR/y3edqA8Zvf34tzQfaqCwpYJxN0/H1O/bTcqRnweFTywsj76T0a34+ZzIr75zJrKrRjl32Tj+uCJd43ELBn0s7wn0PepIeraQHahTC0Jt6hPqmVv789jbcQV2b2jq7eXDJZoKSh/x+3PqmVtteyXYdp6JleH5Wj49V+hbnnmCfOnmko8t/8Z523DG2+3xuzFCy3IENZnra4MnJDeo0rqQPKXMficg2oBXoAjqNMdUicgzwLFCOp0/zHGNMSpTaelOPELxsjgZfwHr5JvvsoBGDI2c3ZWe4bF1IL623vzNU+i6ZLqHLGKyZodMri/j+JeNZ9sGKkP2tbsmKYfa+/SnHFvLbFVsCxnqaSOHkBo1HVpKSWFK9UjjHGFNljKn2Pv8esNQYMxZY6n2edOzcPuGW0Vbsls3gaVJuR7bbFaD86NTX4DSHTIhBWW5yMl18/+ITGTkkJ+L8lP7BTTPH8s4PZrLghqn817mVLLhhKk9cN9Vf+GUlOBuotDCXnMzAz2NOposJo4bYqgP3JN06mnko6Um6BZpnA5/3Pv4z8DpwR7InEakewZo15NvfF2xzWh47NSn/+ZzJAcqPmRluMt1CR9fRW8AMF3x60F6G4oHLJ9JyuJOfvLSRbg00DxgunDDCX2vjS530fS5vPG9c2MKvcFl08Wwwk6oCNKV3pNIoGGCJiBjgd8aYx4ASY8wu7/bdQEnwQSJyPXA9QFlZWfDmuBCuHsHqVjrc0YmIkJPh9n+xwi2PL55Qwssbjrpy5k4rC1F9LC3MJbhYtLMb29J7gPtefp+mz9rpiFBhqvQNXOK5AWnrcjbwAiEV7Xbuziuqxzi+RriLfzxF7FJVgKb0nFS6j84yxkwBLga+LSIzrBuNMQYbcRVjzGPGmGpjTPWwYcMSMjGnJjtAgFups9tTSWx1MRXmZYUsmwGeXL2dlzc0IQI5GS6yM1xUHxsa8Nt3sN1WQsAp3bRxf5sahH7E4187hcevrSY7w/mraYC9rUf8z3vq7ow2i04ZWKRspWCM2eH9f4+I/BU4DWgSkZHGmF0iMhJImSaD3Z1UXcP+sFXFPheTb9n81/caefT1wMCdMXCk01k3SbMz+hduF4S56Q+hqqyQovxsHrzC497p7ja0d4Ua/brGFn/ry1T0MVb6LylZKYhInogU+B4DFwDrgcXAtd7drgUWpWJ+PoLvpOzcSlasmRqVJQW4XeF/vdZ0v4VrGrjuz+/QbLkDVPo+d150ItkZkbWCAAqyM/yfh1lVo3nrjnO5Z9YE232tqp3JkF9RBg6pch+VAG+KSB3wT+AlY8wrwH3A+SKyGZjpfZ42BLuVMlyQ6XbuHRtJbtf3xZ36v3/npvlree39Pfz01Q/JDbqIuF1CXrbb4VWOkpeV6mQyJZhxIwp48IrJ/s9MOIIv5EX52Vx1+rFMrwzMPAtW7XRyd+oqQekJKXEfGWO2AJNtxpuB85Ixhyff3sqitbuYPWkk15xREfVxwW4lwDFTw06G10q2W3jjwz3sDmpwc7jTcOeF4ygqyKFqzFAK87L8PRTuXrye7m5PcUcwR9o1+yhVHF+cx0d77bSEJOQz88aHe3hx3W5GDM5hwZrGiDpa0ah2xjNrSBnYiCee2zeprq42NTU1MR83+e5XAiQjhuS4qbv7orDH9EbYq2ZrM4+v2MKr74eGSMaPyGfj7gMh4zNPGs7vrz01ZLz5QBvX/mE163eFygXkZsDhzpBhJQl8rmwI6xpbApRDM1yw+vszw16gncQSFSWRiMi7lvqwANKtTiHhPPn21hANoZYjXTz59lbHFUNvhb2qK4pCKkV9uMTe3/z5scXUNeyntDCXl9fu9K9qVm/Za2sQQA1CIhAgPzuDju5ufnTpeD7ee5DH3tgast/lVaP51zMqApRDH7xicsQLfV/rYaz0fwacUVi0dpfjuJ1RcBL2irXd4KUTR/CazUrhuukV3P/KpoAeyUNy3Pxo8caQfNx3tqVE8WNAcmp5If8+4ziqygpD7uSffWd7yErT99lRF47S1xlwkcnZk0bGNB4vYa/Lpoxh5OBAYbqhuS5WbvmUey+byC/mTGLmScO5d9Z4Wo50OXS/VeLNOQ7JAD/94kTOGz/CNpe/7u6LuHfWeE4tL+TeWeMDXI9vfLiHXy3bzBsfaoc7pW+iMQXCxxTqm1qZ+XCowNhrN8/oUaXmwjUNvLhuN+sb97O79ejq4ISSPF69+fP86K/reMJGDkOJPy7gnR/OpPre1wKMsAA1PwwfC7Bj6v/+PSBpYOTgLFZ+//y4zFVR4km4mMKAWylA+Du9YOIt7HXy6KGUFeYGGATwyGOf8IOXeEYNQsIpKcjim2cey5b7vkDjvsPkB6WK5lvqBaJl4ZqGkCyyXZ+1s3BNQ6/nqyjJZMDFFHxcc0ZF1Kmo8RL2iiSp3dazHjoDDpcQog/lhFvAWhBckJ3BY3NPZbJXoypehV8vrtvtOH7ZFGcNIkVJNwasUYiV3gp7OUlqK1CQ7aKr23CoI7or/fcvPpEHl3xAW+fR/bMzXNx58YmcVemJEdQ27Ke8aBDX/PGfdHUcvejbFYj1pO92ME6JBJdOHBHT6yhKqlGjkCCCi+MeXb451VNKWx796inMGDechWsaeOqfDazZts+2OA881bzXzTie4YNzQi7k1iZIPgMezQU/HoVfl00ZE5JFNnJwlq4SlD7HgAw0Jxq7QHbZMXms2/lZCmeVOPKz4Egn2Am55mYIRoRbZo4jK8PFvS+9T6fF95PpFlbdeZ7/Qtx8oI0z71/GEcvdfZZbuPaMci4cXxJQzRtt4VcyC8R8iQSXThyhBkFJW7R4LYk4FceVH5PTb43C9y4azzVnVPC1368KkPSYWlHInZeMD7gYH5OXxXcXrMXtErq6DQ9ecfTO/bJf/YO1Ow5QOjSHPQfbI7ZCjbbwK5kFYpdNGaPGQOnTqFHoAVbXUH5ORsCdoVNx3O4DHQzJcYcYjL6Cr4rbbhXkC9j3RqOn/Hsv+ffZvt+jFPvst6dpEZiiJBl1H8VI8EXRysjBWXz785X8cPFG2+0j8jP5zrlj+ekrmziYRPG6TBd0RHi7nAzh5vPGkped4Td4U48vts246qmYoBOX/eof1O4I1X+qGp3Pwv88u9ev74TqDikDFXUfxQk715CVXZ+1k5+T4bgi2H2ggx8u3si9s8Y7Go5EcNywPD5oslPwPMoPLzmJgkFZ/paO63a0OLZ0jCWdNxrW2hiEcOPxwK59pZ2LSlEGGgOyeK2nOLmGrLy4bre/OM6pA8KDr24iLyu6xivx4FtnH8+QHOd+DENy3Fw8aVSPWjrGg0mj82Ma7y09bV+ZSpoPtFHXsD+t56j0D9QoxICTPpIVX176NWdUOKZVtrR1c7DdcHp5Ie442oZsl+fHii8tMriKO7ii29fS0Yq1M1wicXIRJcp1lMpz7QmLandw5v3LuOb3qznz/mUsrt2R6ikp/Rh1H8XANWdU8OCSD8LGFKyZJyPyM9l9oMPx9VZv28e/nDycF9b3XjzNqt/klBYZ7PaxPk51S8dt933Bn300KcGxhFSfayxYVzW+Hsx2vb0VJV4k3SiIyBhgHp6WnAZ4zBjzSxG5G/gm8Il31+8bY/6W7PlFou7ui8JmH/l48u2tjCnOZ/eB8HLXZ584wtYo3DtrfFgD5CMvy8WdF50YcIHvSVpkvCp7e0MiDYGVdDjXaPGtanwGAY6uatJxvkrfJ+nZRyIyEhhpjFkjIgXAu8BlwBzggDHmZ9G+Vl8pXguHT231h3+p4+UNTVw8oYR7v+TpVLpwTQM3zV8b9vh7Z42Pa9B3IGXk9IVztSvmy8l08dYd56btnJX0J61UUo0xu4wxa7yPW4H3gX6T9uGUoXTvrPFh1Vbv/dJk3v3RBX6DAPY9GKxYawTihV3/gP5KXzhX36omJ9NFQXYGOZmutF3VKP2DlNYpiEg5sAI4GbgF+DrwGVAD3GqMCfG9iMj1wPUAZWVlp3z88cfJmm5UXPnbt207pJ1aXshz/35Gj3o9W2MEB450xrVGQOkb9IVVjdJ3CLdSSJlREJF84B/A/xhj/iIiJcBePHGGn+BxMX0j3Guko/voybe32tYgxNvNoyiK0lPSyn0EICKZwPPAU8aYvwAYY5qMMV3GmG7gceC0VMytt1xzRkVITUAi3DyKoiiJIBXZRwL8AXjfGPOQZXykMcZXHfZFYH2y5xYvgjOU1CAoitJXSEWdwpnA14B1IlLrHfs+cLWIVOFxH20DbkjB3OJGvKUgFEVRkkHSjYIx5k08vdGDSbuaBEVRlIGGylwoiqIoftQoKIqiKH7UKCiKoih+1CgoiqIofvp05zUR+QRIr5Lm+FKMp6BvIKHnPHAYiOedLud8rDFmmN2GPm0U+jsiUuNUddhf0XMeOAzE8+4L56zuI0VRFMWPGgVFURTFjxqF9OaxVE8gBeg5DxwG4nmn/TlrTEFRFEXxoysFRVEUxY8aBUVRFMWPGoUUISJ/FJE9IhIiES4it4qIEZFi73MRkUdEpF5E1orIlOTPOD44nbeI/KeIbBKRDSLygGX8Tu95fyAiFyZ/xr3H7pxFpEpEVolIrYjUiMhp3vF+8bcWkTEislxENnr/pjd6x48Rkb+LyGbv/4Xe8f5+3g96P99rReSvIjLUckx6fcaNMfqTgh9gBjAFWB80PgZ4FU9RXrF37BLgZTzqslOB1amefzzPGzgHeA3I9j4f7v1/PFAHZAMVwEeAO9XnEKdzXgJcbPn7vt6f/tbASGCK93EB8KH37/kA8D3v+PeA+wfIeV8AZHjH77ecd9p9xnWlkCKMMSuAT202PQzcjqevhI/ZwDzjYRUwVERGJmGaccfhvL8F3GeMafPus8c7Pht4xhjTZozZCtTTBzvyOZyzAQZ7Hw8Bdnof94u/tTFmlzFmjfdxK/A+MBrP+f3Zu9ufgcu8j/v1eRtjlhhjOr27rQJKvY/T7jOuRiGNEJHZwA5jTF3QptFAg+V5o3esvzAOmC4iq0XkHyJyqne8P5/3TcCDItIA/Ay40zve785ZRMqBzwGrgRJztMPibqDE+7i/n7eVb+BZFUEanrcahTRBRAbh6UB3V6rnkgIygGPwuA2+C8z3tm3tz3wLuNkYMwa4GU+L2n6HiOTj6cd+kzHmM+s24/Gf9MuceKfzFpEfAJ3AU6maWyTUKKQPx+PxKdaJyDY8y8s1IjIC2IEn1uCj1DvWX2gE/uJ1HfwT6MYjHNafz/ta4C/ex89x1GXQb85ZRDLxXBifMsb4zrXJ5xby/u9zFfb380ZEvg5cCnzVaxAhDc9bjUKaYIxZZ4wZbowpN8aU47lQTjHG7AYWA3O9GRpTgRbLErw/sBBPsBkRGQdk4VGSXAxcJSLZIlIBjAX+mapJxpmdwNnex+cCm72P+8Xf2rvS+wPwvjHmIcumxXgMIt7/F1nG++15i8hFeGKFs4wxhyyHpN9nPNXR+oH6AzwN7AI68BiAfwvavo2j2UcCPIonM2EdUJ3q+cfzvPEYgSeB9cAa4FzL/j/wnvcHeLN1+tqPwzmfBbyLJ/NkNXBKf/pbe8/PAGuBWu/PJUARsBSPEXwNOGaAnHc9ntiBb+y3lmPS6jOuMheKoiiKH3UfKYqiKH7UKCiKoih+1CgoiqIoftQoKIqiKH7UKCiKoih+1CgoigMiciDo+ddF5NcRjpklIt+LsM/nReRFh203eavbFSUlqFFQlDhijFlsjLmvFy9xE6BGQUkZahQUpQeIyDAReV5E3vH+nOkd968mROR4b8+EdSJyb9DKI19EFng19p/yVvL+FzAKWC4iy1NwWopCRqonoChpTK6I1FqeH4NHlgDgl8DDxpg3RaQMTw+Mk4KO/yXwS2PM0yLy70HbPgdMwCN38RZwpjHmERG5BTjHGLM3zueiKFGhRkFRnDlsjKnyPfEKmlV7n84ExlvEXAd7lTGtTONov4D/h0ci28c/jTGN3tetBcqBN+M2c0XpIWoUFKVnuICpxpgj1sEYFL/bLI+70O+ikiZoTEFResYS4D99T0SkymafVcDl3sdXRfm6rXjaOCpKSlCjoCg947+Aam8j9o1AcMwAPJlEt4jIWqASaInidR8DXtFAs5IqVCVVURKEt97gsDHGiMhVwNXGmNmpnpeihEP9mIqSOE4Bfu1tvLIfT29eRUlrdKWgKIqi+NGYgqIoiuJHjYKiKIriR42CoiiK4keNgqIoiuJHjYKiKIri5/8DAkteMtKXnwgAAAAASUVORK5CYII=\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.plot.scatter(x='Height', y='Weight')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 234, + "outputs": [ + { + "data": { + "text/plain": "Team\nUnited States 5219\nSoviet Union 2451\nGermany 1984\nGreat Britain 1673\nFrance 1550\n ... \nUnited States-1 101\nNigeria 99\nTurkey 95\nSerbia 85\nKazakhstan 77\nName: count, Length: 50, dtype: object" + }, + "execution_count": 234, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
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+ }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Количество медалей по командам\n", + "plt.figure(figsize=(18,9))\n", + "medals = df.groupby('Team')['Medal'].describe(include=object)['count'].sort_values(ascending=False)[:50]\n", + "medals.plot()\n", + "medals" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Количество полученных медалей 50 лучших команд.\n", + "Видно, что United States получили больше всех - 5219, а второе место аж в два раза меньше - 2451." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Интересные факты\n", + "* Возраст самого старого спортсмена 97 лет\n", + "* Возраст самого молодого - 10 лет\n", + "* Наименьший вес спортсмена - 25 кг\n", + "* Раньше спортсменок почти не было, но к настоящему моменту наблюдается тенденция равного кол-ва мужчин и женщин\n", + "* До 1992 года олимпиады проводились каждые 4 года, но начиная с 1992 стали проводить их каждые 2 года, причем каждая вторая олимпиада была менее \"масштабной\".\n", + "* United States выигрывали медали 5219 раз. Это более чем в два раза больше следующей по счету команды." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + } + ], + "metadata": { + "anaconda-cloud": {}, + "colab": { + "collapsed_sections": [ + "UTKVH3sMutTM", + "tiFgQjEcxnu2", + "qy4yj--r07RL", + "USQjKMAIETO8", + "QeOBRH60Wf1F", + "qwyKgedLeIMT", + "u4vIuhNgeNZx", + "nmb5O7vgWrnf", + "v9NMrXW4keP5", + "j_OKzzgAmiW-", + "CYdb_HyBnc-E", + "O1oGo8x3qXN0", + "sH1avRRhrWF2", + "NBLI-3pesJ0m", + "gv3ndpEhssD5", + "cDEsUU-Zu4cS", + "yzb09-GB33KV", + "APcKwPUddmDr", + "QaznDKFNv6jw", + "oWsKclsog0hC", + "mdP3P92kXxsu", + "ZqIWfFVLXRfZ", + "L1jG1C3BX5Cx", + "WTgnPj6raOzL" + ], + "name": "01_Baseline_example.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} \ No newline at end of file diff --git "a/\320\237\321\200\320\265\320\264\320\276\320\261\321\200\320\260\320\261\320\276\321\202\320\272\320\260 \320\264\320\260\320\275\320\275\321\213\321\205, \320\277\320\276\321\201\321\202\321\200\320\276\320\265\320\275\320\275\320\270\320\265 \320\277\321\200\320\276\321\201\321\202\320\276\320\271 \320\274\320\276\320\264\320\265\320\273\320\270, \320\272\320\276\320\275\320\262\320\265\320\271\320\265\321\200\320\270\320\267\320\260\321\206\320\270\321\217.ipynb" "b/\320\237\321\200\320\265\320\264\320\276\320\261\321\200\320\260\320\261\320\276\321\202\320\272\320\260 \320\264\320\260\320\275\320\275\321\213\321\205, \320\277\320\276\321\201\321\202\321\200\320\276\320\265\320\275\320\275\320\270\320\265 \320\277\321\200\320\276\321\201\321\202\320\276\320\271 \320\274\320\276\320\264\320\265\320\273\320\270, \320\272\320\276\320\275\320\262\320\265\320\271\320\265\321\200\320\270\320\267\320\260\321\206\320\270\321\217.ipynb" index ea3bddb..c6e30a5 100644 --- "a/\320\237\321\200\320\265\320\264\320\276\320\261\321\200\320\260\320\261\320\276\321\202\320\272\320\260 \320\264\320\260\320\275\320\275\321\213\321\205, \320\277\320\276\321\201\321\202\321\200\320\276\320\265\320\275\320\275\320\270\320\265 \320\277\321\200\320\276\321\201\321\202\320\276\320\271 \320\274\320\276\320\264\320\265\320\273\320\270, \320\272\320\276\320\275\320\262\320\265\320\271\320\265\321\200\320\270\320\267\320\260\321\206\320\270\321\217.ipynb" +++ "b/\320\237\321\200\320\265\320\264\320\276\320\261\321\200\320\260\320\261\320\276\321\202\320\272\320\260 \320\264\320\260\320\275\320\275\321\213\321\205, \320\277\320\276\321\201\321\202\321\200\320\276\320\265\320\275\320\275\320\270\320\265 \320\277\321\200\320\276\321\201\321\202\320\276\320\271 \320\274\320\276\320\264\320\265\320\273\320\270, \320\272\320\276\320\275\320\262\320\265\320\271\320\265\321\200\320\270\320\267\320\260\321\206\320\270\321\217.ipynb" @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 1, "metadata": { "executionInfo": { "elapsed": 324, @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 2, "metadata": { "executionInfo": { "elapsed": 585, @@ -96,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "id": "SU42i9kWq3H0" }, @@ -108,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -131,113 +131,16 @@ "outputs": [ { "data": { - "text/html": [ - "
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CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATtarget
5020.045270.011.930.00.5736.12076.72.28751.0273.021.0396.99.0820.6
130.629760.08.140.00.5385.94961.84.70754.0307.021.0396.98.2620.4
820.0365925.04.860.00.4266.30232.25.40074.0281.019.0396.96.7224.8
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" - ], - "text/plain": [ - " CRIM ZN INDUS CHAS NOX ... TAX PTRATIO B LSTAT target\n", - "502 0.04527 0.0 11.93 0.0 0.573 ... 273.0 21.0 396.9 9.08 20.6\n", - "13 0.62976 0.0 8.14 0.0 0.538 ... 307.0 21.0 396.9 8.26 20.4\n", - "82 0.03659 25.0 4.86 0.0 0.426 ... 281.0 19.0 396.9 6.72 24.8\n", - "\n", - "[3 rows x 14 columns]" - ] + "text/plain": " CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX \\\n292 0.03615 80.0 4.95 0.0 0.411 6.630 23.4 5.1167 4.0 245.0 \n444 12.80230 0.0 18.10 0.0 0.740 5.854 96.6 1.8956 24.0 666.0 \n336 0.03427 0.0 5.19 0.0 0.515 5.869 46.3 5.2311 5.0 224.0 \n473 4.64689 0.0 18.10 0.0 0.614 6.980 67.6 2.5329 24.0 666.0 \n467 4.42228 0.0 18.10 0.0 0.584 6.003 94.5 2.5403 24.0 666.0 \n\n PTRATIO B LSTAT target \n292 19.2 396.90 4.70 27.9 \n444 20.2 240.52 23.79 10.8 \n336 20.2 396.90 9.80 19.5 \n473 20.2 374.68 11.66 29.8 \n467 20.2 331.29 21.32 19.1 ", + "text/html": "
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CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATtarget
2920.0361580.04.950.00.4116.63023.45.11674.0245.019.2396.904.7027.9
44412.802300.018.100.00.7405.85496.61.895624.0666.020.2240.5223.7910.8
3360.034270.05.190.00.5155.86946.35.23115.0224.020.2396.909.8019.5
4734.646890.018.100.00.6146.98067.62.532924.0666.020.2374.6811.6629.8
4674.422280.018.100.00.5846.00394.52.540324.0666.020.2331.2921.3219.1
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" }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.sample(3)" + "df.sample(5)" ] }, { @@ -605,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -627,11 +530,9 @@ "outputs": [ { "data": { - "text/plain": [ - "(0.0, 1.0, 0.42220830944694204, 0.32131773862477314)" - ] + "text/plain": "(0.0, 1.0, 0.42220830944694204, 0.32131773862477314)" }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -639,7 +540,7 @@ "source": [ "min_max_scaler = preprocessing.MinMaxScaler()\n", "min_max_feature = min_max_scaler.fit_transform(df['TAX'].values.reshape(-1, 1))\n", - "min_max_feature.min(), min_max_feature.max() , min_max_feature.mean() , min_max_feature.std() " + "min_max_feature.min(), min_max_feature.max() , min_max_feature.mean() , min_max_feature.std()" ] }, { @@ -7831,4 +7732,4 @@ }, "nbformat": 4, "nbformat_minor": 1 -} +} \ No newline at end of file