EDA_Ana.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyse Phase\n",
    "\n",
    "##### Locations of the grand Prix since 1950\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing required libraries \n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "circuit_df = pd.read_csv(\"data/circuits.csv\")\n",
    "constructor_results_df = pd.read_csv(\"data/constructor_results.csv\")\n",
    "constructors_df = pd.read_csv(\"data/constructors.csv\")\n",
    "driver_standings_df = pd.read_csv(\"data/driver_standings.csv\")\n",
    "drivers_df = pd.read_csv(\"data/drivers.csv\")\n",
    "lap_times_df = pd.read_csv(\"data/lap_times.csv\")\n",
    "pit_stops_df = pd.read_csv(\"data/pit_stops.csv\")\n",
    "qualifying_df = pd.read_csv(\"data/qualifying.csv\")\n",
    "races_df = pd.read_csv(\"data/races.csv\")\n",
    "results_df = pd.read_csv(\"data/results.csv\")\n",
    "seasons_df = pd.read_csv(\"data/seasons.csv\")\n",
    "sprint_res_df = pd.read_csv(\"data/sprint_results.csv\")\n",
    "status_df = pd.read_csv(\"data/status.csv\")\n",
    "tyre_data_df = pd.read_csv(\"data/tyre_data.csv\")\n",
    "races_with_weather_df = pd.read_csv(\"data/races_with_weather.csv\")\n",
    "races_with_dummies_df = pd.read_csv(\"data/races_with_dummies.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plotting the different locations in a map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What's the most important variables to win a Grand Prix?\n",
    "\n",
    "Our Hypothesis:\n",
    "\n",
    "__Tyres__\n",
    "\n",
    "We know tyres play a significant role in the performance of a Formula One car, and tyres will be our target to predict in our modelling part.\n",
    "\n",
    "But what about the other features?\n",
    "\n",
    "__Pole Position__\n",
    "\n",
    "We believe Pole position plays a very important role on some circuits.\n",
    "\n",
    "Why the Monaco Grand Prix Is F1's Most Boring Race?\n",
    "\n",
    "The Monaco Grand Prix is a favorite among Formula 1 drivers.\n",
    "\n",
    "They love the romance, tradition and history of the place. They love the idea of driving at the limit, knowing that a single mistake could ruin a weekend's work. They love the pressure of knowing that their skills behind the wheel can make all the difference.\n",
    "\n",
    "Indeed, there is much to admire in Monaco from a driving point of view - but what about those of us who don't have that privilege?\n",
    "\n",
    "For spectators, the Monte Carlo weekend can be considered one of the most boring of the season, to the point of thinking that F1 is starting to outgrow its most famous race.\n",
    "\n",
    "It's a horrible prospect, but a question worth asking.\n",
    "\n",
    "One of the reasons is that due to the layout of the circuit, overtaking is almost impossible.\n",
    "\n",
    "The next graph shows the correlation between the driver in pole position and the winner at the Monaco circuit.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# How many pole positions had Aston Martin drivers had on the last 2 years?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Starting Grid Position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Are those 3 first most fast in Q3 were in Podiums? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# column Q3 vs. winner filtered by Monaco"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Running at Home__\n",
    "\n",
    "And what is the impact of \"running at home\"?\n",
    "\n",
    "Driving in their home country can be attributed to the psychological impact that the support of the fans has on the drivers, as well as the fact that they drive close to home in a familiar context.\n",
    "\n",
    "Let's see if their is any correlation between these 2 variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#nationality vs circuit and winner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Pit Stop__\n",
    "\n",
    "Let's see how Aston Martin is playing on Pit Stops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pit Stop Duration by Constructor from 2011"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.box(newPitStops[newPitStops['seconds']<50].groupby(by=['raceId','raceName','date','constructorName']).mean().reset_index().sort_values(by='seconds',ascending=True),\n",
    "                 x='constructorName',\n",
    "                 y='seconds',\n",
    "                 color='constructorName',\n",
    "                 color_discrete_map=constructor_color_map,\n",
    "                )\n",
    "fig.update_layout(\n",
    "    title_text='Pit Stop Durations by Constructor from 2011 to date',\n",
    ")\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Baseline Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Avoid the multicollinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To avoid or remove multicollinearity in the dataset after one-hot encoding using pd. get_dummies, you can drop one of the categories and hence removing collinearity between the categorical features. Sklearn provides this feature by including drop_first=True in pd. get_dummies.\n",
    "#Plot a graph to see which variables are very correlated between them and drop those columns before modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ana Hypothesis: \n",
    "\n",
    "Qualifiying -> the ones higher up on the grid (1st 4) are more likely to win"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "   qualifyId  raceId  driverId  constructorId  number  position        q1   \n",
       "0          1      18         1              1      22         1  1:26.572  \\\n",
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       "3          4      18        13              6       2         4  1:25.994   \n",
       "4          5      18         2              2       3         5  1:25.960   \n",
       "\n",
       "         q2        q3  \n",
       "0  1:25.187  1:26.714  \n",
       "1  1:25.315  1:26.869  \n",
       "2  1:25.452  1:27.079  \n",
       "3  1:25.691  1:27.178  \n",
       "4  1:25.518  1:27.236  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qualifying_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['qualifyId',\n",
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       " 'constructorId',\n",
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       " 'q1',\n",
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       " 'q3']"
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     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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    "qualifying_df.columns.values.tolist()"
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   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['driverId',\n",
       " 'driverRef',\n",
       " 'number',\n",
       " 'code',\n",
       " 'forename',\n",
       " 'surname',\n",
       " 'dob',\n",
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       " 'url']"
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     "execution_count": 17,
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    "drivers_df.columns.values.tolist()"
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   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
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    {
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     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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    "results_df.columns.values.tolist()"
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   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
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       "      <td>47</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>1:28.753</td>\n",
       "      <td>215.100</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>\\N</td>\n",
       "      <td>R</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>1:29.558</td>\n",
       "      <td>213.166</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>\\N</td>\n",
       "      <td>R</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</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>11</th>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>\\N</td>\n",
       "      <td>R</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</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>12</th>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>\\N</td>\n",
       "      <td>R</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</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>13</th>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>\\N</td>\n",
       "      <td>R</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>1:29.502</td>\n",
       "      <td>213.300</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>\\N</td>\n",
       "      <td>R</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>1:29.310</td>\n",
       "      <td>213.758</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>22</td>\n",
       "      <td>\\N</td>\n",
       "      <td>R</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>1:32.021</td>\n",
       "      <td>207.461</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>\\N</td>\n",
       "      <td>R</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>\\N</td>\n",
       "      <td>R</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>\\N</td>\n",
       "      <td>R</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>\\N</td>\n",
       "      <td>R</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>\\N</td>\n",
       "      <td>R</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>\\N</td>\n",
       "      <td>D</td>\n",
       "      <td>22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>44</td>\n",
       "      <td>8</td>\n",
       "      <td>1:28.736</td>\n",
       "      <td>215.141</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>56</td>\n",
       "      <td>1:31:18.555</td>\n",
       "      <td>5478555</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>1:35.405</td>\n",
       "      <td>209.158</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>56</td>\n",
       "      <td>+19.570</td>\n",
       "      <td>5498125</td>\n",
       "      <td>39</td>\n",
       "      <td>6</td>\n",
       "      <td>1:35.921</td>\n",
       "      <td>208.033</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>56</td>\n",
       "      <td>+38.450</td>\n",
       "      <td>5517005</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>1:35.922</td>\n",
       "      <td>208.031</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>56</td>\n",
       "      <td>+45.832</td>\n",
       "      <td>5524387</td>\n",
       "      <td>53</td>\n",
       "      <td>8</td>\n",
       "      <td>1:36.068</td>\n",
       "      <td>207.715</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>56</td>\n",
       "      <td>+46.548</td>\n",
       "      <td>5525103</td>\n",
       "      <td>53</td>\n",
       "      <td>3</td>\n",
       "      <td>1:35.462</td>\n",
       "      <td>209.033</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>56</td>\n",
       "      <td>+49.833</td>\n",
       "      <td>5528388</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>1:35.366</td>\n",
       "      <td>209.244</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56</td>\n",
       "      <td>+1:08.130</td>\n",
       "      <td>5546685</td>\n",
       "      <td>53</td>\n",
       "      <td>12</td>\n",
       "      <td>1:36.696</td>\n",
       "      <td>206.366</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56</td>\n",
       "      <td>+1:10.041</td>\n",
       "      <td>5548596</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "      <td>1:36.288</td>\n",
       "      <td>207.240</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    resultId  raceId  driverId  constructorId number  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",
       "5          6      18         6              3      8    13        6   \n",
       "6          7      18         7              5     14    17        7   \n",
       "7          8      18         8              6      1    15        8   \n",
       "8          9      18         9              2      4     2       \\N   \n",
       "9         10      18        10              7     12    18       \\N   \n",
       "10        11      18        11              8     18    19       \\N   \n",
       "11        12      18        12              4      6    20       \\N   \n",
       "12        13      18        13              6      2     4       \\N   \n",
       "13        14      18        14              9      9     8       \\N   \n",
       "14        15      18        15              7     11     6       \\N   \n",
       "15        16      18        16             10     20    22       \\N   \n",
       "16        17      18        17              9     10    14       \\N   \n",
       "17        18      18        18             11     16    12       \\N   \n",
       "18        19      18        19              8     19    21       \\N   \n",
       "19        20      18        20              5     15     9       \\N   \n",
       "20        21      18        21             10     21    16       \\N   \n",
       "21        22      18        22             11     17    10       \\N   \n",
       "22        23      19         8              6      1     2        1   \n",
       "23        24      19         9              2      4     4        2   \n",
       "24        25      19         5              1     23     8        3   \n",
       "25        26      19        15              7     11     3        4   \n",
       "26        27      19         1              1     22     9        5   \n",
       "27        28      19         2              2      3     5        6   \n",
       "28        29      19        17              9     10     6        7   \n",
       "29        30      19         4              4      5     7        8   \n",
       "\n",
       "   positionText  positionOrder  points  laps         time 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",
       "5             6              6     3.0    57           \\N           \\N   \n",
       "6             7              7     2.0    55           \\N           \\N   \n",
       "7             8              8     1.0    53           \\N           \\N   \n",
       "8             R              9     0.0    47           \\N           \\N   \n",
       "9             R             10     0.0    43           \\N           \\N   \n",
       "10            R             11     0.0    32           \\N           \\N   \n",
       "11            R             12     0.0    30           \\N           \\N   \n",
       "12            R             13     0.0    29           \\N           \\N   \n",
       "13            R             14     0.0    25           \\N           \\N   \n",
       "14            R             15     0.0    19           \\N           \\N   \n",
       "15            R             16     0.0     8           \\N           \\N   \n",
       "16            R             17     0.0     0           \\N           \\N   \n",
       "17            R             18     0.0     0           \\N           \\N   \n",
       "18            R             19     0.0     0           \\N           \\N   \n",
       "19            R             20     0.0     0           \\N           \\N   \n",
       "20            R             21     0.0     0           \\N           \\N   \n",
       "21            D             22     0.0    58           \\N           \\N   \n",
       "22            1              1    10.0    56  1:31:18.555      5478555   \n",
       "23            2              2     8.0    56      +19.570      5498125   \n",
       "24            3              3     6.0    56      +38.450      5517005   \n",
       "25            4              4     5.0    56      +45.832      5524387   \n",
       "26            5              5     4.0    56      +46.548      5525103   \n",
       "27            6              6     3.0    56      +49.833      5528388   \n",
       "28            7              7     2.0    56    +1:08.130      5546685   \n",
       "29            8              8     1.0    56    +1:10.041      5548596   \n",
       "\n",
       "   fastestLap rank fastestLapTime fastestLapSpeed  statusId  \n",
       "0          39    2       1:27.452         218.300         1  \n",
       "1          41    3       1:27.739         217.586         1  \n",
       "2          41    5       1:28.090         216.719         1  \n",
       "3          58    7       1:28.603         215.464         1  \n",
       "4          43    1       1:27.418         218.385         1  \n",
       "5          50   14       1:29.639         212.974        11  \n",
       "6          22   12       1:29.534         213.224         5  \n",
       "7          20    4       1:27.903         217.180         5  \n",
       "8          15    9       1:28.753         215.100         4  \n",
       "9          23   13       1:29.558         213.166         3  \n",
       "10         24   15       1:30.892         210.038         7  \n",
       "11         20   16       1:31.384         208.907         8  \n",
       "12         23    6       1:28.175         216.510         5  \n",
       "13         21   11       1:29.502         213.300         4  \n",
       "14         18   10       1:29.310         213.758        10  \n",
       "15          8   17       1:32.021         207.461         9  \n",
       "16         \\N   \\N             \\N              \\N         4  \n",
       "17         \\N   \\N             \\N              \\N         4  \n",
       "18         \\N   \\N             \\N              \\N         4  \n",
       "19         \\N   \\N             \\N              \\N         4  \n",
       "20         \\N   \\N             \\N              \\N         4  \n",
       "21         44    8       1:28.736         215.141         2  \n",
       "22         37    2       1:35.405         209.158         1  \n",
       "23         39    6       1:35.921         208.033         1  \n",
       "24         19    7       1:35.922         208.031         1  \n",
       "25         53    8       1:36.068         207.715         1  \n",
       "26         53    3       1:35.462         209.033         1  \n",
       "27         55    1       1:35.366         209.244         1  \n",
       "28         53   12       1:36.696         206.366         1  \n",
       "29         40   10       1:36.288         207.240         1  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['circuitId',\n",
       " 'circuitRef',\n",
       " 'name',\n",
       " 'location',\n",
       " 'country',\n",
       " 'lat',\n",
       " 'lng',\n",
       " 'alt',\n",
       " 'url']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit_df.columns.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['raceId',\n",
       " 'year',\n",
       " 'round',\n",
       " 'circuitId',\n",
       " 'name',\n",
       " 'date',\n",
       " 'time',\n",
       " 'url',\n",
       " 'fp1_date',\n",
       " 'fp1_time',\n",
       " 'fp2_date',\n",
       " 'fp2_time',\n",
       " 'fp3_date',\n",
       " 'fp3_time',\n",
       " 'quali_date',\n",
       " 'quali_time',\n",
       " 'sprint_date',\n",
       " 'sprint_time']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "races_df.columns.values.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check the hypothesis, we will need tp merge 5 tables.\n",
    "- __qualifying_df__ + __results_df__ based on the columns __race_Id__ and __driver_Id__\n",
    "- Plus __drivers_Id__ based on the __driver_Id__ column\n",
    "- Plus __races_df__ based on __raceId__ column\n",
    "- And last __circuit_df__ based on __circuitId__ column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      qualifyId  raceId  driverId  constructorId_x  number_x  position_x   \n",
      "0             1      18         1                1        22           1  \\\n",
      "1             2      18         9                2         4           2   \n",
      "2             3      18         5                1        23           3   \n",
      "3             4      18        13                6         2           4   \n",
      "4             5      18         2                2         3           5   \n",
      "...         ...     ...       ...              ...       ...         ...   \n",
      "9570       2929     270       103               32        11          22   \n",
      "9571       2927     270       105               18        24          20   \n",
      "9572       2932     270       101               31        31          25   \n",
      "9573       2925     270       110               15        29          18   \n",
      "9574       2931     270       115               33        19          24   \n",
      "\n",
      "            q1        q2        q3  resultId  constructorId_y number_y  grid   \n",
      "0     1:26.572  1:25.187  1:26.714         1                1       22     1  \\\n",
      "1     1:26.103  1:25.315  1:26.869         9                2        4     2   \n",
      "2     1:25.664  1:25.452  1:27.079         5                1       23     3   \n",
      "3     1:25.994  1:25.691  1:27.178        13                6        2     4   \n",
      "4     1:25.960  1:25.518  1:27.236         2                2        3     5   \n",
      "...        ...       ...       ...       ...              ...      ...   ...   \n",
      "9570  1:25.595        \\N        \\N      5516               32       11    22   \n",
      "9571  1:25.511        \\N        \\N      5512               18       24    20   \n",
      "9572  1:27.201        \\N        \\N      5519               31       31    25   \n",
      "9573  1:25.407        \\N        \\N      5520               15       29    18   \n",
      "9574  1:27.168        \\N        \\N      5523               33       19    24   \n",
      "\n",
      "     position_y positionText  positionOrder  points  laps       time_x   \n",
      "0             1            1              1    10.0    58  1:34:50.616  \\\n",
      "1            \\N            R              9     0.0    47           \\N   \n",
      "2             5            5              5     4.0    58      +18.014   \n",
      "3            \\N            R             13     0.0    29           \\N   \n",
      "4             2            2              2     8.0    58       +5.478   \n",
      "...         ...          ...            ...     ...   ...          ...   \n",
      "9570         18           18             18     0.0    66           \\N   \n",
      "9571         14           14             14     0.0    67           \\N   \n",
      "9572         \\N            R             21     0.0    42           \\N   \n",
      "9573         \\N            R             22     0.0    37           \\N   \n",
      "9574         \\N            R             25     0.0    10           \\N   \n",
      "\n",
      "     milliseconds fastestLap rank fastestLapTime fastestLapSpeed  statusId   \n",
      "0         5690616         39    2       1:27.452         218.300         1  \\\n",
      "1              \\N         15    9       1:28.753         215.100         4   \n",
      "2         5708630         43    1       1:27.418         218.385         1   \n",
      "3              \\N         23    6       1:28.175         216.510         5   \n",
      "4         5696094         41    3       1:27.739         217.586         1   \n",
      "...           ...        ...  ...            ...             ...       ...   \n",
      "9570           \\N         \\N   \\N             \\N              \\N        13   \n",
      "9571           \\N         \\N   \\N             \\N              \\N        12   \n",
      "9572           \\N         \\N   \\N             \\N              \\N         5   \n",
      "9573           \\N         \\N   \\N             \\N              \\N        37   \n",
      "9574           \\N         \\N   \\N             \\N              \\N         6   \n",
      "\n",
      "       driverRef number code forename     surname         dob nationality   \n",
      "0       hamilton     44  HAM    Lewis    Hamilton  1985-01-07     British  \\\n",
      "1         kubica     88  KUB   Robert      Kubica  1984-12-07      Polish   \n",
      "2     kovalainen     \\N  KOV   Heikki  Kovalainen  1981-10-19     Finnish   \n",
      "3          massa     19  MAS   Felipe       Massa  1981-04-25   Brazilian   \n",
      "4       heidfeld     \\N  HEI     Nick    Heidfeld  1977-05-10      German   \n",
      "...          ...    ...  ...      ...         ...         ...         ...   \n",
      "9570     bernard     \\N   \\N     Éric     Bernard  1964-08-24      French   \n",
      "9571    alboreto     \\N   \\N  Michele    Alboreto  1956-12-23     Italian   \n",
      "9572     brabham     \\N   \\N    David     Brabham  1965-09-05  Australian   \n",
      "9573     cesaris     \\N   \\N   Andrea  de Cesaris  1959-05-31     Italian   \n",
      "9574        noda     \\N   \\N   Hideki        Noda  1969-03-07    Japanese   \n",
      "\n",
      "                                               url_x  year  round  circuitId   \n",
      "0        http://en.wikipedia.org/wiki/Lewis_Hamilton  2008      1          1  \\\n",
      "1         http://en.wikipedia.org/wiki/Robert_Kubica  2008      1          1   \n",
      "2     http://en.wikipedia.org/wiki/Heikki_Kovalainen  2008      1          1   \n",
      "3          http://en.wikipedia.org/wiki/Felipe_Massa  2008      1          1   \n",
      "4         http://en.wikipedia.org/wiki/Nick_Heidfeld  2008      1          1   \n",
      "...                                              ...   ...    ...        ...   \n",
      "9570  http://en.wikipedia.org/wiki/%C3%89ric_Bernard  1994     14         26   \n",
      "9571   http://en.wikipedia.org/wiki/Michele_Alboreto  1994     14         26   \n",
      "9572      http://en.wikipedia.org/wiki/David_Brabham  1994     14         26   \n",
      "9573  http://en.wikipedia.org/wiki/Andrea_de_Cesaris  1994     14         26   \n",
      "9574        http://en.wikipedia.org/wiki/Hideki_Noda  1994     14         26   \n",
      "\n",
      "                     name_x        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",
      "9570    European Grand Prix  1994-10-16        \\N   \n",
      "9571    European Grand Prix  1994-10-16        \\N   \n",
      "9572    European Grand Prix  1994-10-16        \\N   \n",
      "9573    European Grand Prix  1994-10-16        \\N   \n",
      "9574    European Grand Prix  1994-10-16        \\N   \n",
      "\n",
      "                                                  url_y fp1_date fp1_time   \n",
      "0     http://en.wikipedia.org/wiki/2008_Australian_G...       \\N       \\N  \\\n",
      "1     http://en.wikipedia.org/wiki/2008_Australian_G...       \\N       \\N   \n",
      "2     http://en.wikipedia.org/wiki/2008_Australian_G...       \\N       \\N   \n",
      "3     http://en.wikipedia.org/wiki/2008_Australian_G...       \\N       \\N   \n",
      "4     http://en.wikipedia.org/wiki/2008_Australian_G...       \\N       \\N   \n",
      "...                                                 ...      ...      ...   \n",
      "9570  http://en.wikipedia.org/wiki/1994_European_Gra...       \\N       \\N   \n",
      "9571  http://en.wikipedia.org/wiki/1994_European_Gra...       \\N       \\N   \n",
      "9572  http://en.wikipedia.org/wiki/1994_European_Gra...       \\N       \\N   \n",
      "9573  http://en.wikipedia.org/wiki/1994_European_Gra...       \\N       \\N   \n",
      "9574  http://en.wikipedia.org/wiki/1994_European_Gra...       \\N       \\N   \n",
      "\n",
      "     fp2_date fp2_time fp3_date fp3_time quali_date quali_time sprint_date   \n",
      "0          \\N       \\N       \\N       \\N         \\N         \\N          \\N  \\\n",
      "1          \\N       \\N       \\N       \\N         \\N         \\N          \\N   \n",
      "2          \\N       \\N       \\N       \\N         \\N         \\N          \\N   \n",
      "3          \\N       \\N       \\N       \\N         \\N         \\N          \\N   \n",
      "4          \\N       \\N       \\N       \\N         \\N         \\N          \\N   \n",
      "...       ...      ...      ...      ...        ...        ...         ...   \n",
      "9570       \\N       \\N       \\N       \\N         \\N         \\N          \\N   \n",
      "9571       \\N       \\N       \\N       \\N         \\N         \\N          \\N   \n",
      "9572       \\N       \\N       \\N       \\N         \\N         \\N          \\N   \n",
      "9573       \\N       \\N       \\N       \\N         \\N         \\N          \\N   \n",
      "9574       \\N       \\N       \\N       \\N         \\N         \\N          \\N   \n",
      "\n",
      "     sprint_time   circuitRef                          name_y   \n",
      "0             \\N  albert_park  Albert Park Grand Prix Circuit  \\\n",
      "1             \\N  albert_park  Albert Park Grand Prix Circuit   \n",
      "2             \\N  albert_park  Albert Park Grand Prix Circuit   \n",
      "3             \\N  albert_park  Albert Park Grand Prix Circuit   \n",
      "4             \\N  albert_park  Albert Park Grand Prix Circuit   \n",
      "...          ...          ...                             ...   \n",
      "9570          \\N        jerez               Circuito de Jerez   \n",
      "9571          \\N        jerez               Circuito de Jerez   \n",
      "9572          \\N        jerez               Circuito de Jerez   \n",
      "9573          \\N        jerez               Circuito de Jerez   \n",
      "9574          \\N        jerez               Circuito de Jerez   \n",
      "\n",
      "                  location    country      lat        lng alt   \n",
      "0                Melbourne  Australia -37.8497  144.96800  10  \\\n",
      "1                Melbourne  Australia -37.8497  144.96800  10   \n",
      "2                Melbourne  Australia -37.8497  144.96800  10   \n",
      "3                Melbourne  Australia -37.8497  144.96800  10   \n",
      "4                Melbourne  Australia -37.8497  144.96800  10   \n",
      "...                    ...        ...      ...        ...  ..   \n",
      "9570  Jerez de la Frontera      Spain  36.7083   -6.03417  37   \n",
      "9571  Jerez de la Frontera      Spain  36.7083   -6.03417  37   \n",
      "9572  Jerez de la Frontera      Spain  36.7083   -6.03417  37   \n",
      "9573  Jerez de la Frontera      Spain  36.7083   -6.03417  37   \n",
      "9574  Jerez de la Frontera      Spain  36.7083   -6.03417  37   \n",
      "\n",
      "                                                    url  \n",
      "0     http://en.wikipedia.org/wiki/Melbourne_Grand_P...  \n",
      "1     http://en.wikipedia.org/wiki/Melbourne_Grand_P...  \n",
      "2     http://en.wikipedia.org/wiki/Melbourne_Grand_P...  \n",
      "3     http://en.wikipedia.org/wiki/Melbourne_Grand_P...  \n",
      "4     http://en.wikipedia.org/wiki/Melbourne_Grand_P...  \n",
      "...                                                 ...  \n",
      "9570  http://en.wikipedia.org/wiki/Circuito_Permanen...  \n",
      "9571  http://en.wikipedia.org/wiki/Circuito_Permanen...  \n",
      "9572  http://en.wikipedia.org/wiki/Circuito_Permanen...  \n",
      "9573  http://en.wikipedia.org/wiki/Circuito_Permanen...  \n",
      "9574  http://en.wikipedia.org/wiki/Circuito_Permanen...  \n",
      "\n",
      "[9575 rows x 58 columns]\n"
     ]
    }
   ],
   "source": [
    "merged_qualifying_race_results = pd.merge(qualifying_df,results_df, on=['raceId', 'driverId'])\n",
    "\n",
    "merged_qualifying_race_results_drivers = pd.merge(merged_qualifying_race_results, drivers_df, on='driverId')\n",
    "\n",
    "merged_data_with_races = pd.merge(merged_qualifying_race_results_drivers, races_df, on='raceId')\n",
    "\n",
    "final_merged_data = pd.merge(merged_data_with_races, circuit_df, on='circuitId')\n",
    "\n",
    "print(final_merged_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>qualifyId</th>\n",
       "      <th>raceId</th>\n",
       "      <th>driverId</th>\n",
       "      <th>constructorId_x</th>\n",
       "      <th>number_x</th>\n",
       "      <th>position_x</th>\n",
       "      <th>q1</th>\n",
       "      <th>q2</th>\n",
       "      <th>q3</th>\n",
       "      <th>resultId</th>\n",
       "      <th>constructorId_y</th>\n",
       "      <th>number_y</th>\n",
       "      <th>grid</th>\n",
       "      <th>position_y</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>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_x</th>\n",
       "      <th>year</th>\n",
       "      <th>round</th>\n",
       "      <th>circuitId</th>\n",
       "      <th>name_x</th>\n",
       "      <th>date</th>\n",
       "      <th>time_y</th>\n",
       "      <th>url_y</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",
       "      <th>circuitRef</th>\n",
       "      <th>name_y</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",
       "  </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>1:26.572</td>\n",
       "      <td>1:25.187</td>\n",
       "      <td>1:26.714</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>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>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>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1:26.103</td>\n",
       "      <td>1:25.315</td>\n",
       "      <td>1:26.869</td>\n",
       "      <td>9</td>\n",
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       "      <td>4</td>\n",
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       "      <td>47</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>1:28.753</td>\n",
       "      <td>215.100</td>\n",
       "      <td>4</td>\n",
       "      <td>kubica</td>\n",
       "      <td>88</td>\n",
       "      <td>KUB</td>\n",
       "      <td>Robert</td>\n",
       "      <td>Kubica</td>\n",
       "      <td>1984-12-07</td>\n",
       "      <td>Polish</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Robert_Kubica</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>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>1:25.664</td>\n",
       "      <td>1:25.452</td>\n",
       "      <td>1:27.079</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>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>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>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1:25.994</td>\n",
       "      <td>1:25.691</td>\n",
       "      <td>1:27.178</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>\\N</td>\n",
       "      <td>R</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</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",
       "      <td>massa</td>\n",
       "      <td>19</td>\n",
       "      <td>MAS</td>\n",
       "      <td>Felipe</td>\n",
       "      <td>Massa</td>\n",
       "      <td>1981-04-25</td>\n",
       "      <td>Brazilian</td>\n",
       "      <td>http://en.wikipedia.org/wiki/Felipe_Massa</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>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1:25.960</td>\n",
       "      <td>1:25.518</td>\n",
       "      <td>1:27.236</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>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>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>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   qualifyId  raceId  driverId  constructorId_x  number_x  position_x   \n",
       "0          1      18         1                1        22           1  \\\n",
       "1          2      18         9                2         4           2   \n",
       "2          3      18         5                1        23           3   \n",
       "3          4      18        13                6         2           4   \n",
       "4          5      18         2                2         3           5   \n",
       "\n",
       "         q1        q2        q3  resultId  constructorId_y number_y  grid   \n",
       "0  1:26.572  1:25.187  1:26.714         1                1       22     1  \\\n",
       "1  1:26.103  1:25.315  1:26.869         9                2        4     2   \n",
       "2  1:25.664  1:25.452  1:27.079         5                1       23     3   \n",
       "3  1:25.994  1:25.691  1:27.178        13                6        2     4   \n",
       "4  1:25.960  1:25.518  1:27.236         2                2        3     5   \n",
       "\n",
       "  position_y positionText  positionOrder  points  laps       time_x   \n",
       "0          1            1              1    10.0    58  1:34:50.616  \\\n",
       "1         \\N            R              9     0.0    47           \\N   \n",
       "2          5            5              5     4.0    58      +18.014   \n",
       "3         \\N            R             13     0.0    29           \\N   \n",
       "4          2            2              2     8.0    58       +5.478   \n",
       "\n",
       "  milliseconds fastestLap rank fastestLapTime fastestLapSpeed  statusId   \n",
       "0      5690616         39    2       1:27.452         218.300         1  \\\n",
       "1           \\N         15    9       1:28.753         215.100         4   \n",
       "2      5708630         43    1       1:27.418         218.385         1   \n",
       "3           \\N         23    6       1:28.175         216.510         5   \n",
       "4      5696094         41    3       1:27.739         217.586         1   \n",
       "\n",
       "    driverRef number code forename     surname         dob nationality   \n",
       "0    hamilton     44  HAM    Lewis    Hamilton  1985-01-07     British  \\\n",
       "1      kubica     88  KUB   Robert      Kubica  1984-12-07      Polish   \n",
       "2  kovalainen     \\N  KOV   Heikki  Kovalainen  1981-10-19     Finnish   \n",
       "3       massa     19  MAS   Felipe       Massa  1981-04-25   Brazilian   \n",
       "4    heidfeld     \\N  HEI     Nick    Heidfeld  1977-05-10      German   \n",
       "\n",
       "                                            url_x  year  round  circuitId   \n",
       "0     http://en.wikipedia.org/wiki/Lewis_Hamilton  2008      1          1  \\\n",
       "1      http://en.wikipedia.org/wiki/Robert_Kubica  2008      1          1   \n",
       "2  http://en.wikipedia.org/wiki/Heikki_Kovalainen  2008      1          1   \n",
       "3       http://en.wikipedia.org/wiki/Felipe_Massa  2008      1          1   \n",
       "4      http://en.wikipedia.org/wiki/Nick_Heidfeld  2008      1          1   \n",
       "\n",
       "                  name_x        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 fp1_date fp1_time   \n",
       "0  http://en.wikipedia.org/wiki/2008_Australian_G...       \\N       \\N  \\\n",
       "1  http://en.wikipedia.org/wiki/2008_Australian_G...       \\N       \\N   \n",
       "2  http://en.wikipedia.org/wiki/2008_Australian_G...       \\N       \\N   \n",
       "3  http://en.wikipedia.org/wiki/2008_Australian_G...       \\N       \\N   \n",
       "4  http://en.wikipedia.org/wiki/2008_Australian_G...       \\N       \\N   \n",
       "\n",
       "  fp2_date fp2_time fp3_date fp3_time quali_date quali_time sprint_date   \n",
       "0       \\N       \\N       \\N       \\N         \\N         \\N          \\N  \\\n",
       "1       \\N       \\N       \\N       \\N         \\N         \\N          \\N   \n",
       "2       \\N       \\N       \\N       \\N         \\N         \\N          \\N   \n",
       "3       \\N       \\N       \\N       \\N         \\N         \\N          \\N   \n",
       "4       \\N       \\N       \\N       \\N         \\N         \\N          \\N   \n",
       "\n",
       "  sprint_time   circuitRef                          name_y   location   \n",
       "0          \\N  albert_park  Albert Park Grand Prix Circuit  Melbourne  \\\n",
       "1          \\N  albert_park  Albert Park Grand Prix Circuit  Melbourne   \n",
       "2          \\N  albert_park  Albert Park Grand Prix Circuit  Melbourne   \n",
       "3          \\N  albert_park  Albert Park Grand Prix Circuit  Melbourne   \n",
       "4          \\N  albert_park  Albert Park Grand Prix Circuit  Melbourne   \n",
       "\n",
       "     country      lat      lng alt   \n",
       "0  Australia -37.8497  144.968  10  \\\n",
       "1  Australia -37.8497  144.968  10   \n",
       "2  Australia -37.8497  144.968  10   \n",
       "3  Australia -37.8497  144.968  10   \n",
       "4  Australia -37.8497  144.968  10   \n",
       "\n",
       "                                                 url  \n",
       "0  http://en.wikipedia.org/wiki/Melbourne_Grand_P...  \n",
       "1  http://en.wikipedia.org/wiki/Melbourne_Grand_P...  \n",
       "2  http://en.wikipedia.org/wiki/Melbourne_Grand_P...  \n",
       "3  http://en.wikipedia.org/wiki/Melbourne_Grand_P...  \n",
       "4  http://en.wikipedia.org/wiki/Melbourne_Grand_P...  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_merged_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9575 entries, 0 to 9574\n",
      "Data columns (total 58 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   qualifyId        9575 non-null   int64  \n",
      " 1   raceId           9575 non-null   int64  \n",
      " 2   driverId         9575 non-null   int64  \n",
      " 3   constructorId_x  9575 non-null   int64  \n",
      " 4   number_x         9575 non-null   int64  \n",
      " 5   position_x       9575 non-null   int64  \n",
      " 6   q1               9567 non-null   object \n",
      " 7   q2               9424 non-null   object \n",
      " 8   q3               9276 non-null   object \n",
      " 9   resultId         9575 non-null   int64  \n",
      " 10  constructorId_y  9575 non-null   int64  \n",
      " 11  number_y         9575 non-null   object \n",
      " 12  grid             9575 non-null   int64  \n",
      " 13  position_y       9575 non-null   object \n",
      " 14  positionText     9575 non-null   object \n",
      " 15  positionOrder    9575 non-null   int64  \n",
      " 16  points           9575 non-null   float64\n",
      " 17  laps             9575 non-null   int64  \n",
      " 18  time_x           9575 non-null   object \n",
      " 19  milliseconds     9575 non-null   object \n",
      " 20  fastestLap       9575 non-null   object \n",
      " 21  rank             9575 non-null   object \n",
      " 22  fastestLapTime   9575 non-null   object \n",
      " 23  fastestLapSpeed  9575 non-null   object \n",
      " 24  statusId         9575 non-null   int64  \n",
      " 25  driverRef        9575 non-null   object \n",
      " 26  number           9575 non-null   object \n",
      " 27  code             9575 non-null   object \n",
      " 28  forename         9575 non-null   object \n",
      " 29  surname          9575 non-null   object \n",
      " 30  dob              9575 non-null   object \n",
      " 31  nationality      9575 non-null   object \n",
      " 32  url_x            9575 non-null   object \n",
      " 33  year             9575 non-null   int64  \n",
      " 34  round            9575 non-null   int64  \n",
      " 35  circuitId        9575 non-null   int64  \n",
      " 36  name_x           9575 non-null   object \n",
      " 37  date             9575 non-null   object \n",
      " 38  time_y           9575 non-null   object \n",
      " 39  url_y            9575 non-null   object \n",
      " 40  fp1_date         9575 non-null   object \n",
      " 41  fp1_time         9575 non-null   object \n",
      " 42  fp2_date         9575 non-null   object \n",
      " 43  fp2_time         9575 non-null   object \n",
      " 44  fp3_date         9575 non-null   object \n",
      " 45  fp3_time         9575 non-null   object \n",
      " 46  quali_date       9575 non-null   object \n",
      " 47  quali_time       9575 non-null   object \n",
      " 48  sprint_date      9575 non-null   object \n",
      " 49  sprint_time      9575 non-null   object \n",
      " 50  circuitRef       9575 non-null   object \n",
      " 51  name_y           9575 non-null   object \n",
      " 52  location         9575 non-null   object \n",
      " 53  country          9575 non-null   object \n",
      " 54  lat              9575 non-null   float64\n",
      " 55  lng              9575 non-null   float64\n",
      " 56  alt              9575 non-null   object \n",
      " 57  url              9575 non-null   object \n",
      "dtypes: float64(3), int64(15), object(40)\n",
      "memory usage: 4.2+ MB\n"
     ]
    }
   ],
   "source": [
    "final_merged_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8, 18,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 22, 19, 20, 21, 23, 24,  0, 26, 25])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_merged_data.grid.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "qualifyId            0\n",
       "raceId               0\n",
       "driverId             0\n",
       "constructorId_x      0\n",
       "number_x             0\n",
       "position_x           0\n",
       "q1                   8\n",
       "q2                 151\n",
       "q3                 299\n",
       "resultId             0\n",
       "constructorId_y      0\n",
       "number_y             0\n",
       "grid                 0\n",
       "position_y           0\n",
       "positionText         0\n",
       "positionOrder        0\n",
       "points               0\n",
       "laps                 0\n",
       "time_x               0\n",
       "milliseconds         0\n",
       "fastestLap           0\n",
       "rank                 0\n",
       "fastestLapTime       0\n",
       "fastestLapSpeed      0\n",
       "statusId             0\n",
       "driverRef            0\n",
       "number               0\n",
       "code                 0\n",
       "forename             0\n",
       "surname              0\n",
       "dob                  0\n",
       "nationality          0\n",
       "url_x                0\n",
       "year                 0\n",
       "round                0\n",
       "circuitId            0\n",
       "name_x               0\n",
       "date                 0\n",
       "time_y               0\n",
       "url_y                0\n",
       "fp1_date             0\n",
       "fp1_time             0\n",
       "fp2_date             0\n",
       "fp2_time             0\n",
       "fp3_date             0\n",
       "fp3_time             0\n",
       "quali_date           0\n",
       "quali_time           0\n",
       "sprint_date          0\n",
       "sprint_time          0\n",
       "circuitRef           0\n",
       "name_y               0\n",
       "location             0\n",
       "country              0\n",
       "lat                  0\n",
       "lng                  0\n",
       "alt                  0\n",
       "url                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_merged_data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have some Nan values in our __q3__ column, which is the one we need to check our hypothesis. But, since we have a __grid__ column, we just ignore this __q3__ column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter data for only winning positions\n",
    "winning_positions_data = final_merged_data[final_merged_data['positionOrder'] == 1]\n",
    "\n",
    "# Create a bar plot using Seaborn\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(data=winning_positions_data, x='grid', palette='viridis')\n",
    "plt.xlabel('Starting Position')\n",
    "plt.ylabel('Number of Wins')\n",
    "plt.title('Distribution of Wins Based on Starting Position')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now filter our data for Fernando Alonso's wins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fernando_alonso_wins = final_merged_data[\n",
    "    (final_merged_data['forename'] == 'Fernando') &\n",
    "    (final_merged_data['surname'] == 'Alonso') &\n",
    "    (final_merged_data['positionOrder'] == 1)\n",
    "]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(data=fernando_alonso_wins, x='grid', palette='viridis')\n",
    "plt.xlabel('Starting Position')\n",
    "plt.ylabel('Number of Wins')\n",
    "plt.title('Distribution of Wins Based on Starting Position for Fernando Alonso')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}