Add python checks to the report
This commit is contained in:
Jip J. Dekker
parent
787989ca2d
commit
f73766cfa2
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wk9/pearson.png
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wk9/spearman.png
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@ -12,6 +12,7 @@
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\usepackage[utf8]{inputenc} %support umlauts in the input
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\usepackage[utf8]{inputenc} %support umlauts in the input
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% Easier compilation
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% Easier compilation
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\usepackage{bookmark}
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\usepackage{bookmark}
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\usepackage{graphicx}
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\begin{document}
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\begin{document}
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\title{Week 9 - Correlation and Regression}
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\title{Week 9 - Correlation and Regression}
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@ -35,6 +36,8 @@
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samples and samples of ranking ranges within the top 200. To this end, we made
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samples and samples of ranking ranges within the top 200. To this end, we made
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use of Microsoft Excel tools and functions of the Python library SciPy.
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use of Microsoft Excel tools and functions of the Python library SciPy.
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TODO: Describe Python method as a sanity check
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\section{Results} \label{sec:results}
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\section{Results} \label{sec:results}
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We performed separate statistical analyses on 10 different samples of the
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We performed separate statistical analyses on 10 different samples of the
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population, as well as the population itself. This included 11 separate
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population, as well as the population itself. This included 11 separate
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@ -75,6 +78,15 @@
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places}
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places}
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\end{table}
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\end{table}
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\begin{figure}[ht]
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\centering
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\label{fig:scipy}
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\includegraphics[width=0.6\textwidth]{pearson.png}
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\includegraphics[width=0.6\textwidth]{spearman.png}
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\caption{The Pearsion (top) and Spearman (bottom) correlations coefficients
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of the data set as computed by the Pandas Python library}
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\end{figure}
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\section{Discussion} \label{sec:discussion}
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\section{Discussion} \label{sec:discussion}
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The results generally indicate that there is a fairly strong positive
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The results generally indicate that there is a fairly strong positive
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correlation between the weight and weight of an individual tennis player,
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correlation between the weight and weight of an individual tennis player,
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252
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Using matplotlib backend: MacOSX\n",
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"Populating the interactive namespace from numpy and matplotlib\n"
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]
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}
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],
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"source": [
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"%pylab\n",
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"%matplotlib inline\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from scipy import stats\n",
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"from matplotlib import colors\n",
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"\n",
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"data = pd.read_csv(\"Tennis players 2017-09.csv\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<style type=\"text/css\" >\n",
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" #T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row0_col0 {\n",
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" background-color: #fc7f00;\n",
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" } #T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row0_col1 {\n",
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" background-color: #ffd20c;\n",
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" background-color: #fc7f00;\n",
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" } #T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row1_col2 {\n",
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" background-color: #e4ff7a;\n",
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" background-color: #e8fc6c;\n",
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" background-color: #ffe619;\n",
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" background-color: #e4ff7a;\n",
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" background-color: #f1f44d;\n",
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" background-color: #e8fc6c;\n",
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" } #T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row3_col2 {\n",
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" background-color: #fe9800;\n",
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" } #T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row3_col3 {\n",
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" background-color: #fc7f00;\n",
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" }</style> \n",
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"<thead> <tr> \n",
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" <th class=\"blank level0\" ></th> \n",
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" <th class=\"col_heading level0 col0\" >DOB</th> \n",
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" <th class=\"col_heading level0 col1\" >RANK</th> \n",
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" <th class=\"col_heading level0 col2\" >HEIGHT</th> \n",
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" <th class=\"col_heading level0 col3\" >Weight</th> \n",
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" </tr></thead> \n",
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"<tbody> <tr> \n",
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" <th id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2level0_row0\" class=\"row_heading level0 row0\" >DOB</th> \n",
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||||||
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" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row0_col0\" class=\"data row0 col0\" >1</td> \n",
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" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row0_col1\" class=\"data row0 col1\" >0.277766</td> \n",
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" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row0_col2\" class=\"data row0 col2\" >0.139684</td> \n",
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" </tr> <tr> \n",
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" <th id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2level0_row1\" class=\"row_heading level0 row1\" >RANK</th> \n",
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" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row1_col0\" class=\"data row1 col0\" >0.277766</td> \n",
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" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row1_col1\" class=\"data row1 col1\" >1</td> \n",
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" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row1_col2\" class=\"data row1 col2\" >-0.16755</td> \n",
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" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row1_col3\" class=\"data row1 col3\" >-0.121946</td> \n",
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||||||
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" </tr> <tr> \n",
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" <th id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2level0_row2\" class=\"row_heading level0 row2\" >HEIGHT</th> \n",
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||||||
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" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row2_col0\" class=\"data row2 col0\" >0.139684</td> \n",
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" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row2_col1\" class=\"data row2 col1\" >-0.16755</td> \n",
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" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row2_col2\" class=\"data row2 col2\" >1</td> \n",
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" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row2_col3\" class=\"data row2 col3\" >0.779526</td> \n",
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||||||
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" </tr> <tr> \n",
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" <th id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2level0_row3\" class=\"row_heading level0 row3\" >Weight</th> \n",
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||||||
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" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row3_col0\" class=\"data row3 col0\" >-0.030479</td> \n",
|
||||||
|
" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row3_col1\" class=\"data row3 col1\" >-0.121946</td> \n",
|
||||||
|
" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row3_col2\" class=\"data row3 col2\" >0.779526</td> \n",
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||||||
|
" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row3_col3\" class=\"data row3 col3\" >1</td> \n",
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" </tr></tbody> \n",
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"</table> "
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],
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"text/plain": [
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"<pandas.io.formats.style.Styler at 0x1a197d7b38>"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"def background_gradient(s, m, M, cmap='Wistia', low=0, high=0):\n",
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" rng = M - m\n",
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" norm = colors.Normalize(m - (rng * low),\n",
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" M + (rng * high))\n",
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" normed = norm(s.values)\n",
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" c = [colors.rgb2hex(x) for x in plt.cm.get_cmap(cmap)(normed)]\n",
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" return ['background-color: %s' % color for color in c]\n",
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"\n",
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"data = data[[\"SEX\", \"DOB\", \"RANK\", \"HANDED\", \"Country\", \"HEIGHT\", \"Weight\"]]\n",
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"data.drop_duplicates\n",
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"\n",
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"pearson = data.corr()\n",
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"pearson.style.apply(background_gradient,\n",
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" cmap='Wistia',\n",
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" m=pearson.min().min(),\n",
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" M=pearson.max().max()\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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||||||
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"<style type=\"text/css\" >\n",
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" background-color: #fc7f00;\n",
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" background-color: #ffd20c;\n",
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" background-color: #fee91d;\n",
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||||||
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" background-color: #ffd20c;\n",
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||||||
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||||||
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" background-color: #fc7f00;\n",
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" } #T_727bef98_4f3e_11e8_a315_787b8ab7acb2row1_col2 {\n",
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||||||
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" background-color: #e4ff7a;\n",
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||||||
|
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|
" background-color: #eafa63;\n",
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||||||
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||||||
|
" background-color: #fee91d;\n",
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||||||
|
" } #T_727bef98_4f3e_11e8_a315_787b8ab7acb2row2_col1 {\n",
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||||||
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" background-color: #e4ff7a;\n",
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||||||
|
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||||||
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" background-color: #fc7f00;\n",
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||||||
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" background-color: #ff9d00;\n",
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||||||
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||||||
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" background-color: #f4f242;\n",
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||||||
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" } #T_727bef98_4f3e_11e8_a315_787b8ab7acb2row3_col1 {\n",
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||||||
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" background-color: #eafa63;\n",
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||||||
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" } #T_727bef98_4f3e_11e8_a315_787b8ab7acb2row3_col2 {\n",
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||||||
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" background-color: #ff9d00;\n",
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||||||
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" } #T_727bef98_4f3e_11e8_a315_787b8ab7acb2row3_col3 {\n",
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||||||
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" background-color: #fc7f00;\n",
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||||||
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" }</style> \n",
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||||||
|
"<table id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2\" > \n",
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||||||
|
"<thead> <tr> \n",
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||||||
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" <th class=\"blank level0\" ></th> \n",
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" <th class=\"col_heading level0 col0\" >DOB</th> \n",
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" <th class=\"col_heading level0 col1\" >RANK</th> \n",
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" <th class=\"col_heading level0 col2\" >HEIGHT</th> \n",
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" <th class=\"col_heading level0 col3\" >Weight</th> \n",
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" </tr></thead> \n",
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"<tbody> <tr> \n",
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||||||
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" <th id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2level0_row0\" class=\"row_heading level0 row0\" >DOB</th> \n",
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" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row0_col0\" class=\"data row0 col0\" >1</td> \n",
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||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row0_col1\" class=\"data row0 col1\" >0.280386</td> \n",
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||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row0_col2\" class=\"data row0 col2\" >0.122412</td> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row0_col3\" class=\"data row0 col3\" >0.00769861</td> \n",
|
||||||
|
" </tr> <tr> \n",
|
||||||
|
" <th id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2level0_row1\" class=\"row_heading level0 row1\" >RANK</th> \n",
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||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row1_col0\" class=\"data row1 col0\" >0.280386</td> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row1_col1\" class=\"data row1 col1\" >1</td> \n",
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||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row1_col2\" class=\"data row1 col2\" >-0.160006</td> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row1_col3\" class=\"data row1 col3\" >-0.0908714</td> \n",
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||||||
|
" </tr> <tr> \n",
|
||||||
|
" <th id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2level0_row2\" class=\"row_heading level0 row2\" >HEIGHT</th> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row2_col0\" class=\"data row2 col0\" >0.122412</td> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row2_col1\" class=\"data row2 col1\" >-0.160006</td> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row2_col2\" class=\"data row2 col2\" >1</td> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row2_col3\" class=\"data row2 col3\" >0.739246</td> \n",
|
||||||
|
" </tr> <tr> \n",
|
||||||
|
" <th id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2level0_row3\" class=\"row_heading level0 row3\" >Weight</th> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row3_col0\" class=\"data row3 col0\" >0.00769861</td> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row3_col1\" class=\"data row3 col1\" >-0.0908714</td> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row3_col2\" class=\"data row3 col2\" >0.739246</td> \n",
|
||||||
|
" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row3_col3\" class=\"data row3 col3\" >1</td> \n",
|
||||||
|
" </tr></tbody> \n",
|
||||||
|
"</table> "
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
"<pandas.io.formats.style.Styler at 0x111a3b198>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 3,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"spearman = data.corr(method=\"spearman\")\n",
|
||||||
|
"spearman.style.apply(background_gradient,\n",
|
||||||
|
" cmap='Wistia',\n",
|
||||||
|
" m=spearman.min().min(),\n",
|
||||||
|
" M=spearman.max().max()\n",
|
||||||
|
")"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.6.4"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 2
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user