predicting_motogp_winners\data_exploration

data_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting MotoGP Winners"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_motogpsession = pd.read_csv('data/motogpsession.tsv', sep='\\t', encoding='utf-8')\n",
    "df_motogpqresult = pd.read_csv('data/motogpqresult.tsv', sep='\\t', encoding='utf-8')\n",
    "df_motogprresult = pd.read_csv('data/motogprresult.tsv', sep='\\t', encoding='utf-8')\n",
    "df_motogprider = pd.read_csv('data/motogprider.tsv', sep='\\t', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_motogpsession = df_motogpsession.loc[:, ~df_motogpsession.columns.str.contains('^Unnamed')]\n",
    "df_motogpqresult = df_motogpqresult.loc[:, ~df_motogpqresult.columns.str.contains('^Unnamed')]\n",
    "df_motogprresult = df_motogprresult.loc[:, ~df_motogprresult.columns.str.contains('^Unnamed')]\n",
    "df_motogprider = df_motogprider.loc[:, ~df_motogprider.columns.str.contains('^Unnamed')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_motogpdata = {}\n",
    "\n",
    "dict_motogpdata['session'] = df_motogpsession\n",
    "dict_motogpdata['qresult'] = df_motogpqresult\n",
    "dict_motogpdata['rresult'] = df_motogprresult\n",
    "dict_motogpdata['rider'] = df_motogprider"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What fields do we have to work with?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_datasummary(dict_motodata):\n",
    "    \"\"\" \"\"\"\n",
    "\n",
    "    import pandas as pd\n",
    "\n",
    "    list_datasummary = [dict_motodata['session'].columns,\n",
    "                        dict_motodata['qresult'].columns,\n",
    "                        dict_motodata['rresult'].columns,\n",
    "                        dict_motodata['rider'].columns]\n",
    "\n",
    "    df_datasummary = pd.DataFrame(list_datasummary).T\n",
    "\n",
    "    df_datasummary.columns = ['session',\n",
    "                              'qresult',\n",
    "                              'rresult',\n",
    "                              'rider']\n",
    "\n",
    "    return df_datasummary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               session          qresult           rresult             rider\n",
      "0            sessionId        sessionId         sessionId           riderId\n",
      "1        sessionSeason          riderId           riderId         riderName\n",
      "2       sessionCountry     qresultPlace      rresultPlace       riderNumber\n",
      "3     sessionTrackname  qresultBesttime  rresultTotaltime  riderNationality\n",
      "4         sessionClass   qresultBestlap   rresultAvgspeed         riderTeam\n",
      "5       sessionSession  qresultTotallap              None    riderMotortype\n",
      "6          sessionDate  qresultTopspeed              None              None\n",
      "7   sessionTracklength             None              None              None\n",
      "8   sessionWeathertype             None              None              None\n",
      "9       sessionAirtemp             None              None              None\n",
      "10   sessionGroundtemp             None              None              None\n",
      "11     sessionHumidity             None              None              None\n"
     ]
    }
   ],
   "source": [
    "df_datasummary = generate_datasummary(dict_motogpdata)\n",
    "\n",
    "print(df_datasummary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are some of the key variable counts?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_datacounts(dict_motodata, list_years):\n",
    "    \"\"\" \"\"\"\n",
    "\n",
    "    df_motosession = dict_motodata['session']\n",
    "    df_motoqresult = dict_motodata['qresult']\n",
    "    df_motorresult = dict_motodata['rresult']\n",
    "    df_motorider = dict_motodata['rider']\n",
    "\n",
    "    list_allsessiondata = [['Number of tracks',\n",
    "                            'Types of unique sessions',\n",
    "                            'Number of unique riders',\n",
    "                            'Number of unique teams',\n",
    "                            'Number of unique manufacturers']]\n",
    "\n",
    "    for y in list_years:\n",
    "        df_tempsession = df_motosession[df_motosession['sessionSeason'] == y]\n",
    "\n",
    "        list_sessiondata = []\n",
    "        \n",
    "        list_sessiondata.append(len(df_tempsession['sessionTrackname'].unique()))\n",
    "        list_sessiondata.append(len(df_tempsession['sessionSession'].unique()))\n",
    "\n",
    "        df_tempqresult = df_motoqresult[df_motoqresult['sessionId'].isin(df_tempsession['sessionId'])]\n",
    "        df_temprresult = df_motorresult[df_motorresult['sessionId'].isin(df_tempsession['sessionId'])]\n",
    "\n",
    "        for r in ['riderId', 'riderTeam', 'riderMotortype']:\n",
    "            list_sessiondata.append(len(df_motorider[(df_motorider['riderId'].isin(df_tempqresult['riderId'])) | \\\n",
    "                                    (df_motorider['riderId'].isin(df_temprresult['riderId']))]['riderId'].unique()))\n",
    "\n",
    "        list_allsessiondata.append(list_sessiondata)\n",
    "\n",
    "    df_datacounts = pd.DataFrame(list_allsessiondata).T\n",
    "    df_datacounts = df_datacounts.set_index(0)\n",
    "    df_datacounts.index.name = None\n",
    "    df_datacounts.columns = list_years\n",
    "\n",
    "    return df_datacounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\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>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Number of tracks</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Types of unique sessions</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of unique riders</th>\n",
       "      <td>37</td>\n",
       "      <td>28</td>\n",
       "      <td>26</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>31</td>\n",
       "      <td>47</td>\n",
       "      <td>38</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of unique teams</th>\n",
       "      <td>37</td>\n",
       "      <td>28</td>\n",
       "      <td>26</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>31</td>\n",
       "      <td>47</td>\n",
       "      <td>38</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of unique manufacturers</th>\n",
       "      <td>37</td>\n",
       "      <td>28</td>\n",
       "      <td>26</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>31</td>\n",
       "      <td>47</td>\n",
       "      <td>38</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               2007 2008 2009 2010 2011 2012 2013 2014 2015  \\\n",
       "Number of tracks                 18   18   17   18   18   18   18   18   18   \n",
       "Types of unique sessions          6    7    6    7    6    6   10    8    8   \n",
       "Number of unique riders          37   28   26   24   28   36   36   31   47   \n",
       "Number of unique teams           37   28   26   24   28   36   36   31   47   \n",
       "Number of unique manufacturers   37   28   26   24   28   36   36   31   47   \n",
       "\n",
       "                               2016 2017  \n",
       "Number of tracks                 18   18  \n",
       "Types of unique sessions          9    8  \n",
       "Number of unique riders          38   32  \n",
       "Number of unique teams           38   32  \n",
       "Number of unique manufacturers   38   32  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_years = np.arange(2007, 2018, 1)\n",
    "\n",
    "df_datacounts = generate_datacounts(dict_motogpdata, list_years)\n",
    "\n",
    "df_datacounts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What types of sessions were held each year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_sessioncounts(dict_motodata, list_years):\n",
    "    \"\"\" \"\"\"\n",
    "\n",
    "    df_motosession = dict_motodata['session']\n",
    "\n",
    "    list_sessions = sorted(df_motosession[\"sessionSession\"].unique().tolist())\n",
    "\n",
    "    list_allsessiondata = []\n",
    "\n",
    "    for y in list_years:\n",
    "        list_sessiondata = []\n",
    "\n",
    "        df_tempsession = df_motosession[df_motosession[\"sessionSeason\"] == y]\n",
    "\n",
    "        for s in list_sessions:\n",
    "            if len(df_tempsession[df_tempsession[\"sessionSession\"] == s]) > 0:\n",
    "                list_sessiondata.append(\"Y\")\n",
    "                \n",
    "            else:\n",
    "                list_sessiondata.append(\"N\")\n",
    "\n",
    "        list_allsessiondata.append(list_sessiondata)\n",
    "\n",
    "    list_allsessiondata.append(list_sessions)\n",
    "    list_allsessiondata = list_allsessiondata[-1:] + list_allsessiondata[:-1]\n",
    "\n",
    "    df_sessioncounts = pd.DataFrame(list_allsessiondata).T\n",
    "    df_sessioncounts = df_sessioncounts.set_index(0)\n",
    "    df_sessioncounts.index.name = None\n",
    "    df_sessioncounts.columns = list_years\n",
    "\n",
    "    return df_sessioncounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FP</th>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FP1</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FP2</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FP3</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FP4</th>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q1</th>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q2</th>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QP</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QP1</th>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QP2</th>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAC</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAC2</th>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WUP</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WUP2</th>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017\n",
       "FP      N    N    N    N    N    N    Y    N    N    N    N\n",
       "FP1     Y    Y    Y    Y    Y    Y    Y    Y    Y    Y    Y\n",
       "FP2     Y    Y    Y    Y    Y    Y    Y    Y    Y    Y    Y\n",
       "FP3     Y    Y    N    Y    Y    Y    Y    Y    Y    Y    Y\n",
       "FP4     N    Y    N    N    N    N    Y    Y    Y    Y    Y\n",
       "Q1      N    N    N    N    N    N    Y    Y    Y    Y    Y\n",
       "Q2      N    N    N    N    N    N    Y    Y    Y    Y    Y\n",
       "QP      Y    Y    Y    Y    Y    Y    Y    N    N    N    N\n",
       "QP1     N    N    N    N    N    N    N    N    N    N    N\n",
       "QP2     N    N    N    N    N    N    N    N    N    N    N\n",
       "RAC     Y    Y    Y    Y    Y    Y    Y    Y    Y    Y    Y\n",
       "RAC2    N    N    N    Y    N    N    N    N    N    Y    N\n",
       "WUP     Y    Y    Y    Y    Y    Y    Y    Y    Y    Y    Y\n",
       "WUP2    N    N    Y    N    N    N    N    N    N    N    N"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_years = np.arange(2007, 2018, 1)\n",
    "\n",
    "df_sessioncounts = generate_sessioncounts(dict_motogpdata, list_years)\n",
    "\n",
    "df_sessioncounts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just how long is each track?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_tracklenplot(dict_motodata):\n",
    "    \"\"\" \"\"\"\n",
    "\n",
    "    df_tempsession = dict_motodata['session'].sort_values(by='sessionTracklength',\n",
    "                                                          ascending=False)\n",
    "\n",
    "    plt.clf()\n",
    "\n",
    "    fig = plt.figure(figsize=(12, 6))\n",
    "\n",
    "    ax = sns.barplot(x='sessionTrackname',\n",
    "                     y='sessionTracklength',\n",
    "                     data=df_tempsession, color='c')\n",
    "\n",
    "    ax.set_xticklabels(ax.get_xticklabels(),\n",
    "                       rotation=45, ha='right')\n",
    "\n",
    "    ax.set_title('MotoGP 2007-17 - Track Length Comparison')\n",
    "    ax.set_ylabel('Track length (meters)')\n",
    "    \n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = generate_tracklenplot(dict_motogpdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('images/motogptracktemp.png', bbox_inches='tight', pad_inches=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What sort of temperatures have been recorded at each track over race sessions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_tracktempplot(dict_motodata):\n",
    "    \"\"\" \"\"\"\n",
    "\n",
    "    df_tempsession = dict_motodata['session'][dict_motodata['session']['sessionSession'] == 'RAC']\n",
    "\n",
    "    df_tempsession = df_tempsession.sort_values(by='sessionAirtemp',\n",
    "                                                ascending=False)\n",
    "\n",
    "    plt.clf()\n",
    "\n",
    "    fig = plt.figure(figsize=(12, 6))\n",
    "\n",
    "    ax = sns.barplot(x='sessionTrackname',\n",
    "                     y='sessionAirtemp',\n",
    "                     data=df_tempsession, color='c')\n",
    "\n",
    "    ax.set_xticklabels(ax.get_xticklabels(),\n",
    "                       rotation=45, ha='right')\n",
    "\n",
    "    ax.set_title('MotoGP 2007-17 - Track Air Temperature Comparison')\n",
    "    ax.set_ylabel('Air temperature at track (degrees celsius)')\n",
    "\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = generate_tracktempplot(dict_motogpdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('images/motogptracktemp.png', bbox_inches='tight', pad_inches=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can we get a snapsot of riders best lap times recorded over some targeted sessions? Let's say, free practice sessions at Phillip Island in 2014."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convertdatetime(dt):\n",
    "    \"\"\" \"\"\"\n",
    "\n",
    "    import re\n",
    "    import numpy as np\n",
    "\n",
    "    from datetime import datetime\n",
    "\n",
    "    dt = str(dt)\n",
    "\n",
    "    if dt == 'None':\n",
    "        return np.NaN\n",
    "\n",
    "    else:\n",
    "        f = '\\d{4}-\\d{2}-\\d{2} \\d{2}:\\d{2}:\\d{2}.\\d{6}'\n",
    "        r = re.compile(f)\n",
    "        if r.match(dt) is None:\n",
    "            dt = dt + '.000000'\n",
    "\n",
    "        try:\n",
    "            f = '%Y-%m-%d %H:%M:%S.%f'\n",
    "            a = datetime.strptime(dt, f)\n",
    "            b = datetime(1900, 1, 1)\n",
    "        except:\n",
    "            return np.NaN\n",
    "\n",
    "        return (a - b).total_seconds()\n",
    "\n",
    "    \n",
    "def generate_fptimeplot(dict_motogpdata, year, track):\n",
    "    \"\"\" \"\"\"\n",
    "\n",
    "    df_motogpsession = dict_motogpdata['session']\n",
    "\n",
    "    df_motogpsessionfp = df_motogpsession[(df_motogpsession['sessionTrackname'] == track) & \\\n",
    "                                          (df_motogpsession['sessionSeason'] == year) & \\\n",
    "                                          (df_motogpsession['sessionSession'].isin(['FP1', 'FP2', 'FP3', 'FP4']))]\n",
    "    \n",
    "    df_motogpqresult = dict_motogpdata['qresult']\n",
    "\n",
    "    df_motogpqresultfp = df_motogpqresult[df_motogpqresult['sessionId'].isin(df_motogpsessionfp['sessionId'])]\n",
    "    df_motogpqresultfp = df_motogpqresultfp[['riderId', 'sessionId', 'qresultBesttime']]\n",
    "    df_motogpqresultfp = df_motogpqresultfp.set_index('riderId')\n",
    "\n",
    "    dict_sessionidsession = dict_motogpdata['session'].set_index('sessionId')['sessionSession'].to_dict()\n",
    "    df_motogpqresultfp['sessionId'] = df_motogpqresultfp['sessionId'].replace(dict_sessionidsession)\n",
    "    df_motogpqresultfp['qresultBesttime'] = df_motogpqresultfp.apply(lambda row: convertdatetime(row[1]), axis=1)\n",
    "\n",
    "    plt.clf()\n",
    "\n",
    "    fig = plt.figure(figsize=(12, 6))\n",
    "\n",
    "    ax = sns.boxplot(x='sessionId',\n",
    "                     y='qresultBesttime',\n",
    "                     data=df_motogpqresultfp)\n",
    "\n",
    "    ax.set_title('MotoGP ' + str(year) + ' - ' + track + ' Free Practice Session Times')\n",
    "    ax.set_ylabel('Riders Best Lap Time (seconds)')\n",
    "    ax.set_xlabel('Session')\n",
    "\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = generate_fptimeplot(dict_motogpdata, 2014, 'Phillip Island')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('images/motogpfpt.png', bbox_inches='tight', pad_inches=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a closer look at the championship progression over 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_raceresults(dict_motodata, year):\n",
    "    \"\"\" \"\"\"\n",
    "\n",
    "    df_motosession = dict_motodata['session']\n",
    "    df_motorresult = dict_motodata['rresult']\n",
    "    df_motorider = dict_motodata['rider']\n",
    "\n",
    "    df_motosessionyr = df_motosession[(df_motosession['sessionSeason'] == year)]\n",
    "    df_motorresultyr = df_motorresult[df_motorresult['sessionId'].isin(df_motosessionyr['sessionId'])]\n",
    "\n",
    "    list_motorresultyrid = df_motorresultyr['sessionId'].unique()\n",
    "    dict_rideridname = df_motorider.set_index('riderId')['riderName'].to_dict()\n",
    "\n",
    "    dict_sessionidcountry = df_motosession.set_index('sessionId')['sessionCountry'].to_dict()\n",
    "\n",
    "    df_raceresults = pd.DataFrame([])\n",
    "\n",
    "    for i in list_motorresultyrid:\n",
    "        df_temprresult = df_motorresultyr[df_motorresultyr['sessionId'] == i][['riderId', 'rresultPlace']]\n",
    "        df_temprresult = df_temprresult[['riderId', 'rresultPlace']].replace({'riderId': dict_rideridname})\n",
    "        df_temprresult = df_temprresult.set_index('riderId')\n",
    "        df_temprresult.columns = [i]\n",
    "        df_raceresults = pd.concat([df_raceresults, df_temprresult], axis=1, sort=False)\n",
    "\n",
    "    df_raceresults = df_raceresults.rename(columns=dict_sessionidcountry)\n",
    "    df_raceresults = df_raceresults.replace(np.nan, 'DNF', regex=True)\n",
    "\n",
    "    return df_raceresults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>QAT</th>\n",
       "      <th>ARG</th>\n",
       "      <th>AME</th>\n",
       "      <th>SPA</th>\n",
       "      <th>FRA</th>\n",
       "      <th>ITA</th>\n",
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       "      <th>MAL</th>\n",
       "      <th>VAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Maverick VIALES</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DNF</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>DNF</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
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       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrea DOVIZIOSO</th>\n",
       "      <td>2</td>\n",
       "      <td>DNF</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>DNF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valentino ROSSI</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>DNF</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>DNF</td>\n",
       "      <td>5</td>\n",
       "      <td>DNF</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marc MARQUEZ</th>\n",
       "      <td>4</td>\n",
       "      <td>DNF</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DNF</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DNF</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dani PEDROSA</th>\n",
       "      <td>5</td>\n",
       "      <td>DNF</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DNF</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>DNF</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aleix ESPARGARO</th>\n",
       "      <td>6</td>\n",
       "      <td>DNF</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scott REDDING</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>DNF</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>DNF</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>DNF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jack MILLER</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>DNF</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>DNF</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>DNF</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>DNF</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alex RINS</th>\n",
       "      <td>9</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>DNF</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonas FOLGER</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>DNF</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jorge LORENZO</th>\n",
       "      <td>11</td>\n",
       "      <td>DNF</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>DNF</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>DNF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loris BAZ</th>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>DNF</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>DNF</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>DNF</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hector BARBERA</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>DNF</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>DNF</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>DNF</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karel ABRAHAM</th>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>DNF</td>\n",
       "      <td>15</td>\n",
       "      <td>DNF</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>14</td>\n",
       "      <td>DNF</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tito RABAT</th>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>DNF</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>DNF</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pol ESPARGARO</th>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>12</td>\n",
       "      <td>DNF</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>DNF</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>DNF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bradley SMITH</th>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>14</td>\n",
       "      <td>DNF</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sam LOWES</th>\n",
       "      <td>18</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>19</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cal CRUTCHLOW</th>\n",
       "      <td>DNF</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>DNF</td>\n",
       "      <td>5</td>\n",
       "      <td>DNF</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alvaro BAUTISTA</th>\n",
       "      <td>DNF</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>DNF</td>\n",
       "      <td>6</td>\n",
       "      <td>DNF</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>DNF</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>DNF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Johann ZARCO</th>\n",
       "      <td>DNF</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Danilo PETRUCCI</th>\n",
       "      <td>DNF</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>DNF</td>\n",
       "      <td>3</td>\n",
       "      <td>DNF</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrea IANNONE</th>\n",
       "      <td>DNF</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>DNF</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>DNF</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Takuya TSUDA</th>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>17</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sylvain GUINTOLI</th>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michele PIRRO</th>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>9</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>5</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mika KALLIO</th>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>16</td>\n",
       "      <td>DNF</td>\n",
       "      <td>10</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>11</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Katsuyuki NAKASUGA</th>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>12</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hiroshi AOYAMA</th>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>18</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Broc PARKES</th>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>22</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michael VAN DER MARK</th>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      QAT  ARG  AME  SPA  FRA  ITA  CAT  NED  GER  CZE  AUT  \\\n",
       "Maverick VIALES         1    1  DNF    6    1    2   10  DNF    4    3    6   \n",
       "Andrea DOVIZIOSO        2  DNF    6    5    4    1    1    5    8    6    1   \n",
       "Valentino ROSSI         3    2    2   10  DNF    4    8    1    5    4    7   \n",
       "Marc MARQUEZ            4  DNF    1    2  DNF    6    2    3    1    1    2   \n",
       "Dani PEDROSA            5  DNF    3    1    3  DNF    3   13    3    2    3   \n",
       "Aleix ESPARGARO         6  DNF   17    9  DNF  DNF  DNF   10    7    8   13   \n",
       "Scott REDDING           7    8   12   11  DNF   12   13  DNF   20   16   12   \n",
       "Jack MILLER             8    9   10  DNF    8   15  DNF    6   15   14  DNF   \n",
       "Alex RINS               9  DNF  DNF  DNF  DNF  DNF  DNF   17   21   11   16   \n",
       "Jonas FOLGER           10    6   11    8    7   13    6  DNF    2   10  DNF   \n",
       "Jorge LORENZO          11  DNF    9    3    6    8    4   15   11   15    4   \n",
       "Loris BAZ              12   11  DNF   13    9   18   12    8   19  DNF    9   \n",
       "Hector BARBERA         13   13   14   12  DNF   14    9   16  DNF   20   17   \n",
       "Karel ABRAHAM          14   10  DNF   15  DNF   16   14    7   17   13   14   \n",
       "Tito RABAT             15   12   13  DNF   11   11   15   12   18   17   19   \n",
       "Pol ESPARGARO          16   14  DNF  DNF   12  DNF   18   11   13    9  DNF   \n",
       "Bradley SMITH          17   15   16   14   13   20  DNF  DNF   14  DNF   18   \n",
       "Sam LOWES              18  DNF  DNF   16   14   19   19  DNF  DNF   18   20   \n",
       "Cal CRUTCHLOW         DNF    3    4  DNF    5  DNF   11    4   10    5   15   \n",
       "Alvaro BAUTISTA       DNF    4   15  DNF  DNF    5    7  DNF    6  DNF    8   \n",
       "Johann ZARCO          DNF    5    5    4    2    7    5   14    9   12    5   \n",
       "Danilo PETRUCCI       DNF    7    8    7  DNF    3  DNF    2   12    7  DNF   \n",
       "Andrea IANNONE        DNF   16    7  DNF   10   10   16    9  DNF   19   11   \n",
       "Takuya TSUDA          DNF  DNF  DNF   17  DNF  DNF  DNF  DNF  DNF  DNF  DNF   \n",
       "Sylvain GUINTOLI      DNF  DNF  DNF  DNF   15   17   17  DNF  DNF  DNF  DNF   \n",
       "Michele PIRRO         DNF  DNF  DNF  DNF  DNF    9  DNF  DNF  DNF  DNF  DNF   \n",
       "Mika KALLIO           DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF   16  DNF   10   \n",
       "Katsuyuki NAKASUGA    DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF   \n",
       "Hiroshi AOYAMA        DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF   \n",
       "Broc PARKES           DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF   \n",
       "Michael VAN DER MARK  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF   \n",
       "\n",
       "                      GBR  RSM  ARA  JPN  AUS  MAL  VAL  \n",
       "Maverick VIALES         2    4    4    9    3    9   12  \n",
       "Andrea DOVIZIOSO        1    3    7    1   13    1  DNF  \n",
       "Valentino ROSSI         3  DNF    5  DNF    2    7    5  \n",
       "Marc MARQUEZ          DNF    1    1    2    1    4    3  \n",
       "Dani PEDROSA            7   14    2  DNF   12    5    1  \n",
       "Aleix ESPARGARO       DNF  DNF    6    7  DNF  DNF  DNF  \n",
       "Scott REDDING           8    7   14   16   11   13  DNF  \n",
       "Jack MILLER            16    6   13  DNF    7    8    7  \n",
       "Alex RINS               9    8   17    5    8  DNF    4  \n",
       "Jonas FOLGER          DNF    9   16  DNF  DNF  DNF  DNF  \n",
       "Jorge LORENZO           5  DNF    3    6   15    2  DNF  \n",
       "Loris BAZ              15   16   21   10   18  DNF   16  \n",
       "Hector BARBERA         14  DNF   18   14   20   14   15  \n",
       "Karel ABRAHAM          13   17  DNF  DNF   14  DNF   14  \n",
       "Tito RABAT             12  DNF   15   15   16   18   10  \n",
       "Pol ESPARGARO          11   11   10   11    9   10  DNF  \n",
       "Bradley SMITH          17   10   19   17   10   12   11  \n",
       "Sam LOWES             DNF  DNF   22   13   19  DNF  DNF  \n",
       "Cal CRUTCHLOW           4   13  DNF  DNF    5   15    8  \n",
       "Alvaro BAUTISTA        10   12    8  DNF   17   11  DNF  \n",
       "Johann ZARCO            6   15    9    8    4    3    2  \n",
       "Danilo PETRUCCI       DNF    2   20    3   21    6   13  \n",
       "Andrea IANNONE        DNF  DNF   12    4    6   17    6  \n",
       "Takuya TSUDA          DNF  DNF  DNF  DNF  DNF  DNF  DNF  \n",
       "Sylvain GUINTOLI      DNF  DNF  DNF  DNF  DNF  DNF  DNF  \n",
       "Michele PIRRO         DNF    5  DNF  DNF  DNF  DNF    9  \n",
       "Mika KALLIO           DNF  DNF   11  DNF  DNF  DNF  DNF  \n",
       "Katsuyuki NAKASUGA    DNF  DNF  DNF   12  DNF  DNF  DNF  \n",
       "Hiroshi AOYAMA        DNF  DNF  DNF   18  DNF  DNF  DNF  \n",
       "Broc PARKES           DNF  DNF  DNF  DNF   22  DNF  DNF  \n",
       "Michael VAN DER MARK  DNF  DNF  DNF  DNF  DNF   16   17  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raceresults = generate_raceresults(dict_motogpdata, 2017)\n",
    "\n",
    "df_raceresults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_champpoints(dict_motodata, year):\n",
    "    \"\"\" \"\"\"\n",
    "\n",
    "    dict_champpoints = {1: 25, 2: 20, 3: 16, 4: 13, 5: 11, 6: 10, 7: 9, 8: 8, 9: 7, 10: 6,\n",
    "                        11: 5, 12: 4, 13: 3, 14: 2, 15: 1, 16: 0, 17: 0, 18: 0, 19: 0, 20: 0,\n",
    "                        21: 0, 22: 0, 23: 0, 24: 0, 25: 0, 26: 0, 27: 0, 28: 0, 29: 0, 30: 0,\n",
    "                        31: 0, 32: 0, 33: 0, 34: 0, 35: 0, 36: 0, 37: 0, 38: 0, 39: 0, 40: 0}\n",
    "\n",
    "    df_champpoints = generate_raceresults(dict_motodata, year)\n",
    "\n",
    "    df_champpoints = df_champpoints.replace('DNF', np.nan, regex=True)\n",
    "    df_champpoints = df_champpoints.replace(dict_champpoints)\n",
    "    df_champpoints['Total'] = df_champpoints.sum(axis=1)\n",
    "    df_champpoints = df_champpoints.sort_values(by='Total', ascending=False)\n",
    "    df_champpoints = df_champpoints.replace(np.nan, 'DNF', regex=True)\n",
    "\n",
    "    return df_champpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>QAT</th>\n",
       "      <th>ARG</th>\n",
       "      <th>AME</th>\n",
       "      <th>SPA</th>\n",
       "      <th>FRA</th>\n",
       "      <th>ITA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>NED</th>\n",
       "      <th>GER</th>\n",
       "      <th>CZE</th>\n",
       "      <th>AUT</th>\n",
       "      <th>GBR</th>\n",
       "      <th>RSM</th>\n",
       "      <th>ARA</th>\n",
       "      <th>JPN</th>\n",
       "      <th>AUS</th>\n",
       "      <th>MAL</th>\n",
       "      <th>VAL</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Marc MARQUEZ</th>\n",
       "      <td>13</td>\n",
       "      <td>DNF</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>DNF</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>DNF</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>25</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>298.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrea DOVIZIOSO</th>\n",
       "      <td>20</td>\n",
       "      <td>DNF</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>DNF</td>\n",
       "      <td>261.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maverick VIALES</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>DNF</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>DNF</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dani PEDROSA</th>\n",
       "      <td>11</td>\n",
       "      <td>DNF</td>\n",
       "      <td>16</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>DNF</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>DNF</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valentino ROSSI</th>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>DNF</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>DNF</td>\n",
       "      <td>11</td>\n",
       "      <td>DNF</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>208.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Johann ZARCO</th>\n",
       "      <td>DNF</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jorge LORENZO</th>\n",
       "      <td>5</td>\n",
       "      <td>DNF</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>DNF</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>DNF</td>\n",
       "      <td>137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Danilo PETRUCCI</th>\n",
       "      <td>DNF</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>DNF</td>\n",
       "      <td>16</td>\n",
       "      <td>DNF</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cal CRUTCHLOW</th>\n",
       "      <td>DNF</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>DNF</td>\n",
       "      <td>11</td>\n",
       "      <td>DNF</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonas FOLGER</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>DNF</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jack MILLER</th>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>DNF</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>DNF</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DNF</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>DNF</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alvaro BAUTISTA</th>\n",
       "      <td>DNF</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>DNF</td>\n",
       "      <td>10</td>\n",
       "      <td>DNF</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>DNF</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>DNF</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrea IANNONE</th>\n",
       "      <td>DNF</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>DNF</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>DNF</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scott REDDING</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>DNF</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>DNF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>DNF</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aleix ESPARGARO</th>\n",
       "      <td>10</td>\n",
       "      <td>DNF</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alex RINS</th>\n",
       "      <td>7</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>DNF</td>\n",
       "      <td>13</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pol ESPARGARO</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>4</td>\n",
       "      <td>DNF</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>DNF</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>DNF</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loris BAZ</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>DNF</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>DNF</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>DNF</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tito RABAT</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>DNF</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>DNF</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karel ABRAHAM</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>DNF</td>\n",
       "      <td>1</td>\n",
       "      <td>DNF</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>2</td>\n",
       "      <td>DNF</td>\n",
       "      <td>2</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bradley SMITH</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>2</td>\n",
       "      <td>DNF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hector BARBERA</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>DNF</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>DNF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>DNF</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michele PIRRO</th>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>7</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>11</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>7</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mika KALLIO</th>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>0</td>\n",
       "      <td>DNF</td>\n",
       "      <td>6</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>5</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sam LOWES</th>\n",
       "      <td>0</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Katsuyuki NAKASUGA</th>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>4</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sylvain GUINTOLI</th>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Takuya TSUDA</th>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>0</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hiroshi AOYAMA</th>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>0</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Broc PARKES</th>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>0</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michael VAN DER MARK</th>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>DNF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      QAT  ARG  AME  SPA  FRA  ITA  CAT  NED  GER  CZE  AUT  \\\n",
       "Marc MARQUEZ           13  DNF   25   20  DNF   10   20   16   25   25   20   \n",
       "Andrea DOVIZIOSO       20  DNF   10   11   13   25   25   11    8   10   25   \n",
       "Maverick VIALES        25   25  DNF   10   25   20    6  DNF   13   16   10   \n",
       "Dani PEDROSA           11  DNF   16   25   16  DNF   16    3   16   20   16   \n",
       "Valentino ROSSI        16   20   20    6  DNF   13    8   25   11   13    9   \n",
       "Johann ZARCO          DNF   11   11   13   20    9   11    2    7    4   11   \n",
       "Jorge LORENZO           5  DNF    7   16   10    8   13    1    5    1   13   \n",
       "Danilo PETRUCCI       DNF    9    8    9  DNF   16  DNF   20    4    9  DNF   \n",
       "Cal CRUTCHLOW         DNF   16   13  DNF   11  DNF    5   13    6   11    1   \n",
       "Jonas FOLGER            6   10    5    8    9    3   10  DNF   20    6  DNF   \n",
       "Jack MILLER             8    7    6  DNF    8    1  DNF   10    1    2  DNF   \n",
       "Alvaro BAUTISTA       DNF   13    1  DNF  DNF   11    9  DNF   10  DNF    8   \n",
       "Andrea IANNONE        DNF    0    9  DNF    6    6    0    7  DNF    0    5   \n",
       "Scott REDDING           9    8    4    5  DNF    4    3  DNF    0    0    4   \n",
       "Aleix ESPARGARO        10  DNF    0    7  DNF  DNF  DNF    6    9    8    3   \n",
       "Alex RINS               7  DNF  DNF  DNF  DNF  DNF  DNF    0    0    5    0   \n",
       "Pol ESPARGARO           0    2  DNF  DNF    4  DNF    0    5    3    7  DNF   \n",
       "Loris BAZ               4    5  DNF    3    7    0    4    8    0  DNF    7   \n",
       "Tito RABAT              1    4    3  DNF    5    5    1    4    0    0    0   \n",
       "Karel ABRAHAM           2    6  DNF    1  DNF    0    2    9    0    3    2   \n",
       "Bradley SMITH           0    1    0    2    3    0  DNF  DNF    2  DNF    0   \n",
       "Hector BARBERA          3    3    2    4  DNF    2    7    0  DNF    0    0   \n",
       "Michele PIRRO         DNF  DNF  DNF  DNF  DNF    7  DNF  DNF  DNF  DNF  DNF   \n",
       "Mika KALLIO           DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF    0  DNF    6   \n",
       "Sam LOWES               0  DNF  DNF    0    2    0    0  DNF  DNF    0    0   \n",
       "Katsuyuki NAKASUGA    DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF   \n",
       "Sylvain GUINTOLI      DNF  DNF  DNF  DNF    1    0    0  DNF  DNF  DNF  DNF   \n",
       "Takuya TSUDA          DNF  DNF  DNF    0  DNF  DNF  DNF  DNF  DNF  DNF  DNF   \n",
       "Hiroshi AOYAMA        DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF   \n",
       "Broc PARKES           DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF   \n",
       "Michael VAN DER MARK  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF  DNF   \n",
       "\n",
       "                      GBR  RSM  ARA  JPN  AUS  MAL  VAL  Total  \n",
       "Marc MARQUEZ          DNF   25   25   20   25   13   16  298.0  \n",
       "Andrea DOVIZIOSO       25   16    9   25    3   25  DNF  261.0  \n",
       "Maverick VIALES        20   13   13    7   16    7    4  230.0  \n",
       "Dani PEDROSA            9    2   20  DNF    4   11   25  210.0  \n",
       "Valentino ROSSI        16  DNF   11  DNF   20    9   11  208.0  \n",
       "Johann ZARCO           10    1    7    8   13   16   20  174.0  \n",
       "Jorge LORENZO          11  DNF   16   10    1   20  DNF  137.0  \n",
       "Danilo PETRUCCI       DNF   20    0   16    0   10    3  124.0  \n",
       "Cal CRUTCHLOW          13    3  DNF  DNF   11    1    8  112.0  \n",
       "Jonas FOLGER          DNF    7    0  DNF  DNF  DNF  DNF   84.0  \n",
       "Jack MILLER             0   10    3  DNF    9    8    9   82.0  \n",
       "Alvaro BAUTISTA         6    4    8  DNF    0    5  DNF   75.0  \n",
       "Andrea IANNONE        DNF  DNF    4   13   10    0   10   70.0  \n",
       "Scott REDDING           8    9    2    0    5    3  DNF   64.0  \n",
       "Aleix ESPARGARO       DNF  DNF   10    9  DNF  DNF  DNF   62.0  \n",
       "Alex RINS               7    8    0   11    8  DNF   13   59.0  \n",
       "Pol ESPARGARO           5    5    6    5    7    6  DNF   55.0  \n",
       "Loris BAZ               1    0    0    6    0  DNF    0   45.0  \n",
       "Tito RABAT              4  DNF    1    1    0    0    6   35.0  \n",
       "Karel ABRAHAM           3    0  DNF  DNF    2  DNF    2   32.0  \n",
       "Bradley SMITH           0    6    0    0    6    4    5   29.0  \n",
       "Hector BARBERA          2  DNF    0    2    0    2    1   28.0  \n",
       "Michele PIRRO         DNF   11  DNF  DNF  DNF  DNF    7   25.0  \n",
       "Mika KALLIO           DNF  DNF    5  DNF  DNF  DNF  DNF   11.0  \n",
       "Sam LOWES             DNF  DNF    0    3    0  DNF  DNF    5.0  \n",
       "Katsuyuki NAKASUGA    DNF  DNF  DNF    4  DNF  DNF  DNF    4.0  \n",
       "Sylvain GUINTOLI      DNF  DNF  DNF  DNF  DNF  DNF  DNF    1.0  \n",
       "Takuya TSUDA          DNF  DNF  DNF  DNF  DNF  DNF  DNF    0.0  \n",
       "Hiroshi AOYAMA        DNF  DNF  DNF    0  DNF  DNF  DNF    0.0  \n",
       "Broc PARKES           DNF  DNF  DNF  DNF    0  DNF  DNF    0.0  \n",
       "Michael VAN DER MARK  DNF  DNF  DNF  DNF  DNF    0    0    0.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_champpoints = generate_champpoints(dict_motogpdata, 2017)\n",
    "\n",
    "df_champpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_cumpoints(dict_motodata, year):\n",
    "    \"\"\" \"\"\"\n",
    "\n",
    "    df_cumpoints = generate_champpoints(dict_motodata, year)\n",
    "\n",
    "    df_cumpoints = df_cumpoints.drop('Total', 1)\n",
    "    df_cumpoints = df_cumpoints.replace('DNF', np.nan, regex=True)\n",
    "    df_cumpoints = df_cumpoints.replace(np.nan, 0, regex=True)\n",
    "    df_cumpoints = df_cumpoints.cumsum(axis=1)\n",
    "    df_cumpoints['Total'] = df_cumpoints.sum(axis=1)\n",
    "    df_cumpoints = df_cumpoints.sort_values(by='Total', ascending=False)\n",
    "    df_cumpoints = df_cumpoints.drop('Total', 1)\n",
    "    df_cumpoints = df_cumpoints.sort_values(by='VAL', ascending=False)\n",
    "\n",
    "    return df_cumpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\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>QAT</th>\n",
       "      <th>ARG</th>\n",
       "      <th>AME</th>\n",
       "      <th>SPA</th>\n",
       "      <th>FRA</th>\n",
       "      <th>ITA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>NED</th>\n",
       "      <th>GER</th>\n",
       "      <th>CZE</th>\n",
       "      <th>AUT</th>\n",
       "      <th>GBR</th>\n",
       "      <th>RSM</th>\n",
       "      <th>ARA</th>\n",
       "      <th>JPN</th>\n",
       "      <th>AUS</th>\n",
       "      <th>MAL</th>\n",
       "      <th>VAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Marc MARQUEZ</th>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>298.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrea DOVIZIOSO</th>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>261.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maverick VIALES</th>\n",
       "      <td>25.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dani PEDROSA</th>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valentino ROSSI</th>\n",
       "      <td>16.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>208.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Johann ZARCO</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jorge LORENZO</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Danilo PETRUCCI</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cal CRUTCHLOW</th>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonas FOLGER</th>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jack MILLER</th>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alvaro BAUTISTA</th>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrea IANNONE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scott REDDING</th>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aleix ESPARGARO</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alex RINS</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pol ESPARGARO</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loris BAZ</th>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tito RABAT</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karel ABRAHAM</th>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bradley SMITH</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hector BARBERA</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michele PIRRO</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mika KALLIO</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sam LOWES</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Katsuyuki NAKASUGA</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sylvain GUINTOLI</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Takuya TSUDA</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hiroshi AOYAMA</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Broc PARKES</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michael VAN DER MARK</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       QAT   ARG   AME   SPA   FRA    ITA    CAT    NED  \\\n",
       "Marc MARQUEZ          13.0  13.0  38.0  58.0  58.0   68.0   88.0  104.0   \n",
       "Andrea DOVIZIOSO      20.0  20.0  30.0  41.0  54.0   79.0  104.0  115.0   \n",
       "Maverick VIALES       25.0  50.0  50.0  60.0  85.0  105.0  111.0  111.0   \n",
       "Dani PEDROSA          11.0  11.0  27.0  52.0  68.0   68.0   84.0   87.0   \n",
       "Valentino ROSSI       16.0  36.0  56.0  62.0  62.0   75.0   83.0  108.0   \n",
       "Johann ZARCO           0.0  11.0  22.0  35.0  55.0   64.0   75.0   77.0   \n",
       "Jorge LORENZO          5.0   5.0  12.0  28.0  38.0   46.0   59.0   60.0   \n",
       "Danilo PETRUCCI        0.0   9.0  17.0  26.0  26.0   42.0   42.0   62.0   \n",
       "Cal CRUTCHLOW          0.0  16.0  29.0  29.0  40.0   40.0   45.0   58.0   \n",
       "Jonas FOLGER           6.0  16.0  21.0  29.0  38.0   41.0   51.0   51.0   \n",
       "Jack MILLER            8.0  15.0  21.0  21.0  29.0   30.0   30.0   40.0   \n",
       "Alvaro BAUTISTA        0.0  13.0  14.0  14.0  14.0   25.0   34.0   34.0   \n",
       "Andrea IANNONE         0.0   0.0   9.0   9.0  15.0   21.0   21.0   28.0   \n",
       "Scott REDDING          9.0  17.0  21.0  26.0  26.0   30.0   33.0   33.0   \n",
       "Aleix ESPARGARO       10.0  10.0  10.0  17.0  17.0   17.0   17.0   23.0   \n",
       "Alex RINS              7.0   7.0   7.0   7.0   7.0    7.0    7.0    7.0   \n",
       "Pol ESPARGARO          0.0   2.0   2.0   2.0   6.0    6.0    6.0   11.0   \n",
       "Loris BAZ              4.0   9.0   9.0  12.0  19.0   19.0   23.0   31.0   \n",
       "Tito RABAT             1.0   5.0   8.0   8.0  13.0   18.0   19.0   23.0   \n",
       "Karel ABRAHAM          2.0   8.0   8.0   9.0   9.0    9.0   11.0   20.0   \n",
       "Bradley SMITH          0.0   1.0   1.0   3.0   6.0    6.0    6.0    6.0   \n",
       "Hector BARBERA         3.0   6.0   8.0  12.0  12.0   14.0   21.0   21.0   \n",
       "Michele PIRRO          0.0   0.0   0.0   0.0   0.0    7.0    7.0    7.0   \n",
       "Mika KALLIO            0.0   0.0   0.0   0.0   0.0    0.0    0.0    0.0   \n",
       "Sam LOWES              0.0   0.0   0.0   0.0   2.0    2.0    2.0    2.0   \n",
       "Katsuyuki NAKASUGA     0.0   0.0   0.0   0.0   0.0    0.0    0.0    0.0   \n",
       "Sylvain GUINTOLI       0.0   0.0   0.0   0.0   1.0    1.0    1.0    1.0   \n",
       "Takuya TSUDA           0.0   0.0   0.0   0.0   0.0    0.0    0.0    0.0   \n",
       "Hiroshi AOYAMA         0.0   0.0   0.0   0.0   0.0    0.0    0.0    0.0   \n",
       "Broc PARKES            0.0   0.0   0.0   0.0   0.0    0.0    0.0    0.0   \n",
       "Michael VAN DER MARK   0.0   0.0   0.0   0.0   0.0    0.0    0.0    0.0   \n",
       "\n",
       "                        GER    CZE    AUT    GBR    RSM    ARA    JPN    AUS  \\\n",
       "Marc MARQUEZ          129.0  154.0  174.0  174.0  199.0  224.0  244.0  269.0   \n",
       "Andrea DOVIZIOSO      123.0  133.0  158.0  183.0  199.0  208.0  233.0  236.0   \n",
       "Maverick VIALES       124.0  140.0  150.0  170.0  183.0  196.0  203.0  219.0   \n",
       "Dani PEDROSA          103.0  123.0  139.0  148.0  150.0  170.0  170.0  174.0   \n",
       "Valentino ROSSI       119.0  132.0  141.0  157.0  157.0  168.0  168.0  188.0   \n",
       "Johann ZARCO           84.0   88.0   99.0  109.0  110.0  117.0  125.0  138.0   \n",
       "Jorge LORENZO          65.0   66.0   79.0   90.0   90.0  106.0  116.0  117.0   \n",
       "Danilo PETRUCCI        66.0   75.0   75.0   75.0   95.0   95.0  111.0  111.0   \n",
       "Cal CRUTCHLOW          64.0   75.0   76.0   89.0   92.0   92.0   92.0  103.0   \n",
       "Jonas FOLGER           71.0   77.0   77.0   77.0   84.0   84.0   84.0   84.0   \n",
       "Jack MILLER            41.0   43.0   43.0   43.0   53.0   56.0   56.0   65.0   \n",
       "Alvaro BAUTISTA        44.0   44.0   52.0   58.0   62.0   70.0   70.0   70.0   \n",
       "Andrea IANNONE         28.0   28.0   33.0   33.0   33.0   37.0   50.0   60.0   \n",
       "Scott REDDING          33.0   33.0   37.0   45.0   54.0   56.0   56.0   61.0   \n",
       "Aleix ESPARGARO        32.0   40.0   43.0   43.0   43.0   53.0   62.0   62.0   \n",
       "Alex RINS               7.0   12.0   12.0   19.0   27.0   27.0   38.0   46.0   \n",
       "Pol ESPARGARO          14.0   21.0   21.0   26.0   31.0   37.0   42.0   49.0   \n",
       "Loris BAZ              31.0   31.0   38.0   39.0   39.0   39.0   45.0   45.0   \n",
       "Tito RABAT             23.0   23.0   23.0   27.0   27.0   28.0   29.0   29.0   \n",
       "Karel ABRAHAM          20.0   23.0   25.0   28.0   28.0   28.0   28.0   30.0   \n",
       "Bradley SMITH           8.0    8.0    8.0    8.0   14.0   14.0   14.0   20.0   \n",
       "Hector BARBERA         21.0   21.0   21.0   23.0   23.0   23.0   25.0   25.0   \n",
       "Michele PIRRO           7.0    7.0    7.0    7.0   18.0   18.0   18.0   18.0   \n",
       "Mika KALLIO             0.0    0.0    6.0    6.0    6.0   11.0   11.0   11.0   \n",
       "Sam LOWES               2.0    2.0    2.0    2.0    2.0    2.0    5.0    5.0   \n",
       "Katsuyuki NAKASUGA      0.0    0.0    0.0    0.0    0.0    0.0    4.0    4.0   \n",
       "Sylvain GUINTOLI        1.0    1.0    1.0    1.0    1.0    1.0    1.0    1.0   \n",
       "Takuya TSUDA            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "Hiroshi AOYAMA          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "Broc PARKES             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "Michael VAN DER MARK    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "                        MAL    VAL  \n",
       "Marc MARQUEZ          282.0  298.0  \n",
       "Andrea DOVIZIOSO      261.0  261.0  \n",
       "Maverick VIALES       226.0  230.0  \n",
       "Dani PEDROSA          185.0  210.0  \n",
       "Valentino ROSSI       197.0  208.0  \n",
       "Johann ZARCO          154.0  174.0  \n",
       "Jorge LORENZO         137.0  137.0  \n",
       "Danilo PETRUCCI       121.0  124.0  \n",
       "Cal CRUTCHLOW         104.0  112.0  \n",
       "Jonas FOLGER           84.0   84.0  \n",
       "Jack MILLER            73.0   82.0  \n",
       "Alvaro BAUTISTA        75.0   75.0  \n",
       "Andrea IANNONE         60.0   70.0  \n",
       "Scott REDDING          64.0   64.0  \n",
       "Aleix ESPARGARO        62.0   62.0  \n",
       "Alex RINS              46.0   59.0  \n",
       "Pol ESPARGARO          55.0   55.0  \n",
       "Loris BAZ              45.0   45.0  \n",
       "Tito RABAT             29.0   35.0  \n",
       "Karel ABRAHAM          30.0   32.0  \n",
       "Bradley SMITH          24.0   29.0  \n",
       "Hector BARBERA         27.0   28.0  \n",
       "Michele PIRRO          18.0   25.0  \n",
       "Mika KALLIO            11.0   11.0  \n",
       "Sam LOWES               5.0    5.0  \n",
       "Katsuyuki NAKASUGA      4.0    4.0  \n",
       "Sylvain GUINTOLI        1.0    1.0  \n",
       "Takuya TSUDA            0.0    0.0  \n",
       "Hiroshi AOYAMA          0.0    0.0  \n",
       "Broc PARKES             0.0    0.0  \n",
       "Michael VAN DER MARK    0.0    0.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cumpoints = generate_cumpoints(dict_motogpdata, 2017)\n",
    "\n",
    "df_cumpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_qualresults(dict_motogpdata, year):\n",
    "    \"\"\" \"\"\"\n",
    "\n",
    "    def aggregateq1q2(q1, q2, n):\n",
    "        if q1 > 2:\n",
    "            return q1 + n\n",
    "        else:\n",
    "            return q2\n",
    "\n",
    "    df_motogpsession = dict_motogpdata['session']\n",
    "    df_motogpqresult = dict_motogpdata['qresult']\n",
    "    df_motogprider = dict_motogpdata['rider']\n",
    "\n",
    "    df_motogpsessionyr = df_motogpsession[(df_motogpsession['sessionSeason'] == year)]\n",
    "    df_motogpqresultyr = df_motogpqresult[df_motogpqresult['sessionId'].isin(df_motogpsession['sessionId'])]\n",
    "\n",
    "    list_motogpsessionid = df_motogpsessionyr[(df_motogpsessionyr['sessionSession'] == 'Q1') | \\\n",
    "                                              (df_motogpsessionyr['sessionSession'] == 'Q2')]['sessionId'].unique()\n",
    "\n",
    "    dict_rideridname = df_motogprider.set_index('riderId')['riderName'].to_dict()\n",
    "    dict_sessionidcountry = df_motogpsessionyr.set_index('sessionId')['sessionCountry'].to_dict()\n",
    "\n",
    "    df_qualresults = pd.DataFrame([])\n",
    "\n",
    "    for i in range(0, len(list_motogpsessionid), 2):\n",
    "        df_temprresults = pd.DataFrame([])\n",
    "\n",
    "        for j in range(0, 2):\n",
    "            q = list_motogpsessionid[i + j]\n",
    "            df_temprresult = df_motogpqresult[df_motogpqresult['sessionId'] == q][['riderId', 'qresultPlace']]\n",
    "            df_temprresult = df_temprresult[['riderId', 'qresultPlace']].replace({'riderId': dict_rideridname})\n",
    "            df_temprresult = df_temprresult.set_index('riderId')\n",
    "            df_temprresult.columns = [q]\n",
    "\n",
    "            df_temprresults = pd.concat([df_temprresults, df_temprresult], axis=1, sort=False)\n",
    "\n",
    "        n = float(df_temprresults[[q]].max().values - 2)\n",
    "\n",
    "        df_temprresults[q] = df_temprresults.apply(lambda row: aggregateq1q2(row[q - 1], row[q], n), axis=1)\n",
    "        df_temprresults = df_temprresults.drop(q - 1, 1)\n",
    "\n",
    "        df_qualresults = pd.concat([df_qualresults, df_temprresults], axis=1, sort=False)\n",
    "\n",
    "    df_qualresults = df_qualresults.rename(columns=dict_sessionidcountry)\n",
    "    df_qualresults = df_qualresults.replace(np.nan, 'DNQ', regex=True)\n",
    "\n",
    "    return df_qualresults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ARG</th>\n",
       "      <th>AME</th>\n",
       "      <th>SPA</th>\n",
       "      <th>FRA</th>\n",
       "      <th>ITA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>NED</th>\n",
       "      <th>GER</th>\n",
       "      <th>CZE</th>\n",
       "      <th>AUT</th>\n",
       "      <th>GBR</th>\n",
       "      <th>RSM</th>\n",
       "      <th>ARA</th>\n",
       "      <th>JPN</th>\n",
       "      <th>AUS</th>\n",
       "      <th>MAL</th>\n",
       "      <th>VAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dani PEDROSA</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valentino ROSSI</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrea DOVIZIOSO</th>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Johann ZARCO</th>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scott REDDING</th>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jorge LORENZO</th>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jack MILLER</th>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pol ESPARGARO</th>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bradley SMITH</th>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tito RABAT</th>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hector BARBERA</th>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sam LOWES</th>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alex RINS</th>\n",
       "      <td>23</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marc MARQUEZ</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karel ABRAHAM</th>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cal CRUTCHLOW</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Danilo PETRUCCI</th>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maverick VIALES</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aleix ESPARGARO</th>\n",
       "      <td>8</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loris BAZ</th>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alvaro BAUTISTA</th>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonas FOLGER</th>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrea IANNONE</th>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Takuya TSUDA</th>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>23</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sylvain GUINTOLI</th>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michele PIRRO</th>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>4</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>11</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mika KALLIO</th>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>17</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>18</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>12</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hiroshi AOYAMA</th>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>21</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Katsuyuki NAKASUGA</th>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>23</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kohta NOZANE</th>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>24</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Broc PARKES</th>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>21</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michael VAN DER MARK</th>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>DNQ</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ARG  AME  SPA  FRA  ITA  CAT  NED  GER  CZE  AUT  GBR  \\\n",
       "Dani PEDROSA            5    4    1   13    5    1   12    3    3    8    7   \n",
       "Valentino ROSSI         7    3    7    2    2   13    4    9    2    7    2   \n",
       "Andrea DOVIZIOSO       13    7   14    6    3    7    9   10    4    2    6   \n",
       "Johann ZARCO           14    5    6    3   11   14    1   19   10    6    8   \n",
       "Scott REDDING          15   10   11    7   20   11    5   23   23   15   12   \n",
       "Jorge LORENZO          16    6    8   16    7    2   21    6    6    3    5   \n",
       "Jack MILLER            17   12   10   11   19   15   13   13   15   19   17   \n",
       "Pol ESPARGARO          18   21   15    8   18   20   19    7   18   16   11   \n",
       "Bradley SMITH          19   18   16   10   23  DNQ   22   15   19   22   19   \n",
       "Tito RABAT             20   16   18   22   10   19   23   24   21   24   22   \n",
       "Hector BARBERA         21   15   21   20   14    6   20   18   16   14   16   \n",
       "Sam LOWES              22   20   22   21   22   21   10   21   22   23   23   \n",
       "Alex RINS              23  DNQ  DNQ  DNQ  DNQ  DNQ   17   22   13   21   13   \n",
       "Marc MARQUEZ            1    1    2    5    6    4    2    1    1    1    1   \n",
       "Karel ABRAHAM           2   17   19    9   21   18   18   20   17   11   20   \n",
       "Cal CRUTCHLOW           3    9    3    4   13   17    8    4    5    9    3   \n",
       "Danilo PETRUCCI         4   13   13   19    9    3    3    2    8    5   18   \n",
       "Maverick VIALES         6    2    4    1    1    9   11   11    7    4    4   \n",
       "Aleix ESPARGARO         8  DNQ   12   18   12    5   15    8   11   20    9   \n",
       "Loris BAZ               9   14   20   12   17   16   14   14   12   12   21   \n",
       "Alvaro BAUTISTA        10   19   17   14    8   10    7   12    9   17   14   \n",
       "Jonas FOLGER           11    8    9   15   15    8    6    5   14   13   10   \n",
       "Andrea IANNONE         12   11    5   17   16   12   16   16   20   10   15   \n",
       "Takuya TSUDA          DNQ  DNQ   23  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ   \n",
       "Sylvain GUINTOLI      DNQ  DNQ  DNQ   23   24  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ   \n",
       "Michele PIRRO         DNQ  DNQ  DNQ  DNQ    4  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ   \n",
       "Mika KALLIO           DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ   17  DNQ   18  DNQ   \n",
       "Hiroshi AOYAMA        DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ   \n",
       "Katsuyuki NAKASUGA    DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ   \n",
       "Kohta NOZANE          DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ   \n",
       "Broc PARKES           DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ   \n",
       "Michael VAN DER MARK  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  DNQ   \n",
       "\n",
       "                      RSM  ARA  JPN  AUS  MAL  VAL  \n",
       "Dani PEDROSA            7    6    6   12    1    5  \n",
       "Valentino ROSSI       DNQ    3   12    7    4    7  \n",
       "Andrea DOVIZIOSO        2    7    9   11    3    9  \n",
       "Johann ZARCO            6   11    1    3    2    2  \n",
       "Scott REDDING          19   22   22   20   14   22  \n",
       "Jorge LORENZO           5    2    5   16    6    4  \n",
       "Jack MILLER            14   13  DNQ    5   11   12  \n",
       "Pol ESPARGARO          17   14    8    6   12   11  \n",
       "Bradley SMITH          22   23    7    9   16   17  \n",
       "Tito RABAT             18   21   19   14   19   14  \n",
       "Hector BARBERA         13   19   17   19   20   20  \n",
       "Sam LOWES              23   24   18   23   18   24  \n",
       "Alex RINS              20   20   10   13    8   10  \n",
       "Marc MARQUEZ            3    5    3    1    7    1  \n",
       "Karel ABRAHAM          12   15   20   15   21   18  \n",
       "Cal CRUTCHLOW           4    4   15   10   10   16  \n",
       "Danilo PETRUCCI         8   16    2   18   13   15  \n",
       "Maverick VIALES         1    1   14    2    5   13  \n",
       "Aleix ESPARGARO         9    8    4    8  DNQ    8  \n",
       "Loris BAZ              15   17   13   17   17   23  \n",
       "Alvaro BAUTISTA        10    9   16   22   15   21  \n",
       "Jonas FOLGER           16   18  DNQ  DNQ  DNQ  DNQ  \n",
       "Andrea IANNONE         21   10   11    4    9    3  \n",
       "Takuya TSUDA          DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  \n",
       "Sylvain GUINTOLI      DNQ  DNQ  DNQ  DNQ  DNQ  DNQ  \n",
       "Michele PIRRO          11  DNQ  DNQ  DNQ  DNQ    6  \n",
       "Mika KALLIO           DNQ   12  DNQ  DNQ  DNQ   19  \n",
       "Hiroshi AOYAMA        DNQ  DNQ   21  DNQ  DNQ  DNQ  \n",
       "Katsuyuki NAKASUGA    DNQ  DNQ   23  DNQ  DNQ  DNQ  \n",
       "Kohta NOZANE          DNQ  DNQ   24  DNQ  DNQ  DNQ  \n",
       "Broc PARKES           DNQ  DNQ  DNQ   21  DNQ  DNQ  \n",
       "Michael VAN DER MARK  DNQ  DNQ  DNQ  DNQ   22   25  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_qualresults = generate_qualresults(dict_motogpdata, 2017)\n",
    "\n",
    "df_qualresults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_cumpointplot(dict_motodata, year):\n",
    "    \"\"\" \"\"\"\n",
    "\n",
    "    df_cumpoints = generate_cumpoints(dict_motodata, year)\n",
    "\n",
    "    df_cumpointstop5 = df_cumpoints[:5].T\n",
    "    df_cumpointstop5 = df_cumpointstop5.reset_index()\n",
    "\n",
    "    list_sessioncountry = df_cumpointstop5['index'].tolist()\n",
    "    df_cumpointstop5 = df_cumpointstop5.drop('index', 1)\n",
    "\n",
    "    plt.clf()\n",
    "\n",
    "    fig = df_cumpointstop5.plot(figsize=(16, 8))\n",
    "\n",
    "    plt.xticks(np.arange(0, len(df_cumpointstop5)), list_sessioncountry)\n",
    "    plt.title('MotoGP ' + str(year) + ' Cumulative Championship Points (top 5 riders)')\n",
    "    plt.ylabel('Cumulative Championship Points')\n",
    "    plt.xlabel('Session Country')\n",
    "    plt.legend()\n",
    "\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = generate_cumpointplot(dict_motogpdata, 2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = fig.get_figure()\n",
    "fig.savefig('images/motogpcumpoint.png', bbox_inches='tight', pad_inches=0.2)"
   ]
  }
 ],
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