Testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "115e114e-cd7d-4557-a5e1-d111f9a70edc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "194fb9e8-593d-4e96-9dc4-ae1cbd68a155",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_circuits = pd.read_csv(\"Data/circuits.csv\")\n",
    "# Do not use constructor_results, constructor_standings is the updated version\n",
    "# df_constructor_results = pd.read_csv(\"Data/constructor_results.csv\")\n",
    "df_constructor_standings = pd.read_csv(\"Data/constructor_standings.csv\")\n",
    "df_constructors = pd.read_csv(\"Data/constructors.csv\")\n",
    "df_driver_standings = pd.read_csv(\"Data/driver_standings.csv\")\n",
    "df_drivers = pd.read_csv(\"Data/drivers.csv\")\n",
    "df_lap_times = pd.read_csv(\"Data/lap_times.csv\")\n",
    "df_pit_stops = pd.read_csv(\"Data/pit_stops.csv\")\n",
    "df_qualifying = pd.read_csv(\"Data/qualifying.csv\")\n",
    "df_races = pd.read_csv(\"Data/races.csv\")\n",
    "df_results = pd.read_csv(\"Data/results.csv\")\n",
    "df_seasons = pd.read_csv(\"Data/seasons.csv\")\n",
    "df_sprint_results = pd.read_csv(\"Data/sprint_results.csv\")\n",
    "df_status = pd.read_csv(\"Data/status.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f07eee2-2476-479b-86c5-ad36ea593314",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d11ecc7e-af04-4d3b-b6a9-7f51b9a9cad6",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Circuits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e8d54384-c197-480d-a3cb-b1c7ba7f830c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_circuits = pd.read_csv(\"Data/circuits.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "357af63f-3ed6-408e-9760-7caaedb416d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 77 entries, 1 to 79\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   circuitRef  77 non-null     object \n",
      " 1   name        77 non-null     object \n",
      " 2   location    77 non-null     object \n",
      " 3   country     77 non-null     object \n",
      " 4   lat         77 non-null     float64\n",
      " 5   lng         77 non-null     float64\n",
      " 6   alt         77 non-null     int64  \n",
      " 7   url         77 non-null     object \n",
      "dtypes: float64(2), int64(1), object(5)\n",
      "memory usage: 5.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_circuits.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "83778dbe-a055-4be6-bd19-154960af994b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>circuitRef</th>\n",
       "      <th>name</th>\n",
       "      <th>location</th>\n",
       "      <th>country</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>alt</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>circuitId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>albert_park</td>\n",
       "      <td>Albert Park Grand Prix Circuit</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-37.84970</td>\n",
       "      <td>144.96800</td>\n",
       "      <td>10</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Melbourne_Grand_P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sepang</td>\n",
       "      <td>Sepang International Circuit</td>\n",
       "      <td>Kuala Lumpur</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>2.76083</td>\n",
       "      <td>101.73800</td>\n",
       "      <td>18</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Sepang_Internatio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bahrain</td>\n",
       "      <td>Bahrain International Circuit</td>\n",
       "      <td>Sakhir</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>26.03250</td>\n",
       "      <td>50.51060</td>\n",
       "      <td>7</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Bahrain_Internati...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>catalunya</td>\n",
       "      <td>Circuit de Barcelona-Catalunya</td>\n",
       "      <td>Montmeló</td>\n",
       "      <td>Spain</td>\n",
       "      <td>41.57000</td>\n",
       "      <td>2.26111</td>\n",
       "      <td>109</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Circuit_de_Barcel...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>istanbul</td>\n",
       "      <td>Istanbul Park</td>\n",
       "      <td>Istanbul</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>40.95170</td>\n",
       "      <td>29.40500</td>\n",
       "      <td>130</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Istanbul_Park</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            circuitRef                            name      location  \\\n",
       "circuitId                                                              \n",
       "1          albert_park  Albert Park Grand Prix Circuit     Melbourne   \n",
       "2               sepang    Sepang International Circuit  Kuala Lumpur   \n",
       "3              bahrain   Bahrain International Circuit        Sakhir   \n",
       "4            catalunya  Circuit de Barcelona-Catalunya      Montmeló   \n",
       "5             istanbul                   Istanbul Park      Istanbul   \n",
       "\n",
       "             country       lat        lng  alt  \\\n",
       "circuitId                                        \n",
       "1          Australia -37.84970  144.96800   10   \n",
       "2           Malaysia   2.76083  101.73800   18   \n",
       "3            Bahrain  26.03250   50.51060    7   \n",
       "4              Spain  41.57000    2.26111  109   \n",
       "5             Turkey  40.95170   29.40500  130   \n",
       "\n",
       "                                                         url  \n",
       "circuitId                                                     \n",
       "1          http://en.wikipedia.org/wiki/Melbourne_Grand_P...  \n",
       "2          http://en.wikipedia.org/wiki/Sepang_Internatio...  \n",
       "3          http://en.wikipedia.org/wiki/Bahrain_Internati...  \n",
       "4          http://en.wikipedia.org/wiki/Circuit_de_Barcel...  \n",
       "5                 http://en.wikipedia.org/wiki/Istanbul_Park  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_circuits.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "55e585fb-07e3-4fdd-b304-ce092b1919a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 77 entries, 1 to 79\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   circuitRef  77 non-null     object \n",
      " 1   name        77 non-null     object \n",
      " 2   location    77 non-null     object \n",
      " 3   country     77 non-null     object \n",
      " 4   lat         77 non-null     float64\n",
      " 5   lng         77 non-null     float64\n",
      " 6   alt         77 non-null     int64  \n",
      " 7   url         77 non-null     object \n",
      "dtypes: float64(2), int64(1), object(5)\n",
      "memory usage: 5.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_circuits = pd.read_csv(\"Data/circuits.csv\", index_col=0)\n",
    "df_circuits.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1917aeb6-13e9-40fc-8948-a89ab11c9a27",
   "metadata": {},
   "source": [
    "The methods below do not use (), while .info() & .head() do."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c4e1e5b4-05dc-4bce-86cc-b054942091fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77, 8)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_circuits.dtypes\n",
    "df_circuits.columns\n",
    "df_circuits.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "fada0c02-d8e0-4c60-926a-df98893837be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'http://en.wikipedia.org/wiki/Melbourne_Grand_Prix_Circuit'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_circuits.loc[1, 'url']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "516b5cc5-3ba7-469a-bde8-2cd1b578b011",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "circuitRef                                          albert_park\n",
       "name                             Albert Park Grand Prix Circuit\n",
       "location                                              Melbourne\n",
       "country                                               Australia\n",
       "lat                                                    -37.8497\n",
       "lng                                                     144.968\n",
       "alt                                                          10\n",
       "url           http://en.wikipedia.org/wiki/Melbourne_Grand_P...\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_circuits.loc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "1bb7c2cb-7b93-469d-8b3b-ab783384dabc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>circuitId</th>\n",
       "      <th>name</th>\n",
       "      <th>location</th>\n",
       "      <th>country</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>alt</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>circuitRef</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>albert_park</th>\n",
       "      <td>1</td>\n",
       "      <td>Albert Park Grand Prix Circuit</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-37.84970</td>\n",
       "      <td>144.96800</td>\n",
       "      <td>10</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Melbourne_Grand_P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepang</th>\n",
       "      <td>2</td>\n",
       "      <td>Sepang International Circuit</td>\n",
       "      <td>Kuala Lumpur</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>2.76083</td>\n",
       "      <td>101.73800</td>\n",
       "      <td>18</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Sepang_Internatio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bahrain</th>\n",
       "      <td>3</td>\n",
       "      <td>Bahrain International Circuit</td>\n",
       "      <td>Sakhir</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>26.03250</td>\n",
       "      <td>50.51060</td>\n",
       "      <td>7</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Bahrain_Internati...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>catalunya</th>\n",
       "      <td>4</td>\n",
       "      <td>Circuit de Barcelona-Catalunya</td>\n",
       "      <td>Montmeló</td>\n",
       "      <td>Spain</td>\n",
       "      <td>41.57000</td>\n",
       "      <td>2.26111</td>\n",
       "      <td>109</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Circuit_de_Barcel...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>istanbul</th>\n",
       "      <td>5</td>\n",
       "      <td>Istanbul Park</td>\n",
       "      <td>Istanbul</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>40.95170</td>\n",
       "      <td>29.40500</td>\n",
       "      <td>130</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Istanbul_Park</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             circuitId                            name      location  \\\n",
       "circuitRef                                                             \n",
       "albert_park          1  Albert Park Grand Prix Circuit     Melbourne   \n",
       "sepang               2    Sepang International Circuit  Kuala Lumpur   \n",
       "bahrain              3   Bahrain International Circuit        Sakhir   \n",
       "catalunya            4  Circuit de Barcelona-Catalunya      Montmeló   \n",
       "istanbul             5                   Istanbul Park      Istanbul   \n",
       "\n",
       "               country       lat        lng  alt  \\\n",
       "circuitRef                                         \n",
       "albert_park  Australia -37.84970  144.96800   10   \n",
       "sepang        Malaysia   2.76083  101.73800   18   \n",
       "bahrain        Bahrain  26.03250   50.51060    7   \n",
       "catalunya        Spain  41.57000    2.26111  109   \n",
       "istanbul        Turkey  40.95170   29.40500  130   \n",
       "\n",
       "                                                           url  \n",
       "circuitRef                                                      \n",
       "albert_park  http://en.wikipedia.org/wiki/Melbourne_Grand_P...  \n",
       "sepang       http://en.wikipedia.org/wiki/Sepang_Internatio...  \n",
       "bahrain      http://en.wikipedia.org/wiki/Bahrain_Internati...  \n",
       "catalunya    http://en.wikipedia.org/wiki/Circuit_de_Barcel...  \n",
       "istanbul            http://en.wikipedia.org/wiki/Istanbul_Park  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_circuits = pd.read_csv(\"Data/circuits.csv\", index_col=1)\n",
    "df_circuits.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d240a18-3a15-4beb-bb00-394ff64c5d70",
   "metadata": {},
   "source": [
    "Since the database is relational and relies on the use of these predetermined IDs, it is necessary that we continue to use them as the indexes for the dataframes. Despite the existence of missing ID values, the database relies on the IDs currently present so I will not modify the circuitIDs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "36a6b6a8-c0ff-4336-8217-7a498ac68cff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>circuitId</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>alt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39.883117</td>\n",
       "      <td>33.442925</td>\n",
       "      <td>1.076683</td>\n",
       "      <td>247.012987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23.001701</td>\n",
       "      <td>22.808866</td>\n",
       "      <td>65.516951</td>\n",
       "      <td>362.738469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-37.849700</td>\n",
       "      <td>-118.189000</td>\n",
       "      <td>-7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>32.777400</td>\n",
       "      <td>-9.394170</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.951700</td>\n",
       "      <td>3.930830</td>\n",
       "      <td>129.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>46.958900</td>\n",
       "      <td>19.248600</td>\n",
       "      <td>332.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>57.265300</td>\n",
       "      <td>144.968000</td>\n",
       "      <td>2227.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       circuitId        lat         lng          alt\n",
       "count  77.000000  77.000000   77.000000    77.000000\n",
       "mean   39.883117  33.442925    1.076683   247.012987\n",
       "std    23.001701  22.808866   65.516951   362.738469\n",
       "min     1.000000 -37.849700 -118.189000    -7.000000\n",
       "25%    20.000000  32.777400   -9.394170    18.000000\n",
       "50%    40.000000  40.951700    3.930830   129.000000\n",
       "75%    59.000000  46.958900   19.248600   332.000000\n",
       "max    80.000000  57.265300  144.968000  2227.000000"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_circuits.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6ad131cf-c021-4c87-bbdc-ec6bebc1ef1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Final command:\n",
    "df_circuits = pd.read_csv(\"Data/circuits.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9e0d376-d8c4-44da-aff2-e29afa073f73",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Constructors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "29f95207-a30e-46d9-a754-21210b3a5036",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_constructor_results = pd.read_csv(\"Data/constructor_results.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "506d307f-57ee-4e39-80b2-e030b30be029",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 12505 entries, 1 to 17009\n",
      "Data columns (total 4 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   raceId         12505 non-null  int64  \n",
      " 1   constructorId  12505 non-null  int64  \n",
      " 2   points         12505 non-null  float64\n",
      " 3   status         12505 non-null  object \n",
      "dtypes: float64(1), int64(2), object(1)\n",
      "memory usage: 488.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df_constructor_results.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "50be7455-9055-46b1-a35b-307406402ff0",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                      raceId  constructorId  points status\n",
       "constructorResultsId                                      \n",
       "1                         18              1    14.0     \\N\n",
       "2                         18              2     8.0     \\N\n",
       "3                         18              3     9.0     \\N\n",
       "4                         18              4     5.0     \\N\n",
       "5                         18              5     2.0     \\N"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_constructor_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "7138557a-f374-4820-bb2e-22cb90a5c63d",
   "metadata": {},
   "outputs": [
    {
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       "      <th>317</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "                      raceId  constructorId  points status\n",
       "constructorResultsId                                      \n",
       "186                       36              1    14.0      D\n",
       "196                       37              1    18.0      D\n",
       "208                       38              1    12.0      D\n",
       "219                       39              1    14.0      D\n",
       "229                       40              1    18.0      D\n",
       "240                       41              1    12.0      D\n",
       "251                       42              1    18.0      D\n",
       "263                       43              1     8.0      D\n",
       "274                       44              1    14.0      D\n",
       "284                       45              1    10.0      D\n",
       "295                       46              1    15.0      D\n",
       "307                       47              1    10.0      D\n",
       "317                       48              1    18.0      D\n",
       "329                       49              1    11.0      D\n",
       "339                       50              1    10.0      D\n",
       "351                       51              1     8.0      D\n",
       "362                       52              1     8.0      D"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_constructor_results[df_constructor_results['status'] != '\\\\N']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1f568a84-8fed-4292-9cde-a25839a322c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 13271 entries, 1 to 28852\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   raceId         13271 non-null  int64  \n",
      " 1   constructorId  13271 non-null  int64  \n",
      " 2   points         13271 non-null  float64\n",
      " 3   position       13271 non-null  int64  \n",
      " 4   positionText   13271 non-null  object \n",
      " 5   wins           13271 non-null  int64  \n",
      "dtypes: float64(1), int64(4), object(1)\n",
      "memory usage: 725.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_constructor_standings = pd.read_csv(\"Data/constructor_standings.csv\", index_col = 0)\n",
    "df_constructor_standings.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "a81a1463-f6b5-490a-b4c2-0eb68b860780",
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
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       "                        raceId  constructorId  points  position positionText  \\\n",
       "constructorStandingsId                                                         \n",
       "8515                       320             22    38.5         4            4   \n",
       "8514                       320              6    55.5         3            3   \n",
       "8540                         2              9     1.5         7            7   \n",
       "8536                         2              3     3.5         5            5   \n",
       "8534                         2              7    16.5         2            2   \n",
       "...                        ...            ...     ...       ...          ...   \n",
       "27914                     1072              9   559.5         2            2   \n",
       "27916                     1072              6   307.5         3            3   \n",
       "27923                     1073            131   613.5         1            1   \n",
       "27924                     1073              9   585.5         2            2   \n",
       "27926                     1073              6   323.5         3            3   \n",
       "\n",
       "                        wins  \n",
       "constructorStandingsId        \n",
       "8515                       1  \n",
       "8514                       0  \n",
       "8540                       0  \n",
       "8536                       0  \n",
       "8534                       0  \n",
       "...                      ...  \n",
       "27914                     10  \n",
       "27916                      0  \n",
       "27923                      9  \n",
       "27924                     11  \n",
       "27926                      0  \n",
       "\n",
       "[141 rows x 6 columns]"
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     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df_constructor_standings[df_constructor_standings['points'] % 1 != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "a4f1b549-3025-43d4-9553-afb36f184a24",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>11</td>\n",
       "      <td>E</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25778</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>11</td>\n",
       "      <td>E</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25789</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>88.0</td>\n",
       "      <td>11</td>\n",
       "      <td>E</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25800</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>106.0</td>\n",
       "      <td>11</td>\n",
       "      <td>E</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25811</th>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>114.0</td>\n",
       "      <td>11</td>\n",
       "      <td>E</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25822</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>128.0</td>\n",
       "      <td>11</td>\n",
       "      <td>E</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25833</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>138.0</td>\n",
       "      <td>11</td>\n",
       "      <td>E</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25844</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>138.0</td>\n",
       "      <td>11</td>\n",
       "      <td>E</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25855</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>148.0</td>\n",
       "      <td>11</td>\n",
       "      <td>E</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25866</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>166.0</td>\n",
       "      <td>11</td>\n",
       "      <td>E</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25877</th>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>192.0</td>\n",
       "      <td>11</td>\n",
       "      <td>E</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25888</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>202.0</td>\n",
       "      <td>11</td>\n",
       "      <td>E</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25899</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>210.0</td>\n",
       "      <td>11</td>\n",
       "      <td>E</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25910</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>218.0</td>\n",
       "      <td>11</td>\n",
       "      <td>E</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25734</th>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11</td>\n",
       "      <td>E</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25745</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>32.0</td>\n",
       "      <td>11</td>\n",
       "      <td>E</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        raceId  constructorId  points  position positionText  \\\n",
       "constructorStandingsId                                                         \n",
       "25756                       38              1    44.0        11            E   \n",
       "25767                       39              1    58.0        11            E   \n",
       "25778                       40              1    76.0        11            E   \n",
       "25789                       41              1    88.0        11            E   \n",
       "25800                       42              1   106.0        11            E   \n",
       "25811                       43              1   114.0        11            E   \n",
       "25822                       44              1   128.0        11            E   \n",
       "25833                       45              1   138.0        11            E   \n",
       "25844                       46              1   138.0        11            E   \n",
       "25855                       47              1   148.0        11            E   \n",
       "25866                       48              1   166.0        11            E   \n",
       "25877                       49              1   192.0        11            E   \n",
       "25888                       50              1   202.0        11            E   \n",
       "25899                       51              1   210.0        11            E   \n",
       "25910                       52              1   218.0        11            E   \n",
       "25734                       36              1    14.0        11            E   \n",
       "25745                       37              1    32.0        11            E   \n",
       "\n",
       "                        wins  \n",
       "constructorStandingsId        \n",
       "25756                      1  \n",
       "25767                      1  \n",
       "25778                      2  \n",
       "25789                      3  \n",
       "25800                      4  \n",
       "25811                      4  \n",
       "25822                      5  \n",
       "25833                      5  \n",
       "25844                      6  \n",
       "25855                      6  \n",
       "25866                      7  \n",
       "25877                      7  \n",
       "25888                      8  \n",
       "25899                      8  \n",
       "25910                      8  \n",
       "25734                      0  \n",
       "25745                      1  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_constructor_standings[df_constructor_standings['positionText'] != df_constructor_standings['position'].astype(str)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ec503ef-79d7-481c-b5e0-e2c47f54c358",
   "metadata": {},
   "source": [
    "To solve the 10-ish rows in constructor_standings where positionText is E, the constructor standings are modified such that the positionText matches the position as a string as seen below. First, the mismatched rows are identified using boolean arrays. Then the array is multiindexed using the .loc method to replace all the index rows' positionText with their new value. The last line is just to confirm that the process has been successful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "cc270cc0-ec03-4321-87df-7e313508d183",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>raceId</th>\n",
       "      <th>constructorId</th>\n",
       "      <th>points</th>\n",
       "      <th>position</th>\n",
       "      <th>positionText</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [raceId, constructorId, points, position, positionText, wins]\n",
       "Index: []"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mismatched_rows = df_constructor_standings['positionText'] != df_constructor_standings['position'].astype(str)\n",
    "df_constructor_standings.loc[mismatched_rows, 'positionText'] = df_constructor_standings.loc[mismatched_rows, 'position'].astype(str)\n",
    "df_constructor_standings[df_constructor_standings['positionText'] != df_constructor_standings['position'].astype(str)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "b3a799a9-1068-4158-a554-724fa2e8d28f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_constructor_standings.loc[1, 'positionText'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "d15ee1a7-a63e-4c5a-8e9c-f536370565ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>constructorRef</th>\n",
       "      <th>name</th>\n",
       "      <th>nationality</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>constructorId</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mclaren</td>\n",
       "      <td>McLaren</td>\n",
       "      <td>British</td>\n",
       "      <td>http://en.wikipedia.org/wiki/McLaren</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bmw_sauber</td>\n",
       "      <td>BMW Sauber</td>\n",
       "      <td>German</td>\n",
       "      <td>http://en.wikipedia.org/wiki/BMW_Sauber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>williams</td>\n",
       "      <td>Williams</td>\n",
       "      <td>British</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Williams_Grand_Pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>renault</td>\n",
       "      <td>Renault</td>\n",
       "      <td>French</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Renault_in_Formul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>toro_rosso</td>\n",
       "      <td>Toro Rosso</td>\n",
       "      <td>Italian</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Scuderia_Toro_Rosso</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              constructorRef        name nationality  \\\n",
       "constructorId                                          \n",
       "1                    mclaren     McLaren     British   \n",
       "2                 bmw_sauber  BMW Sauber      German   \n",
       "3                   williams    Williams     British   \n",
       "4                    renault     Renault      French   \n",
       "5                 toro_rosso  Toro Rosso     Italian   \n",
       "\n",
       "                                                             url  \n",
       "constructorId                                                     \n",
       "1                           http://en.wikipedia.org/wiki/McLaren  \n",
       "2                        http://en.wikipedia.org/wiki/BMW_Sauber  \n",
       "3              http://en.wikipedia.org/wiki/Williams_Grand_Pr...  \n",
       "4              http://en.wikipedia.org/wiki/Renault_in_Formul...  \n",
       "5               http://en.wikipedia.org/wiki/Scuderia_Toro_Rosso  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_constructors = pd.read_csv(\"Data/constructors.csv\", index_col = 0)\n",
    "df_constructors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "9118e648-f396-4040-a6f6-dc75ad077fc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 212 entries, 1 to 215\n",
      "Data columns (total 4 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   constructorRef  212 non-null    object\n",
      " 1   name            212 non-null    object\n",
      " 2   nationality     212 non-null    object\n",
      " 3   url             212 non-null    object\n",
      "dtypes: object(4)\n",
      "memory usage: 8.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df_constructors.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d528ad8e-a202-4942-a562-5070f2bb90cd",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Drivers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "8bf5f5bc-ff44-422c-bd54-f7ad8072366c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_driver_standings = pd.read_csv(\"Data/driver_standings.csv\", index_col=0)\n",
    "df_drivers = pd.read_csv(\"Data/drivers.csv\", index_col=0, parse_dates = [6], na_values = ['\\\\N'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "36378d10-922b-4635-a79b-464677df4497",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>raceId</th>\n",
       "      <th>driverId</th>\n",
       "      <th>points</th>\n",
       "      <th>position</th>\n",
       "      <th>positionText</th>\n",
       "      <th>wins</th>\n",
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       "    <tr>\n",
       "      <th>driverStandingsId</th>\n",
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       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                   raceId  driverId  points  position positionText  wins\n",
       "driverStandingsId                                                       \n",
       "1                      18         1    10.0         1            1     1\n",
       "2                      18         2     8.0         2            2     0\n",
       "3                      18         3     6.0         3            3     0\n",
       "4                      18         4     5.0         4            4     0\n",
       "5                      18         5     4.0         5            5     0"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_driver_standings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "a8bd987d-391a-431f-9be3-8843601c3d71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 34595 entries, 1 to 72871\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   raceId        34595 non-null  int64  \n",
      " 1   driverId      34595 non-null  int64  \n",
      " 2   points        34595 non-null  float64\n",
      " 3   position      34595 non-null  int64  \n",
      " 4   positionText  34595 non-null  object \n",
      " 5   wins          34595 non-null  int64  \n",
      "dtypes: float64(1), int64(4), object(1)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df_driver_standings.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "24cfbe52-29ee-4407-8880-896942909dd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>223</td>\n",
       "      <td>30</td>\n",
       "      <td>78.0</td>\n",
       "      <td>26</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   raceId  driverId  points  position positionText  wins\n",
       "driverStandingsId                                                       \n",
       "11031                 223        30    78.0        26            D     5"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_driver_standings\n",
    "mismatched_rows = df['positionText'] != df['position'].astype(str)\n",
    "df[mismatched_rows]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "f4236d11-8104-4cf3-8350-0505506cc4fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>raceId</th>\n",
       "      <th>driverId</th>\n",
       "      <th>points</th>\n",
       "      <th>position</th>\n",
       "      <th>positionText</th>\n",
       "      <th>wins</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>driverStandingsId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11031</th>\n",
       "      <td>223</td>\n",
       "      <td>30</td>\n",
       "      <td>78.0</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   raceId  driverId  points  position positionText  wins\n",
       "driverStandingsId                                                       \n",
       "11031                 223        30    78.0        26           26     5"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[mismatched_rows, 'positionText'] = df['position'].astype(str)\n",
    "df[mismatched_rows]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "3ebfa2c4-240a-4059-aa14-b93175fe2371",
   "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>driverRef</th>\n",
       "      <th>number</th>\n",
       "      <th>code</th>\n",
       "      <th>forename</th>\n",
       "      <th>surname</th>\n",
       "      <th>dob</th>\n",
       "      <th>nationality</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>driverId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hamilton</td>\n",
       "      <td>44.0</td>\n",
       "      <td>HAM</td>\n",
       "      <td>Lewis</td>\n",
       "      <td>Hamilton</td>\n",
       "      <td>1985-01-07</td>\n",
       "      <td>British</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Lewis_Hamilton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>heidfeld</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HEI</td>\n",
       "      <td>Nick</td>\n",
       "      <td>Heidfeld</td>\n",
       "      <td>1977-05-10</td>\n",
       "      <td>German</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Nick_Heidfeld</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rosberg</td>\n",
       "      <td>6.0</td>\n",
       "      <td>ROS</td>\n",
       "      <td>Nico</td>\n",
       "      <td>Rosberg</td>\n",
       "      <td>1985-06-27</td>\n",
       "      <td>German</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Nico_Rosberg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>alonso</td>\n",
       "      <td>14.0</td>\n",
       "      <td>ALO</td>\n",
       "      <td>Fernando</td>\n",
       "      <td>Alonso</td>\n",
       "      <td>1981-07-29</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Fernando_Alonso</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>kovalainen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KOV</td>\n",
       "      <td>Heikki</td>\n",
       "      <td>Kovalainen</td>\n",
       "      <td>1981-10-19</td>\n",
       "      <td>Finnish</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Heikki_Kovalainen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           driverRef  number code  forename     surname        dob  \\\n",
       "driverId                                                             \n",
       "1           hamilton    44.0  HAM     Lewis    Hamilton 1985-01-07   \n",
       "2           heidfeld     NaN  HEI      Nick    Heidfeld 1977-05-10   \n",
       "3            rosberg     6.0  ROS      Nico     Rosberg 1985-06-27   \n",
       "4             alonso    14.0  ALO  Fernando      Alonso 1981-07-29   \n",
       "5         kovalainen     NaN  KOV    Heikki  Kovalainen 1981-10-19   \n",
       "\n",
       "         nationality                                             url  \n",
       "driverId                                                              \n",
       "1            British     http://en.wikipedia.org/wiki/Lewis_Hamilton  \n",
       "2             German      http://en.wikipedia.org/wiki/Nick_Heidfeld  \n",
       "3             German       http://en.wikipedia.org/wiki/Nico_Rosberg  \n",
       "4            Spanish    http://en.wikipedia.org/wiki/Fernando_Alonso  \n",
       "5            Finnish  http://en.wikipedia.org/wiki/Heikki_Kovalainen  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drivers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "2d189f64-bdfa-435a-98b9-96c438194965",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 859 entries, 1 to 860\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count  Dtype         \n",
      "---  ------       --------------  -----         \n",
      " 0   driverRef    859 non-null    object        \n",
      " 1   number       57 non-null     float64       \n",
      " 2   code         102 non-null    object        \n",
      " 3   forename     859 non-null    object        \n",
      " 4   surname      859 non-null    object        \n",
      " 5   dob          859 non-null    datetime64[ns]\n",
      " 6   nationality  859 non-null    object        \n",
      " 7   url          859 non-null    object        \n",
      "dtypes: datetime64[ns](1), float64(1), object(6)\n",
      "memory usage: 60.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_drivers.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb957983-71ee-4956-b3d3-923dec0fbaf7",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Races"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "7bcf2db8-480b-47c3-b3f5-298841039f73",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_races = pd.read_csv(\"Data/races.csv\",\n",
    "                       index_col = 0,\n",
    "                       na_values = ['\\\\N'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "4d73d361-52df-4032-90b7-fe3d57995ed6",
   "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>year</th>\n",
       "      <th>round</th>\n",
       "      <th>circuitId</th>\n",
       "      <th>name</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>url</th>\n",
       "      <th>fp1_date</th>\n",
       "      <th>fp1_time</th>\n",
       "      <th>fp2_date</th>\n",
       "      <th>fp2_time</th>\n",
       "      <th>fp3_date</th>\n",
       "      <th>fp3_time</th>\n",
       "      <th>quali_date</th>\n",
       "      <th>quali_time</th>\n",
       "      <th>sprint_date</th>\n",
       "      <th>sprint_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>raceId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Australian Grand Prix</td>\n",
       "      <td>2009-03-29</td>\n",
       "      <td>06:00:00</td>\n",
       "      <td>http://en.wikipedia.org/wiki/2009_Australian_G...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Malaysian Grand Prix</td>\n",
       "      <td>2009-04-05</td>\n",
       "      <td>09:00:00</td>\n",
       "      <td>http://en.wikipedia.org/wiki/2009_Malaysian_Gr...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>Chinese Grand Prix</td>\n",
       "      <td>2009-04-19</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>http://en.wikipedia.org/wiki/2009_Chinese_Gran...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Bahrain Grand Prix</td>\n",
       "      <td>2009-04-26</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>http://en.wikipedia.org/wiki/2009_Bahrain_Gran...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2009</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Spanish Grand Prix</td>\n",
       "      <td>2009-05-10</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>http://en.wikipedia.org/wiki/2009_Spanish_Gran...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        year  round  circuitId                   name        date      time  \\\n",
       "raceId                                                                        \n",
       "1       2009      1          1  Australian Grand Prix  2009-03-29  06:00:00   \n",
       "2       2009      2          2   Malaysian Grand Prix  2009-04-05  09:00:00   \n",
       "3       2009      3         17     Chinese Grand Prix  2009-04-19  07:00:00   \n",
       "4       2009      4          3     Bahrain Grand Prix  2009-04-26  12:00:00   \n",
       "5       2009      5          4     Spanish Grand Prix  2009-05-10  12:00:00   \n",
       "\n",
       "                                                      url fp1_date fp1_time  \\\n",
       "raceId                                                                        \n",
       "1       http://en.wikipedia.org/wiki/2009_Australian_G...      NaN      NaN   \n",
       "2       http://en.wikipedia.org/wiki/2009_Malaysian_Gr...      NaN      NaN   \n",
       "3       http://en.wikipedia.org/wiki/2009_Chinese_Gran...      NaN      NaN   \n",
       "4       http://en.wikipedia.org/wiki/2009_Bahrain_Gran...      NaN      NaN   \n",
       "5       http://en.wikipedia.org/wiki/2009_Spanish_Gran...      NaN      NaN   \n",
       "\n",
       "       fp2_date fp2_time fp3_date fp3_time quali_date quali_time sprint_date  \\\n",
       "raceId                                                                         \n",
       "1           NaN      NaN      NaN      NaN        NaN        NaN         NaN   \n",
       "2           NaN      NaN      NaN      NaN        NaN        NaN         NaN   \n",
       "3           NaN      NaN      NaN      NaN        NaN        NaN         NaN   \n",
       "4           NaN      NaN      NaN      NaN        NaN        NaN         NaN   \n",
       "5           NaN      NaN      NaN      NaN        NaN        NaN         NaN   \n",
       "\n",
       "       sprint_time  \n",
       "raceId              \n",
       "1              NaN  \n",
       "2              NaN  \n",
       "3              NaN  \n",
       "4              NaN  \n",
       "5              NaN  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_races.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "5b65240a-6b79-4a31-bcc8-b9ad8a248982",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1125 entries, 1 to 1144\n",
      "Data columns (total 17 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   year         1125 non-null   int64 \n",
      " 1   round        1125 non-null   int64 \n",
      " 2   circuitId    1125 non-null   int64 \n",
      " 3   name         1125 non-null   object\n",
      " 4   date         1125 non-null   object\n",
      " 5   time         394 non-null    object\n",
      " 6   url          1125 non-null   object\n",
      " 7   fp1_date     90 non-null     object\n",
      " 8   fp1_time     68 non-null     object\n",
      " 9   fp2_date     90 non-null     object\n",
      " 10  fp2_time     68 non-null     object\n",
      " 11  fp3_date     72 non-null     object\n",
      " 12  fp3_time     53 non-null     object\n",
      " 13  quali_date   90 non-null     object\n",
      " 14  quali_time   68 non-null     object\n",
      " 15  sprint_date  18 non-null     object\n",
      " 16  sprint_time  15 non-null     object\n",
      "dtypes: int64(3), object(14)\n",
      "memory usage: 158.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_races.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "067d3171-6ab2-4457-950b-3759f78ce1fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1125 entries, 1 to 1144\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   year       1125 non-null   int64 \n",
      " 1   round      1125 non-null   int64 \n",
      " 2   circuitId  1125 non-null   int64 \n",
      " 3   name       1125 non-null   object\n",
      " 4   date       1125 non-null   object\n",
      " 5   time       394 non-null    object\n",
      " 6   url        1125 non-null   object\n",
      "dtypes: int64(3), object(4)\n",
      "memory usage: 70.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df_races = df_races.drop(columns=df_races.columns[7:])\n",
    "df_races.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "7ff0b49a-58a1-4b92-94bf-8db64e64ed7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1125 entries, 1 to 1144\n",
      "Data columns (total 8 columns):\n",
      " #   Column     Non-Null Count  Dtype         \n",
      "---  ------     --------------  -----         \n",
      " 0   year       1125 non-null   int64         \n",
      " 1   round      1125 non-null   int64         \n",
      " 2   circuitId  1125 non-null   int64         \n",
      " 3   name       1125 non-null   object        \n",
      " 4   date       1125 non-null   object        \n",
      " 5   time       394 non-null    object        \n",
      " 6   url        1125 non-null   object        \n",
      " 7   datetime   394 non-null    datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(3), object(4)\n",
      "memory usage: 79.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_races['datetime'] = pd.to_datetime(df_races['date'] + ' ' + df_races['time'])\n",
    "df_races.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "1cb70d07-324d-45ca-81e5-dc1b5479f274",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>round</th>\n",
       "      <th>circuitId</th>\n",
       "      <th>name</th>\n",
       "      <th>datetime</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>raceId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Australian Grand Prix</td>\n",
       "      <td>2009-03-29 06:00:00</td>\n",
       "      <td>http://en.wikipedia.org/wiki/2009_Australian_G...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Malaysian Grand Prix</td>\n",
       "      <td>2009-04-05 09:00:00</td>\n",
       "      <td>http://en.wikipedia.org/wiki/2009_Malaysian_Gr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>Chinese Grand Prix</td>\n",
       "      <td>2009-04-19 07:00:00</td>\n",
       "      <td>http://en.wikipedia.org/wiki/2009_Chinese_Gran...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Bahrain Grand Prix</td>\n",
       "      <td>2009-04-26 12:00:00</td>\n",
       "      <td>http://en.wikipedia.org/wiki/2009_Bahrain_Gran...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Spanish Grand Prix</td>\n",
       "      <td>2009-05-10 12:00:00</td>\n",
       "      <td>http://en.wikipedia.org/wiki/2009_Spanish_Gran...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1140</th>\n",
       "      <td>20</td>\n",
       "      <td>32</td>\n",
       "      <td>Mexico City Grand Prix</td>\n",
       "      <td>2024-10-27 20:00:00</td>\n",
       "      <td>https://en.wikipedia.org/wiki/2024_Mexico_City...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>São Paulo Grand Prix</td>\n",
       "      <td>2024-11-03 17:00:00</td>\n",
       "      <td>https://en.wikipedia.org/wiki/2024_S%C3%A3o_Pa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1142</th>\n",
       "      <td>22</td>\n",
       "      <td>80</td>\n",
       "      <td>Las Vegas Grand Prix</td>\n",
       "      <td>2024-11-23 06:00:00</td>\n",
       "      <td>https://en.wikipedia.org/wiki/2024_Las_Vegas_G...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1143</th>\n",
       "      <td>23</td>\n",
       "      <td>78</td>\n",
       "      <td>Qatar Grand Prix</td>\n",
       "      <td>2024-12-01 17:00:00</td>\n",
       "      <td>https://en.wikipedia.org/wiki/2024_Qatar_Grand...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1144</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>Abu Dhabi Grand Prix</td>\n",
       "      <td>2024-12-08 13:00:00</td>\n",
       "      <td>https://en.wikipedia.org/wiki/2024_Abu_Dhabi_G...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1125 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        round  circuitId                    name            datetime  \\\n",
       "raceId                                                                 \n",
       "1           1          1   Australian Grand Prix 2009-03-29 06:00:00   \n",
       "2           2          2    Malaysian Grand Prix 2009-04-05 09:00:00   \n",
       "3           3         17      Chinese Grand Prix 2009-04-19 07:00:00   \n",
       "4           4          3      Bahrain Grand Prix 2009-04-26 12:00:00   \n",
       "5           5          4      Spanish Grand Prix 2009-05-10 12:00:00   \n",
       "...       ...        ...                     ...                 ...   \n",
       "1140       20         32  Mexico City Grand Prix 2024-10-27 20:00:00   \n",
       "1141       21         18    São Paulo Grand Prix 2024-11-03 17:00:00   \n",
       "1142       22         80    Las Vegas Grand Prix 2024-11-23 06:00:00   \n",
       "1143       23         78        Qatar Grand Prix 2024-12-01 17:00:00   \n",
       "1144       24         24    Abu Dhabi Grand Prix 2024-12-08 13:00:00   \n",
       "\n",
       "                                                      url  \n",
       "raceId                                                     \n",
       "1       http://en.wikipedia.org/wiki/2009_Australian_G...  \n",
       "2       http://en.wikipedia.org/wiki/2009_Malaysian_Gr...  \n",
       "3       http://en.wikipedia.org/wiki/2009_Chinese_Gran...  \n",
       "4       http://en.wikipedia.org/wiki/2009_Bahrain_Gran...  \n",
       "5       http://en.wikipedia.org/wiki/2009_Spanish_Gran...  \n",
       "...                                                   ...  \n",
       "1140    https://en.wikipedia.org/wiki/2024_Mexico_City...  \n",
       "1141    https://en.wikipedia.org/wiki/2024_S%C3%A3o_Pa...  \n",
       "1142    https://en.wikipedia.org/wiki/2024_Las_Vegas_G...  \n",
       "1143    https://en.wikipedia.org/wiki/2024_Qatar_Grand...  \n",
       "1144    https://en.wikipedia.org/wiki/2024_Abu_Dhabi_G...  \n",
       "\n",
       "[1125 rows x 5 columns]"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_races = df_races.drop(columns = ['date', 'time', 'year'])\n",
    "columns = df_races.columns.to_list()\n",
    "columns[3], columns[4] = columns[4], columns[3]\n",
    "df_races[columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22ec00c8-ceef-4c81-b516-0c2accb4b6e5",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "26b6d4db-b51e-4137-a7a5-da991303bf43",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_results = pd.read_csv(\"Data/results.csv\",\n",
    "                         index_col = 0,\n",
    "                         na_values = {'position': '\\\\N',\n",
    "                                      'number': '\\\\N',\n",
    "                                      'milliseconds': '\\\\N',\n",
    "                                      'fastestLap': '\\\\N',\n",
    "                                      'rank': '\\\\N'})\n",
    "int_columns = ['position', 'number', 'milliseconds', 'fastestLap', 'rank']\n",
    "for col in int_columns:\n",
    "    df_results[col] = df_results[col].dropna().astype('Int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "1b12fb24-0e01-4def-bec6-9a2e34b26f4b",
   "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>raceId</th>\n",
       "      <th>driverId</th>\n",
       "      <th>constructorId</th>\n",
       "      <th>number</th>\n",
       "      <th>grid</th>\n",
       "      <th>position</th>\n",
       "      <th>positionText</th>\n",
       "      <th>positionOrder</th>\n",
       "      <th>points</th>\n",
       "      <th>laps</th>\n",
       "      <th>time</th>\n",
       "      <th>milliseconds</th>\n",
       "      <th>fastestLap</th>\n",
       "      <th>rank</th>\n",
       "      <th>fastestLapTime</th>\n",
       "      <th>fastestLapSpeed</th>\n",
       "      <th>statusId</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resultId</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>58</td>\n",
       "      <td>1:34:50.616</td>\n",
       "      <td>5690616</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>1:27.452</td>\n",
       "      <td>218.300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>58</td>\n",
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       "      <td>5696094</td>\n",
       "      <td>41</td>\n",
       "      <td>3</td>\n",
       "      <td>1:27.739</td>\n",
       "      <td>217.586</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
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       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>58</td>\n",
       "      <td>+8.163</td>\n",
       "      <td>5698779</td>\n",
       "      <td>41</td>\n",
       "      <td>5</td>\n",
       "      <td>1:28.090</td>\n",
       "      <td>216.719</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>+17.181</td>\n",
       "      <td>5707797</td>\n",
       "      <td>58</td>\n",
       "      <td>7</td>\n",
       "      <td>1:28.603</td>\n",
       "      <td>215.464</td>\n",
       "      <td>1</td>\n",
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       "      <th>5</th>\n",
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       "      <td>3</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>58</td>\n",
       "      <td>+18.014</td>\n",
       "      <td>5708630</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>1:27.418</td>\n",
       "      <td>218.385</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          raceId  driverId  constructorId  number  grid  position  \\\n",
       "resultId                                                            \n",
       "1             18         1              1      22     1         1   \n",
       "2             18         2              2       3     5         2   \n",
       "3             18         3              3       7     7         3   \n",
       "4             18         4              4       5    11         4   \n",
       "5             18         5              1      23     3         5   \n",
       "\n",
       "         positionText  positionOrder  points  laps         time  milliseconds  \\\n",
       "resultId                                                                        \n",
       "1                   1              1    10.0    58  1:34:50.616       5690616   \n",
       "2                   2              2     8.0    58       +5.478       5696094   \n",
       "3                   3              3     6.0    58       +8.163       5698779   \n",
       "4                   4              4     5.0    58      +17.181       5707797   \n",
       "5                   5              5     4.0    58      +18.014       5708630   \n",
       "\n",
       "          fastestLap  rank fastestLapTime fastestLapSpeed  statusId  \n",
       "resultId                                                             \n",
       "1                 39     2       1:27.452         218.300         1  \n",
       "2                 41     3       1:27.739         217.586         1  \n",
       "3                 41     5       1:28.090         216.719         1  \n",
       "4                 58     7       1:28.603         215.464         1  \n",
       "5                 43     1       1:27.418         218.385         1  "
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "e9659192-6d31-4298-bc37-40b9ab1f9fe0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 26519 entries, 1 to 26524\n",
      "Data columns (total 17 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   raceId           26519 non-null  int64  \n",
      " 1   driverId         26519 non-null  int64  \n",
      " 2   constructorId    26519 non-null  int64  \n",
      " 3   number           26513 non-null  Int64  \n",
      " 4   grid             26519 non-null  int64  \n",
      " 5   position         15591 non-null  Int64  \n",
      " 6   positionText     26519 non-null  object \n",
      " 7   positionOrder    26519 non-null  int64  \n",
      " 8   points           26519 non-null  float64\n",
      " 9   laps             26519 non-null  int64  \n",
      " 10  time             26519 non-null  object \n",
      " 11  milliseconds     7533 non-null   Int64  \n",
      " 12  fastestLap       8020 non-null   Int64  \n",
      " 13  rank             8270 non-null   Int64  \n",
      " 14  fastestLapTime   26519 non-null  object \n",
      " 15  fastestLapSpeed  26519 non-null  object \n",
      " 16  statusId         26519 non-null  int64  \n",
      "dtypes: Int64(5), float64(1), int64(7), object(4)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df_results.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "84d1d52d-3c7f-4f07-861a-d319df75a6d0",
   "metadata": {},
   "outputs": [
    {
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       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>R</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32</td>\n",
       "      <td>\\N</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>1:30.892</td>\n",
       "      <td>210.038</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>R</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "      <td>\\N</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>1:31.384</td>\n",
       "      <td>208.907</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>R</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29</td>\n",
       "      <td>\\N</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>1:28.175</td>\n",
       "      <td>216.510</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26462</th>\n",
       "      <td>1129</td>\n",
       "      <td>815</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>R</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51</td>\n",
       "      <td>\\N</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>51</td>\n",
       "      <td>16</td>\n",
       "      <td>1:18.819</td>\n",
       "      <td>199.185</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26463</th>\n",
       "      <td>1129</td>\n",
       "      <td>844</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>R</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>\\N</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>39</td>\n",
       "      <td>19</td>\n",
       "      <td>1:26.012</td>\n",
       "      <td>182.528</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26464</th>\n",
       "      <td>1129</td>\n",
       "      <td>858</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>R</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>\\N</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>1:26.484</td>\n",
       "      <td>181.531</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26523</th>\n",
       "      <td>1132</td>\n",
       "      <td>847</td>\n",
       "      <td>131</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>R</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33</td>\n",
       "      <td>\\N</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>1:31.298</td>\n",
       "      <td>232.289</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26524</th>\n",
       "      <td>1132</td>\n",
       "      <td>842</td>\n",
       "      <td>214</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>W</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>\\N</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>0</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10928 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          raceId  driverId  constructorId  number  grid  position  \\\n",
       "resultId                                                            \n",
       "9             18         9              2       4     2      <NA>   \n",
       "10            18        10              7      12    18      <NA>   \n",
       "11            18        11              8      18    19      <NA>   \n",
       "12            18        12              4       6    20      <NA>   \n",
       "13            18        13              6       2     4      <NA>   \n",
       "...          ...       ...            ...     ...   ...       ...   \n",
       "26462       1129       815              9      11    16      <NA>   \n",
       "26463       1129       844              6      16    11      <NA>   \n",
       "26464       1129       858              3       2    13      <NA>   \n",
       "26523       1132       847            131      63     1      <NA>   \n",
       "26524       1132       842            214      10    19      <NA>   \n",
       "\n",
       "         positionText  positionOrder  points  laps time  milliseconds  \\\n",
       "resultId                                                                \n",
       "9                   R              9     0.0    47   \\N          <NA>   \n",
       "10                  R             10     0.0    43   \\N          <NA>   \n",
       "11                  R             11     0.0    32   \\N          <NA>   \n",
       "12                  R             12     0.0    30   \\N          <NA>   \n",
       "13                  R             13     0.0    29   \\N          <NA>   \n",
       "...               ...            ...     ...   ...  ...           ...   \n",
       "26462               R             18     0.0    51   \\N          <NA>   \n",
       "26463               R             19     0.0    40   \\N          <NA>   \n",
       "26464               R             20     0.0    23   \\N          <NA>   \n",
       "26523               R             19     0.0    33   \\N          <NA>   \n",
       "26524               W             20     0.0     0   \\N          <NA>   \n",
       "\n",
       "          fastestLap  rank fastestLapTime fastestLapSpeed  statusId  \n",
       "resultId                                                             \n",
       "9                 15     9       1:28.753         215.100         4  \n",
       "10                23    13       1:29.558         213.166         3  \n",
       "11                24    15       1:30.892         210.038         7  \n",
       "12                20    16       1:31.384         208.907         8  \n",
       "13                23     6       1:28.175         216.510         5  \n",
       "...              ...   ...            ...             ...       ...  \n",
       "26462             51    16       1:18.819         199.185         3  \n",
       "26463             39    19       1:26.012         182.528         5  \n",
       "26464             19    20       1:26.484         181.531         3  \n",
       "26523              3    19       1:31.298         232.289        34  \n",
       "26524           <NA>     0             \\N              \\N         6  \n",
       "\n",
       "[10928 rows x 17 columns]"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mismatched_rows = df_results['positionText'] != df_results['position'].astype(str)\n",
    "df_results[mismatched_rows]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "3d33ee7a-a05d-4d38-945b-6c4fb381f5a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['R', 'D', 'N', '11', '14', 'W', 'F', 'E'], dtype=object)"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results[mismatched_rows]['positionText'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "68b4dfa9-aadd-4ac2-b5c5-818609005850",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 22.,   3.,   7.,   5.,  23.,   8.,  14.,   1.,   4.,  12.,  18.,\n",
       "         6.,   2.,   9.,  11.,  20.,  10.,  16.,  19.,  15.,  21.,  17.,\n",
       "        24.,  25.,  28.,  27.,  29.,  30.,  26.,   0.,  31.,  34.,  32.,\n",
       "        33.,  35.,  36.,  39.,  38.,  41.,  37.,  40.,  42.,  50.,  43.,\n",
       "        51.,  55.,  66.,  44.,  45.,  52.,  13.,  77.,  54., 208.,  46.,\n",
       "        48.,  56.,  69.,  71.,  58.,  64.,  62.,  60.,  nan,  74.,  72.,\n",
       "        68.,  70.,  99.,  98.,  65.,  73.,  97.,  76.,  88.,  89.,  57.,\n",
       "        53.,  49.,  47.,  87.,  61.,  59.,  83.,  92.,  95.,  82.,  81.,\n",
       "        67.,  93., 101., 102., 117., 103., 108., 119., 121., 125., 124.,\n",
       "       113., 128., 135., 107., 123., 120., 126., 110., 130., 116., 136.,\n",
       "       118., 114., 109., 127., 105., 112., 122., 129., 104., 115.,  75.,\n",
       "        91.,  84.,  90.,  94.,  78.,  86.,  85.,  79.,  63.])"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results['number'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "0223ebcf-852f-4aad-989e-9b21d72e781d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IntegerArray>\n",
       "[22,  3,  7,  5, 23,  8, 14,  1,  4, 12,\n",
       " ...\n",
       " 75, 91, 84, 90, 94, 78, 86, 85, 79, 63]\n",
       "Length: 130, dtype: Int64"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results['number'] = df_results['number'].astype('Int64')\n",
    "df_results['number'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "2f331309-5026-465b-a663-b1d3156daa55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IntegerArray>\n",
       "[   2,    3,    5,    7,    1,   14,    8,    4,    9,   13,   15,   16,    6,\n",
       "   11,   10,   17, <NA>,   12,   18,   19,   20,   21,   22,   23,   24,    0]\n",
       "Length: 26, dtype: Int64"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results['rank'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cb96288-1da8-44e1-aed9-cbb99fa26366",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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