From a9a2e7497f15fc3fde846f5f395cae45f5466a19 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Javier=20S=C3=A1nchez?= Date: Mon, 5 Jun 2023 06:12:12 +0100 Subject: [PATCH] [Descriptive-stats] Javier Sanchez Camacho --- your-code/main.ipynb | 476 +++++++++++++++++++++++++++++++++++-------- 1 file changed, 393 insertions(+), 83 deletions(-) diff --git a/your-code/main.ipynb b/your-code/main.ipynb index a0a5b66..f0d62d1 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -11,11 +11,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "# Libraries" + "# Libraries\n", + "import numpy as np\n", + "import pandas as pd\n", + "import random\n", + "import matplotlib.pyplot as plt" ] }, { @@ -29,11 +33,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "def dice_throw(n):\n", + " return pd.DataFrame(np.array([random.choice(np.arange(1, 7, 1)) for i in range(n)]))" ] }, { @@ -45,11 +50,35 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dice = dice_throw(10).sort_values(by=0).reset_index()[0]\n", + "\n", + "# plot ??? like a line ??? OK.\n", + "plt.plot(range(len(dice)), dice, color= 'gray')" ] }, { @@ -61,11 +90,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(array([2., 2., 0., 3., 3., 0.]),\n", + " array([0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5]),\n", + " )" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "plt.hist(dice, bins= 6, range=(0.5, 6.5), color= 'gray')" ] }, { @@ -75,7 +127,7 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "They're both growing in y, but we can see the steps more clearly in the first plot\n", "\"\"\"" ] }, @@ -91,11 +143,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "def average(data):\n", + " return sum(data)/len(data)" ] }, { @@ -107,11 +160,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.3\n", + "3.3\n" + ] + } + ], "source": [ - "# your code here" + "def freq(vals):\n", + " \n", + " freq_dist = {}\n", + " for x in vals:\n", + " if x in freq_dist:\n", + " freq_dist[x] += 1\n", + " else:\n", + " freq_dist[x] = 1\n", + " \n", + " return freq_dist, sum([k*v for k, v in freq_dist.items()])/sum([v for k, v in freq_dist.items()])\n", + "\n", + "freq_dist, mean_from_freq = freq(dice)\n", + "\n", + "print(mean_from_freq)\n", + "print(np.mean(dice))" ] }, { @@ -124,11 +200,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "def median(val):\n", + " \n", + " \"\"\"\n", + " Input: list of values\n", + " Ouput: median value of input list\n", + " \"\"\"\n", + " \n", + " if len(val) % 2 == 0:\n", + " return sum(val[int(len(val)/2)-1:int(len(val)/2)+1])/2\n", + " \n", + " else:\n", + " return val[int(len(val)/2)]" ] }, { @@ -140,11 +227,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "def quartiles(vals):\n", + " \n", + " half = median(vals)\n", + " \n", + " if len(vals) % 2 == 0:\n", + " first_quartile = median(vals[:int(len(vals)/2)])\n", + " third_quartile = median(vals[int(len(vals)/2):])\n", + " \n", + " else:\n", + " first_quartile = median(vals[:int(len(vals)/2)-1])\n", + " third_quartile = median(vals[int(len(vals)/2)+1:])\n", + " \n", + " return first_quartile, half, third_quartile" ] }, { @@ -158,11 +257,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(array([12., 17., 14., 22., 12., 23.]),\n", + " array([0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5]),\n", + " )" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "dice_df = pd.read_csv('../data/roll_the_dice_hundred.csv')\n", + "dice_values = sorted(dice_df['value'])\n", + "plt.hist(dice_values, bins=6, range= (0.5, 6.5))" ] }, { @@ -172,7 +296,7 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "The random throws of dice. Since there is only one die being thrown, there should be no visual correlation, as opposed of throwing two dice (the pyramid)\n", "\"\"\"" ] }, @@ -185,11 +309,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.74\n", + "(2.0, 4.0, 5.0)\n" + ] + } + ], "source": [ - "# your code here" + "print(average(dice_values))\n", + "print(quartiles(dice_values))" ] }, { @@ -201,11 +335,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "freq_dist = freq(dice_values)" ] }, { @@ -217,21 +351,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "plt.hist(dice_values, bins=6, range= (0.5, 6.5))\n", + "plt.vlines(dice_df['value'].mean(), ymin=0, ymax=25, lw=2)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "\"\\nIt's the mean, almost the median, a little to the right\\n\"" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", - "your comments here\n", + "It's the mean, almost the median, a little to the right\n", "\"\"\"" ] }, @@ -244,11 +411,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(array([175., 167., 175., 168., 149., 166.]),\n", + " array([0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5]),\n", + " )" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "thousand = pd.read_csv('../data/roll_the_dice_thousand.csv')\n", + "plt.hist(thousand['value'], bins= 6, range= (0.5, 6.5))" ] }, { @@ -258,7 +449,8 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "Since the number of throws is much higher, the values of the plots are more normalized because there is less chance for extremes to happen.\n", + "That is why in clinical trials a bigger population yields better results with less bias.\n", "\"\"\"" ] }, @@ -274,11 +466,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[]], dtype=object)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ages1 = pd.read_csv('../data/ages_population.csv')\n", + "ages1.hist()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "# Between 30-40 for the mean and 2 for the deviation" ] }, { @@ -290,11 +513,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "36.56\n", + "12.816499625976762\n" + ] + } + ], "source": [ - "# your code here" + "print(ages1.observation.mean())\n", + "print(ages1.observation.std())" ] }, { @@ -304,7 +537,7 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "More or less \n", "\"\"\"" ] }, @@ -317,11 +550,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[]], dtype=object)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "ages2 = pd.read_csv('../data/ages_population2.csv')\n", + "ages2.hist()" ] }, { @@ -333,12 +588,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nThat there is a much higher number of young people than before, and the population criteria is stricter,\\nsince here it goes from around 18-37 and before 0-75\\n'" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", - "your comments here\n", + "That there is a much higher number of young people than before, and the population criteria is stricter,\n", + "since here it goes from around 18-37 and before 0-75\n", "\"\"\"" ] }, @@ -351,11 +618,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "27.155\n", + "2.969813932689186\n" + ] + } + ], "source": [ - "# your code here" + "print(ages2.observation.mean())\n", + "print(ages2.observation.std())" ] }, { @@ -365,7 +642,7 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "As we could see for the plot, most of the people were young, around 27,5, and since most of them were in that age, the deviation is very little\n", "\"\"\"" ] }, @@ -381,11 +658,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[]], dtype=object)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "ages3 = pd.read_csv('../data/ages_population3.csv')\n", + "ages3.hist()" ] }, { @@ -397,11 +696,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "41.989\n", + "16.144705959865934\n" + ] + } + ], "source": [ - "# your code here" + "print(ages3.observation.mean())\n", + "print(ages3.observation.std())" ] }, { @@ -411,7 +720,7 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "That they went to a nursing home to conduct the survey, so there is a lot of old people living there, and then the younger staff (admins, nurses...)\n", "\"\"\"" ] }, @@ -424,11 +733,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "30.0\n", + "40.0\n", + "53.0\n" + ] + } + ], "source": [ - "# your code here" + "print(ages3.observation.quantile(.25))\n", + "print(ages3.observation.quantile(.50))\n", + "print(ages3.observation.quantile(.75))" ] }, { @@ -438,7 +759,7 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "Don't know how to relate it to the plot, I'm dumb. That the 3th quartile is larger because of that decrease before 65 years?\n", "\"\"\"" ] }, @@ -455,18 +776,7 @@ "metadata": {}, "outputs": [], "source": [ - "# your code here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" + "# Sorry, I don't know which ones to use" ] }, { @@ -500,9 +810,9 @@ ], "metadata": { "kernelspec": { - "display_name": "ironhack-3.7", + "display_name": "Python 3", "language": "python", - "name": "ironhack-3.7" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -514,7 +824,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.10.9" } }, "nbformat": 4,