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ResearchMethods/wk9/wk9.ipynb
2018-05-04 11:57:16 +10:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using matplotlib backend: MacOSX\n",
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab\n",
"%matplotlib inline\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy import stats\n",
"from matplotlib import colors\n",
"\n",
"data = pd.read_csv(\"Tennis players 2017-09.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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" <th class=\"col_heading level0 col0\" >DOB</th> \n",
" <th class=\"col_heading level0 col1\" >RANK</th> \n",
" <th class=\"col_heading level0 col2\" >HEIGHT</th> \n",
" <th class=\"col_heading level0 col3\" >Weight</th> \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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" <th id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2level0_row1\" class=\"row_heading level0 row1\" >RANK</th> \n",
" <td id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2row1_col0\" class=\"data row1 col0\" >0.277766</td> \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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" <th id=\"T_7277b07a_4f3e_11e8_b8a3_787b8ab7acb2level0_row3\" class=\"row_heading level0 row3\" >Weight</th> \n",
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],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x1a197d7b38>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def background_gradient(s, m, M, cmap='Wistia', low=0, high=0):\n",
" rng = M - m\n",
" norm = colors.Normalize(m - (rng * low),\n",
" M + (rng * high))\n",
" normed = norm(s.values)\n",
" c = [colors.rgb2hex(x) for x in plt.cm.get_cmap(cmap)(normed)]\n",
" return ['background-color: %s' % color for color in c]\n",
"\n",
"data = data[[\"SEX\", \"DOB\", \"RANK\", \"HANDED\", \"Country\", \"HEIGHT\", \"Weight\"]]\n",
"data.drop_duplicates\n",
"\n",
"pearson = data.corr()\n",
"pearson.style.apply(background_gradient,\n",
" cmap='Wistia',\n",
" m=pearson.min().min(),\n",
" M=pearson.max().max()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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" <th class=\"col_heading level0 col1\" >RANK</th> \n",
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" <th id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2level0_row1\" class=\"row_heading level0 row1\" >RANK</th> \n",
" <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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" </tr> <tr> \n",
" <th id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2level0_row2\" class=\"row_heading level0 row2\" >HEIGHT</th> \n",
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" <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",
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" <td id=\"T_727bef98_4f3e_11e8_a315_787b8ab7acb2row3_col3\" class=\"data row3 col3\" >1</td> \n",
" </tr></tbody> \n",
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],
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"<pandas.io.formats.style.Styler at 0x111a3b198>"
]
},
"execution_count": 3,
"metadata": {},
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}
],
"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",
")"
]
}
],
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