malssong.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>split</th>\n",
       "      <th>final</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33</td>\n",
       "      <td>M</td>\n",
       "      <td>01:05:38</td>\n",
       "      <td>02:08:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:26</td>\n",
       "      <td>02:09:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:49</td>\n",
       "      <td>02:10:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:16</td>\n",
       "      <td>02:13:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:32</td>\n",
       "      <td>02:13:59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age gender     split     final\n",
       "0   33      M  01:05:38  02:08:51\n",
       "1   32      M  01:06:26  02:09:28\n",
       "2   31      M  01:06:49  02:10:42\n",
       "3   38      M  01:06:16  02:13:45\n",
       "4   31      M  01:06:32  02:13:59"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "data = pd.read_csv('marathon-data.csv')\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>split</th>\n",
       "      <th>final</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33</td>\n",
       "      <td>M</td>\n",
       "      <td>01:05:38</td>\n",
       "      <td>02:08:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:26</td>\n",
       "      <td>02:09:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:49</td>\n",
       "      <td>02:10:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:16</td>\n",
       "      <td>02:13:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:32</td>\n",
       "      <td>02:13:59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age gender    split    final\n",
       "0   33      M 01:05:38 02:08:51\n",
       "1   32      M 01:06:26 02:09:28\n",
       "2   31      M 01:06:49 02:10:42\n",
       "3   38      M 01:06:16 02:13:45\n",
       "4   31      M 01:06:32 02:13:59"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def covert_time(s):\n",
    "    return pd.to_timedelta(s)\n",
    "data =pd.read_csv('marathon-data.csv',converters={'split':covert_time,'final':covert_time})\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>split</th>\n",
       "      <th>final</th>\n",
       "      <th>split_sec</th>\n",
       "      <th>final_sec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33</td>\n",
       "      <td>M</td>\n",
       "      <td>01:05:38</td>\n",
       "      <td>02:08:51</td>\n",
       "      <td>3938.0</td>\n",
       "      <td>7731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:26</td>\n",
       "      <td>02:09:28</td>\n",
       "      <td>3986.0</td>\n",
       "      <td>7768.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:49</td>\n",
       "      <td>02:10:42</td>\n",
       "      <td>4009.0</td>\n",
       "      <td>7842.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:16</td>\n",
       "      <td>02:13:45</td>\n",
       "      <td>3976.0</td>\n",
       "      <td>8025.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:32</td>\n",
       "      <td>02:13:59</td>\n",
       "      <td>3992.0</td>\n",
       "      <td>8039.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age gender    split    final  split_sec  final_sec\n",
       "0   33      M 01:05:38 02:08:51     3938.0     7731.0\n",
       "1   32      M 01:06:26 02:09:28     3986.0     7768.0\n",
       "2   31      M 01:06:49 02:10:42     4009.0     7842.0\n",
       "3   38      M 01:06:16 02:13:45     3976.0     8025.0\n",
       "4   31      M 01:06:32 02:13:59     3992.0     8039.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data['split_sec']=data['split'].astype(int)/1E9\n",
    "data['final_sec']=data['final'].astype(int)/1E9\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'JointGrid' object has no attribute 'ax_jointplot'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-aed87a331a16>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes_style\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'white'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjointplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'split_sec'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'final_sec'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'hex'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     g.ax_jointplot(np.linspace(4000,16000),\n\u001b[0m\u001b[1;32m      4\u001b[0m                   np.linspace(8000,32000),':k')\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'JointGrid' object has no attribute 'ax_jointplot'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18a002b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style('white'):\n",
    "    g = sns.jointplot('split_sec','final_sec',data,kind='hex')\n",
    "    g.ax_jointplot(np.linspace(4000,16000),\n",
    "                  np.linspace(8000,32000),':k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>split</th>\n",
       "      <th>final</th>\n",
       "      <th>split_sec</th>\n",
       "      <th>final_sec</th>\n",
       "      <th>split_frac</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33</td>\n",
       "      <td>M</td>\n",
       "      <td>01:05:38</td>\n",
       "      <td>02:08:51</td>\n",
       "      <td>3938.0</td>\n",
       "      <td>7731.0</td>\n",
       "      <td>-0.018756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:26</td>\n",
       "      <td>02:09:28</td>\n",
       "      <td>3986.0</td>\n",
       "      <td>7768.0</td>\n",
       "      <td>-0.026262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:49</td>\n",
       "      <td>02:10:42</td>\n",
       "      <td>4009.0</td>\n",
       "      <td>7842.0</td>\n",
       "      <td>-0.022443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:16</td>\n",
       "      <td>02:13:45</td>\n",
       "      <td>3976.0</td>\n",
       "      <td>8025.0</td>\n",
       "      <td>0.009097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:32</td>\n",
       "      <td>02:13:59</td>\n",
       "      <td>3992.0</td>\n",
       "      <td>8039.0</td>\n",
       "      <td>0.006842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age gender    split    final  split_sec  final_sec  split_frac\n",
       "0   33      M 01:05:38 02:08:51     3938.0     7731.0   -0.018756\n",
       "1   32      M 01:06:26 02:09:28     3986.0     7768.0   -0.026262\n",
       "2   31      M 01:06:49 02:10:42     4009.0     7842.0   -0.022443\n",
       "3   38      M 01:06:16 02:13:45     3976.0     8025.0    0.009097\n",
       "4   31      M 01:06:32 02:13:59     3992.0     8039.0    0.006842"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['split_frac']=1-2*data['split_sec']/data['final_sec']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1a1a9b7b70>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a9f50f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['split_frac'])\n",
    "plt.axvline(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "251"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(data.split_frac < 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'Series' object cannot be interpreted as an integer",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-ad0616bbf5f3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mnew_final\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'split_sec'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_final\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfinal_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mpermuted_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpermutation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0msample1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpermuted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0msample2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpermuted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'Series' object cannot be interpreted as an integer"
     ]
    }
   ],
   "source": [
    "new_final=data['split_sec']*2\n",
    "df = np.concatenate(new_final,data.final_sec)\n",
    "permuted_data=np.permutation(df)\n",
    "sample1=permuted_data[:len(data.split_sec)]\n",
    "sample2=permuted_data[len(data.split_sec):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}