diff --git a/Numpy (26.02)/Numpy_Lecture.ipynb b/Numpy (26.02)/Numpy_Lecture.ipynb index b7b1491..dae3ab6 100644 --- a/Numpy (26.02)/Numpy_Lecture.ipynb +++ b/Numpy (26.02)/Numpy_Lecture.ipynb @@ -1271,10 +1271,10 @@ "evalue": "ignored", "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[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) " ] } ], @@ -1733,11 +1733,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "id": "q7IVVJ4X9q__" }, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'np' is not defined", + "output_type": "error", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mNameError\u001B[0m Traceback (most recent call last)", + "\u001B[0;32m/tmp/ipykernel_86487/3986160085.py\u001B[0m in \u001B[0;36m\u001B[0;34m\u001B[0m\n\u001B[0;32m----> 1\u001B[0;31m b = np.array([[ 0, 1, 2, 3],\n\u001B[0m\u001B[1;32m 2\u001B[0m \u001B[0;34m[\u001B[0m\u001B[0;36m10\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m11\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m12\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m13\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 3\u001B[0m \u001B[0;34m[\u001B[0m\u001B[0;36m20\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m21\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m22\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m23\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 4\u001B[0m \u001B[0;34m[\u001B[0m\u001B[0;36m30\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m31\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m32\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m33\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 [40, 41, 42, 43]])\n", + "\u001B[0;31mNameError\u001B[0m: name 'np' is not defined" + ] + } + ], "source": [ "b = np.array([[ 0, 1, 2, 3],\n", " [10, 11, 12, 13],\n", @@ -1748,7 +1760,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1769,16 +1781,15 @@ }, "outputs": [ { - "data": { - "text/plain": [ - "23" - ] - }, - "execution_count": 115, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" + "ename": "NameError", + "evalue": "name 'b' is not defined", + "output_type": "error", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mNameError\u001B[0m Traceback (most recent call last)", + "\u001B[0;32m/tmp/ipykernel_86487/1437053575.py\u001B[0m in \u001B[0;36m\u001B[0;34m\u001B[0m\n\u001B[0;32m----> 1\u001B[0;31m \u001B[0mb\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m2\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;36m3\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;31m# Вторая строка, третий столбец\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 2\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n", + "\u001B[0;31mNameError\u001B[0m: name 'b' is not defined" + ] } ], "source": [ @@ -3345,10 +3356,10 @@ "evalue": "ignored", "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[0;32m----> 1\u001b[0;31m \u001b[0mb\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mValueError\u001b[0m: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 4 is different from 8)" + "\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[0;32m----> 1\u001B[0;31m \u001B[0mb\u001B[0m \u001B[0;34m@\u001B[0m \u001B[0ma\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m", + "\u001B[0;31mValueError\u001B[0m: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 4 is different from 8)" ] } ], @@ -3509,4 +3520,4 @@ }, "nbformat": 4, "nbformat_minor": 1 -} +} \ No newline at end of file diff --git a/Numpy (26.02)/Numpy_Task.ipynb b/Numpy (26.02)/Numpy_Task.ipynb index 593ba20..49c7178 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": "5655e521", "metadata": { "id": "medieval-detail" }, @@ -13,6 +14,7 @@ }, { "cell_type": "markdown", + "id": "448eaa6f", "metadata": { "id": "abstract-istanbul" }, @@ -25,20 +27,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, + "id": "8c8e7d2d", "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": "b1d6a033", "metadata": { "id": "loose-tobago" }, @@ -49,18 +62,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, + "id": "71caca79", "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.array([1.5 for i in range(10)])\n", "print(z)" ] }, { "cell_type": "markdown", + "id": "76d87fe1", "metadata": { "id": "threatened-theme" }, @@ -71,18 +94,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, + "id": "5b9b1205", "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 = np.zeros((5, 5))\n", "print(z)" ] }, { "cell_type": "markdown", + "id": "175ae51a", "metadata": { "id": "federal-blackberry" }, @@ -93,18 +130,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, + "id": "6f6b349b", "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.array([1 for j in range(12)])\n", "print(ones)" ] }, { "cell_type": "markdown", + "id": "2ad1e242", "metadata": { "id": "whole-chassis" }, @@ -116,18 +163,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, + "id": "a8db6ca3", "metadata": { "id": "outstanding-deviation" }, - "outputs": [], - "source": [ - "ones = \n", + "outputs": [ + { + "data": { + "text/plain": "(3, 4)" + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ones = ones.reshape(3, 4)\n", "ones.shape" ] }, { "cell_type": "markdown", + "id": "2a80b8ee", "metadata": { "id": "cubic-noise" }, @@ -139,20 +197,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, + "id": "6ed564be", "metadata": { "id": "foster-memory" }, - "outputs": [], - "source": [ - "Z = \n", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[17 18 16 19 2]\n", + " [18 17 14 16 5]\n", + " [ 4 6 12 19 9]\n", + " [18 3 4 1 8]]\n", + "[[ 17 18 16 19 2]\n", + " [ 18 17 14 16 5]\n", + " [ 4 6 12 -99 9]\n", + " [ 18 3 4 1 8]]\n" + ] + } + ], + "source": [ + "Z = np.random.randint(1, 20, 20).reshape(4, 5)\n", "print(Z)\n", - "\n", + "Z[2, 3] = -99\n", "print(Z)" ] }, { "cell_type": "markdown", + "id": "45130b98", "metadata": { "id": "helpful-table" }, @@ -164,20 +239,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, + "id": "5a7e5cd4", "metadata": { "id": "magnetic-leone" }, - "outputs": [], - "source": [ - "first = \n", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ -7 -4 -2 -9 -9 -6 7 6 -6 1 -10 8 1 -10 -3]\n", + "[ -3 -10 1 8 -10 1 -6 6 7 -6 -9 -9 -2 -4 -7]\n" + ] + } + ], + "source": [ + "first = np.array([np.random.randint(-10, 10) for z in range(15)])\n", "print(first)\n", - "second = \n", + "second = first[::-1]\n", "print(second)" ] }, { "cell_type": "markdown", + "id": "8591f998", "metadata": { "id": "executed-september" }, @@ -189,20 +275,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, + "id": "18e5b4f8", "metadata": { "id": "pharmaceutical-sigma" }, - "outputs": [], - "source": [ - "first = \n", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 6 -1 -10 11 -6]\n", + " [-12 3 9 1 -9]\n", + " [-11 4 1 -4 4]\n", + " [ -1 9 11 -7 -10]\n", + " [ 12 12 7 9 5]]\n", + "[[ 6 1 100 11 36]\n", + " [144 3 9 1 81]\n", + " [121 4 1 16 4]\n", + " [ 1 9 11 49 100]\n", + " [ 12 12 7 9 5]]\n" + ] + } + ], + "source": [ + "first = np.array([np.random.randint(-15, 15) for x in range(25)]).reshape(5, 5)\n", "print(first)\n", - "\n", + "first[first < 0] **= 2\n", "print(first)" ] }, { "cell_type": "markdown", + "id": "bb8a9355", "metadata": { "id": "floral-difference" }, @@ -216,18 +321,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, + "id": "0b725d3e", "metadata": { "id": "saving-conference" }, - "outputs": [], - "source": [ - "first = \n", - "print(first)\n" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 7 6 9 13 -6]\n", + " [ -5 -15 9 14 10]\n", + " [-11 -6 2 -14 1]]\n", + "14\n", + "-15\n", + "[-3. -5. 6.66666667 4.33333333 1.66666667]\n", + "[ 5.8 2.6 -5.6]\n" + ] + } + ], + "source": [ + "first = np.array([np.random.randint(-15, 15, 15)]).reshape(3, 5)\n", + "print(first)\n", + "print(np.max(first))\n", + "print(np.min(first))\n", + "print(np.mean(first, axis=0))\n", + "print(np.mean(first, axis=1))" ] }, { "cell_type": "markdown", + "id": "8b528f10", "metadata": { "id": "diagnostic-departure" }, @@ -240,23 +365,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, + "id": "3c2ba189", "metadata": { "id": "olympic-qatar" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-25 1 71]\n", + " [-51 44 75]]\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('Ошибка')" ] }, { "cell_type": "markdown", + "id": "ff698968", "metadata": { "id": "governmental-austin" }, @@ -268,20 +404,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, + "id": "097206d6", "metadata": { "id": "suffering-mauritius" }, - "outputs": [], - "source": [ - "mask = \n", - "matrix = \n", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 2. 2.41666667 2.83333333 3.25 3.66666667]\n", + " [ 0. 4.5 4.91666667 5.33333333 5.75 ]\n", + " [ 0. 0. 7. 7.41666667 7.83333333]\n", + " [ 0. 0. 0. 9.5 9.91666667]\n", + " [ 0. 0. 0. 0. 12. ]]\n" + ] + } + ], + "source": [ + "#mask =\n", + "matrix = np.array(np.linspace(2, 12, 25)).reshape(5, 5)\n", + "matrix[np.tril_indices(5, -1)] = 0\n", "\n", "print(matrix)" ] }, { "cell_type": "markdown", + "id": "7ee69ca6", "metadata": { "id": "altered-baghdad" }, @@ -293,20 +444,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, + "id": "0b061fd0", "metadata": { "id": "refined-stuff" }, - "outputs": [], - "source": [ - "mask = \n", - "matrix = \n", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0. 6.32629905 7.58269858 10.19420403 10.05776825]\n", + " [ 8.93394227 0. 12.62405164 13.09684805 10.07054908]\n", + " [ 8.58286701 8.63528701 0. 7.37779475 8.86826475]\n", + " [ 7.37686875 7.86347024 11.37640817 0. 8.93931273]\n", + " [ 6.871157 14.36250541 8.41168836 13.4345607 0. ]]\n" + ] + } + ], + "source": [ + "#mask =\n", + "matrix = np.array([np.random.normal(10, 2) for c in range(25)]).reshape(5, 5)\n", + "matrix[np.diag_indices(5)] = 0\n", "\n", "print(matrix)" ] }, { "cell_type": "markdown", + "id": "a58dd8e7", "metadata": { "id": "quiet-complement" }, @@ -317,22 +483,44 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "french-fighter" - }, - "outputs": [], + "execution_count": 71, + "id": "7b92ead2", + "metadata": { + "id": "french-fighter", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 0 0 1 0]\n", + "[1 0 0 0 0]\n" + ] + }, + { + "data": { + "text/plain": "False" + }, + "execution_count": 71, + "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" + "equal = a == b\n", + "equal.all()" ] }, { "cell_type": "markdown", + "id": "c9a66126", "metadata": { "id": "color-amplifier" }, @@ -347,23 +535,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 76, + "id": "a7fb4ee1", "metadata": { "id": "close-daisy" }, - "outputs": [], - "source": [ - "r, c = \n", - "a = \n", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ -9 -4 0 -3 5 4 -7 -1 -1]\n", + " [ 4 -6 -1 2 -8 -8 5 2 -2]\n", + " [ -7 5 5 8 -4 -8 -5 -6 5]\n", + " [ 8 8 5 -6 -2 8 -1 0 1]\n", + " [ 2 6 -1 7 -1 4 3 -10 -4]\n", + " [ 9 -8 -5 -10 3 4 2 4 -5]]\n", + "27\n", + "[ -8 -8 4 1 -5 4 -6 -7 2 5 -7 -4 -9 -7 5 -8 -10 5\n", + " -1 -6 4 -7 4 5 -5 2 -4]\n" + ] + } + ], + "source": [ + "r, c = np.random.randint(3, 7), np.random.randint(2, 12)\n", + "a = np.random.randint(-10, 10, (r, c))\n", "print(a)\n", - "N = \n", + "N = (r * c) // 2\n", "print(N)\n", - "sample = \n", + "sample = np.random.choice(a.ravel(), size=N)\n", "print(sample)" ] }, { "cell_type": "markdown", + "id": "10cd98a7", "metadata": { "id": "patent-african" }, @@ -376,20 +582,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 77, + "id": "d8b22150", "metadata": { "id": "taken-fabric" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[False True False]\n", + "False\n", + "[False False True]\n" + ] + } + ], "source": [ "a = np.array([1, np.NaN, np.Inf], float)\n", "\n", - "\n", - "a" + "print(np.isnan(a))\n", + "print(np.isinf(a))\n" ] }, { "cell_type": "markdown", + "id": "1ccd3a2f", "metadata": { "id": "analyzed-ireland" }, @@ -401,20 +619,111 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 122, + "id": "3e105cff", "metadata": { "id": "imposed-digest" }, - "outputs": [], - "source": [ - "axis = \n", - "print(axis)\n", - "matrix = \n", - "print(...)" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4, 4, 4, 4)\n", + "[[[[1 0 0 1]\n", + " [0 0 0 0]\n", + " [0 0 0 1]\n", + " [0 0 1 1]]\n", + "\n", + " [[0 0 0 0]\n", + " [1 1 0 0]\n", + " [0 1 0 1]\n", + " [1 1 0 1]]\n", + "\n", + " [[0 0 1 0]\n", + " [1 1 0 0]\n", + " [1 1 1 0]\n", + " [0 1 0 1]]\n", + "\n", + " [[1 0 1 0]\n", + " [1 1 0 0]\n", + " [0 0 1 1]\n", + " [1 1 0 0]]]\n", + "\n", + "\n", + " [[[0 0 1 0]\n", + " [1 1 1 0]\n", + " [1 1 0 0]\n", + " [1 1 1 1]]\n", + "\n", + " [[0 0 0 1]\n", + " [0 0 1 1]\n", + " [1 0 0 1]\n", + " [0 0 0 1]]\n", + "\n", + " [[0 1 0 1]\n", + " [0 1 1 0]\n", + " [1 1 0 1]\n", + " [0 0 0 1]]\n", + "\n", + " [[0 1 1 1]\n", + " [1 0 0 1]\n", + " [1 1 1 0]\n", + " [1 1 0 1]]]\n", + "\n", + "\n", + " [[[1 1 1 0]\n", + " [1 0 0 1]\n", + " [0 0 1 0]\n", + " [1 0 0 0]]\n", + "\n", + " [[0 1 0 1]\n", + " [0 0 0 0]\n", + " [1 0 0 0]\n", + " [0 0 0 0]]\n", + "\n", + " [[0 1 1 0]\n", + " [1 1 1 1]\n", + " [0 1 0 1]\n", + " [0 1 1 0]]\n", + "\n", + " [[0 1 1 1]\n", + " [1 0 1 1]\n", + " [0 1 0 0]\n", + " [0 1 1 1]]]\n", + "\n", + "\n", + " [[[0 0 1 0]\n", + " [1 1 0 0]\n", + " [1 0 1 1]\n", + " [1 1 0 1]]\n", + "\n", + " [[1 0 1 1]\n", + " [0 1 0 0]\n", + " [0 1 0 0]\n", + " [0 0 1 1]]\n", + "\n", + " [[0 0 1 1]\n", + " [1 0 0 1]\n", + " [1 1 0 0]\n", + " [0 1 0 0]]\n", + "\n", + " [[0 0 1 0]\n", + " [1 0 1 1]\n", + " [1 1 1 0]\n", + " [0 0 1 0]]]]\n" + ] + } + ], + "source": [ + "matrix = np.random.randint(0, 2, ([np.random.randint(1, 5)]*np.random.randint(2, 6)))\n", + "print(matrix.shape)\n", + "print(matrix)" ] }, { "cell_type": "markdown", + "id": "b0933ec4", "metadata": { "id": "regulation-colleague" }, @@ -427,17 +736,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 79, + "id": "375dee38", "metadata": { "id": "concerned-anthropology" }, - "outputs": [], - "source": [ - "matrix = np.random.normal(50, 10, (10,3))\n", - "print(matrix)\n", - "indexes = \n", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[51.40027567 33.30430468 45.264732 ]\n", + " [62.8625294 45.78707053 42.27779131]\n", + " [40.22214076 47.34649647 60.93443534]\n", + " [20.97587447 54.30548878 38.21993906]\n", + " [52.08064334 46.40567518 35.6579447 ]\n", + " [52.8761884 27.7944046 42.42578905]\n", + " [50.24637064 57.46672647 47.25224784]\n", + " [47.63133328 61.54324542 61.83160697]\n", + " [58.86384325 42.28252043 63.90076771]\n", + " [66.51882066 35.04164702 42.58267977]]\n", + "[0 0 2 1 0 0 1 2 2 0]\n", + "[51.40027567 62.8625294 60.93443534 54.30548878 52.08064334 52.8761884\n", + " 57.46672647 61.83160697 63.90076771 66.51882066]\n" + ] + } + ], + "source": [ + "Matrix = np.random.normal(50, 10, (10,3))\n", + "print(Matrix)\n", + "indexes = np.argmax(Matrix, axis=1)\n", "print(indexes)\n", - "print(...)" + "print(np.amax(Matrix, axis=1))" ] } ], @@ -448,7 +778,7 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -462,9 +792,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/Pandas (06.03)/Pandas. Task. Part 1.ipynb b/Pandas (06.03)/Pandas. Task. Part 1.ipynb index 5172e85..fab9e1d 100644 --- a/Pandas (06.03)/Pandas. Task. Part 1.ipynb +++ b/Pandas (06.03)/Pandas. Task. Part 1.ipynb @@ -1 +1,564 @@ -{"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
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"],"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
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32561 rows × 16 columns

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"],"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 +{ + "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\n", + "import numpy as np" + ], + "execution_count": 6, + "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": 7, + "outputs": [ + { + "data": { + "text/plain": " age workclass fnlwgt education education-num \\\n0 39 State-gov 77516 Bachelors 13 \n1 50 Self-emp-not-inc 83311 Bachelors 13 \n2 38 Private 215646 HS-grad 9 \n3 53 Private 234721 11th 7 \n4 28 Private 338409 Bachelors 13 \n\n marital-status occupation relationship race sex \\\n0 Never-married Adm-clerical Not-in-family White Male \n1 Married-civ-spouse Exec-managerial Husband White Male \n2 Divorced Handlers-cleaners Not-in-family White Male \n3 Married-civ-spouse Handlers-cleaners Husband Black Male \n4 Married-civ-spouse Prof-specialty Wife Black Female \n\n capital-gain capital-loss hours-per-week native-country salary \n0 2174 0 40 United-States <=50K \n1 0 0 13 United-States <=50K \n2 0 0 40 United-States <=50K \n3 0 0 40 United-States <=50K \n4 0 0 40 Cuba <=50K ", + "text/html": "
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ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipracesexcapital-gaincapital-losshours-per-weeknative-countrysalary
039State-gov77516Bachelors13Never-marriedAdm-clericalNot-in-familyWhiteMale2174040United-States<=50K
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428Private338409Bachelors13Married-civ-spouseProf-specialtyWifeBlackFemale0040Cuba<=50K
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" + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "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": 8, + "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": 3, + "outputs": [ + { + "data": { + "text/plain": " age workclass fnlwgt education education-num \\\n0 39 State-gov 77516 Bachelors 13 \n1 50 Self-emp-not-inc 83311 Bachelors 13 \n2 38 Private 215646 HS-grad 9 \n3 53 Private 234721 11th 7 \n4 28 Private 338409 Bachelors 13 \n... ... ... ... ... ... \n32556 27 Private 257302 Assoc-acdm 12 \n32557 40 Private 154374 HS-grad 9 \n32558 58 Private 151910 HS-grad 9 \n32559 22 Private 201490 HS-grad 9 \n32560 52 Self-emp-inc 287927 HS-grad 9 \n\n marital-status occupation relationship race sex \\\n0 Never-married Adm-clerical Not-in-family White Male \n1 Married-civ-spouse Exec-managerial Husband White Male \n2 Divorced Handlers-cleaners Not-in-family White Male \n3 Married-civ-spouse Handlers-cleaners Husband Black Male \n4 Married-civ-spouse Prof-specialty Wife Black Female \n... ... ... ... ... ... \n32556 Married-civ-spouse Tech-support Wife White Female \n32557 Married-civ-spouse Machine-op-inspct Husband White Male \n32558 Widowed Adm-clerical Unmarried White Female \n32559 Never-married Adm-clerical Own-child White Male \n32560 Married-civ-spouse Exec-managerial Wife White Female \n\n capital-gain capital-loss hours-per-week native-country salary \n0 2174 0 40 United-States <=50K \n1 0 0 13 United-States <=50K \n2 0 0 40 United-States <=50K \n3 0 0 40 United-States <=50K \n4 0 0 40 Cuba <=50K \n... ... ... ... ... ... \n32556 0 0 38 United-States <=50K \n32557 0 0 40 United-States >50K \n32558 0 0 40 United-States <=50K \n32559 0 0 20 United-States <=50K \n32560 15024 0 40 United-States >50K \n\n[32561 rows x 15 columns]", + "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
................................................
3255627Private257302Assoc-acdm12Married-civ-spouseTech-supportWifeWhiteFemale0038United-States<=50K
3255740Private154374HS-grad9Married-civ-spouseMachine-op-inspctHusbandWhiteMale0040United-States>50K
3255858Private151910HS-grad9WidowedAdm-clericalUnmarriedWhiteFemale0040United-States<=50K
3255922Private201490HS-grad9Never-marriedAdm-clericalOwn-childWhiteMale0020United-States<=50K
3256052Self-emp-inc287927HS-grad9Married-civ-spouseExec-managerialWifeWhiteFemale15024040United-States>50K
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\n
" + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "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": [ + "print(data[data.sex == 'Male'].shape[0])\n", + "data[data.sex == 'Female'].shape[0]" + ], + "execution_count": 21, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "21790\n" + ] + }, + { + "data": { + "text/plain": "10771" + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "adF8lgVbutTZ" + }, + "source": [ + "**2. Каков средний возраст (признак *age*) женщин?**" + ] + }, + { + "cell_type": "code", + "metadata": { + "collapsed": true, + "id": "K6C2qZ_zutTb" + }, + "source": [ + "data[data.sex == 'Female'].age.mean()" + ], + "execution_count": 19, + "outputs": [ + { + "data": { + "text/plain": "36.85823043357163" + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "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": [ + "data[data['native-country'] == 'Germany'].shape[0] / data.shape[0]" + ], + "execution_count": 23, + "outputs": [ + { + "data": { + "text/plain": "0.004207487485028101" + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "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": [ + "print(f\"среднее значение для >50K: {data.age[data.salary == '>50K'].mean()}\")\n", + "print(f\"отклонение для >50K: {data.age[data.salary == '>50K'].std()}\")\n", + "print(f\"среднее значение для <=50K: {data.age[data.salary == '<=50K'].mean()}\")\n", + "print(f\"отклонение для <=50K: {data.age[data.salary == '<=50K'].std()}\")" + ], + "execution_count": 35, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "среднее значение для >50K: 44.24984058155847\n", + "отклонение для >50K: 10.519027719851826\n", + "среднее значение для <=50K: 36.78373786407767\n", + "отклонение для <=50K: 14.02008849082488\n" + ] + } + ] + }, + { + "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": [ + "def check_education(value):\n", + " education = ['Bachelors', 'Prof-school', 'Assoc-acdm', 'Assoc-voc', 'Masters', 'Doctorate']\n", + " return value in education\n", + "\n", + "\n", + "data[data.salary == '>50K'].education.apply(check_education).all()\n" + ], + "execution_count": 115, + "outputs": [ + { + "data": { + "text/plain": "False" + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "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": [ + "print(f\"Статситика по расе: {data.groupby('age').race.describe()}\")\n", + "print(f\"Статистика по полу: {data.groupby('age').sex.describe()}\")\n", + "print(f\"Максимальный возраст \\\"таким образом\\\": \")\n", + "data[data.race == 'Amer-Indian-Eskimo'].groupby('age').describe()[::-1]\n", + "\n", + "# интересно, можно вывести статистику примерно таким способом?\n", + "# data.groupby('age').agg({'race':[data.race.describe()], 'sex':[data.sex.describe()]})\n", + "\n", + "# не понял, зачем выводить максимальный возраст предложенным способом,\n", + "# на всякий случай вот еще вариант:\n", + "# data[data.race == 'Amer-Indian-Eskimo'].age.max()" + ], + "execution_count": 16, + "outputs": [ + { + "data": { + "text/plain": " fnlwgt \\\n count mean std min 25% 50% \nage \n82 1.0 52921.000000 NaN 52921.0 52921.00 52921.0 \n80 1.0 20101.000000 NaN 20101.0 20101.00 20101.0 \n74 1.0 33114.000000 NaN 33114.0 33114.00 33114.0 \n71 1.0 322789.000000 NaN 322789.0 322789.00 322789.0 \n68 2.0 87848.000000 48080.432694 53850.0 70849.00 87848.0 \n67 1.0 126849.000000 NaN 126849.0 126849.00 126849.0 \n65 1.0 178931.000000 NaN 178931.0 178931.00 178931.0 \n63 2.0 237885.000000 293142.429849 30602.0 134243.50 237885.0 \n62 1.0 68461.000000 NaN 68461.0 68461.00 68461.0 \n61 1.0 130466.000000 NaN 130466.0 130466.00 130466.0 \n60 6.0 44603.833333 50954.030951 21101.0 22460.75 24543.5 \n59 2.0 161504.000000 62854.721780 117059.0 139281.50 161504.0 \n58 3.0 56484.666667 36365.178761 30111.0 35742.50 41374.0 \n57 3.0 172688.000000 142288.315111 29375.0 102067.50 174760.0 \n56 3.0 107913.000000 51293.126801 60166.0 80801.00 101436.0 \n55 3.0 92913.666667 113284.739971 26290.0 27512.50 28735.0 \n53 5.0 87174.000000 64892.475388 20438.0 20676.00 105728.0 \n52 2.0 262507.500000 28519.737806 242341.0 252424.25 262507.5 \n51 10.0 126893.200000 91869.051011 35211.0 44834.75 90176.5 \n50 4.0 120766.250000 82460.258045 32801.0 67187.00 114812.0 \n49 2.0 176587.500000 19419.273532 162856.0 169721.75 176587.5 \n48 2.0 152808.000000 128443.118375 61985.0 107396.50 152808.0 \n47 8.0 128878.500000 67380.633053 24723.0 89641.00 120711.0 \n46 13.0 170797.846154 88035.609467 26781.0 133969.00 147640.0 \n45 5.0 87854.200000 93655.957457 26781.0 26781.00 26781.0 \n44 4.0 106413.500000 65717.346388 33105.0 82518.00 99786.5 \n43 8.0 150311.000000 77209.211109 19914.0 93100.50 178305.5 \n42 13.0 113468.384615 85920.970203 23813.0 31387.00 108506.0 \n41 5.0 138658.000000 80349.040819 47170.0 113555.00 118721.0 \n40 11.0 119502.181818 93649.945804 20109.0 73040.50 96509.0 \n39 7.0 83539.000000 33041.571931 19914.0 72679.00 89419.0 \n38 6.0 87902.666667 65457.626672 31352.0 35226.50 63080.5 \n37 7.0 115490.714286 69269.328756 25864.0 68014.00 116358.0 \n36 5.0 107067.400000 65314.823301 32587.0 68089.00 88967.0 \n35 16.0 98522.625000 51866.462059 22055.0 71101.75 105223.5 \n34 8.0 115391.625000 88294.804799 22641.0 33020.00 108362.5 \n33 7.0 122588.857143 113206.419007 27959.0 50806.50 98145.0 \n32 7.0 136745.714286 85103.432402 27939.0 64975.50 163530.0 \n31 16.0 118253.062500 96582.076694 22201.0 57409.50 97237.0 \n30 10.0 181404.000000 128468.828473 23037.0 69468.50 158156.5 \n29 11.0 95232.636364 67714.025110 22641.0 28158.00 100405.0 \n28 14.0 87593.000000 93787.897013 24153.0 25955.00 39275.5 \n27 11.0 115923.454545 68421.330878 23740.0 61741.00 129661.0 \n26 3.0 106531.666667 40411.040327 73392.0 84022.00 94652.0 \n25 9.0 144497.222222 116454.389611 12285.0 44216.00 130397.0 \n24 6.0 153098.000000 137543.252751 19410.0 32603.25 138534.0 \n23 13.0 109204.000000 88232.594202 13769.0 33105.00 99399.0 \n22 9.0 147079.222222 80070.263533 29444.0 108506.00 145477.0 \n21 6.0 181713.000000 91018.186890 75763.0 103932.75 190530.5 \n20 3.0 66616.666667 61632.489730 27337.0 31099.50 34862.0 \n19 6.0 82268.333333 87982.956676 29083.0 30048.50 32143.5 \n18 4.0 147675.500000 124316.647074 40829.0 50039.00 124003.0 \n17 3.0 85667.333333 52726.233000 27415.0 63438.50 99462.0 \n\n education-num ... capital-loss \\\n 75% max count mean ... 75% max \nage ... \n82 52921.00 52921.0 1.0 10.000000 ... 0.0 0.0 \n80 20101.00 20101.0 1.0 9.000000 ... 0.0 0.0 \n74 33114.00 33114.0 1.0 6.000000 ... 0.0 0.0 \n71 322789.00 322789.0 1.0 9.000000 ... 0.0 0.0 \n68 104847.00 121846.0 2.0 4.000000 ... 0.0 0.0 \n67 126849.00 126849.0 1.0 6.000000 ... 0.0 0.0 \n65 178931.00 178931.0 1.0 9.000000 ... 0.0 0.0 \n63 341526.50 445168.0 2.0 8.500000 ... 0.0 0.0 \n62 68461.00 68461.0 1.0 16.000000 ... 0.0 0.0 \n61 130466.00 130466.0 1.0 9.000000 ... 0.0 0.0 \n60 26495.75 148522.0 6.0 9.000000 ... 0.0 0.0 \n59 183726.50 205949.0 2.0 8.000000 ... 0.0 0.0 \n58 69671.50 97969.0 3.0 7.000000 ... 0.0 0.0 \n57 244344.50 313929.0 3.0 10.333333 ... 0.0 0.0 \n56 131786.50 162137.0 3.0 6.666667 ... 0.0 0.0 \n55 126225.50 223716.0 3.0 10.000000 ... 0.0 0.0 \n53 121618.00 167410.0 5.0 9.000000 ... 0.0 0.0 \n52 272590.75 282674.0 2.0 6.500000 ... 0.0 0.0 \n51 214031.00 251487.0 10.0 9.500000 ... 0.0 0.0 \n50 168391.25 220640.0 4.0 11.500000 ... 0.0 0.0 \n49 183453.25 190319.0 2.0 11.500000 ... 0.0 0.0 \n48 198219.50 243631.0 2.0 7.500000 ... 0.0 0.0 \n47 164427.00 244025.0 8.0 8.625000 ... 0.0 0.0 \n46 197988.00 363875.0 13.0 8.461538 ... 0.0 1902.0 \n45 119835.00 239093.0 5.0 8.400000 ... 0.0 0.0 \n44 123682.00 192976.0 4.0 10.000000 ... 0.0 0.0 \n43 201248.50 239161.0 8.0 10.375000 ... 0.0 0.0 \n42 140474.00 304302.0 13.0 11.307692 ... 0.0 0.0 \n41 147314.00 266530.0 5.0 9.800000 ... 0.0 0.0 \n40 113341.00 310101.0 11.0 9.818182 ... 0.0 1977.0 \n39 106708.00 116666.0 7.0 9.571429 ... 0.0 0.0 \n38 141942.00 175732.0 6.0 10.666667 ... 0.0 0.0 \n37 156248.00 217689.0 7.0 9.714286 ... 0.0 0.0 \n36 150309.00 195385.0 5.0 9.200000 ... 0.0 0.0 \n35 128745.50 197719.0 16.0 9.812500 ... 0.0 0.0 \n34 170104.00 245487.0 8.0 9.375000 ... 0.0 1564.0 \n33 137195.00 356015.0 7.0 10.142857 ... 0.0 0.0 \n32 195156.50 245487.0 7.0 7.857143 ... 0.0 1980.0 \n31 122401.75 395170.0 16.0 9.687500 ... 0.0 0.0 \n30 297535.25 356015.0 10.0 9.000000 ... 0.0 0.0 \n29 151753.50 190636.0 11.0 9.000000 ... 0.0 1485.0 \n28 112592.00 314649.0 14.0 9.000000 ... 0.0 0.0 \n27 156266.00 221252.0 11.0 9.272727 ... 0.0 0.0 \n26 123101.50 151551.0 3.0 9.666667 ... 0.0 0.0 \n25 258276.00 312338.0 9.0 9.444444 ... 0.0 0.0 \n24 251180.25 336088.0 6.0 9.333333 ... 0.0 0.0 \n23 178207.00 306601.0 13.0 9.384615 ... 0.0 0.0 \n22 216984.00 233955.0 9.0 9.666667 ... 0.0 0.0 \n21 238196.50 304302.0 6.0 9.166667 ... 0.0 0.0 \n20 86256.50 137651.0 3.0 9.666667 ... 0.0 0.0 \n19 103247.50 243941.0 6.0 8.333333 ... 0.0 1721.0 \n18 221639.50 301867.0 4.0 8.000000 ... 0.0 0.0 \n17 114793.50 130125.0 3.0 6.666667 ... 0.0 0.0 \n\n hours-per-week \n count mean std min 25% 50% 75% max \nage \n82 1.0 3.000000 NaN 3.0 3.00 3.0 3.00 3.0 \n80 1.0 32.000000 NaN 32.0 32.00 32.0 32.00 32.0 \n74 1.0 30.000000 NaN 30.0 30.00 30.0 30.00 30.0 \n71 1.0 35.000000 NaN 35.0 35.00 35.0 35.00 35.0 \n68 2.0 30.000000 14.142136 20.0 25.00 30.0 35.00 40.0 \n67 1.0 20.000000 NaN 20.0 20.00 20.0 20.00 20.0 \n65 1.0 40.000000 NaN 40.0 40.00 40.0 40.00 40.0 \n63 2.0 48.000000 11.313708 40.0 44.00 48.0 52.00 56.0 \n62 1.0 40.000000 NaN 40.0 40.00 40.0 40.00 40.0 \n61 1.0 40.000000 NaN 40.0 40.00 40.0 40.00 40.0 \n60 6.0 35.000000 13.784049 10.0 32.50 40.0 40.00 50.0 \n59 2.0 40.000000 0.000000 40.0 40.00 40.0 40.00 40.0 \n58 3.0 40.000000 0.000000 40.0 40.00 40.0 40.00 40.0 \n57 3.0 38.333333 2.886751 35.0 37.50 40.0 40.00 40.0 \n56 3.0 46.666667 16.072751 35.0 37.50 40.0 52.50 65.0 \n55 3.0 41.000000 3.605551 38.0 39.00 40.0 42.50 45.0 \n53 5.0 34.200000 12.891858 15.0 28.00 40.0 40.00 48.0 \n52 2.0 40.000000 0.000000 40.0 40.00 40.0 40.00 40.0 \n51 10.0 44.000000 6.649979 40.0 40.00 40.0 46.00 60.0 \n50 4.0 42.500000 5.000000 40.0 40.00 40.0 42.50 50.0 \n49 2.0 40.000000 0.000000 40.0 40.00 40.0 40.00 40.0 \n48 2.0 30.000000 14.142136 20.0 25.00 30.0 35.00 40.0 \n47 8.0 42.125000 6.664136 35.0 39.00 40.0 45.00 56.0 \n46 13.0 41.769231 9.867065 20.0 40.00 40.0 40.00 60.0 \n45 5.0 35.200000 15.594871 8.0 40.00 40.0 40.00 48.0 \n44 4.0 39.500000 1.000000 38.0 39.50 40.0 40.00 40.0 \n43 8.0 36.875000 10.329396 15.0 37.50 40.0 40.00 50.0 \n42 13.0 41.923077 8.548504 30.0 40.00 40.0 40.00 60.0 \n41 5.0 46.600000 4.219005 40.0 45.00 48.0 50.00 50.0 \n40 11.0 46.545455 14.137635 35.0 40.00 40.0 46.50 84.0 \n39 7.0 41.142857 3.023716 40.0 40.00 40.0 40.00 48.0 \n38 6.0 40.833333 18.551729 15.0 32.50 40.0 47.50 70.0 \n37 7.0 35.714286 9.123491 20.0 31.00 40.0 40.00 48.0 \n36 5.0 45.000000 11.180340 40.0 40.00 40.0 40.00 65.0 \n35 16.0 41.500000 12.738393 15.0 39.50 40.0 43.75 60.0 \n34 8.0 44.625000 8.667468 32.0 40.00 42.5 49.00 60.0 \n33 7.0 41.285714 9.945087 30.0 35.50 40.0 44.00 60.0 \n32 7.0 45.714286 9.759001 40.0 40.00 40.0 50.00 60.0 \n31 16.0 37.562500 5.773142 24.0 38.75 40.0 40.00 45.0 \n30 10.0 43.500000 18.638371 8.0 40.00 40.0 46.00 84.0 \n29 11.0 46.181818 8.908627 40.0 40.00 40.0 49.00 65.0 \n28 14.0 43.214286 8.902710 30.0 40.00 40.0 43.75 60.0 \n27 11.0 36.090909 18.822617 8.0 29.00 40.0 41.50 75.0 \n26 3.0 39.333333 9.018500 30.0 35.00 40.0 44.00 48.0 \n25 9.0 35.000000 8.660254 20.0 35.00 40.0 40.00 40.0 \n24 6.0 42.166667 5.671567 35.0 40.00 40.0 46.00 50.0 \n23 13.0 35.769231 5.340940 25.0 30.00 40.0 40.00 40.0 \n22 9.0 48.666667 18.794946 25.0 40.00 40.0 50.00 84.0 \n21 6.0 34.666667 15.266521 4.0 38.50 40.0 40.00 46.0 \n20 3.0 53.333333 16.653328 40.0 44.00 48.0 60.00 72.0 \n19 6.0 27.500000 10.368221 15.0 21.25 25.0 36.25 40.0 \n18 4.0 26.250000 9.464847 20.0 20.00 22.5 28.75 40.0 \n17 3.0 20.000000 0.000000 20.0 20.00 20.0 20.00 20.0 \n\n[53 rows x 40 columns]", + "text/html": "
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fnlwgteducation-num...capital-losshours-per-week
countmeanstdmin25%50%75%maxcountmean...75%maxcountmeanstdmin25%50%75%max
age
821.052921.000000NaN52921.052921.0052921.052921.0052921.01.010.000000...0.00.01.03.000000NaN3.03.003.03.003.0
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741.033114.000000NaN33114.033114.0033114.033114.0033114.01.06.000000...0.00.01.030.000000NaN30.030.0030.030.0030.0
711.0322789.000000NaN322789.0322789.00322789.0322789.00322789.01.09.000000...0.00.01.035.000000NaN35.035.0035.035.0035.0
682.087848.00000048080.43269453850.070849.0087848.0104847.00121846.02.04.000000...0.00.02.030.00000014.14213620.025.0030.035.0040.0
671.0126849.000000NaN126849.0126849.00126849.0126849.00126849.01.06.000000...0.00.01.020.000000NaN20.020.0020.020.0020.0
651.0178931.000000NaN178931.0178931.00178931.0178931.00178931.01.09.000000...0.00.01.040.000000NaN40.040.0040.040.0040.0
632.0237885.000000293142.42984930602.0134243.50237885.0341526.50445168.02.08.500000...0.00.02.048.00000011.31370840.044.0048.052.0056.0
621.068461.000000NaN68461.068461.0068461.068461.0068461.01.016.000000...0.00.01.040.000000NaN40.040.0040.040.0040.0
611.0130466.000000NaN130466.0130466.00130466.0130466.00130466.01.09.000000...0.00.01.040.000000NaN40.040.0040.040.0040.0
606.044603.83333350954.03095121101.022460.7524543.526495.75148522.06.09.000000...0.00.06.035.00000013.78404910.032.5040.040.0050.0
592.0161504.00000062854.721780117059.0139281.50161504.0183726.50205949.02.08.000000...0.00.02.040.0000000.00000040.040.0040.040.0040.0
583.056484.66666736365.17876130111.035742.5041374.069671.5097969.03.07.000000...0.00.03.040.0000000.00000040.040.0040.040.0040.0
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\n

53 rows × 40 columns

\n
" + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "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": [ + "# эта строчка уже была в документе\n", + "# наверно, к этому нужно было бы прийти самостоятельно\n", + "# data[\"married\"] = data[\"marital-status\"].apply(lambda row: \"Married\" in row)\n", + "\n", + "all_men = (data[data.sex == 'Male'].married == True).shape[0]\n", + "married_men = data.query('sex == \"Male\" and married == True and salary == \">50K\"').shape[0]\n", + "single_men = data.query('sex == \"Male\" and married == False and salary == \">50K\"').shape[0]\n", + "married_percent = married_men / all_men\n", + "single_percent = single_men / all_men\n", + "\n", + "print(f\"Доля среди женатых: {married_percent}\")\n", + "print(f\"Доля среди холостых: {single_percent}\")" + ], + "execution_count": 14, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Доля среди женатых: 0.2737494263423589\n", + "Доля среди холостых: 0.03198715006883892\n" + ] + } + ] + }, + { + "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": [ + "max_hours = data['hours-per-week'].max()\n", + "salary = \">50K\"\n", + "print(f\"Максимальное число часов: {max_hours}\")\n", + "print(f\"Столько людей работает: {data[data['hours-per-week'] == max_hours].shape[0]}\")\n", + "print(f\"Столько из них зарабатывает >50K: {(data[data['hours-per-week'] == max_hours].query('salary == @salary')).shape[0]}\")" + ], + "execution_count": 23, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Максимальное число часов: 99\n", + "Столько людей работает: 85\n", + "Столько из них зарабатывает >50K: 25\n" + ] + } + ] + }, + { + "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": [ + "print(f\"Для зарабатывающих много: {data[data.salary == '>50K'].groupby('native-country')['hours-per-week'].mean()}\")\n", + "print(f\"Для зарабатывающих мало: {data[data.salary == '<=50K'].groupby('native-country')['hours-per-week'].mean()}\")" + ], + "execution_count": 27, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Для зарабатывающих много: native-country\n", + "? 45.547945\n", + "Cambodia 40.000000\n", + "Canada 45.641026\n", + "China 38.900000\n", + "Columbia 50.000000\n", + "Cuba 42.440000\n", + "Dominican-Republic 47.000000\n", + "Ecuador 48.750000\n", + "El-Salvador 45.000000\n", + "England 44.533333\n", + "France 50.750000\n", + "Germany 44.977273\n", + "Greece 50.625000\n", + "Guatemala 36.666667\n", + "Haiti 42.750000\n", + "Honduras 60.000000\n", + "Hong 45.000000\n", + "Hungary 50.000000\n", + "India 46.475000\n", + "Iran 47.500000\n", + "Ireland 48.000000\n", + "Italy 45.400000\n", + "Jamaica 41.100000\n", + "Japan 47.958333\n", + "Laos 40.000000\n", + "Mexico 46.575758\n", + "Nicaragua 37.500000\n", + "Peru 40.000000\n", + "Philippines 43.032787\n", + "Poland 39.000000\n", + "Portugal 41.500000\n", + "Puerto-Rico 39.416667\n", + "Scotland 46.666667\n", + "South 51.437500\n", + "Taiwan 46.800000\n", + "Thailand 58.333333\n", + "Trinadad&Tobago 40.000000\n", + "United-States 45.505369\n", + "Vietnam 39.200000\n", + "Yugoslavia 49.500000\n", + "Name: hours-per-week, dtype: float64\n", + "Для зарабатывающих мало: native-country\n", + "? 40.164760\n", + "Cambodia 41.416667\n", + "Canada 37.914634\n", + "China 37.381818\n", + "Columbia 38.684211\n", + "Cuba 37.985714\n", + "Dominican-Republic 42.338235\n", + "Ecuador 38.041667\n", + "El-Salvador 36.030928\n", + "England 40.483333\n", + "France 41.058824\n", + "Germany 39.139785\n", + "Greece 41.809524\n", + "Guatemala 39.360656\n", + "Haiti 36.325000\n", + "Holand-Netherlands 40.000000\n", + "Honduras 34.333333\n", + "Hong 39.142857\n", + "Hungary 31.300000\n", + "India 38.233333\n", + "Iran 41.440000\n", + "Ireland 40.947368\n", + "Italy 39.625000\n", + "Jamaica 38.239437\n", + "Japan 41.000000\n", + "Laos 40.375000\n", + "Mexico 40.003279\n", + "Nicaragua 36.093750\n", + "Outlying-US(Guam-USVI-etc) 41.857143\n", + "Peru 35.068966\n", + "Philippines 38.065693\n", + "Poland 38.166667\n", + "Portugal 41.939394\n", + "Puerto-Rico 38.470588\n", + "Scotland 39.444444\n", + "South 40.156250\n", + "Taiwan 33.774194\n", + "Thailand 42.866667\n", + "Trinadad&Tobago 37.058824\n", + "United-States 38.799127\n", + "Vietnam 37.193548\n", + "Yugoslavia 41.600000\n", + "Name: hours-per-week, dtype: float64\n" + ] + } + ] + } + ] +} \ No newline at end of file