{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "a1FSGmDGeo7Z" }, "source": [ "# Section 1.3 — Descriptive statistics\n", "\n", "This notebook contains all the code from [Section 1.3 Descriptive Statistics](https://minireference.com/static/excerpts/noBSstats/noBSstats_ch01_DATA.pdf#page=80) of the **No Bullshit Guide to Statistics**.\n", "\n", "All the data manipulations are done using the `pandas` library,\n", "and data visualizations are based on the `seaborn` library.\n", "\n", "Code cells containing an `ALT` comment and commented out show alternative way for computing the same quantities or additional details that were not included in the book. It's up to you if you want to learn about these alternative options—just uncomment the code and execute." ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "#### Notebook setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "VFzPCGo6I1NJ" }, "outputs": [], "source": [ "import os\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Pandas setup\n", "pd.set_option(\"display.precision\", 2)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Figures setup\n", "sns.set_theme(\n", " context=\"paper\",\n", " style=\"whitegrid\",\n", " palette=\"colorblind\",\n", " rc={'figure.figsize': (7,4)},\n", ")\n", "\n", "%config InlineBackend.figure_format = 'retina'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Numerical variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Definitions" ] }, { "cell_type": "markdown", "metadata": { "id": "evCXgKk6ND5B" }, "source": [ "### Descriptive statistics for the students dataset" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "#### Load the `students` dataset from CSV" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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student_IDbackgroundcurriculumeffortscore
01artsdebate10.9675.0
12sciencelecture8.6975.0
23artsdebate8.6067.0
34artslecture7.9270.3
45sciencedebate9.9076.1
56businessdebate10.8079.8
67sciencelecture7.8172.7
78businesslecture9.1375.4
89businesslecture5.2157.0
910sciencelecture7.7169.0
1011businessdebate9.8270.4
1112artsdebate11.5396.2
1213sciencedebate7.1062.9
1314sciencelecture6.3957.6
1415artsdebate12.0084.3
\n", "
" ], "text/plain": [ " student_ID background curriculum effort score\n", "0 1 arts debate 10.96 75.0\n", "1 2 science lecture 8.69 75.0\n", "2 3 arts debate 8.60 67.0\n", "3 4 arts lecture 7.92 70.3\n", "4 5 science debate 9.90 76.1\n", "5 6 business debate 10.80 79.8\n", "6 7 science lecture 7.81 72.7\n", "7 8 business lecture 9.13 75.4\n", "8 9 business lecture 5.21 57.0\n", "9 10 science lecture 7.71 69.0\n", "10 11 business debate 9.82 70.4\n", "11 12 arts debate 11.53 96.2\n", "12 13 science debate 7.10 62.9\n", "13 14 science lecture 6.39 57.6\n", "14 15 arts debate 12.00 84.3" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "students = pd.read_csv(\"../datasets/students.csv\")\n", "students" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# what type of object is `students`?\n", "type(students)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 415, "status": "ok", "timestamp": 1620080348254, "user": { "displayName": "Ivan Savov", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgM13MuJiia2gUK23ZS4gJbjbYEAe1_3dxb5acpnQ=s64", "userId": "13320182563968762712" }, "user_tz": 240 }, "id": "UZqKw-bFLqcz", "outputId": "da6c0cd7-12e2-4d1b-d326-4e932aa5f603" }, "outputs": [ { "data": { "text/plain": [ "RangeIndex(start=0, stop=15, step=1)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# rows\n", "students.index" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 377, "status": "ok", "timestamp": 1620080350533, "user": { "displayName": "Ivan Savov", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgM13MuJiia2gUK23ZS4gJbjbYEAe1_3dxb5acpnQ=s64", "userId": "13320182563968762712" }, "user_tz": 240 }, "id": "-5FJptNcLWvN", "outputId": "cd7bdc3d-38f3-4e8e-c6c8-d9789739edeb" }, "outputs": [ { "data": { "text/plain": [ "Index(['student_ID', 'background', 'curriculum', 'effort', 'score'], dtype='object')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# columns\n", "students.columns" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 15 entries, 0 to 14\n", "Data columns (total 5 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 student_ID 15 non-null int64 \n", " 1 background 15 non-null object \n", " 2 curriculum 15 non-null object \n", " 3 effort 15 non-null float64\n", " 4 score 15 non-null float64\n", "dtypes: float64(2), int64(1), object(2)\n", "memory usage: 728.0+ bytes\n" ] } ], "source": [ "# info about memory and data types of each column\n", "students.info()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Focus on the `score` variable" ] }, { "cell_type": "markdown", "metadata": { "id": "evCXgKk6ND5B" }, "source": [ "Let's look at the `score` variable." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "scores = students[\"score\"]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 612, "status": "ok", "timestamp": 1620080353830, "user": { "displayName": "Ivan Savov", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgM13MuJiia2gUK23ZS4gJbjbYEAe1_3dxb5acpnQ=s64", "userId": "13320182563968762712" }, "user_tz": 240 }, "id": "QlBxDHISMmAe", "outputId": "85bd7eba-a218-4875-e741-0a8605e0cb4c" }, "outputs": [ { "data": { "text/plain": [ "15" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scores.count()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8 57.0\n", "13 57.6\n", "12 62.9\n", "2 67.0\n", "9 69.0\n", "3 70.3\n", "10 70.4\n", "6 72.7\n", "0 75.0\n", "1 75.0\n", "7 75.4\n", "4 76.1\n", "5 79.8\n", "14 84.3\n", "11 96.2\n", "Name: score, dtype: float64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scores.sort_values()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# # ALT\n", "# sorted(scores)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Min, max, and median" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 407, "status": "ok", "timestamp": 1620080250818, "user": { "displayName": "Ivan Savov", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgM13MuJiia2gUK23ZS4gJbjbYEAe1_3dxb5acpnQ=s64", "userId": "13320182563968762712" }, "user_tz": 240 }, "id": "3Xk2-OFnNr6b", "outputId": "b912a5ee-022f-43f0-b9f2-e77423e0da0f" }, "outputs": [ { "data": { "text/plain": [ "57.0" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scores.min()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "96.2" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scores.max()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "39.2" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scores.max() - scores.min() # Range" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 399, "status": "ok", "timestamp": 1620078690252, "user": { "displayName": "Ivan Savov", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgM13MuJiia2gUK23ZS4gJbjbYEAe1_3dxb5acpnQ=s64", "userId": "13320182563968762712" }, "user_tz": 240 }, "id": "1jOb5cuoJlj9", "outputId": "2e81f0e6-afb4-4a7b-a0d9-07962e67199d" }, "outputs": [ { "data": { "text/plain": [ "72.7" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scores.median()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Strip plot" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 368, "width": 562 } }, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "sns.stripplot(data=students, x=\"score\", jitter=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We used the option `jitter=0` to disable the Seaborn default behaviour\n", "of adding a small amount of random vertical displacement to each data point,\n", "which we don't need for this plot." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# # ALT also show mean via https://stackoverflow.com/a/67579487/127114\n", "# with plt.rc_context({\"figure.figsize\":(7,2)}):\n", "# sns.stripplot(x=\"score\", data=students, jitter=0.03, alpha=0.6)\n", "# color0 = sns.color_palette()[0]\n", "# sns.boxplot(data=students,\n", "# showmeans=True,\n", "# meanline=True,\n", "# meanprops={'color': color0, 'ls': '-', 'lw': 2},\n", "# medianprops={'visible': False},\n", "# whiskerprops={'visible': False},\n", "# zorder=10,\n", "# x=\"score\",\n", "# showfliers=False,\n", "# showbox=False,\n", "# showcaps=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Mean, variance, and standard deviation" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "72.58" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scores.mean()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 435, "status": "ok", "timestamp": 1620078956726, "user": { "displayName": "Ivan Savov", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgM13MuJiia2gUK23ZS4gJbjbYEAe1_3dxb5acpnQ=s64", "userId": "13320182563968762712" }, "user_tz": 240 }, "id": "-ZNyl9hBN8FR", "outputId": "604032e8-6785-4eae-d34f-97ce8f169369" }, "outputs": [ { "data": { "text/plain": [ "99.58600000000001" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scores.var() # sample variance, 1/(n-1) EE(x-mean)^2" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 463, "status": "ok", "timestamp": 1620078957060, "user": { "displayName": "Ivan Savov", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgM13MuJiia2gUK23ZS4gJbjbYEAe1_3dxb5acpnQ=s64", "userId": "13320182563968762712" }, "user_tz": 240 }, "id": "Tm-Xu3bBOnoj", "outputId": "886f7d7c-e042-4de8-f7ac-d2a612988194" }, "outputs": [ { "data": { "text/plain": [ "9.979278531036199" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scores.std()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Histograms" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 406, "status": "ok", "timestamp": 1620080883499, "user": { "displayName": "Ivan Savov", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgM13MuJiia2gUK23ZS4gJbjbYEAe1_3dxb5acpnQ=s64", "userId": "13320182563968762712" }, "user_tz": 240 }, "id": "-eTLlvMWOPPP", "outputId": "2350d6fe-4574-4a92-83b4-8552b28c9c45" }, "outputs": [ { "data": { "text/plain": [ "(49.999, 60.0] 2\n", "(60.0, 70.0] 3\n", "(70.0, 80.0] 8\n", "(80.0, 90.0] 1\n", "(90.0, 100.0] 1\n", "Name: score, dtype: int64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bins = [50, 60, 70, 80, 90, 100]\n", "scores.value_counts(bins=bins, sort=False)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "id": "LRTdFsHBWUZh" }, "outputs": [], "source": [ "# note mode is the bin 70--80, which contains 8 values" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 368, "width": 598 } }, "output_type": "display_data" } ], "source": [ "bins = [50, 60, 70, 80, 90, 100]\n", "sns.histplot(data=students, x=\"score\", bins=bins)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Quartiles" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 383, "status": "ok", "timestamp": 1620080750460, "user": { "displayName": "Ivan Savov", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgM13MuJiia2gUK23ZS4gJbjbYEAe1_3dxb5acpnQ=s64", "userId": "13320182563968762712" }, "user_tz": 240 }, "id": "Qw5J6BZPSkSC", "outputId": "82577f28-6900-48a9-a9a8-f3bfd0e9a8d8" }, "outputs": [ { "data": { "text/plain": [ "68.0" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Q1 = scores.quantile(q=0.25)\n", "Q1" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 414, "status": "ok", "timestamp": 1620080766403, "user": { "displayName": "Ivan Savov", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgM13MuJiia2gUK23ZS4gJbjbYEAe1_3dxb5acpnQ=s64", "userId": "13320182563968762712" }, "user_tz": 240 }, "id": "mL38GGdfVogt", "outputId": "36aa4b43-2407-4c40-eaf8-a79238a6e61c" }, "outputs": [ { "data": { "text/plain": [ "72.7" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Q2 = scores.quantile(q=0.5)\n", "Q2" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 432, "status": "ok", "timestamp": 1620080772519, "user": { "displayName": "Ivan Savov", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgM13MuJiia2gUK23ZS4gJbjbYEAe1_3dxb5acpnQ=s64", "userId": "13320182563968762712" }, "user_tz": 240 }, "id": "cBJGFJu5VoFF", "outputId": "f18d097d-f902-4ebc-ec7d-7997306c04ce" }, "outputs": [ { "data": { "text/plain": [ "75.75" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Q3 = scores.quantile(q=0.75)\n", "Q3" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 399, "status": "ok", "timestamp": 1620080823892, "user": { "displayName": "Ivan Savov", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgM13MuJiia2gUK23ZS4gJbjbYEAe1_3dxb5acpnQ=s64", "userId": "13320182563968762712" }, "user_tz": 240 }, "id": "OvyUz9iuUjbg", "outputId": "5e0e4b00-d5ee-411b-95ef-3d202e1cff31" }, "outputs": [ { "data": { "text/plain": [ "7.75" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "IQR = Q3 - Q1\n", "IQR" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Box plots" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 296 }, "executionInfo": { "elapsed": 624, "status": "ok", "timestamp": 1620080747866, "user": { "displayName": "Ivan Savov", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgM13MuJiia2gUK23ZS4gJbjbYEAe1_3dxb5acpnQ=s64", "userId": "13320182563968762712" }, "user_tz": 240 }, "id": "Y2XAO_XcRZxp", "outputId": "9ae53dfa-db78-49a1-ebc7-bba8b698cb4e" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "image/png": { "height": 368, "width": 562 } }, "output_type": "display_data" } ], "source": [ "# box plot\n", "sns.boxplot(data=students, x=\"score\", width=0.4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## All summary statistics" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 15.00\n", "mean 72.58\n", "std 9.98\n", "min 57.00\n", "25% 68.00\n", "50% 72.70\n", "75% 75.75\n", "max 96.20\n", "Name: score, dtype: float64" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scores.describe()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countmeanstdmin25%50%75%max
score15.072.589.9857.068.072.775.7596.2
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" ], "text/plain": [ " count mean std min 25% 50% 75% max\n", "score 15.0 72.58 9.98 57.0 68.0 72.7 75.75 96.2" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "students[[\"score\"]].describe().T" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "id": "-xLQ3EThbblV" }, "source": [ "## Comparing two numeric variables\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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effortscore
count15.0015.00
mean8.9072.58
std1.959.98
min5.2157.00
25%7.7668.00
50%8.6972.70
75%10.3575.75
max12.0096.20
\n", "
" ], "text/plain": [ " effort score\n", "count 15.00 15.00\n", "mean 8.90 72.58\n", "std 1.95 9.98\n", "min 5.21 57.00\n", "25% 7.76 68.00\n", "50% 8.69 72.70\n", "75% 10.35 75.75\n", "max 12.00 96.20" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "students[ [\"effort\",\"score\"] ].describe()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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student_IDeffortscore
count15.0015.0015.00
mean8.008.9072.58
std4.471.959.98
min1.005.2157.00
25%4.507.7668.00
50%8.008.6972.70
75%11.5010.3575.75
max15.0012.0096.20
\n", "
" ], "text/plain": [ " student_ID effort score\n", "count 15.00 15.00 15.00\n", "mean 8.00 8.90 72.58\n", "std 4.47 1.95 9.98\n", "min 1.00 5.21 57.00\n", "25% 4.50 7.76 68.00\n", "50% 8.00 8.69 72.70\n", "75% 11.50 10.35 75.75\n", "max 15.00 12.00 96.20" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "students.describe()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 296 }, "executionInfo": { "elapsed": 569, "status": "ok", "timestamp": 1620082364267, "user": { "displayName": "Ivan Savov", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgM13MuJiia2gUK23ZS4gJbjbYEAe1_3dxb5acpnQ=s64", "userId": "13320182563968762712" }, "user_tz": 240 }, "id": "ud0vh9Uabc6c", "outputId": "264b7d2a-b5fe-4e8c-86e4-ffd806e8ac9b" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { 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" ] }, "metadata": { "image/png": { "height": 368, "width": 605 } }, "output_type": "display_data" } ], "source": [ "sns.scatterplot(data=students, x=\"effort\", y=\"score\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Covariance and correlation" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 375, "status": "ok", "timestamp": 1620082617201, "user": { "displayName": "Ivan Savov", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgM13MuJiia2gUK23ZS4gJbjbYEAe1_3dxb5acpnQ=s64", "userId": "13320182563968762712" }, "user_tz": 240 }, "id": "PEmO45ppcCte", "outputId": "4ff85942-38ce-41ce-cee3-8c9791d1c3d5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " effort score\n", "effort 3.8 17.10\n", "score 17.1 99.59\n" ] } ], "source": [ "print(students[[\"effort\", \"score\"]].cov())" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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effortscore
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score0.881.00
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" ], "text/plain": [ " effort score\n", "effort 1.00 0.88\n", "score 0.88 1.00" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "students[[\"effort\", \"score\"]].corr()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8794375135614694" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "students[[\"effort\",\"score\"]].corr().loc[\"score\",\"effort\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multiple numerical variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Extract data for the two groups:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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student_IDbackgroundcurriculumeffortscore
01artsdebate10.9675.0
23artsdebate8.6067.0
45sciencedebate9.9076.1
56businessdebate10.8079.8
1011businessdebate9.8270.4
1112artsdebate11.5396.2
1213sciencedebate7.1062.9
1415artsdebate12.0084.3
\n", "
" ], "text/plain": [ " student_ID background curriculum effort score\n", "0 1 arts debate 10.96 75.0\n", "2 3 arts debate 8.60 67.0\n", "4 5 science debate 9.90 76.1\n", "5 6 business debate 10.80 79.8\n", "10 11 business debate 9.82 70.4\n", "11 12 arts debate 11.53 96.2\n", "12 13 science debate 7.10 62.9\n", "14 15 arts debate 12.00 84.3" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dstudents = students[students[\"curriculum\"]==\"debate\"]\n", "dstudents" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 8.00\n", "mean 76.46\n", "std 10.52\n", "min 62.90\n", "25% 69.55\n", "50% 75.55\n", "75% 80.92\n", "max 96.20\n", "Name: score, dtype: float64" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dstudents[\"score\"].describe()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 7.00\n", "mean 68.14\n", "std 7.76\n", "min 57.00\n", "25% 63.30\n", "50% 70.30\n", "75% 73.85\n", "max 75.40\n", "Name: score, dtype: float64" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lstudents = students[students[\"curriculum\"]==\"lecture\"]\n", "lstudents[\"score\"].describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Strip plots" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 368, "width": 628 } }, "output_type": "display_data" } ], "source": [ "sns.stripplot(data=students, x=\"score\", y=\"curriculum\", hue=\"curriculum\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Box plots" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 368, "width": 628 } }, "output_type": "display_data" } ], "source": [ "sns.boxplot(data=students, x=\"score\", y=\"curriculum\", width=0.4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Histograms" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 368, "width": 598 } }, "output_type": "display_data" } ], "source": [ "# histograms\n", "fig, axs = plt.subplots(2, 1, sharex=True)\n", "\n", "# prepare data\n", "dstudents = students[students[\"curriculum\"]==\"debate\"]\n", "lstudents = students[students[\"curriculum\"]==\"lecture\"]\n", "\n", "# select colors to use for the two groups \n", "blue, yellow = sns.color_palette()[0], sns.color_palette()[1]\n", "\n", "# plot histograms\n", "bins = [50, 60, 70, 80, 90, 100]\n", "sns.histplot(data=dstudents, x=\"score\", color=blue, ax=axs[0], bins=bins)\n", "sns.histplot(data=lstudents, x=\"score\", color=yellow, ax=axs[1], bins=bins)\n", "\n", "# add labels\n", "axs[0].legend(labels=[\"debate\"])\n", "axs[1].legend(labels=[\"lecture\"])" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "# # ALT grouped bar charts\n", "# sns.histplot(data=students, x=\"score\", hue=\"curriculum\",\n", "# bins=bins, multiple=\"dodge\")" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "# # ALT2 use `displot` with curriculum as row-variable\n", "# bins = [50, 60, 70, 80, 90, 100]\n", "# sns.displot(data=students, x='score', row='curriculum',\n", "# hue='curriculum', alpha=0.8, bins=bins, aspect=3.3, height=2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "id": "97XxbO8sXQUV" }, "source": [ "## Categorical data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analysis of the `background` variable" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 636, "status": "ok", "timestamp": 1620082054403, "user": { "displayName": "Ivan Savov", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgM13MuJiia2gUK23ZS4gJbjbYEAe1_3dxb5acpnQ=s64", "userId": "13320182563968762712" }, "user_tz": 240 }, "id": "diobFFhyXT-B", "outputId": "dddc612f-7d15-47c6-8ed8-f652416b5dbb" }, "outputs": [], "source": [ "backgrounds = students[\"background\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Pandas methods `.value_counts()` can be used to compute the frequencies of any series or data frame." ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "science 6\n", "arts 5\n", "business 4\n", "Name: background, dtype: int64" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "backgrounds.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We often chain the `.sort_index()` after `.value_counts()` to sort the results in alphabetical order." ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 417, "status": "ok", "timestamp": 1620082183672, "user": { "displayName": "Ivan Savov", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgM13MuJiia2gUK23ZS4gJbjbYEAe1_3dxb5acpnQ=s64", "userId": "13320182563968762712" }, "user_tz": 240 }, "id": "z5Vn51LVaguu", "outputId": "550e6870-4a23-4ae1-ba1b-95e820991bb0" }, "outputs": [ { "data": { "text/plain": [ "arts 5\n", "business 4\n", "science 6\n", "Name: background, dtype: int64" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "backgrounds.value_counts().sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the option `normalize=True` for the methods `.value_counts()` to compute relative frequencies." ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 740, "status": "ok", "timestamp": 1620082150174, "user": { "displayName": "Ivan Savov", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgM13MuJiia2gUK23ZS4gJbjbYEAe1_3dxb5acpnQ=s64", "userId": "13320182563968762712" }, "user_tz": 240 }, "id": "-OqH32ARa1v7", "outputId": "9e1ae4da-898a-46f5-cb9e-17b1ae7d1fe2" }, "outputs": [ { "data": { "text/plain": [ "science 0.40\n", "arts 0.33\n", "business 0.27\n", "Name: background, dtype: float64" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# relative frequencies\n", "backgrounds.value_counts(normalize=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The method `.describe()` shows some useful facts about the variable." ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 15\n", "unique 3\n", "top science\n", "freq 6\n", "Name: background, dtype: object" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "backgrounds.describe()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "# # ALT\n", "# backgrounds.value_counts() / backgrounds.count()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "# # ALT combiend table with both frequency and relative frequency\n", "# df2 = pd.DataFrame({\n", "# \"frequency\": backgrounds.value_counts(),\n", "# \"relative frequency\": backgrounds.value_counts(normalize=True)\n", "# })\n", "# df2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 53, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 296 }, "executionInfo": { "elapsed": 434, "status": "ok", "timestamp": 1620082206959, "user": { "displayName": "Ivan Savov", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgM13MuJiia2gUK23ZS4gJbjbYEAe1_3dxb5acpnQ=s64", "userId": "13320182563968762712" }, "user_tz": 240 }, "id": "dVE6Zx6CZ0Ik", "outputId": "d23577d2-1acb-4c31-8938-ed6c595d193e" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 368, "width": 598 } }, "output_type": "display_data" } ], "source": [ "# bar chart of counts\n", "sns.countplot(data=students, x=\"background\")" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "# # ALT bar chart with relative frequencies\n", "# df3 = (backgrounds\n", "# .value_counts(normalize=True)\n", "# .sort_index()\n", "# .rename(\"relative frequency\")\n", "# .rename_axis(\"background\")\n", "# .reset_index()\n", "# )\n", "# sns.barplot(data=df3, x=\"background\", y=\"relative frequency\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "UN7U2DAedX9t" }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "id": "YixN6-HpdY4W" }, "source": [ "## Comparing two categodescribeal variables" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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backgroundcurriculum
0artsdebate
1sciencelecture
2artsdebate
3artslecture
4sciencedebate
5businessdebate
6sciencelecture
7businesslecture
8businesslecture
9sciencelecture
10businessdebate
11artsdebate
12sciencedebate
13sciencelecture
14artsdebate
\n", "
" ], "text/plain": [ " background curriculum\n", "0 arts debate\n", "1 science lecture\n", "2 arts debate\n", "3 arts lecture\n", "4 science debate\n", "5 business debate\n", "6 science lecture\n", "7 business lecture\n", "8 business lecture\n", "9 science lecture\n", "10 business debate\n", "11 arts debate\n", "12 science debate\n", "13 science lecture\n", "14 arts debate" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "students[ [\"background\",\"curriculum\"] ]" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "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", "
backgroundartsbusinessscienceTOTAL
curriculum
debate4228
lecture1247
TOTAL54615
\n", "
" ], "text/plain": [ "background arts business science TOTAL\n", "curriculum \n", "debate 4 2 2 8\n", "lecture 1 2 4 7\n", "TOTAL 5 4 6 15" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# joint frequencies\n", "pd.crosstab(index=students[\"curriculum\"],\n", " columns=students[\"background\"],\n", " margins=True, margins_name=\"TOTAL\")" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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backgroundartsbusinessscienceTOTAL
curriculum
debate0.270.130.130.53
lecture0.070.130.270.47
TOTAL0.330.270.401.00
\n", "
" ], "text/plain": [ "background arts business science TOTAL\n", "curriculum \n", "debate 0.27 0.13 0.13 0.53\n", "lecture 0.07 0.13 0.27 0.47\n", "TOTAL 0.33 0.27 0.40 1.00" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# joint relative frequencies\n", "pd.crosstab(index=students[\"curriculum\"],\n", " columns=students[\"background\"],\n", " margins=True, margins_name=\"TOTAL\",\n", " normalize=True)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "image/png": { "height": 368, "width": 608 } }, "output_type": "display_data" } ], "source": [ "sns.countplot(data=students, x=\"background\",\n", " hue=\"curriculum\", alpha=0.8)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "id": "Bf4WUuZudaPC" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "image/png": { "height": 368, "width": 598 } }, "output_type": "display_data" } ], "source": [ "sns.histplot(data=students, x=\"background\",\n", " hue=\"curriculum\", multiple=\"stack\", shrink=.7)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "# # ALT using displot\n", "# sns.displot(data=students, x=\"background\", hue=\"curriculum\", multiple=\"stack\", shrink=0.8)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "# # ALT2 using Pandas plot function for crosstab\n", "# pd.crosstab(\n", "# index=students[\"curriculum\"],\n", "# columns=students[\"background\"],\n", "# ).T.plot(kind=\"bar\", stacked=True, rot=0)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [], "source": [ "# # stacked joint relative frequencies\n", "# sns.histplot(data=students, x=\"background\",\n", "# hue=\"curriculum\", shrink=.7,\n", "# multiple=\"stack\", stat=\"proportion\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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backgroundartsbusinessscienceTOTAL
curriculum
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\n", "
" ], "text/plain": [ "background arts business science TOTAL\n", "curriculum \n", "debate 0.8 0.5 0.33 0.53\n", "lecture 0.2 0.5 0.67 0.47" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# curriculum relative frequencies conditional on background\n", "pd.crosstab(index=students[\"curriculum\"],\n", " columns=students[\"background\"],\n", " margins=True, margins_name=\"TOTAL\",\n", " normalize=\"columns\")" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'conditional relative frequency')" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 368, "width": 706 } }, "output_type": "display_data" } ], "source": [ "ct1 = pd.crosstab(\n", " index=students[\"curriculum\"],\n", " columns=students[\"background\"],\n", " normalize=\"columns\",\n", ")\n", "axct1 = ct1.T.plot(kind=\"bar\", stacked=True, rot=0, alpha=0.8)\n", "plt.legend(loc = \"upper left\", bbox_to_anchor=(1.02,1))\n", "plt.ylabel(\"conditional relative frequency\")" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "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", "
backgroundartsbusinessscience
curriculum
debate0.500.250.25
lecture0.140.290.57
TOTAL0.330.270.40
\n", "
" ], "text/plain": [ "background arts business science\n", "curriculum \n", "debate 0.50 0.25 0.25\n", "lecture 0.14 0.29 0.57\n", "TOTAL 0.33 0.27 0.40" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# background relative frequencies conditional on curriculum\n", "pd.crosstab(index=students[\"curriculum\"],\n", " columns=students[\"background\"],\n", " margins=True, margins_name=\"TOTAL\",\n", " normalize=\"index\")" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'conditional relative frequency')" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 368, "width": 738 } }, "output_type": "display_data" } ], "source": [ "ct2 = pd.crosstab(\n", " index=students[\"curriculum\"],\n", " columns=students[\"background\"],\n", " normalize=\"index\",\n", ")\n", "axct2 = ct2.plot(kind=\"barh\", stacked=True, rot=0, alpha=0.8, )\n", "plt.legend(loc = \"upper left\", bbox_to_anchor=(1.02,1))\n", "plt.xlabel(\"conditional relative frequency\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explanations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Kernel density plots" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "image/png": { "height": 368, "width": 615 } }, "output_type": "display_data" } ], "source": [ "sns.kdeplot(data=students, x=\"score\", bw_adjust=0.2)\n", "sns.kdeplot(data=students, x=\"score\", bw_adjust=0.5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Discussion" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Code implementations of `median` and `quantile` functions (bonus material)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Linear interpolation for the median\n", "\n", "When computing the median of a list of values,\n", "it's not always possible to find a value from the list\n", "that satisfies the definition \"splits the list into equal parts.\"\n", "For example, if the list has an even number of elements, then there is no single \"middle\" number.\n", "The convention in this situation is to create a new number that consists of a 50-50 mix of\n", "the two middle numbers, a process known as *linear interpolation*.\n", "\n", "The code below shows the procedure for calculating the median:\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [], "source": [ "def median(values):\n", " n = len(values)\n", " svalues = sorted(values)\n", " if n % 2 == 1: # Case A: n is odd\n", " mid = n // 2\n", " return svalues[mid]\n", " else: # Case B: n is even\n", " j = n // 2\n", " return 0.5*svalues[j-1] + 0.5*svalues[j]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The logic in the above function splits into two cases, depending on $n$,\n", "the length of the list of values. If $n$ is odd, we return the middle\n", "value from the list of sorted values `svalues[mid]`. If $n$ is even, we\n", "return a mixture of the two values straddling the midpoint.\n", "\n", "It's not important that you understand the details of the above code\n", "snippet, since Pandas will perform median calculations for you. I just\n", "want you to be aware this interpolation is happening behind the scenes,\n", "so you won't be surprised if the $\\mathbf{Med}$ value you obtain is not one of\n", "the observations in your dataset. For example, the median of a list of\n", "integers is a decimal `median([1,2]) = 1.5`.\n" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "assert median([1,300]) == np.median([1,300])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Linear interpolation for quantiles\n", "\n", "Linear interpolation is also used when computing quartiles, percentiles, and quantiles.\n", "In this section, we'll explain the calculations and code for computing the $q$th quantile\n", "for the dataset for anyone interested in learning the technical details.\n", "Feel free to skip if you're not passionate about code.\n", "\n", "Suppose we want to compute the $q$th quantile for a list of $n$ values\n", "$\\mathbf{x}=[x_0, x_1, x_2, \\ldots, x_{n-1}]$.\n", "We'll assume the values are already in sorted order.\n", "The $q$th quantile corresponds to the position $p = q(n-1)$ within the list,\n", "where $p$ is a decimal number between $0$ (index of the first element) and $n-1$ (index of the last element).\n", "We can split the position into an integer part $i$ and a fractional part $g$,\n", "where $i = \\lfloor p \\rfloor$ is the greatest\n", "integer that's less than or equal to $p$,\n", "and $g = p - \\lfloor p \\rfloor$ is what remains of $p$ once we remove the integer part.\n", "\n", "The $q$th quantile lies somewhere between the values $x_i$ and $x_{i+1}$ in the list.\n", "Specifically, the quantile computed by linear interpolation is a mixture\n", "with proportion $g$ of $x_{i+1}$ and $(1-g)$ of $x_i$:\n", "\n", "$$ \\texttt{quantile}(\\mathbf{x}, q) = (1-g) x_i + g x_{i+1}. $$\n", "\n", "The code below shows the practical implementation of the math calculations described above." ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "def quantile(values, q):\n", " svalues = sorted(values)\n", " p = q * (len(values)-1)\n", " i = int(p)\n", " g = p - int(p)\n", " return (1-g)*svalues[i] + g*svalues[i+1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function we defined above is equivalent to the `np.quantile(q)` method\n", "in Pandas and NumPy, which uses the linear interpolation by default.\n", "Note however, there are other valid interpolation methods for computing\n", "quantiles that may be used in other statistics software, so don't freak\n", "out if you get different values when computing quantiles.\n", "\n", "Everything we discussed about the quantile function also applies to\n", "percentiles and quartiles. For example, the $k$^th^ quartile is given by\n", "`quantile(q=k/4)`, and the $k$^th^ percentile is `quantile(q=k/100)`." ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "arr = [21, 22, 24, 24, 26, 97]\n", "assert quantile(arr, 0.33) == np.quantile(arr, 0.33)\n", "assert quantile(arr, 0.25) == np.quantile(arr, 0.25)\n", "assert quantile(arr, 0.75) == np.quantile(arr, 0.75)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "The function `quantile` we defined above:\n", "- Equivalent to ``quantile(values, q, method=\"linear\")`` in `numpy`.\n", "- Equivalent to ``quantile(values, q, type=7)`` in R." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "colab": { "authorship_tag": "ABX9TyPw9/JPOAyxhg8PCV/LtSU1", "name": "DescriptiveStatistics.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.4" } }, "nbformat": 4, "nbformat_minor": 4 }