5_Random_F1_Facts-Part_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 Random F1 Facts- Part 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Analysis Edition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's import some modules:\n",
    "\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting all graphs to be a specific color and size\n",
    "sns.set_palette('Set3')\n",
    "plt.rcParams['figure.figsize']=10,6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read in the 13 CSV files:\n",
    "\n",
    "folder = '../f1db_csv/'\n",
    "\n",
    "filelist = [file for file in os.listdir(folder) if file.endswith('.csv')]\n",
    "for file in filelist:\n",
    "    exec(\"%s = pd.read_csv('%s')\" % (file.split('.')[0], os.path.join(folder,file)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['circuits.csv',\n",
       " 'status.csv',\n",
       " 'lap_times.csv',\n",
       " 'drivers.csv',\n",
       " 'races.csv',\n",
       " 'constructors.csv',\n",
       " 'constructor_standings.csv',\n",
       " 'qualifying.csv',\n",
       " 'driver_standings.csv',\n",
       " 'constructor_results.csv',\n",
       " 'pit_stops.csv',\n",
       " 'seasons.csv',\n",
       " 'results.csv']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the dataframes were created correctly:\n",
    "\n",
    "filelist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>resultId</th>\n",
       "      <th>raceId</th>\n",
       "      <th>driverId</th>\n",
       "      <th>constructorId</th>\n",
       "      <th>number_x</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_x</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",
       "      <th>constructorRef</th>\n",
       "      <th>name_x</th>\n",
       "      <th>nationality_x</th>\n",
       "      <th>url_x</th>\n",
       "      <th>year</th>\n",
       "      <th>round</th>\n",
       "      <th>circuitId</th>\n",
       "      <th>name_y</th>\n",
       "      <th>date</th>\n",
       "      <th>time_y</th>\n",
       "      <th>url_y</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_x</th>\n",
       "      <th>driverRef</th>\n",
       "      <th>number_y</th>\n",
       "      <th>code</th>\n",
       "      <th>forename</th>\n",
       "      <th>surname</th>\n",
       "      <th>dob</th>\n",
       "      <th>nationality_y</th>\n",
       "      <th>url_y</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\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",
       "      <td>mclaren</td>\n",
       "      <td>McLaren</td>\n",
       "      <td>British</td>\n",
       "      <td>http://en.wikipedia.org/wiki/McLaren</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Australian Grand Prix</td>\n",
       "      <td>2008-03-16</td>\n",
       "      <td>04:30:00</td>\n",
       "      <td>http://en.wikipedia.org/wiki/2008_Australian_G...</td>\n",
       "      <td>albert_park</td>\n",
       "      <td>Albert Park Grand Prix Circuit</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-37.8497</td>\n",
       "      <td>144.968</td>\n",
       "      <td>10</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Melbourne_Grand_P...</td>\n",
       "      <td>hamilton</td>\n",
       "      <td>44</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",
       "      <td>Finished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\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",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>58</td>\n",
       "      <td>+5.478</td>\n",
       "      <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",
       "      <td>bmw_sauber</td>\n",
       "      <td>BMW Sauber</td>\n",
       "      <td>German</td>\n",
       "      <td>http://en.wikipedia.org/wiki/BMW_Sauber</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Australian Grand Prix</td>\n",
       "      <td>2008-03-16</td>\n",
       "      <td>04:30:00</td>\n",
       "      <td>http://en.wikipedia.org/wiki/2008_Australian_G...</td>\n",
       "      <td>albert_park</td>\n",
       "      <td>Albert Park Grand Prix Circuit</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-37.8497</td>\n",
       "      <td>144.968</td>\n",
       "      <td>10</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Melbourne_Grand_P...</td>\n",
       "      <td>heidfeld</td>\n",
       "      <td>\\N</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",
       "      <td>Finished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <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",
       "      <td>williams</td>\n",
       "      <td>Williams</td>\n",
       "      <td>British</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Williams_Grand_Pr...</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Australian Grand Prix</td>\n",
       "      <td>2008-03-16</td>\n",
       "      <td>04:30:00</td>\n",
       "      <td>http://en.wikipedia.org/wiki/2008_Australian_G...</td>\n",
       "      <td>albert_park</td>\n",
       "      <td>Albert Park Grand Prix Circuit</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-37.8497</td>\n",
       "      <td>144.968</td>\n",
       "      <td>10</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Melbourne_Grand_P...</td>\n",
       "      <td>rosberg</td>\n",
       "      <td>6</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",
       "      <td>Finished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\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",
       "      <td>58</td>\n",
       "      <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",
       "      <td>renault</td>\n",
       "      <td>Renault</td>\n",
       "      <td>French</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Renault_in_Formul...</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Australian Grand Prix</td>\n",
       "      <td>2008-03-16</td>\n",
       "      <td>04:30:00</td>\n",
       "      <td>http://en.wikipedia.org/wiki/2008_Australian_G...</td>\n",
       "      <td>albert_park</td>\n",
       "      <td>Albert Park Grand Prix Circuit</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-37.8497</td>\n",
       "      <td>144.968</td>\n",
       "      <td>10</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Melbourne_Grand_P...</td>\n",
       "      <td>alonso</td>\n",
       "      <td>14</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",
       "      <td>Finished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <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",
       "      <td>mclaren</td>\n",
       "      <td>McLaren</td>\n",
       "      <td>British</td>\n",
       "      <td>http://en.wikipedia.org/wiki/McLaren</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Australian Grand Prix</td>\n",
       "      <td>2008-03-16</td>\n",
       "      <td>04:30:00</td>\n",
       "      <td>http://en.wikipedia.org/wiki/2008_Australian_G...</td>\n",
       "      <td>albert_park</td>\n",
       "      <td>Albert Park Grand Prix Circuit</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-37.8497</td>\n",
       "      <td>144.968</td>\n",
       "      <td>10</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Melbourne_Grand_P...</td>\n",
       "      <td>kovalainen</td>\n",
       "      <td>\\N</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",
       "      <td>Finished</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   resultId  raceId  driverId  constructorId number_x  grid position  \\\n",
       "0         1      18         1              1       22     1        1   \n",
       "1         2      18         2              2        3     5        2   \n",
       "2         3      18         3              3        7     7        3   \n",
       "3         4      18         4              4        5    11        4   \n",
       "4         5      18         5              1       23     3        5   \n",
       "\n",
       "  positionText  positionOrder  points  laps       time_x milliseconds  \\\n",
       "0            1              1    10.0    58  1:34:50.616      5690616   \n",
       "1            2              2     8.0    58       +5.478      5696094   \n",
       "2            3              3     6.0    58       +8.163      5698779   \n",
       "3            4              4     5.0    58      +17.181      5707797   \n",
       "4            5              5     4.0    58      +18.014      5708630   \n",
       "\n",
       "  fastestLap rank fastestLapTime fastestLapSpeed  statusId constructorRef  \\\n",
       "0         39    2       1:27.452         218.300         1        mclaren   \n",
       "1         41    3       1:27.739         217.586         1     bmw_sauber   \n",
       "2         41    5       1:28.090         216.719         1       williams   \n",
       "3         58    7       1:28.603         215.464         1        renault   \n",
       "4         43    1       1:27.418         218.385         1        mclaren   \n",
       "\n",
       "       name_x nationality_x  \\\n",
       "0     McLaren       British   \n",
       "1  BMW Sauber        German   \n",
       "2    Williams       British   \n",
       "3     Renault        French   \n",
       "4     McLaren       British   \n",
       "\n",
       "                                               url_x  year  round  circuitId  \\\n",
       "0               http://en.wikipedia.org/wiki/McLaren  2008      1          1   \n",
       "1            http://en.wikipedia.org/wiki/BMW_Sauber  2008      1          1   \n",
       "2  http://en.wikipedia.org/wiki/Williams_Grand_Pr...  2008      1          1   \n",
       "3  http://en.wikipedia.org/wiki/Renault_in_Formul...  2008      1          1   \n",
       "4               http://en.wikipedia.org/wiki/McLaren  2008      1          1   \n",
       "\n",
       "                  name_y        date    time_y  \\\n",
       "0  Australian Grand Prix  2008-03-16  04:30:00   \n",
       "1  Australian Grand Prix  2008-03-16  04:30:00   \n",
       "2  Australian Grand Prix  2008-03-16  04:30:00   \n",
       "3  Australian Grand Prix  2008-03-16  04:30:00   \n",
       "4  Australian Grand Prix  2008-03-16  04:30:00   \n",
       "\n",
       "                                               url_y   circuitRef  \\\n",
       "0  http://en.wikipedia.org/wiki/2008_Australian_G...  albert_park   \n",
       "1  http://en.wikipedia.org/wiki/2008_Australian_G...  albert_park   \n",
       "2  http://en.wikipedia.org/wiki/2008_Australian_G...  albert_park   \n",
       "3  http://en.wikipedia.org/wiki/2008_Australian_G...  albert_park   \n",
       "4  http://en.wikipedia.org/wiki/2008_Australian_G...  albert_park   \n",
       "\n",
       "                             name   location    country      lat      lng alt  \\\n",
       "0  Albert Park Grand Prix Circuit  Melbourne  Australia -37.8497  144.968  10   \n",
       "1  Albert Park Grand Prix Circuit  Melbourne  Australia -37.8497  144.968  10   \n",
       "2  Albert Park Grand Prix Circuit  Melbourne  Australia -37.8497  144.968  10   \n",
       "3  Albert Park Grand Prix Circuit  Melbourne  Australia -37.8497  144.968  10   \n",
       "4  Albert Park Grand Prix Circuit  Melbourne  Australia -37.8497  144.968  10   \n",
       "\n",
       "                                               url_x   driverRef number_y  \\\n",
       "0  http://en.wikipedia.org/wiki/Melbourne_Grand_P...    hamilton       44   \n",
       "1  http://en.wikipedia.org/wiki/Melbourne_Grand_P...    heidfeld       \\N   \n",
       "2  http://en.wikipedia.org/wiki/Melbourne_Grand_P...     rosberg        6   \n",
       "3  http://en.wikipedia.org/wiki/Melbourne_Grand_P...      alonso       14   \n",
       "4  http://en.wikipedia.org/wiki/Melbourne_Grand_P...  kovalainen       \\N   \n",
       "\n",
       "  code  forename     surname         dob nationality_y  \\\n",
       "0  HAM     Lewis    Hamilton  1985-01-07       British   \n",
       "1  HEI      Nick    Heidfeld  1977-05-10        German   \n",
       "2  ROS      Nico     Rosberg  1985-06-27        German   \n",
       "3  ALO  Fernando      Alonso  1981-07-29       Spanish   \n",
       "4  KOV    Heikki  Kovalainen  1981-10-19       Finnish   \n",
       "\n",
       "                                            url_y    status  \n",
       "0     http://en.wikipedia.org/wiki/Lewis_Hamilton  Finished  \n",
       "1      http://en.wikipedia.org/wiki/Nick_Heidfeld  Finished  \n",
       "2       http://en.wikipedia.org/wiki/Nico_Rosberg  Finished  \n",
       "3    http://en.wikipedia.org/wiki/Fernando_Alonso  Finished  \n",
       "4  http://en.wikipedia.org/wiki/Heikki_Kovalainen  Finished  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merging into 1 dataframe:\n",
    "f1 = results.copy()\n",
    "f1 = f1.merge(constructors, how='left', on='constructorId')\n",
    "f1 = f1.merge(races, how='left', on='raceId')\n",
    "f1 = f1.merge(circuits, how='left', on='circuitId')\n",
    "f1 = f1.merge(drivers, how='left', on='driverId')\n",
    "f1 = f1.merge(status, how='left', on='statusId')\n",
    "f1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 24880 entries, 0 to 24879\n",
      "Data columns (total 46 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   resultId         24880 non-null  int64  \n",
      " 1   raceId           24880 non-null  int64  \n",
      " 2   driverId         24880 non-null  int64  \n",
      " 3   constructorId    24880 non-null  int64  \n",
      " 4   number_x         24880 non-null  object \n",
      " 5   grid             24880 non-null  int64  \n",
      " 6   position         24880 non-null  object \n",
      " 7   positionText     24880 non-null  object \n",
      " 8   positionOrder    24880 non-null  int64  \n",
      " 9   points           24880 non-null  float64\n",
      " 10  laps             24880 non-null  int64  \n",
      " 11  time_x           24880 non-null  object \n",
      " 12  milliseconds     24880 non-null  object \n",
      " 13  fastestLap       24880 non-null  object \n",
      " 14  rank             24880 non-null  object \n",
      " 15  fastestLapTime   24880 non-null  object \n",
      " 16  fastestLapSpeed  24880 non-null  object \n",
      " 17  statusId         24880 non-null  int64  \n",
      " 18  constructorRef   24880 non-null  object \n",
      " 19  name_x           24880 non-null  object \n",
      " 20  nationality_x    24880 non-null  object \n",
      " 21  url_x            24880 non-null  object \n",
      " 22  year             24880 non-null  int64  \n",
      " 23  round            24880 non-null  int64  \n",
      " 24  circuitId        24880 non-null  int64  \n",
      " 25  name_y           24880 non-null  object \n",
      " 26  date             24880 non-null  object \n",
      " 27  time_y           24880 non-null  object \n",
      " 28  url_y            24880 non-null  object \n",
      " 29  circuitRef       24880 non-null  object \n",
      " 30  name             24880 non-null  object \n",
      " 31  location         24880 non-null  object \n",
      " 32  country          24880 non-null  object \n",
      " 33  lat              24880 non-null  float64\n",
      " 34  lng              24880 non-null  float64\n",
      " 35  alt              24880 non-null  object \n",
      " 36  url_x            24880 non-null  object \n",
      " 37  driverRef        24880 non-null  object \n",
      " 38  number_y         24880 non-null  object \n",
      " 39  code             24880 non-null  object \n",
      " 40  forename         24880 non-null  object \n",
      " 41  surname          24880 non-null  object \n",
      " 42  dob              24880 non-null  object \n",
      " 43  nationality_y    24880 non-null  object \n",
      " 44  url_y            24879 non-null  object \n",
      " 45  status           24880 non-null  object \n",
      "dtypes: float64(3), int64(11), object(32)\n",
      "memory usage: 8.9+ MB\n"
     ]
    }
   ],
   "source": [
    "f1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replacing any empty vales with NaN\n",
    "f1.replace(r'\\N', np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Focusing the dataframe:\n",
    "f1 = f1[['year', 'name', 'date', 'driverRef', 'name_x', 'grid',\n",
    "       'position', 'points', 'laps', 'time_x',\n",
    "       'milliseconds', 'fastestLap', 'rank', 'fastestLapTime',\n",
    "       'fastestLapSpeed',  'round',\n",
    "        'dob', 'nationality_y', 'status']]\n",
    "f1.position = f1.position.astype(float)\n",
    "f1 = f1.set_index('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>driverRef</th>\n",
       "      <th>constructor</th>\n",
       "      <th>grid</th>\n",
       "      <th>position</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>round</th>\n",
       "      <th>dob</th>\n",
       "      <th>nationality</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1950-05-13</th>\n",
       "      <td>1950</td>\n",
       "      <td>Silverstone Circuit</td>\n",
       "      <td>claes</td>\n",
       "      <td>Talbot-Lago</td>\n",
       "      <td>21</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64</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>1</td>\n",
       "      <td>1916-08-11</td>\n",
       "      <td>Belgian</td>\n",
       "      <td>+6 Laps</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-06-18</th>\n",
       "      <td>1950</td>\n",
       "      <td>Circuit de Spa-Francorchamps</td>\n",
       "      <td>farina</td>\n",
       "      <td>Alfa Romeo</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>35</td>\n",
       "      <td>+4:05.0</td>\n",
       "      <td>10291000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>1906-10-30</td>\n",
       "      <td>Italian</td>\n",
       "      <td>Finished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-06-18</th>\n",
       "      <td>1950</td>\n",
       "      <td>Circuit de Spa-Francorchamps</td>\n",
       "      <td>ascari</td>\n",
       "      <td>Ferrari</td>\n",
       "      <td>7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>34</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>5</td>\n",
       "      <td>1918-07-13</td>\n",
       "      <td>Italian</td>\n",
       "      <td>+1 Lap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-06-18</th>\n",
       "      <td>1950</td>\n",
       "      <td>Circuit de Spa-Francorchamps</td>\n",
       "      <td>villoresi</td>\n",
       "      <td>Ferrari</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33</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>5</td>\n",
       "      <td>1909-05-16</td>\n",
       "      <td>Italian</td>\n",
       "      <td>+2 Laps</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-06-18</th>\n",
       "      <td>1950</td>\n",
       "      <td>Circuit de Spa-Francorchamps</td>\n",
       "      <td>levegh</td>\n",
       "      <td>Talbot-Lago</td>\n",
       "      <td>10</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33</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>5</td>\n",
       "      <td>1905-12-22</td>\n",
       "      <td>French</td>\n",
       "      <td>+2 Laps</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            year                          name  driverRef  constructor  grid  \\\n",
       "date                                                                           \n",
       "1950-05-13  1950           Silverstone Circuit      claes  Talbot-Lago    21   \n",
       "1950-06-18  1950  Circuit de Spa-Francorchamps     farina   Alfa Romeo     1   \n",
       "1950-06-18  1950  Circuit de Spa-Francorchamps     ascari      Ferrari     7   \n",
       "1950-06-18  1950  Circuit de Spa-Francorchamps  villoresi      Ferrari     4   \n",
       "1950-06-18  1950  Circuit de Spa-Francorchamps     levegh  Talbot-Lago    10   \n",
       "\n",
       "            position  points  laps     time milliseconds fastestLap rank  \\\n",
       "date                                                                       \n",
       "1950-05-13      11.0     0.0    64      NaN          NaN        NaN  NaN   \n",
       "1950-06-18       4.0     4.0    35  +4:05.0     10291000        NaN  NaN   \n",
       "1950-06-18       5.0     2.0    34      NaN          NaN        NaN  NaN   \n",
       "1950-06-18       6.0     0.0    33      NaN          NaN        NaN  NaN   \n",
       "1950-06-18       7.0     0.0    33      NaN          NaN        NaN  NaN   \n",
       "\n",
       "           fastestLapTime fastestLapSpeed  round         dob nationality  \\\n",
       "date                                                                       \n",
       "1950-05-13            NaN             NaN      1  1916-08-11     Belgian   \n",
       "1950-06-18            NaN             NaN      5  1906-10-30     Italian   \n",
       "1950-06-18            NaN             NaN      5  1918-07-13     Italian   \n",
       "1950-06-18            NaN             NaN      5  1909-05-16     Italian   \n",
       "1950-06-18            NaN             NaN      5  1905-12-22      French   \n",
       "\n",
       "              status  \n",
       "date                  \n",
       "1950-05-13   +6 Laps  \n",
       "1950-06-18  Finished  \n",
       "1950-06-18    +1 Lap  \n",
       "1950-06-18   +2 Laps  \n",
       "1950-06-18   +2 Laps  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1 = f1.rename(columns={'name_x':'constructor', 'time_x': 'time', 'nationality_y':'nationality'})\n",
    "f1 = f1.sort_values('year')\n",
    "f1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 24880 entries, 1950-05-13 to 2020-11-01\n",
      "Data columns (total 18 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   year             24880 non-null  int64  \n",
      " 1   name             24880 non-null  object \n",
      " 2   driverRef        24880 non-null  object \n",
      " 3   constructor      24880 non-null  object \n",
      " 4   grid             24880 non-null  int64  \n",
      " 5   position         14154 non-null  float64\n",
      " 6   points           24880 non-null  float64\n",
      " 7   laps             24880 non-null  int64  \n",
      " 8   time             6546 non-null   object \n",
      " 9   milliseconds     6545 non-null   object \n",
      " 10  fastestLap       6474 non-null   object \n",
      " 11  rank             6631 non-null   object \n",
      " 12  fastestLapTime   6474 non-null   object \n",
      " 13  fastestLapSpeed  6474 non-null   object \n",
      " 14  round            24880 non-null  int64  \n",
      " 15  dob              24880 non-null  object \n",
      " 16  nationality      24880 non-null  object \n",
      " 17  status           24880 non-null  object \n",
      "dtypes: float64(2), int64(4), object(12)\n",
      "memory usage: 3.6+ MB\n"
     ]
    }
   ],
   "source": [
    "f1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1: Who participated in the most races?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>driver</th>\n",
       "      <th>number_of_races</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>raikkonen</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>barrichello</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alonso</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>button</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>michael_schumacher</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               driver  number_of_races\n",
       "0           raikkonen              328\n",
       "1         barrichello              326\n",
       "2              alonso              314\n",
       "3              button              309\n",
       "4  michael_schumacher              308"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_races = f1.driverRef.value_counts().head().copy()\n",
    "most_races = pd.DataFrame(most_races)\n",
    "most_races = most_races.reset_index()\n",
    "most_races.columns = ['driver', 'number_of_races']\n",
    "most_races"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='driver', y='number_of_races', data=most_races)\n",
    "plt.title('Who has the most races in F1?', fontsize=20)\n",
    "plt.ylabel('Number of Races', fontsize=15)\n",
    "plt.xlabel('Driver', fontsize=15)\n",
    "plt.xticks(np.arange(5), most_races.driver.str.capitalize(), fontsize=11);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2: Who has driven in F1 the longest?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_6fdd6280_279d_11eb_a0c9_3af9d31835f5\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >driver</th>        <th class=\"col_heading level0 col1\" >number_of_years</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_6fdd6280_279d_11eb_a0c9_3af9d31835f5row0_col0\" class=\"data row0 col0\" >barrichello</td>\n",
       "                        <td id=\"T_6fdd6280_279d_11eb_a0c9_3af9d31835f5row0_col1\" class=\"data row0 col1\" >19</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_6fdd6280_279d_11eb_a0c9_3af9d31835f5row1_col0\" class=\"data row1 col0\" >michael_schumacher</td>\n",
       "                        <td id=\"T_6fdd6280_279d_11eb_a0c9_3af9d31835f5row1_col1\" class=\"data row1 col1\" >19</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_6fdd6280_279d_11eb_a0c9_3af9d31835f5row2_col0\" class=\"data row2 col0\" >hill</td>\n",
       "                        <td id=\"T_6fdd6280_279d_11eb_a0c9_3af9d31835f5row2_col1\" class=\"data row2 col1\" >18</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_6fdd6280_279d_11eb_a0c9_3af9d31835f5row3_col0\" class=\"data row3 col0\" >button</td>\n",
       "                        <td id=\"T_6fdd6280_279d_11eb_a0c9_3af9d31835f5row3_col1\" class=\"data row3 col1\" >18</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_6fdd6280_279d_11eb_a0c9_3af9d31835f5row4_col0\" class=\"data row4 col0\" >raikkonen</td>\n",
       "                        <td id=\"T_6fdd6280_279d_11eb_a0c9_3af9d31835f5row4_col1\" class=\"data row4 col1\" >18</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fec1107d0d0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_years = f1[['year', 'driverRef']].copy()\n",
    "most_years = most_years.drop_duplicates(['year', 'driverRef'])[['year', 'driverRef']]\n",
    "most_years = pd.DataFrame(most_years)\n",
    "most_years = most_years.value_counts('driverRef').reset_index().head(5)\n",
    "most_years.columns = ['driver', 'number_of_years']\n",
    "most_years.style.hide_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kdcRa8x3AQvKxU74/3yGs9l76sMfOynr/+tfvzcp6tbx11tl5vkOQpEXNFjdJkqSOMHGTJEnqCBM3SZKkjjBxkyRJ6ggTN0mSpI4wcZMkSeoIEzdJkqSOMHGTJEnqCBM3SZKkjjBxkyRJ6ggTN0mSpI4wcZMkSeoIEzdJkqSOMHGTJEnqCBM3SZKkjjBxkyRJ6oi15jsASfr+8b+c7xBWe4/d6f6ztu4rPvBvs7ZuNTZ+9VtmZb0HHf7TWVmvlvnnp28/0fXZ4iZJktQRJm6SJEkdYeImSZLUEXN6jluSQ4Hdgcur6gFt2ZeA+7ZV1gOurqrthix7EXAdcDNwU1UtmYOQJUmSFoy5vjjhMODDwGd7BVX1rN7rJP8OXDPF8jtV1ZWzFp0kSdICNqeJW1V9P8mWw+YlCfBM4PFzGZMkSVJXLKRz3B4D/LGqfj1ifgHHJDktyX5zGJckSdKCsJDGcdsH+MIU8x9VVZcm2QQ4Nsm5VfX9YRXbxG4/gC222GLykUqSJM2DBdHilmQt4BnAl0bVqapL2+fLga8DI0e0q6pDqmpJVS3ZeOONJx2uJEnSvFgQiRuwC3BuVV08bGaSOyRZt/caeCJw9hzGJ0mSNO/mNHFL8gXgx8B9k1yc5IXtrL0Z6CZNcrckR7aTdwFOSnIm8FPg21V11FzFLUmStBDM9VWl+4wo33dI2aXAbu3rC4BtZzU4SZKkBW6hdJVKkiRpJUzcJEmSOsLETZIkqSNM3CRJkjrCxE2SJKkjTNwkSZI6wsRNkiSpI0zcJEmSOsLETZIkqSNM3CRJkjrCxE2SJKkjTNwkSZI6wsRNkiSpI0zcJEmSOsLETZIkqSNM3CRJkjrCxE2SJKkjTNwkSZI6wsRNkiSpI0zcJEmSOsLETZIkqSNM3CRJkjrCxE2SJKkjTNwkSZI6wsRNkiSpI0zcJEmSOsLETZIkqSNM3CRJkjrCxE2SJKkjTNwkSZI6Yq1xKiU5borZtwDXAmcAn66q308gLkmSJA0Yt8XtT8C9gUcD6wDXt8+PBu4D3AF4JXBOkofNQpySJEmL3riJ27eAK4Atq+qRVfW0qnokcE/gSuArwL2AXwDvGrWSJIcmuTzJ2X1lBya5JMkZ7WO3EcvumuS8JOcn2X/MuCVJklYb4yZuBwBvr6pL+wur6hLg7cCbq+pa4P3Aw6dYz2HArkPKP1BV27WPIwdnJlkT+AjwZGBrYJ8kW48ZuyRJ0mph3MTtrsDaI+bdDrhL+/pyIKNWUlXfB64aO7pltgfOr6oLqmop8EVgjxmsR5IkqbPGTdxOBN6d5KH9hUmW0HSNntAWbQX8bgZxvCLJWW1X6vpD5m8G9F/0cHFbJkmStGiMm7jtR3Pl6E/7zke7BPgJcDXwD33re+80Y/gY8DfAdsBlwL8PqTOsFa9GrTDJfklOTXLqFVdcMc1wJEmSFqaxhgNph/jYLslTgCXApsAfgFP6z0mrqo9PN4Cq+mPvdZJP0FwIMehi4O5905sDlw6p11vnIcAhAEuWLBmZ4EmSJHXJWIlbT1V9G/j2JANIctequqyd/Fvg7CHVTgG2SnJP4BJgb+DZk4xDkiRpoZtW4pZkbZpzy243OK+qfjHG8l8AdgQ2SnIx8FZgxyTb0XR9XkTb7ZrkbsAnq2q3qropySuAo4E1gUOr6pzpxC5JktR149454W40XY9PHjabJulac2Xrqap9hhR/akTdS4Hd+qaPBFYYKkSSJGmxGLfF7ZPAQ4DX0Ayyu3TWIpIkSdJQ4yZujwJeXFVfns1gJEmSNNq4w4FcDvx1NgORJEnS1KZzy6s3JLnTbAYjSZKk0cbtKn0GsAXw2ySn0Ay626+q6lmTDEySJEnLGzdx2wj4Tfv6NsDGsxOOJEmSRhn3zgk7zXYgkiRJmtq457hJkiRpno1scUvyMuArVXVF+3pKVfXRiUYmSZKk5UzVVfph4FTgivb1VAowcZMkSZpFIxO3qlpj2GtJkiTNDxMySZKkjhgrcUvyyiTvHjHvXUleMdmwJEmSNGjcFreXAeePmPerdr4kSZJm0biJ2z0YnbhdCGw5kWgkSZI00riJ25+B+46Yd1/g2smEI0mSpFHGTdy+CRyY5IH9hUkeALwV+MakA5MkSdLyxr1X6RuBHYCfJfkZcBlwV+DBwNnA/rMTniRJknrGanGrqquAhwEvp7nZ/Drt80uBh1fVn2ctQkmSJAHjt7hRVTcAH28fkiRJmmNjJ249SdYAbjdYXlV/mUhEkiRJGmrcAXiT5A1Jzgf+D7huyEOSJEmzaNyrSl9JcwHCp4AA7wDeTjP47kXAfrMRnCRJkpYZN3F7Mc2wH+9tpw+vqrcB2wDnAlvNQmySJEnqM27idk/gjKq6maardD2AqroF+CjwvFmJTpIkSbcaN3H7E3DH9vXvaMZv61mfZngQSZIkzaJxryr9Ic04bkcCn6e5i8IGwFKasd2+NzvhSZIkqWfcxO1AYLP29Ttpukr3pWlpOxb4xwnHJUmSpAFjJW5VdR5wXvv6RuCf2ockSZLmyLjnuA2V5DZJnp/knEkFJEmSpOGmbHFL8jfAXsDdgQuAw6rqT0nWAV4BvIrmZvPHz3KckiRJi97IxC3JY4CjaG5vdQWwAfCKJHsBXwTuRXOxwp5V9eM5iFWSJGlRm6qr9G3A2cDmVbUpTeL2Q+BEYG3gsVW1+3SStiSHJrk8ydl9ZQclOTfJWUm+nmS9EctelOTnSc5Icuq47ylJkrS6mCpxeyDwjqq6DKCqrgfeQHMl6eur6qQZvN9hwK4DZccCD6iqB9HcQuuNUyy/U1VtV1VLZvDekiRJnTZV4rYh8IeBst70r2byZlX1feCqgbJjquqmdvJkYPOZrFuSJGl1t7KrSm+X5Pa9B3CHtnzt/vJ23iS8APjOiHkFHJPktCTe1F6SJC06KxvHbdTVoj8YUrbmqgSS5M3ATcB/j6jyqKq6NMkmwLFJzm1b8Iataz9gP4AttthiVcKSJElaMKZK3J4/V0EkeR6wO7BzVdWwOlV1aft8eZKvA9sDQxO3qjoEOARgyZIlQ9cnSZLUNSMTt6r6zFwEkGRXmoseHldVfxlR5w7AGlV1Xfv6icDb5yI+SZKkhWKV7pwwXUm+APwYuG+Si5O8EPgwsC5N9+cZSQ5u694tyZHtoncBTkpyJvBT4NtVddRcxi5JkjTfxr3J/ERU1T5Dij81ou6lwG7t6wuAbWcxNEmSpAVvTlvcJEmSNHMmbpIkSR1h4iZJktQRYyVuSTZJcs++6STZL8l/JHnq7IUnSZKknnFb3A4DXt03/TbgozT3Hf16kn0nG5YkSZIGjZu4PQQ4DiDJGsBLgTdV1f2AdwCvmpXoJEmSdKtxE7c7A39qXz8U2IBlt6Y6Drj3hOOSJEnSgHETt4uBrdvXTwHOrapL2uk7AzdMOjBJkiQtb9wBeA8FDkqyC03i9sa+eY8AfjnpwCRJkrS8sRK3qnpXkotpbuz+jzSJXM8GwCdnITZJkiT1WWnilmRt4HXAt6rqc4Pzq+olsxGYJEmSlrfSc9yq6kbgzcB6sx6NJEmSRhr34oSf0FxNKkmSpHky7sUJrwc+n2QpcCTwR6D6K1TVXyYcmyRJkvqMm7j9pH3+EPDBEXXWXPVwJEmSNMq4idsLGGhhkyRJ0twadziQw2Y5DkmSJK3EuBcnSJIkaZ6N21VKkmcBLwbuA9xucH5VbTLBuCRJkjRgrBa3JM8GPgOcD2wOHAF8q13+WuDDsxWgJEmSGuN2lf4z8K/Ay9vpj1bVC4B7AlcCDgUiSZI0y8ZN3LYCflhVNwM3A3cCqKrrgPcAr5id8CRJktQzbuJ2DbB2+/oS4P598wJsOMmgJEmStKJxL044FXgQcDTN+W0HJLkJWAocwLIBeiVJkjRLxk3c3gXco319QPv6ozR3SzgF2G/yoUmSJKnfuAPwngyc3L6+GtgjydrA2lV17eyFJ0mSpJ5pDcCbxt2T7ACsZdImSZI0d8ZO3JK8jObChN8CPwDu25Z/LcmrZiU6SZIk3WrcAXj/GXg/8Ang8TRXkvacADxr4pFJkiRpOeNenPBy4ICqem+SNQfmnUdzGyxJkiTNonG7SjcFThsx7xaG3LtUkiRJkzVu4nY+8LgR8x4L/GIy4UiSJGmUcRO3/wD2T/IWmttfAWyS5IXAa4APjLOSJIcmuTzJ2X1lGyQ5Nsmv2+f1Ryy7a5LzkpyfZP8x45YkSVptjEzckrwpyVoAVfVJ4M3AG4Bz2ipHAh8EDqyqz4/5focBuw6U7Q98r6q2Ar7XTg/GsibwEeDJwNbAPkm2HvM9JUmSVgtTtbi9BTgzyWMBquog4G40ydPfAbsBm7XlY6mq7wNXDRTvAXymff0Z4OlDFt0eOL+qLqiqpcAX2+UkSZIWjakSt22AC4HjkxyWZMOquq6qjqmqz1fVUVV1zQRiuEtVXQbQPm8ypM5mwO/7pi9uy4ZKsl+SU5OcesUVV0wgREmSpPk3MnGrqguranfgGTQXIJyX5EVzFtnyMqSsRlWuqkOqaklVLdl4441nMSxJkqS5s9KLE6rqGzTnlf0X8PEkV7UXGCz3WIUY/pjkrgDt87B1XQzcvW96c+DSVXhPSZKkzhl3AN6nAXsCVwCfApZOMIYjgOcB726fvzGkzinAVknuSXPbrb2BZ08wBkmSpAVvysQtyVY0V3PuDBwCvLGqrp7pmyX5ArAjsFGSi4G30iRsX26HFvkdsFdb927AJ6tqt6q6KckrgKOBNYFDq+qcYe8hSZK0uhqZuCX5V+B1wC+BHarqJ6v6ZlW1z4hZOw+peynNlau96SNphiCRJElalKY6x+2VNGOqLZlE0iZJkqRVM1VX6f3bVi9JkiQtAFMNB2LSJkmStICMe69SSZIkzTMTN0mSpI6Y6ibzWyS5zVwGI0mSpNGmanG7EHgwQJLjktxvbkKSJEnSMFMlbn8Fbt++3hG406xHI0mSpJGmGg7kZ8AHkxzbTv9jkstG1K2qesNkQ5MkSVK/qRK3FwMHAXsARXN3gxtH1C3AxE2SJGkWjUzcqupc4KkASW4Bnl5VP52rwCRJkrS8KW8y3+eewKhuUkmSJM2BsRK3qvptkrWSPAt4NLABcBXwA+BrVXXTLMYoSZIkxkzckmwCHAM8CLgI+CPwSODlwJlJnlhVV8xWkJIkSRr/zgnvBzYEHl5V96qqR1bVvYCHt+Xvn60AJUmS1Bg3cdsNeENVndJf2E6/EXjKpAOTJEnS8sZN3NYGrhsx7zrgtpMJR5IkSaOMm7idDLwhyR36C9vpN7TzJUmSNIvGHQ7ktcDxwO+THENzccImwJOA0NwSS5IkSbNorBa3qjoD2Ao4BNgYeAJN4nYwsFVVnTlbAUqSJKkxbosbVXUlsP8sxiJJkqQpjHuOmyRJkuaZiZskSVJHmLhJkiR1hImbJElSR6w0cUuydpI3J9l2LgKSJEnScCtN3KrqRuDNwHqzHo0kSZJGGrer9CfAQ2czEEmSJE1t3HHcXg98PslS4EiaOydUf4Wq+suEY5MkSVKfcRO3n7TPHwI+OKLOmqsejiRJkkYZN3F7AQMtbJIkSZpbYyVuVXXYLMchSZKklZjWOG5Jtk7y90nelGTTtuzeSdZdlSCS3DfJGX2Pa5O8aqDOjkmu6atzwKq8pyRJUteM1eKW5I7AocCewP+1yx0F/AF4J/A74HUzDaKqzgO2a99rTeAS4OtDqv6gqnaf6ftIkiR12bgtbu8HdgB2BtYF0jfvSGDXCca0M/CbqvrtBNcpSZLUeeMmbs8A3lBVxwM3D8z7LXCPCca0N/CFEfMemeTMJN9Jss2oFSTZL8mpSU694oorJhiaJEnS/Bk3cVsH+NOIeeuyYjI3I0luCzwN+MqQ2acD96iqbYH/BA4ftZ6qOqSqllTVko033ngSoUmSJM27cRO3U4Dnjpi3J/CjyYTDk4HTq+qPgzOq6tqqur59fSRwmyQbTeh9JUmSFrxxx3F7C/DdJN+laQ0rYLckr6ZJ3B47oXj2YUQ3aXsV6x+rqpJsT5N0jmoFlCRJWu2M1eJWVSfRXDSwNvBhmosT3gbcC9ilqk5Z1UCS3B54AvC1vrKXJHlJO7kncHaSM2nu4LB3VTkosCRJWjTGbXGjqn4IPCbJOsD6wNWTvD9pu64NB8oO7nv9YZqkUZIkaVGa1gC8rRtoxnL764RjkSRJ0hTGTtyS7JbkRzSJ2x+AG5L8KMlTZi06SZIk3WqsxC3JPwDfBK4H/gnYq32+HjiinS9JkqRZNO45bm8CDqmqlw6UH5zkYODNwMcnGpkkSZKWM25X6Yb0Xe054H+ADSYTjiRJkkYZN3E7HnjciHmPA74/mXAkSZI0ysiu0iRb901+CPhkkg1pbjV1ObAJ8Lc0dzt40SzGKEmSJKY+x+1smjsk9AT4h/ZR7XTPUcCaE49OkiRJt5oqcdtpzqKQJEnSSo1M3KrqxLkMRJIkSVMb+5ZXPUnWAm47WD7J219JkiRpReMOwHvnJB9NchnNnROuG/KQJEnSLBq3xe0wmmE/PgGcDyydrYAkSZI03LiJ287AP1TVF2YzGEmSJI027gC8vwM8h02SJGkejZu4vR54S5ItZjMYSZIkjTZWV2lVHZlkF+D8JBcBVw+ps/1kQ5MkSVK/sRK3JO8DXgWcghcnSJIkzYtxL054EfDmqnrXbAYjSZKk0cY9x+0vwGmzGYgkSZKmNm7i9kFgvyRZaU1JkiTNinG7SjcCHg6cl+QEVrw4oarqDROMS5IkSQPGTdz2BG4CbgM8Ycj8AkzcJEmSZtG4w4Hcc7YDkSRJ0tTGPcdNkiRJ82zccdxetrI6VfXRVQ9HkiRJo4x7jtuHp5hX7bOJmyRJ0iwaq6u0qtYYfAAbAPsAZwJbz2aQkiRJGr/FbQVVdTXwpSR3Bj4O7DihmCRJkjTEJC5OuBBYMoH1SJIkaQqrlLgluSvwWprkTZIkSbNo3KtKr2DZRQg9twXWBW4AnjHhuCRJkjRg3HPcPsKKidsNwMXAUVX1p1UNJMlFwHXAzcBNVbVkYH5o7pm6G81N7/etqtNX9X0lSZK6Ytw7Jxw4y3H07FRVV46Y92Rgq/bxcOBj7bMkSdKi0KU7J+wBfLYaJwPrtefYSZIkLQojW9ySHDeN9VRV7byKsRRwTJICPl5VhwzM3wz4fd/0xW3ZZYMrSrIfsB/AFltssYphSZIkLQxTdZWOc97aXYEdWPH8t5l4VFVdmmQT4Ngk51bV9/vmZ8gyQ9+3TfoOAViyZMkkYpMkSZp3IxO3qtpr1LwkWwBvAHYHrgQ+sKqBVNWl7fPlSb4ObA/0J24XA3fvm94cuHRV31eSJKkrpnWOW5J7J/kU8GvgacAbgXtU1btWJYgkd0iybu818ETg7IFqRwDPTeMRwDVVtUI3qSRJ0upq3HHctgHeDOxFc57ZPwGHVtXSCcVxF+DrzYgfrAV8vqqOSvISgKo6GDiSZiiQ82mGA3n+hN5bkiSpE6ZM3JI8lCZh2wP4FfAi4L+q6uZJBlFVFwDbDik/uO91AS+f5PtKkiR1yVRXlX6HpsvyLGDvqvrKnEUlSZKkFUzV4vak9vnuwEeSfGSqFVXVJhOLSpIkSSuYKnF725xFIUmSpJWaajgQEzdJkqQFpEu3vJIkSVrUTNwkSZI6wsRNkiSpI0zcJEmSOsLETZIkqSNM3CRJkjrCxE2SJKkjTNwkSZI6wsRNkiSpI0zcJEmSOsLETZIkqSNM3CRJkjrCxE2SJKkjTNwkSZI6wsRNkiSpI0zcJEmSOsLETZIkqSNM3CRJkjrCxE2SJKkjTNwkSZI6wsRNkiSpI0zcJEmSOsLETZIkqSNM3CRJkjrCxE2SJKkjTNwkSZI6wsRNkiSpIxZE4pbk7kmOT/LLJOck+achdXZMck2SM9rHAfMRqyRJ0nxZa74DaN0EvLaqTk+yLnBakmOr6hcD9X5QVbvPQ3ySJEnzbkG0uFXVZVV1evv6OuCXwGbzG5UkSdLCsiASt35JtgQeDPxkyOxHJjkzyXeSbDO3kUmSJM2vhdJVCkCSOwL/A7yqqq4dmH06cI+quj7JbsDhwFYj1rMfsB/AFltsMXsBS5IkzaEF0+KW5DY0Sdt/V9XXBudX1bVVdX37+kjgNkk2GrauqjqkqpZU1ZKNN954VuOWJEmaKwsicUsS4FPAL6vq/SPqbNrWI8n2NLH/ae6ilCRJml8Lpav0UcDfAz9PckZb9iZgC4CqOhjYE3hpkpuAvwJ7V1XNQ6ySJEnzYkEkblV1EpCV1Pkw8OG5iUiSJGnhWRBdpZIkSVo5EzdJkqSOMHGTJEnqCBM3SZKkjjBxkyRJ6ggTN0mSpI4wcZMkSeoIEzdJkqSOMHGTJEnqCBM3SZKkjjBxkyRJ6ggTN0mSpI4wcZMkSeoIEzdJkqSOMHGTJEnqCBM3SZKkjjBxkyRJ6ggTN0mSpI4wcZMkSeoIEzdJkqSOMHGTJEnqCBM3SZKkjjBxkyRJ6ggTN0mSpI4wcZMkSeoIEzdJkqSOMHGTJEnqCBM3SZKkjjBxkyRJ6ggTN0mSpI4wcZMkSeoIEzdJkqSOWDCJW5Jdk5yX5Pwk+w+ZnyQfauefleQh8xGnJEnSfFkQiVuSNYGPAE8Gtgb2SbL1QLUnA1u1j/2Aj81pkJIkSfNsQSRuwPbA+VV1QVUtBb4I7DFQZw/gs9U4GVgvyV3nOlBJkqT5slASt82A3/dNX9yWTbeOJEnSamut+Q6glSFlNYM6TcVkP5ruVIDrk5y3CrEtZBsBV853ENPxsvkOYGHp3PHTcjx+3dW9Y/eaf5nvCBaSTh2/109/kXtMNXOhJG4XA3fvm94cuHQGdQCoqkOAQyYZ4EKU5NSqWjLfcWhmPH7d5vHrLo9dty3247dQukpPAbZKcs8ktwX2Bo4YqHME8Nz26tJHANdU1WVzHagkSdJ8WRAtblV1U5JXAEcDawKHVtU5SV7Szj8YOBLYDTgf+Avw/PmKV5IkaT4siMQNoKqOpEnO+ssO7ntdwMvnOq4FbrXvDl7Nefy6zePXXR67blvUxy9NPiRJkqSFbqGc4yZJkqSVMHGbsCQXJTk3yZlJfpnkRWMsc2SSv2lfn5Bk9yF19k3y1dmIWcsdtzPa508kuc0E1vvJJI9ZSZ0Dk7xvBus+rD03dMbrWKySrJ/khiT/0VfmPuyAmXxXkzw9yfZ901u2w0ZJnWPiNjv2rKptgb2Ajya521SVq2q3qvrN3ISmKexZVdsB27SPZ4y7YJIVzhdNsmZVvaiqfjC5EDUhzwF+THN7vdvOdzCatul+V59Oc4eeni1ZNtbnaqVNbC9rbyXZK3t+kkryiiQvSfLqlaxjxySnTjiuW39oTmh9WyZZcGO5TXo7hzFxm0VVdTbwZ2CzJM9O8pMkP2sfO/fqtV+0Bwwun2TvJKck2Xyg/O5JTk3yzCRrJnlfkrPbx/t6X9j2A3RwkuOS/DrJZ5OknXentjXop0nOSvLBvuVOSHJQkpOSXJDk3bO5nxag27WPPyfZOcmP22P28yR79yq1++mdSb4HfKNtFT0qyeeSnAY8sL8FNcmdkxzarufMJB/ue8/N2pbXc5N8O8nt22Vu2x6Ln7YtDJ9Lcsepgp/qM6FbvQD4N+DnwNMGZ67C92q/NC3tZ7Tfq/u15Q9rP0dntc8Pm8NtXZ31f1eX+4fZm07yJJpjvH97XJ5Lc2/srdvpr7b1hx6jXoKQ5B3t34Hzkjx67jd1Wi4DntQ3/TzgNGgu+quqD8xLVBpLhjQE9DNxm0VJHkUzuvOZNEOdPKKqHkwzTt1nVrLs64EXAbtU1cV95dsC3wFeU1VfpvnVuB3wkPbxYJb/JfkAmmFUtgEeCuzSlr8fOLGqtm+X34Tmn1nPFsBj2/W9KMlW09v6TvpqkjOAPwAXVtUxwOnAo9vjtgvwviTr9y3zAOBJVfWUdvrRwIFV9dCqOmNg/f8B/C+wbdsie2DfvCXAs4H7A7ehaRGCZtDta6pq+7aF4VLgjSvZjpV9Jha19ju0AXAc8GmW/9z3zPR7dRDwxPZYPQz4XZoWvf8B/qWqHgS8Bfif2NK3KoZ9V4eqqqNpxgF9d1VtV1WfpRmh4Bft9J5jHKMNgR+3fwfeDrxn1rZsMg4D9gVIck/g9sDZ7fRypwQkeWPfj8kfJenlBWsl+XibyJ6Z5P5t/U2THJ/ktCTnJHlv37qm/UOzb9nbJ/lKkl+07/flvnkvaMvOTNOYcZe+eSsk1BloMeyfbl+fmaaL/edJTk+yTZIvt+99dJI7tHWn+uG+WZL/affPWUn6/y4/YMQPu5U1mNzaEDDVvjJxmx1fTXObrROB/atqKfA3wNFJzgG+BGyaZNMRyx8IPALYraqu6St/EPA1YK+q+n5btgtwWFUtbd/n0yz7JwJweFXd0M47vY0Dml+g/9z+8Tud5p/PffqW+0pV3dK+/y/7llud9bpfNgZul+RV7euvJjmbJvneALhv3zKfr6qb+qZPmqLbe3fgoKq6BaCq+pv5j66qq9thb37C8sfp79o/gme00ys7Fiv7TCx2LwQ+2+7rrwGPSDJ43+OZfq+OAz6d5B+BzarqLzSfl6VV9V2AqvoesJTlP0eanmHf1VWxsmN0fVV9q319Mgv/7+HxwIPaH5n7Ap8dVinJ82j+pjyq/TH51N7fJ5ofJQe3ieyXaZJZgKvbeg+l+XGzJMmu7byZ/NDseRKwflVt3cbyD22MOwJvovmBvC2wE9D7vzjThHpr4CNV9UCaUyaOpmkM2Rq4GdinrTfVD/f/Ak6uqge1++gTfeufaYPJYEPAUAtmHLfVzJ5VdXaSvYD/SnIf4AvAa6vq8PYXzV9omviHORl4As39yn7dV34xsC6wI00yBc09XAfHdOmfvqHv9c0sO+YBnl5VF4yIYdRyq72quiHJt2gSrafS/Fp/RlVVkl+x/HG7fmDxwelxDe7vddrXAV5WVcdNY10r+0wsWm0LyrOBG9J0mUHTwvm8warM7Hv1DJqWtscDx6cZRPySIesaXJ9mYOC7ehnLN0aM+vs6zLDjTV/ZjX1lXfh7WDTJ1t7As4BH0bTqD9od+FhVXQtQVX/qm3deVf2sfX0yzd9CaAbJPyjJDjT7bVOaJOQomiTwTkn2bOuuTdPjNI4zgfsl+QhwAvDttvwpND+0/tDGeD1A24g1mFD/+5jvdV5fj8jpwD36erZOA+7dvt4YOLTtcbqJ9od7+0N+B5r/07Rx9f8QP7yqbmjj7P2wO5Zm/2yf5LVtvdvT/F/vGWwIGMoWt1lUVV8BjgH2B9YDLmxnvZDmAz3KUcBLgSOTbNNXfhVN5v73fQf+WGDfJLdJc2XV84DvjhHeETTnfPSaaTdqm9QXvTaxfhzwK5rjdlGbtD2BZV/omfgWTStnr9l8ozGWOQJ4TZJ12mXW7XVZTGGmn4nF4OnAuVW1eVVtWVVbAk9kxTuxTHsfpjkv5V5V9dOqejfNd//BwLnA2kl2auvtRJMs/mpym7U4DXxXf0OTNJPkrjQtMz3XAneeYnp1PEaH0bRCnT2QkPXLFMuP+nHyGmB94OFtS9PhLEuSez80t2sf96+qvRlD24hwf5rv3i7AmUlut5IYRyXUNzF1Ej+4baO29WM0SeQD2xbEi4esa5iVNZj09s99quqf++qO9cPfxG32vZGmKfRVwOFJTqK5omnUFwmAtoVlX+CIJA/pK7+G5h/NU5McQDOC9FnAz9rHWSzfZDvKq2g+UGcm+TlNsjjYXbTY9M6bOZvmu/F2mqT7fUl+DOxJs39n6tU0LaZnJzkTOGCMZd5N80v0lCRnASfR/HGbykw/E4vB84H/7i+oqh/THO/H9hXPZB+uCRzWngtzJnBX4ONtd+r/A97ZHsN30rTKL53A9ixWw76rhwCbt/v+QzSnHPR8Dnh2ll2ccBZwXpoLT766Oh6jNhF6M/CvU1T7JvDSJOsCJNlwjFWvB1zWtnZuBuzRN28mPzRp624O3FxVh9P8rdyYpoXrmzT3Kb9LW++OSaZq+ICmkeReaYb9Ccu6PqdrPYb8cG9b/X7UxtmLf9wf4qvcYOKdEyRJWk0kuQjYvZpRDfrLDwNOBTYC7lhVr2uTmjfSXAx1E3AdzQ+YxwLvq6ol7bI79qaT3AP4Ck2L5O9pWol+VVUHtq3TB9Ikc7fQdNm+raq+1nv/quq/mr4/vifT/FCF5kfQ56rqPe28F9I0NtxC08r2VJrTSU6tqo3aOlsOTP8LzQ+1C4FTaC70W9K/LW29fdv9tWc7fWDf/nkC8FGaiwzPAh4OvKqqTmiT1o/QJHM303RzvmdwO/un2wT5vcBj2n1zY7u+k5Kc0MbV6/odycRNkiSpI+wqlSRJ6oiFfmWMJElaTbTnZg+708UTq+ryuY6ni+wqlSRJ6gi7SiVJkjrCxE2SJKkjTNwkrdbS3Jux2sctSf6c5n6H75jitnOD67goffd3lKT54sUJkhaDa4De/RTvTHPj+JcC+yXZtapOW8nyf8tKBs2WpLngxQmSVmvtgJqv6A3M2Ve+HvB9mvsF3reqbh6y7DpV9de5iLN9vwBr9+5zKEmD7CqVtChV1dXA62luAP2EJFu23anPSfLZJFfT3G5nua7SJM9PcmOb+N0qyTbt8jv3le2R5NQkNyT5Q5L3tqPL9+YfmOTKJI9OcgrNPQ73muVNl9RhJm6SFrPjaW7184i+svfR3PpnL5p7Vg76Wvv8twPlzwIup7kpNUme2db9KfA04G3AfsC7Bpa7PfAZ4JM03bk/ndGWSFoUPMdN0qJVVTcmuRK4S1/xyVX18imWuSbJUTSJ2qf7Zj0L+EpV3dx2eR4EfLaqXtarkORG4CNJ3lVVvXPm1gFeU1XfmNBmSVqN2eImabHLwPS3x1jmS8DOSXo3tN4OuE9bTvt6C+DLSdbqPYDjgNsBD+hbVwHfmXn4khYTEzdJi1aS2wEbAn/sK/7jiOr9jgD+j2W37nkWcAlwUjvduxDiyLZe73FhW373vnX9uaqWTjt4SYuSXaWSFrOdaP4O/rivbKWX2lfV9Um+TZOwHQI8E/hyLbtM/6r2eT/gZ0NWcWHfay/tlzQ2EzdJi1J7Veh7gPOB77J8K9g4vgh8KclTgXu10z3n0bTAbVlVn1j1aCWpYeImaTFYK0nvytF1gYfSDMB7e2DX9oKC6a7z28BfgI8DF1bVrVeDVtUtSV4LfC7JnWjOYVtKk+A9Hdizqv6yCtsjaZEycZO0GNyZpju0gGtpWtn+C/jPqvrDTFZYVTckOQJ4DvDuIfO/lORa4E3AC4CbgQuAb9EkcZI0bd45QZIkqSO8qlSSJKkjTNwkSZI6wsRNkiSpI0zcJEmSOsLETZIkqSNM3CRJkjrCxE2SJKkjTNwkSZI6wsRNkiSpI/4/jLxu7uOrQbgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='driver', y='number_of_years', data=most_years)\n",
    "plt.title('Who raced for the longest in F1?', fontsize=20)\n",
    "plt.ylabel('Number of Years Racing', fontsize=15)\n",
    "plt.xlabel('Driver', fontsize=15)\n",
    "plt.xticks(np.arange(5), most_races.driver.str.capitalize(), fontsize=11)\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3: Which track has been used the most?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_7e3a8b14_279d_11eb_a0c9_3af9d31835f5\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >circuit</th>        <th class=\"col_heading level0 col1\" >years_used</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_7e3a8b14_279d_11eb_a0c9_3af9d31835f5row0_col0\" class=\"data row0 col0\" >Autodromo Nazionale di Monza</td>\n",
       "                        <td id=\"T_7e3a8b14_279d_11eb_a0c9_3af9d31835f5row0_col1\" class=\"data row0 col1\" >70</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_7e3a8b14_279d_11eb_a0c9_3af9d31835f5row1_col0\" class=\"data row1 col0\" >Circuit de Monaco</td>\n",
       "                        <td id=\"T_7e3a8b14_279d_11eb_a0c9_3af9d31835f5row1_col1\" class=\"data row1 col1\" >66</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_7e3a8b14_279d_11eb_a0c9_3af9d31835f5row2_col0\" class=\"data row2 col0\" >Silverstone Circuit</td>\n",
       "                        <td id=\"T_7e3a8b14_279d_11eb_a0c9_3af9d31835f5row2_col1\" class=\"data row2 col1\" >54</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_7e3a8b14_279d_11eb_a0c9_3af9d31835f5row3_col0\" class=\"data row3 col0\" >Circuit de Spa-Francorchamps</td>\n",
       "                        <td id=\"T_7e3a8b14_279d_11eb_a0c9_3af9d31835f5row3_col1\" class=\"data row3 col1\" >53</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_7e3a8b14_279d_11eb_a0c9_3af9d31835f5row4_col0\" class=\"data row4 col0\" >Nürburgring</td>\n",
       "                        <td id=\"T_7e3a8b14_279d_11eb_a0c9_3af9d31835f5row4_col1\" class=\"data row4 col1\" >41</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fec120cec50>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "track_use = f1[['year', 'name']].copy()\n",
    "track_use = track_use.drop_duplicates(['year', 'name'])[['year', 'name']]\n",
    "track_use = pd.DataFrame(track_use)\n",
    "track_use = track_use.value_counts('name').reset_index()\n",
    "track_use.columns = ['circuit', 'years_used']\n",
    "most_track_use = track_use.copy().head()\n",
    "most_track_use.style.hide_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='circuit', y='years_used', data=most_track_use )\n",
    "plt.title('Circuit used for the most years in F1', fontsize=20)\n",
    "plt.ylabel('Number of Years Used', fontsize=15)\n",
    "plt.xlabel('Circuit Name', labelpad=12, fontsize=15)\n",
    "plt.xticks(np.arange(5), most_races.driver.str.capitalize(), rotation=15, size=11);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 4: What tracks have only been visited once?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "a = track_use[track_use['years_used']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "least = f1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'name', 'driverRef', 'constructor', 'grid', 'position',\n",
       "       'points', 'laps', 'time', 'milliseconds', 'fastestLap', 'rank',\n",
       "       'fastestLapTime', 'fastestLapSpeed', 'round', 'dob', 'nationality',\n",
       "       'status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "least.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >year</th>        <th class=\"col_heading level0 col1\" >circuit</th>        <th class=\"col_heading level0 col2\" >years_used</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row0_col0\" class=\"data row0 col0\" >1957</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row0_col1\" class=\"data row0 col1\" >Pescara Circuit</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row0_col2\" class=\"data row0 col2\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row1_col0\" class=\"data row1 col0\" >1958</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row1_col1\" class=\"data row1 col1\" >Ain Diab</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row1_col2\" class=\"data row1 col2\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row2_col0\" class=\"data row2 col0\" >1959</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row2_col1\" class=\"data row2 col1\" >Sebring International Raceway</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row2_col2\" class=\"data row2 col2\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row3_col0\" class=\"data row3 col0\" >1959</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row3_col1\" class=\"data row3 col1\" >AVUS</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row3_col2\" class=\"data row3 col2\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row4_col0\" class=\"data row4 col0\" >1959</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row4_col1\" class=\"data row4 col1\" >Monsanto Park Circuit</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row4_col2\" class=\"data row4 col2\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row5_col0\" class=\"data row5 col0\" >1960</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row5_col1\" class=\"data row5 col1\" >Riverside International Raceway</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row5_col2\" class=\"data row5 col2\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row6_col0\" class=\"data row6 col0\" >1964</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row6_col1\" class=\"data row6 col1\" >Zeltweg</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row6_col2\" class=\"data row6 col2\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row7_col0\" class=\"data row7 col0\" >1967</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row7_col1\" class=\"data row7 col1\" >Le Mans</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row7_col2\" class=\"data row7 col2\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row8_col0\" class=\"data row8 col0\" >1984</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row8_col1\" class=\"data row8 col1\" >Fair Park</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row8_col2\" class=\"data row8 col2\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row9_col0\" class=\"data row9 col0\" >1993</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row9_col1\" class=\"data row9 col1\" >Donington Park</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row9_col2\" class=\"data row9 col2\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row10_col0\" class=\"data row10 col0\" >2020</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row10_col1\" class=\"data row10 col1\" >Autodromo Internazionale del Mugello</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row10_col2\" class=\"data row10 col2\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row11_col0\" class=\"data row11 col0\" >2020</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row11_col1\" class=\"data row11 col1\" >Autódromo Internacional do Algarve</td>\n",
       "                        <td id=\"T_8aa668f0_279d_11eb_a0c9_3af9d31835f5row11_col2\" class=\"data row11 col2\" >1</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fec11083fd0>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "least = a.merge(least, how='left', left_on='circuit', right_on='name')\n",
    "least = least[['year','circuit', 'years_used']].drop_duplicates().sort_values('year')\n",
    "least.style.hide_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 5: Which circuits have the fastest lap times? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>fastestLapTime</th>\n",
       "      <th>driverRef</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-12</th>\n",
       "      <td>Red Bull Ring</td>\n",
       "      <td>1:05.619</td>\n",
       "      <td>sainz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-06-20</th>\n",
       "      <td>Indianapolis Motor Speedway</td>\n",
       "      <td>1:10.399</td>\n",
       "      <td>barrichello</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11</th>\n",
       "      <td>Autódromo José Carlos Pace</td>\n",
       "      <td>1:10.540</td>\n",
       "      <td>bottas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-09</th>\n",
       "      <td>Circuit Gilles Villeneuve</td>\n",
       "      <td>1:13.078</td>\n",
       "      <td>bottas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-25</th>\n",
       "      <td>Hockenheimring</td>\n",
       "      <td>1:13.780</td>\n",
       "      <td>raikkonen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   name fastestLapTime    driverRef\n",
       "date                                                               \n",
       "2020-07-12                Red Bull Ring       1:05.619        sainz\n",
       "2004-06-20  Indianapolis Motor Speedway       1:10.399  barrichello\n",
       "2018-11-11   Autódromo José Carlos Pace       1:10.540       bottas\n",
       "2019-06-09    Circuit Gilles Villeneuve       1:13.078       bottas\n",
       "2004-07-25               Hockenheimring       1:13.780    raikkonen"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastest_lap = f1[['name', 'fastestLapTime', 'driverRef']].copy()\n",
    "fastest_lap = fastest_lap.sort_values(by='fastestLapTime')\n",
    "fastest_lap = fastest_lap.drop_duplicates(['name'])[['name', 'fastestLapTime', 'driverRef']].head(5)\n",
    "fastest_lap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>fastestLapTime</th>\n",
       "      <th>driverRef</th>\n",
       "      <th>fastestLapTimeSec</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-12</th>\n",
       "      <td>Red Bull Ring</td>\n",
       "      <td>1:05.619</td>\n",
       "      <td>sainz</td>\n",
       "      <td>65.619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-06-20</th>\n",
       "      <td>Indianapolis Motor Speedway</td>\n",
       "      <td>1:10.399</td>\n",
       "      <td>barrichello</td>\n",
       "      <td>70.399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11</th>\n",
       "      <td>Autódromo José Carlos Pace</td>\n",
       "      <td>1:10.540</td>\n",
       "      <td>bottas</td>\n",
       "      <td>70.540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-09</th>\n",
       "      <td>Circuit Gilles Villeneuve</td>\n",
       "      <td>1:13.078</td>\n",
       "      <td>bottas</td>\n",
       "      <td>73.078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-25</th>\n",
       "      <td>Hockenheimring</td>\n",
       "      <td>1:13.780</td>\n",
       "      <td>raikkonen</td>\n",
       "      <td>73.780</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   name fastestLapTime    driverRef  \\\n",
       "date                                                                  \n",
       "2020-07-12                Red Bull Ring       1:05.619        sainz   \n",
       "2004-06-20  Indianapolis Motor Speedway       1:10.399  barrichello   \n",
       "2018-11-11   Autódromo José Carlos Pace       1:10.540       bottas   \n",
       "2019-06-09    Circuit Gilles Villeneuve       1:13.078       bottas   \n",
       "2004-07-25               Hockenheimring       1:13.780    raikkonen   \n",
       "\n",
       "            fastestLapTimeSec  \n",
       "date                           \n",
       "2020-07-12             65.619  \n",
       "2004-06-20             70.399  \n",
       "2018-11-11             70.540  \n",
       "2019-06-09             73.078  \n",
       "2004-07-25             73.780  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastest_lap['fastestLapTimeSec'] = (fastest_lap['fastestLapTime'].str.split(':', expand = True)[0].astype('float64') * 60) + fastest_lap['fastestLapTime'].str.split(':', expand = True)[1].astype('float64')\n",
    "fastest_lap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='name', y='fastestLapTimeSec', data=fastest_lap)\n",
    "plt.title('Which tracks have the fastest lap times in F1?', fontsize=20)\n",
    "plt.ylabel('Lap Times (Sec)', fontsize=15)\n",
    "plt.xlabel('Circuit', fontsize=15)\n",
    "plt.xticks(rotation=15, fontsize=11);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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