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ResearchMethods/wk7/wk7.ipynb
Kelvin Davis f4c3da7470 Added titles.
2018-04-30 15:16:55 +10:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Week 7: Tennis Data Exploration"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using matplotlib backend: Qt5Agg\n",
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import seaborn as sn\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy import stats"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"tennis.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1000 entries, 0 to 999\n",
"Data columns (total 11 columns):\n",
"ranking 1000 non-null int64\n",
"country 1000 non-null object\n",
"player 1000 non-null object\n",
"age 1000 non-null int64\n",
"points 1000 non-null int64\n",
"tournplayed 1000 non-null int64\n",
"born 1000 non-null int64\n",
"weight 462 non-null float64\n",
"height 704 non-null float64\n",
"hand 723 non-null object\n",
"gender 1000 non-null object\n",
"dtypes: float64(2), int64(5), object(4)\n",
"memory usage: 86.0+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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"</style>\n",
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" <thead>\n",
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" <th></th>\n",
" <th>ranking</th>\n",
" <th>country</th>\n",
" <th>player</th>\n",
" <th>age</th>\n",
" <th>points</th>\n",
" <th>tournplayed</th>\n",
" <th>born</th>\n",
" <th>weight</th>\n",
" <th>height</th>\n",
" <th>hand</th>\n",
" <th>gender</th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>ESP</td>\n",
" <td>Rafael Nadal</td>\n",
" <td>31</td>\n",
" <td>8770</td>\n",
" <td>14</td>\n",
" <td>1986</td>\n",
" <td>85.0</td>\n",
" <td>185.0</td>\n",
" <td>L</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>SUI</td>\n",
" <td>Roger Federer</td>\n",
" <td>36</td>\n",
" <td>8670</td>\n",
" <td>17</td>\n",
" <td>1981</td>\n",
" <td>85.0</td>\n",
" <td>185.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>CRO</td>\n",
" <td>Marin Cilic</td>\n",
" <td>29</td>\n",
" <td>4985</td>\n",
" <td>20</td>\n",
" <td>1988</td>\n",
" <td>89.0</td>\n",
" <td>198.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>GER</td>\n",
" <td>Alexander Zverev</td>\n",
" <td>20</td>\n",
" <td>4925</td>\n",
" <td>24</td>\n",
" <td>1997</td>\n",
" <td>86.0</td>\n",
" <td>198.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>BUL</td>\n",
" <td>Grigor Dimitrov</td>\n",
" <td>26</td>\n",
" <td>4635</td>\n",
" <td>22</td>\n",
" <td>1991</td>\n",
" <td>80.0</td>\n",
" <td>191.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ranking country player age points tournplayed born weight \\\n",
"0 1 ESP Rafael Nadal 31 8770 14 1986 85.0 \n",
"1 2 SUI Roger Federer 36 8670 17 1981 85.0 \n",
"2 3 CRO Marin Cilic 29 4985 20 1988 89.0 \n",
"3 4 GER Alexander Zverev 20 4925 24 1997 86.0 \n",
"4 5 BUL Grigor Dimitrov 26 4635 22 1991 80.0 \n",
"\n",
" height hand gender \n",
"0 185.0 L M \n",
"1 185.0 R M \n",
"2 198.0 R M \n",
"3 198.0 R M \n",
"4 191.0 R M "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
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" <td id=\"T_b0258f1c_4ad1_11e8_82d6_780cb8866ae6row5_col3\" class=\"data row5 col3\" >-0.0960524</td> \n",
" <td id=\"T_b0258f1c_4ad1_11e8_82d6_780cb8866ae6row5_col4\" class=\"data row5 col4\" >-0.120376</td> \n",
" <td id=\"T_b0258f1c_4ad1_11e8_82d6_780cb8866ae6row5_col5\" class=\"data row5 col5\" >1</td> \n",
" <td id=\"T_b0258f1c_4ad1_11e8_82d6_780cb8866ae6row5_col6\" class=\"data row5 col6\" >0.635301</td> \n",
" </tr> <tr> \n",
" <th id=\"T_b0258f1c_4ad1_11e8_82d6_780cb8866ae6level0_row6\" class=\"row_heading level0 row6\" >height</th> \n",
" <td id=\"T_b0258f1c_4ad1_11e8_82d6_780cb8866ae6row6_col0\" class=\"data row6 col0\" >0.0094234</td> \n",
" <td id=\"T_b0258f1c_4ad1_11e8_82d6_780cb8866ae6row6_col1\" class=\"data row6 col1\" >-0.0215108</td> \n",
" <td id=\"T_b0258f1c_4ad1_11e8_82d6_780cb8866ae6row6_col2\" class=\"data row6 col2\" >-0.00995652</td> \n",
" <td id=\"T_b0258f1c_4ad1_11e8_82d6_780cb8866ae6row6_col3\" class=\"data row6 col3\" >-0.0567095</td> \n",
" <td id=\"T_b0258f1c_4ad1_11e8_82d6_780cb8866ae6row6_col4\" class=\"data row6 col4\" >0.0264355</td> \n",
" <td id=\"T_b0258f1c_4ad1_11e8_82d6_780cb8866ae6row6_col5\" class=\"data row6 col5\" >0.635301</td> \n",
" <td id=\"T_b0258f1c_4ad1_11e8_82d6_780cb8866ae6row6_col6\" class=\"data row6 col6\" >1</td> \n",
" </tr></tbody> \n",
"</table> "
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7fa01f4cfcc0>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.corr().style.background_gradient(cmap='viridis')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average rank of RH: 219.67185069984447\n",
"Average rank of LH: 192.2405063291139\n"
]
}
],
"source": [
"print(\"Average rank of RH:\", df.loc[df[\"hand\"] == \"R\"][\"ranking\"].mean())\n",
"print(\"Average rank of LH:\", df.loc[df[\"hand\"] == \"L\"][\"ranking\"].mean())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average rank of RH: 595.3157076205288\n",
"Average rank of LH: 642.2278481012659\n"
]
}
],
"source": [
"print(\"Average rank of RH:\", df.loc[df[\"hand\"] == \"R\"][\"points\"].mean())\n",
"print(\"Average rank of LH:\", df.loc[df[\"hand\"] == \"L\"][\"points\"].mean())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"dfM = df.loc[df[\"gender\"] == \"M\"]\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ranking</th>\n",
" <th>country</th>\n",
" <th>player</th>\n",
" <th>age</th>\n",
" <th>points</th>\n",
" <th>tournplayed</th>\n",
" <th>born</th>\n",
" <th>weight</th>\n",
" <th>height</th>\n",
" <th>hand</th>\n",
" <th>gender</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>ESP</td>\n",
" <td>Rafael Nadal</td>\n",
" <td>31</td>\n",
" <td>8770</td>\n",
" <td>14</td>\n",
" <td>1986</td>\n",
" <td>85.0</td>\n",
" <td>185.0</td>\n",
" <td>L</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>SUI</td>\n",
" <td>Roger Federer</td>\n",
" <td>36</td>\n",
" <td>8670</td>\n",
" <td>17</td>\n",
" <td>1981</td>\n",
" <td>85.0</td>\n",
" <td>185.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>CRO</td>\n",
" <td>Marin Cilic</td>\n",
" <td>29</td>\n",
" <td>4985</td>\n",
" <td>20</td>\n",
" <td>1988</td>\n",
" <td>89.0</td>\n",
" <td>198.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>GER</td>\n",
" <td>Alexander Zverev</td>\n",
" <td>20</td>\n",
" <td>4925</td>\n",
" <td>24</td>\n",
" <td>1997</td>\n",
" <td>86.0</td>\n",
" <td>198.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>BUL</td>\n",
" <td>Grigor Dimitrov</td>\n",
" <td>26</td>\n",
" <td>4635</td>\n",
" <td>22</td>\n",
" <td>1991</td>\n",
" <td>80.0</td>\n",
" <td>191.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>ARG</td>\n",
" <td>Juan Martin del Potro</td>\n",
" <td>29</td>\n",
" <td>4470</td>\n",
" <td>20</td>\n",
" <td>1988</td>\n",
" <td>97.0</td>\n",
" <td>198.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>AUT</td>\n",
" <td>Dominic Thiem</td>\n",
" <td>24</td>\n",
" <td>3665</td>\n",
" <td>25</td>\n",
" <td>1993</td>\n",
" <td>82.0</td>\n",
" <td>185.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>RSA</td>\n",
" <td>Kevin Anderson</td>\n",
" <td>31</td>\n",
" <td>3390</td>\n",
" <td>22</td>\n",
" <td>1986</td>\n",
" <td>93.0</td>\n",
" <td>203.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>USA</td>\n",
" <td>John Isner</td>\n",
" <td>32</td>\n",
" <td>3125</td>\n",
" <td>25</td>\n",
" <td>1985</td>\n",
" <td>108.0</td>\n",
" <td>208.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>BEL</td>\n",
" <td>David Goffin</td>\n",
" <td>27</td>\n",
" <td>3110</td>\n",
" <td>24</td>\n",
" <td>1990</td>\n",
" <td>68.0</td>\n",
" <td>180.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11</td>\n",
" <td>FRA</td>\n",
" <td>Lucas Pouille</td>\n",
" <td>24</td>\n",
" <td>2410</td>\n",
" <td>24</td>\n",
" <td>1994</td>\n",
" <td>81.0</td>\n",
" <td>185.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>ESP</td>\n",
" <td>Pablo Carreno Busta</td>\n",
" <td>26</td>\n",
" <td>2395</td>\n",
" <td>25</td>\n",
" <td>1991</td>\n",
" <td>78.0</td>\n",
" <td>188.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>13</td>\n",
" <td>SRB</td>\n",
" <td>Novak Djokovic</td>\n",
" <td>30</td>\n",
" <td>2310</td>\n",
" <td>15</td>\n",
" <td>1987</td>\n",
" <td>77.0</td>\n",
" <td>188.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>14</td>\n",
" <td>USA</td>\n",
" <td>Sam Querrey</td>\n",
" <td>30</td>\n",
" <td>2220</td>\n",
" <td>23</td>\n",
" <td>1987</td>\n",
" <td>95.0</td>\n",
" <td>198.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>15</td>\n",
" <td>ARG</td>\n",
" <td>Diego Schwartzman</td>\n",
" <td>25</td>\n",
" <td>2220</td>\n",
" <td>26</td>\n",
" <td>1992</td>\n",
" <td>64.0</td>\n",
" <td>170.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>16</td>\n",
" <td>ESP</td>\n",
" <td>Roberto Bautista Agut</td>\n",
" <td>30</td>\n",
" <td>2175</td>\n",
" <td>25</td>\n",
" <td>1988</td>\n",
" <td>76.0</td>\n",
" <td>183.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>17</td>\n",
" <td>USA</td>\n",
" <td>Jack Sock</td>\n",
" <td>25</td>\n",
" <td>2155</td>\n",
" <td>22</td>\n",
" <td>1992</td>\n",
" <td>84.0</td>\n",
" <td>191.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18</td>\n",
" <td>CZE</td>\n",
" <td>Tomas Berdych</td>\n",
" <td>32</td>\n",
" <td>2140</td>\n",
" <td>19</td>\n",
" <td>1985</td>\n",
" <td>91.0</td>\n",
" <td>196.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>19</td>\n",
" <td>KOR</td>\n",
" <td>Hyeon Chung</td>\n",
" <td>21</td>\n",
" <td>1897</td>\n",
" <td>22</td>\n",
" <td>1996</td>\n",
" <td>87.0</td>\n",
" <td>188.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>ITA</td>\n",
" <td>Fabio Fognini</td>\n",
" <td>30</td>\n",
" <td>1840</td>\n",
" <td>24</td>\n",
" <td>1987</td>\n",
" <td>74.0</td>\n",
" <td>178.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>21</td>\n",
" <td>SUI</td>\n",
" <td>Stan Wawrinka</td>\n",
" <td>33</td>\n",
" <td>1785</td>\n",
" <td>14</td>\n",
" <td>1985</td>\n",
" <td>81.0</td>\n",
" <td>183.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>22</td>\n",
" <td>CAN</td>\n",
" <td>Milos Raonic</td>\n",
" <td>27</td>\n",
" <td>1765</td>\n",
" <td>20</td>\n",
" <td>1990</td>\n",
" <td>98.0</td>\n",
" <td>196.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>23</td>\n",
" <td>GBR</td>\n",
" <td>Kyle Edmund</td>\n",
" <td>23</td>\n",
" <td>1757</td>\n",
" <td>25</td>\n",
" <td>1995</td>\n",
" <td>83.0</td>\n",
" <td>188.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>24</td>\n",
" <td>ESP</td>\n",
" <td>Albert Ramos-Vinolas</td>\n",
" <td>30</td>\n",
" <td>1745</td>\n",
" <td>29</td>\n",
" <td>1988</td>\n",
" <td>80.0</td>\n",
" <td>188.0</td>\n",
" <td>L</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25</td>\n",
" <td>AUS</td>\n",
" <td>Nick Kyrgios</td>\n",
" <td>22</td>\n",
" <td>1720</td>\n",
" <td>19</td>\n",
" <td>1995</td>\n",
" <td>85.0</td>\n",
" <td>193.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>26</td>\n",
" <td>FRA</td>\n",
" <td>Adrian Mannarino</td>\n",
" <td>29</td>\n",
" <td>1655</td>\n",
" <td>26</td>\n",
" <td>1988</td>\n",
" <td>70.0</td>\n",
" <td>180.0</td>\n",
" <td>L</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27</td>\n",
" <td>SRB</td>\n",
" <td>Filip Krajinovic</td>\n",
" <td>26</td>\n",
" <td>1616</td>\n",
" <td>25</td>\n",
" <td>1992</td>\n",
" <td>75.0</td>\n",
" <td>185.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>28</td>\n",
" <td>LUX</td>\n",
" <td>Gilles Muller</td>\n",
" <td>34</td>\n",
" <td>1465</td>\n",
" <td>22</td>\n",
" <td>1983</td>\n",
" <td>89.0</td>\n",
" <td>193.0</td>\n",
" <td>L</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>29</td>\n",
" <td>GBR</td>\n",
" <td>Andy Murray</td>\n",
" <td>30</td>\n",
" <td>1450</td>\n",
" <td>14</td>\n",
" <td>1987</td>\n",
" <td>84.0</td>\n",
" <td>191.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>ESP</td>\n",
" <td>Feliciano Lopez</td>\n",
" <td>36</td>\n",
" <td>1420</td>\n",
" <td>24</td>\n",
" <td>1981</td>\n",
" <td>88.0</td>\n",
" <td>188.0</td>\n",
" <td>L</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>470</th>\n",
" <td>471</td>\n",
" <td>HUN</td>\n",
" <td>Zsombor Piros</td>\n",
" <td>18</td>\n",
" <td>76</td>\n",
" <td>17</td>\n",
" <td>1999</td>\n",
" <td>65.0</td>\n",
" <td>178.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>471</th>\n",
" <td>472</td>\n",
" <td>AUT</td>\n",
" <td>David Pichler</td>\n",
" <td>22</td>\n",
" <td>76</td>\n",
" <td>25</td>\n",
" <td>1996</td>\n",
" <td>70.0</td>\n",
" <td>178.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>472</th>\n",
" <td>473</td>\n",
" <td>ITA</td>\n",
" <td>Pietro Rondoni</td>\n",
" <td>24</td>\n",
" <td>76</td>\n",
" <td>26</td>\n",
" <td>1993</td>\n",
" <td>78.0</td>\n",
" <td>185.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>473</th>\n",
" <td>474</td>\n",
" <td>IRL</td>\n",
" <td>James McGee</td>\n",
" <td>30</td>\n",
" <td>75</td>\n",
" <td>8</td>\n",
" <td>1987</td>\n",
" <td>80.0</td>\n",
" <td>183.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>474</th>\n",
" <td>475</td>\n",
" <td>GBR</td>\n",
" <td>Tom Farquharson</td>\n",
" <td>26</td>\n",
" <td>75</td>\n",
" <td>13</td>\n",
" <td>1992</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>475</th>\n",
" <td>476</td>\n",
" <td>FRA</td>\n",
" <td>Laurent Lokoli</td>\n",
" <td>23</td>\n",
" <td>75</td>\n",
" <td>17</td>\n",
" <td>1994</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>476</th>\n",
" <td>477</td>\n",
" <td>USA</td>\n",
" <td>Ryan Shane</td>\n",
" <td>24</td>\n",
" <td>74</td>\n",
" <td>16</td>\n",
" <td>1994</td>\n",
" <td>89.0</td>\n",
" <td>193.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>477</th>\n",
" <td>478</td>\n",
" <td>GBR</td>\n",
" <td>Lloyd Glasspool</td>\n",
" <td>24</td>\n",
" <td>74</td>\n",
" <td>17</td>\n",
" <td>1993</td>\n",
" <td>85.0</td>\n",
" <td>191.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>478</th>\n",
" <td>479</td>\n",
" <td>ISR</td>\n",
" <td>Igor Smilansky</td>\n",
" <td>23</td>\n",
" <td>74</td>\n",
" <td>23</td>\n",
" <td>1995</td>\n",
" <td>85.0</td>\n",
" <td>188.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>479</th>\n",
" <td>480</td>\n",
" <td>ZIM</td>\n",
" <td>Benjamin Lock</td>\n",
" <td>25</td>\n",
" <td>74</td>\n",
" <td>31</td>\n",
" <td>1993</td>\n",
" <td>86.0</td>\n",
" <td>201.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>480</th>\n",
" <td>481</td>\n",
" <td>FRA</td>\n",
" <td>Gianni Mina</td>\n",
" <td>26</td>\n",
" <td>73</td>\n",
" <td>18</td>\n",
" <td>1992</td>\n",
" <td>85.0</td>\n",
" <td>188.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>481</th>\n",
" <td>482</td>\n",
" <td>BEL</td>\n",
" <td>Clement Geens</td>\n",
" <td>22</td>\n",
" <td>73</td>\n",
" <td>23</td>\n",
" <td>1996</td>\n",
" <td>68.0</td>\n",
" <td>180.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>482</th>\n",
" <td>483</td>\n",
" <td>AUT</td>\n",
" <td>Pascal Brunner</td>\n",
" <td>28</td>\n",
" <td>72</td>\n",
" <td>18</td>\n",
" <td>1989</td>\n",
" <td>75.0</td>\n",
" <td>180.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>483</th>\n",
" <td>484</td>\n",
" <td>GER</td>\n",
" <td>Julian Lenz</td>\n",
" <td>25</td>\n",
" <td>71</td>\n",
" <td>17</td>\n",
" <td>1993</td>\n",
" <td>82.0</td>\n",
" <td>188.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>484</th>\n",
" <td>485</td>\n",
" <td>NED</td>\n",
" <td>Botic Van de Zandschulp</td>\n",
" <td>22</td>\n",
" <td>71</td>\n",
" <td>17</td>\n",
" <td>1995</td>\n",
" <td>83.0</td>\n",
" <td>188.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>485</th>\n",
" <td>486</td>\n",
" <td>GER</td>\n",
" <td>Peter Torebko</td>\n",
" <td>30</td>\n",
" <td>70</td>\n",
" <td>20</td>\n",
" <td>1988</td>\n",
" <td>84.0</td>\n",
" <td>185.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>486</th>\n",
" <td>487</td>\n",
" <td>IRL</td>\n",
" <td>Sam Barry</td>\n",
" <td>26</td>\n",
" <td>70</td>\n",
" <td>20</td>\n",
" <td>1992</td>\n",
" <td>83.0</td>\n",
" <td>191.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>487</th>\n",
" <td>488</td>\n",
" <td>USA</td>\n",
" <td>Ulises Blanch</td>\n",
" <td>20</td>\n",
" <td>70</td>\n",
" <td>22</td>\n",
" <td>1998</td>\n",
" <td>78.0</td>\n",
" <td>191.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>488</th>\n",
" <td>489</td>\n",
" <td>JPN</td>\n",
" <td>Kento Takeuchi</td>\n",
" <td>30</td>\n",
" <td>70</td>\n",
" <td>24</td>\n",
" <td>1987</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>489</th>\n",
" <td>490</td>\n",
" <td>USA</td>\n",
" <td>Ronnie Schneider</td>\n",
" <td>23</td>\n",
" <td>70</td>\n",
" <td>25</td>\n",
" <td>1994</td>\n",
" <td>70.0</td>\n",
" <td>175.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>490</th>\n",
" <td>491</td>\n",
" <td>FRA</td>\n",
" <td>Jonathan Kanar</td>\n",
" <td>23</td>\n",
" <td>70</td>\n",
" <td>29</td>\n",
" <td>1994</td>\n",
" <td>72.0</td>\n",
" <td>180.0</td>\n",
" <td>L</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>491</th>\n",
" <td>492</td>\n",
" <td>ITA</td>\n",
" <td>Edoardo Eremin</td>\n",
" <td>24</td>\n",
" <td>69</td>\n",
" <td>13</td>\n",
" <td>1993</td>\n",
" <td>95.0</td>\n",
" <td>185.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>492</th>\n",
" <td>493</td>\n",
" <td>AUS</td>\n",
" <td>Blake Ellis</td>\n",
" <td>19</td>\n",
" <td>69</td>\n",
" <td>16</td>\n",
" <td>1999</td>\n",
" <td>75.0</td>\n",
" <td>191.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>493</th>\n",
" <td>494</td>\n",
" <td>JPN</td>\n",
" <td>Shuichi Sekiguchi</td>\n",
" <td>26</td>\n",
" <td>69</td>\n",
" <td>19</td>\n",
" <td>1991</td>\n",
" <td>66.0</td>\n",
" <td>168.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>494</th>\n",
" <td>495</td>\n",
" <td>ESP</td>\n",
" <td>Andres Artunedo Martinavarro</td>\n",
" <td>24</td>\n",
" <td>69</td>\n",
" <td>20</td>\n",
" <td>1993</td>\n",
" <td>75.0</td>\n",
" <td>183.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495</th>\n",
" <td>496</td>\n",
" <td>NED</td>\n",
" <td>Jelle Sels</td>\n",
" <td>22</td>\n",
" <td>69</td>\n",
" <td>23</td>\n",
" <td>1995</td>\n",
" <td>83.0</td>\n",
" <td>188.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>496</th>\n",
" <td>497</td>\n",
" <td>USA</td>\n",
" <td>Sekou Bangoura</td>\n",
" <td>26</td>\n",
" <td>69</td>\n",
" <td>25</td>\n",
" <td>1991</td>\n",
" <td>77.0</td>\n",
" <td>183.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>497</th>\n",
" <td>498</td>\n",
" <td>GER</td>\n",
" <td>Elmar Ejupovic</td>\n",
" <td>25</td>\n",
" <td>69</td>\n",
" <td>28</td>\n",
" <td>1993</td>\n",
" <td>90.0</td>\n",
" <td>193.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>498</th>\n",
" <td>499</td>\n",
" <td>NED</td>\n",
" <td>Gijs Brouwer</td>\n",
" <td>22</td>\n",
" <td>68</td>\n",
" <td>21</td>\n",
" <td>1996</td>\n",
" <td>72.0</td>\n",
" <td>191.0</td>\n",
" <td>L</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>499</th>\n",
" <td>500</td>\n",
" <td>BRA</td>\n",
" <td>Joao Menezes</td>\n",
" <td>21</td>\n",
" <td>68</td>\n",
" <td>22</td>\n",
" <td>1996</td>\n",
" <td>80.0</td>\n",
" <td>185.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>500 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" ranking country player age points tournplayed \\\n",
"0 1 ESP Rafael Nadal 31 8770 14 \n",
"1 2 SUI Roger Federer 36 8670 17 \n",
"2 3 CRO Marin Cilic 29 4985 20 \n",
"3 4 GER Alexander Zverev 20 4925 24 \n",
"4 5 BUL Grigor Dimitrov 26 4635 22 \n",
"5 6 ARG Juan Martin del Potro 29 4470 20 \n",
"6 7 AUT Dominic Thiem 24 3665 25 \n",
"7 8 RSA Kevin Anderson 31 3390 22 \n",
"8 9 USA John Isner 32 3125 25 \n",
"9 10 BEL David Goffin 27 3110 24 \n",
"10 11 FRA Lucas Pouille 24 2410 24 \n",
"11 12 ESP Pablo Carreno Busta 26 2395 25 \n",
"12 13 SRB Novak Djokovic 30 2310 15 \n",
"13 14 USA Sam Querrey 30 2220 23 \n",
"14 15 ARG Diego Schwartzman 25 2220 26 \n",
"15 16 ESP Roberto Bautista Agut 30 2175 25 \n",
"16 17 USA Jack Sock 25 2155 22 \n",
"17 18 CZE Tomas Berdych 32 2140 19 \n",
"18 19 KOR Hyeon Chung 21 1897 22 \n",
"19 20 ITA Fabio Fognini 30 1840 24 \n",
"20 21 SUI Stan Wawrinka 33 1785 14 \n",
"21 22 CAN Milos Raonic 27 1765 20 \n",
"22 23 GBR Kyle Edmund 23 1757 25 \n",
"23 24 ESP Albert Ramos-Vinolas 30 1745 29 \n",
"24 25 AUS Nick Kyrgios 22 1720 19 \n",
"25 26 FRA Adrian Mannarino 29 1655 26 \n",
"26 27 SRB Filip Krajinovic 26 1616 25 \n",
"27 28 LUX Gilles Muller 34 1465 22 \n",
"28 29 GBR Andy Murray 30 1450 14 \n",
"29 30 ESP Feliciano Lopez 36 1420 24 \n",
".. ... ... ... ... ... ... \n",
"470 471 HUN Zsombor Piros 18 76 17 \n",
"471 472 AUT David Pichler 22 76 25 \n",
"472 473 ITA Pietro Rondoni 24 76 26 \n",
"473 474 IRL James McGee 30 75 8 \n",
"474 475 GBR Tom Farquharson 26 75 13 \n",
"475 476 FRA Laurent Lokoli 23 75 17 \n",
"476 477 USA Ryan Shane 24 74 16 \n",
"477 478 GBR Lloyd Glasspool 24 74 17 \n",
"478 479 ISR Igor Smilansky 23 74 23 \n",
"479 480 ZIM Benjamin Lock 25 74 31 \n",
"480 481 FRA Gianni Mina 26 73 18 \n",
"481 482 BEL Clement Geens 22 73 23 \n",
"482 483 AUT Pascal Brunner 28 72 18 \n",
"483 484 GER Julian Lenz 25 71 17 \n",
"484 485 NED Botic Van de Zandschulp 22 71 17 \n",
"485 486 GER Peter Torebko 30 70 20 \n",
"486 487 IRL Sam Barry 26 70 20 \n",
"487 488 USA Ulises Blanch 20 70 22 \n",
"488 489 JPN Kento Takeuchi 30 70 24 \n",
"489 490 USA Ronnie Schneider 23 70 25 \n",
"490 491 FRA Jonathan Kanar 23 70 29 \n",
"491 492 ITA Edoardo Eremin 24 69 13 \n",
"492 493 AUS Blake Ellis 19 69 16 \n",
"493 494 JPN Shuichi Sekiguchi 26 69 19 \n",
"494 495 ESP Andres Artunedo Martinavarro 24 69 20 \n",
"495 496 NED Jelle Sels 22 69 23 \n",
"496 497 USA Sekou Bangoura 26 69 25 \n",
"497 498 GER Elmar Ejupovic 25 69 28 \n",
"498 499 NED Gijs Brouwer 22 68 21 \n",
"499 500 BRA Joao Menezes 21 68 22 \n",
"\n",
" born weight height hand gender \n",
"0 1986 85.0 185.0 L M \n",
"1 1981 85.0 185.0 R M \n",
"2 1988 89.0 198.0 R M \n",
"3 1997 86.0 198.0 R M \n",
"4 1991 80.0 191.0 R M \n",
"5 1988 97.0 198.0 R M \n",
"6 1993 82.0 185.0 R M \n",
"7 1986 93.0 203.0 R M \n",
"8 1985 108.0 208.0 R M \n",
"9 1990 68.0 180.0 R M \n",
"10 1994 81.0 185.0 R M \n",
"11 1991 78.0 188.0 R M \n",
"12 1987 77.0 188.0 R M \n",
"13 1987 95.0 198.0 R M \n",
"14 1992 64.0 170.0 R M \n",
"15 1988 76.0 183.0 R M \n",
"16 1992 84.0 191.0 R M \n",
"17 1985 91.0 196.0 R M \n",
"18 1996 87.0 188.0 R M \n",
"19 1987 74.0 178.0 R M \n",
"20 1985 81.0 183.0 R M \n",
"21 1990 98.0 196.0 R M \n",
"22 1995 83.0 188.0 R M \n",
"23 1988 80.0 188.0 L M \n",
"24 1995 85.0 193.0 R M \n",
"25 1988 70.0 180.0 L M \n",
"26 1992 75.0 185.0 R M \n",
"27 1983 89.0 193.0 L M \n",
"28 1987 84.0 191.0 R M \n",
"29 1981 88.0 188.0 L M \n",
".. ... ... ... ... ... \n",
"470 1999 65.0 178.0 R M \n",
"471 1996 70.0 178.0 R M \n",
"472 1993 78.0 185.0 R M \n",
"473 1987 80.0 183.0 R M \n",
"474 1992 NaN NaN NaN M \n",
"475 1994 NaN NaN R M \n",
"476 1994 89.0 193.0 R M \n",
"477 1993 85.0 191.0 R M \n",
"478 1995 85.0 188.0 R M \n",
"479 1993 86.0 201.0 R M \n",
"480 1992 85.0 188.0 R M \n",
"481 1996 68.0 180.0 R M \n",
"482 1989 75.0 180.0 R M \n",
"483 1993 82.0 188.0 R M \n",
"484 1995 83.0 188.0 R M \n",
"485 1988 84.0 185.0 R M \n",
"486 1992 83.0 191.0 R M \n",
"487 1998 78.0 191.0 R M \n",
"488 1987 NaN NaN NaN M \n",
"489 1994 70.0 175.0 R M \n",
"490 1994 72.0 180.0 L M \n",
"491 1993 95.0 185.0 R M \n",
"492 1999 75.0 191.0 R M \n",
"493 1991 66.0 168.0 R M \n",
"494 1993 75.0 183.0 R M \n",
"495 1995 83.0 188.0 R M \n",
"496 1991 77.0 183.0 R M \n",
"497 1993 90.0 193.0 R M \n",
"498 1996 72.0 191.0 L M \n",
"499 1996 80.0 185.0 R M \n",
"\n",
"[500 rows x 11 columns]"
]
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"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ranking</th>\n",
" <th>country</th>\n",
" <th>player</th>\n",
" <th>age</th>\n",
" <th>points</th>\n",
" <th>tournplayed</th>\n",
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" <th>height</th>\n",
" <th>hand</th>\n",
" <th>gender</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>ESP</td>\n",
" <td>Rafael Nadal</td>\n",
" <td>31</td>\n",
" <td>8770</td>\n",
" <td>14</td>\n",
" <td>1986</td>\n",
" <td>85.0</td>\n",
" <td>185.0</td>\n",
" <td>L</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>SUI</td>\n",
" <td>Roger Federer</td>\n",
" <td>36</td>\n",
" <td>8670</td>\n",
" <td>17</td>\n",
" <td>1981</td>\n",
" <td>85.0</td>\n",
" <td>185.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>CRO</td>\n",
" <td>Marin Cilic</td>\n",
" <td>29</td>\n",
" <td>4985</td>\n",
" <td>20</td>\n",
" <td>1988</td>\n",
" <td>89.0</td>\n",
" <td>198.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>GER</td>\n",
" <td>Alexander Zverev</td>\n",
" <td>20</td>\n",
" <td>4925</td>\n",
" <td>24</td>\n",
" <td>1997</td>\n",
" <td>86.0</td>\n",
" <td>198.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>BUL</td>\n",
" <td>Grigor Dimitrov</td>\n",
" <td>26</td>\n",
" <td>4635</td>\n",
" <td>22</td>\n",
" <td>1991</td>\n",
" <td>80.0</td>\n",
" <td>191.0</td>\n",
" <td>R</td>\n",
" <td>M</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ranking country player age points tournplayed born weight \\\n",
"0 1 ESP Rafael Nadal 31 8770 14 1986 85.0 \n",
"1 2 SUI Roger Federer 36 8670 17 1981 85.0 \n",
"2 3 CRO Marin Cilic 29 4985 20 1988 89.0 \n",
"3 4 GER Alexander Zverev 20 4925 24 1997 86.0 \n",
"4 5 BUL Grigor Dimitrov 26 4635 22 1991 80.0 \n",
"\n",
" height hand gender \n",
"0 185.0 L M \n",
"1 185.0 R M \n",
"2 198.0 R M \n",
"3 198.0 R M \n",
"4 191.0 R M "
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"[<matplotlib.lines.Line2D at 0x7fa016ec3f60>,\n",
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa01f4a9a20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = df[\"age\"]\n",
"y = df[\"points\"]\n",
"plt.scatter(x, y)\n",
"plt.xlabel(\"age\")\n",
"plt.ylabel(\"points\")\n",
"p1 = poly1d(polyfit(x, y, 1))\n",
"p2 = poly1d(polyfit(x, y, 2))\n",
"p3 = poly1d(polyfit(x, y, 3))\n",
"xu = x.unique()\n",
"plot(xu, p1(xu), xu, p2(xu), xu, p3(xu))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-255.78883132530993"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"poly1d(polyfit(df[\"age\"], df[\"points\"], 2))(0)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fa016a61240>]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa01f4a19e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot(xu, p1(xu))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fa0169cd518>]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa016a3d198>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot(xu, p2(xu))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PolyCollection at 0x7fa0169a93c8>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa016a32518>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nbins = 15\n",
"title('Hexbin')\n",
"hexbin(x, y, gridsize=nbins, cmap=plt.cm.BuGn_r)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0,0.5,'points')"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa016987a20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot(x, y, linestyle='', marker='o', markersize=0.7)\n",
"xlabel(\"age\")\n",
"ylabel(\"points\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Handedness t-test"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"scoresL = df.loc[df[\"hand\"] == \"L\"][\"points\"]\n",
"scoresR = df.loc[df[\"hand\"] == \"R\"][\"points\"]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"left-handed mean: 642.2278481012659\n",
"left-handed var: 1196169.9474196683\n",
"right-handed mean: 595.3157076205288\n",
"right-handed var: 885851.1384912046\n"
]
}
],
"source": [
"print(\"left-handed mean:\", scoresL.mean())\n",
"print(\"left-handed var:\", scoresL.var())\n",
"print(\"right-handed mean:\", scoresR.mean())\n",
"print(\"right-handed var:\", scoresR.var())"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"ttest = (scoresL.mean() - scoresR.mean()) / (np.sqrt(scoresL.var()/(len(scoresL)-1) + scoresR.var()/(len(scoresR)-1)))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.3628504016994108"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ttest"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"720"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"degfree = (len(scoresL) + len(scoresR)) - 2\n",
"degfree"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Links to the converters:\n",
"- https://surfstat.anu.edu.au/surfstat-home/tables/t.php\n",
"- http://www.socscistatistics.com/pvalues/tdistribution.aspx\n",
"- https://goodcalculators.com/student-t-value-calculator/\n",
"\n",
"TODO:\n",
"- try the stats package imported above to do the t-tests and calculate the alpha and p values"
]
}
],
"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
}
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