hypothesis_1_pole_position.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis\n",
    "\n",
    "The ones higher up on the grid (1st 4) are more likely to win."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing required libraries \n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import plotly.figure_factory as ff\n",
    "import plotly.graph_objects as go\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": 15,
   "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": "markdown",
   "metadata": {},
   "source": [
    "To check the hypothesis, we will need tp merge these 4 tables.\n",
    "- __results_df__ + __drivers_Id__ based on the columns __driver_Id__\n",
    "- Plus __races_df__ based on __raceId__ column\n",
    "- And last __circuit_df__ based on __circuitId__ column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       driverId   driverRef number_x code  forename     surname         dob   \n",
      "0             1    hamilton       44  HAM     Lewis    Hamilton  1985-01-07  \\\n",
      "1             2    heidfeld       \\N  HEI      Nick    Heidfeld  1977-05-10   \n",
      "2             3     rosberg        6  ROS      Nico     Rosberg  1985-06-27   \n",
      "3             4      alonso       14  ALO  Fernando      Alonso  1981-07-29   \n",
      "4             5  kovalainen       \\N  KOV    Heikki  Kovalainen  1981-10-19   \n",
      "...         ...         ...      ...  ...       ...         ...         ...   \n",
      "25835       697     bonetto       \\N   \\N    Felice     Bonetto  1903-06-09   \n",
      "25836       704   cabantous       \\N   \\N      Yves   Cabantous  1904-10-08   \n",
      "25837       741   etancelin       \\N   \\N  Philippe   Étancelin  1896-12-28   \n",
      "25838       782       jover       \\N   \\N      Juan       Jover  1903-11-23   \n",
      "25839       783    grignard       \\N   \\N   Georges    Grignard  1905-07-25   \n",
      "\n",
      "      nationality                                              url_x   \n",
      "0         British        http://en.wikipedia.org/wiki/Lewis_Hamilton  \\\n",
      "1          German         http://en.wikipedia.org/wiki/Nick_Heidfeld   \n",
      "2          German          http://en.wikipedia.org/wiki/Nico_Rosberg   \n",
      "3         Spanish       http://en.wikipedia.org/wiki/Fernando_Alonso   \n",
      "4         Finnish     http://en.wikipedia.org/wiki/Heikki_Kovalainen   \n",
      "...           ...                                                ...   \n",
      "25835     Italian        http://en.wikipedia.org/wiki/Felice_Bonetto   \n",
      "25836      French  http://en.wikipedia.org/wiki/Yves_Giraud_Caban...   \n",
      "25837      French  http://en.wikipedia.org/wiki/Philippe_%C3%89ta...   \n",
      "25838     Spanish            http://en.wikipedia.org/wiki/Juan_Jover   \n",
      "25839      French      http://en.wikipedia.org/wiki/Georges_Grignard   \n",
      "\n",
      "       resultId  raceId  constructorId number_y  grid position positionText   \n",
      "0             1      18              1       22     1        1            1  \\\n",
      "1             2      18              2        3     5        2            2   \n",
      "2             3      18              3        7     7        3            3   \n",
      "3             4      18              4        5    11        4            4   \n",
      "4             5      18              1       23     3        5            5   \n",
      "...         ...     ...            ...      ...   ...      ...          ...   \n",
      "25835     20009     832             51       24     8        5            5   \n",
      "25836     20021     832            154       32    14       \\N            R   \n",
      "25837     20012     832            154       34    13        8            8   \n",
      "25838     20024     832            105       46    18       \\N            W   \n",
      "25839     20020     832            154       38    16       \\N            R   \n",
      "\n",
      "       positionOrder  points  laps       time_x milliseconds fastestLap rank   \n",
      "0                  1    10.0    58  1:34:50.616      5690616         39    2  \\\n",
      "1                  2     8.0    58       +5.478      5696094         41    3   \n",
      "2                  3     6.0    58       +8.163      5698779         41    5   \n",
      "3                  4     5.0    58      +17.181      5707797         58    7   \n",
      "4                  5     4.0    58      +18.014      5708630         43    1   \n",
      "...              ...     ...   ...          ...          ...        ...  ...   \n",
      "25835              5     2.0    68           \\N           \\N         \\N   \\N   \n",
      "25836             17     0.0     7           \\N           \\N         \\N   \\N   \n",
      "25837              8     0.0    63           \\N           \\N         \\N   \\N   \n",
      "25838             20     0.0     0           \\N           \\N         \\N   \\N   \n",
      "25839             16     0.0    23           \\N           \\N         \\N   \\N   \n",
      "\n",
      "      fastestLapTime fastestLapSpeed  statusId  year  round  circuitId   \n",
      "0           1:27.452         218.300         1  2008      1          1  \\\n",
      "1           1:27.739         217.586         1  2008      1          1   \n",
      "2           1:28.090         216.719         1  2008      1          1   \n",
      "3           1:28.603         215.464         1  2008      1          1   \n",
      "4           1:27.418         218.385         1  2008      1          1   \n",
      "...              ...             ...       ...   ...    ...        ...   \n",
      "25835             \\N              \\N        12  1951      8         67   \n",
      "25836             \\N              \\N         3  1951      8         67   \n",
      "25837             \\N              \\N        17  1951      8         67   \n",
      "25838             \\N              \\N         5  1951      8         67   \n",
      "25839             \\N              \\N         5  1951      8         67   \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",
      "25835     Spanish Grand Prix  1951-10-28        \\N   \n",
      "25836     Spanish Grand Prix  1951-10-28        \\N   \n",
      "25837     Spanish Grand Prix  1951-10-28        \\N   \n",
      "25838     Spanish Grand Prix  1951-10-28        \\N   \n",
      "25839     Spanish Grand Prix  1951-10-28        \\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",
      "25835  http://en.wikipedia.org/wiki/1951_Spanish_Gran...       \\N       \\N   \n",
      "25836  http://en.wikipedia.org/wiki/1951_Spanish_Gran...       \\N       \\N   \n",
      "25837  http://en.wikipedia.org/wiki/1951_Spanish_Gran...       \\N       \\N   \n",
      "25838  http://en.wikipedia.org/wiki/1951_Spanish_Gran...       \\N       \\N   \n",
      "25839  http://en.wikipedia.org/wiki/1951_Spanish_Gran...       \\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",
      "25835       \\N       \\N       \\N       \\N         \\N         \\N          \\N   \n",
      "25836       \\N       \\N       \\N       \\N         \\N         \\N          \\N   \n",
      "25837       \\N       \\N       \\N       \\N         \\N         \\N          \\N   \n",
      "25838       \\N       \\N       \\N       \\N         \\N         \\N          \\N   \n",
      "25839       \\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",
      "25835          \\N    pedralbes            Circuit de Pedralbes  Barcelona   \n",
      "25836          \\N    pedralbes            Circuit de Pedralbes  Barcelona   \n",
      "25837          \\N    pedralbes            Circuit de Pedralbes  Barcelona   \n",
      "25838          \\N    pedralbes            Circuit de Pedralbes  Barcelona   \n",
      "25839          \\N    pedralbes            Circuit de Pedralbes  Barcelona   \n",
      "\n",
      "         country      lat        lng alt   \n",
      "0      Australia -37.8497  144.96800  10  \\\n",
      "1      Australia -37.8497  144.96800  10   \n",
      "2      Australia -37.8497  144.96800  10   \n",
      "3      Australia -37.8497  144.96800  10   \n",
      "4      Australia -37.8497  144.96800  10   \n",
      "...          ...      ...        ...  ..   \n",
      "25835      Spain  41.3903    2.11667  85   \n",
      "25836      Spain  41.3903    2.11667  85   \n",
      "25837      Spain  41.3903    2.11667  85   \n",
      "25838      Spain  41.3903    2.11667  85   \n",
      "25839      Spain  41.3903    2.11667  85   \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",
      "25835     http://en.wikipedia.org/wiki/Pedralbes_Circuit  \n",
      "25836     http://en.wikipedia.org/wiki/Pedralbes_Circuit  \n",
      "25837     http://en.wikipedia.org/wiki/Pedralbes_Circuit  \n",
      "25838     http://en.wikipedia.org/wiki/Pedralbes_Circuit  \n",
      "25839     http://en.wikipedia.org/wiki/Pedralbes_Circuit  \n",
      "\n",
      "[25840 rows x 51 columns]\n"
     ]
    }
   ],
   "source": [
    "merged_qualifying_race_results = pd.merge(drivers_df,results_df, on= 'driverId')\n",
    "\n",
    "merged_data_with_races = pd.merge(merged_qualifying_race_results, 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25840 entries, 0 to 25839\n",
      "Data columns (total 51 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   driverId         25840 non-null  int64  \n",
      " 1   driverRef        25840 non-null  object \n",
      " 2   number_x         25840 non-null  object \n",
      " 3   code             25840 non-null  object \n",
      " 4   forename         25840 non-null  object \n",
      " 5   surname          25840 non-null  object \n",
      " 6   dob              25840 non-null  object \n",
      " 7   nationality      25840 non-null  object \n",
      " 8   url_x            25840 non-null  object \n",
      " 9   resultId         25840 non-null  int64  \n",
      " 10  raceId           25840 non-null  int64  \n",
      " 11  constructorId    25840 non-null  int64  \n",
      " 12  number_y         25840 non-null  object \n",
      " 13  grid             25840 non-null  int64  \n",
      " 14  position         25840 non-null  object \n",
      " 15  positionText     25840 non-null  object \n",
      " 16  positionOrder    25840 non-null  int64  \n",
      " 17  points           25840 non-null  float64\n",
      " 18  laps             25840 non-null  int64  \n",
      " 19  time_x           25840 non-null  object \n",
      " 20  milliseconds     25840 non-null  object \n",
      " 21  fastestLap       25840 non-null  object \n",
      " 22  rank             25840 non-null  object \n",
      " 23  fastestLapTime   25840 non-null  object \n",
      " 24  fastestLapSpeed  25840 non-null  object \n",
      " 25  statusId         25840 non-null  int64  \n",
      " 26  year             25840 non-null  int64  \n",
      " 27  round            25840 non-null  int64  \n",
      " 28  circuitId        25840 non-null  int64  \n",
      " 29  name_x           25840 non-null  object \n",
      " 30  date             25840 non-null  object \n",
      " 31  time_y           25840 non-null  object \n",
      " 32  url_y            25840 non-null  object \n",
      " 33  fp1_date         25840 non-null  object \n",
      " 34  fp1_time         25840 non-null  object \n",
      " 35  fp2_date         25840 non-null  object \n",
      " 36  fp2_time         25840 non-null  object \n",
      " 37  fp3_date         25840 non-null  object \n",
      " 38  fp3_time         25840 non-null  object \n",
      " 39  quali_date       25840 non-null  object \n",
      " 40  quali_time       25840 non-null  object \n",
      " 41  sprint_date      25840 non-null  object \n",
      " 42  sprint_time      25840 non-null  object \n",
      " 43  circuitRef       25840 non-null  object \n",
      " 44  name_y           25840 non-null  object \n",
      " 45  location         25840 non-null  object \n",
      " 46  country          25840 non-null  object \n",
      " 47  lat              25840 non-null  float64\n",
      " 48  lng              25840 non-null  float64\n",
      " 49  alt              25840 non-null  object \n",
      " 50  url              25840 non-null  object \n",
      "dtypes: float64(3), int64(11), object(37)\n",
      "memory usage: 10.1+ MB\n"
     ]
    }
   ],
   "source": [
    "final_merged_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our __final_merged_data__ has 25840 rows and 51 columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2008, 2007, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,\n",
       "       2018, 2019, 2022, 2006, 2005, 2004, 2003, 2002, 2001, 2000, 1999,\n",
       "       1998, 1997, 1996, 2020, 2021, 1995, 1994, 1993, 1992, 1991, 1990,\n",
       "       1989, 1988, 1987, 1986, 1985, 1984, 1983, 1982, 1981, 1980, 1979,\n",
       "       1978, 1977, 1976, 1975, 1974, 1973, 1972, 1971, 1970, 1969, 1968,\n",
       "       1967, 1964, 1963, 1966, 1965, 1962, 1961, 1960, 1959, 1958, 1957,\n",
       "       1956, 1955, 1950, 1954, 1953, 1952, 1951])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_merged_data.year.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data frame has data from 1950 and 2022."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "driverId           0\n",
       "driverRef          0\n",
       "number_x           0\n",
       "code               0\n",
       "forename           0\n",
       "surname            0\n",
       "dob                0\n",
       "nationality        0\n",
       "url_x              0\n",
       "resultId           0\n",
       "raceId             0\n",
       "constructorId      0\n",
       "number_y           0\n",
       "grid               0\n",
       "position           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",
       "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_merged_data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And has no Nan Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start to have a look on the distribution of wins versus starting position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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    "winning_positions_data = final_merged_data[\n",
    "    final_merged_data['positionOrder'] == 1\n",
    "]\n",
    "\n",
    "# Get the unique grid positions\n",
    "unique_grid_positions = sorted(winning_positions_data['grid'].unique())\n",
    "\n",
    "# Calculate the histogram data\n",
    "hist, bin_edges = np.histogram(winning_positions_data['grid'], bins=len(unique_grid_positions), range=(min(unique_grid_positions) - 0.5, max(unique_grid_positions) + 0.5))\n",
    "\n",
    "# Create a bar plot\n",
    "fig = go.Figure(data=[go.Bar(x=unique_grid_positions, y=hist, marker_color='#00A08B')])\n",
    "\n",
    "fig.update_traces(\n",
    "    marker=dict(line=dict(width=1, color='black')),  # Add black borders between bars\n",
    "    texttemplate='%{y:.0f}',  # Display integer values for y-axis\n",
    "    textposition='outside',  # Display text labels outside the bars\n",
    ")\n",
    "\n",
    "fig.update_layout(\n",
    "    title='Distribution of Wins Based on Starting Position from 1950',\n",
    "    xaxis_title='Grid Position',\n",
    "    yaxis_title='Number of Wins',\n",
    "    title_font=dict(size=24),  # Increase title size\n",
    "    xaxis=dict(tickvals=unique_grid_positions, ticktext=unique_grid_positions),  # Set custom tick values and labels\n",
    "    bargap=0.1,  # Adjust gap between bars\n",
    "    bargroupgap=0.1,  # Adjust gap between groups of bars\n",
    "    #width=800,  # Set the width of the graph\n",
    "    #height=600,  # Set the height of the graph\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With no surprises, drivers who start up on the grid (1st 4) are more likely to win. Our Hypothesis confirms! \n",
    "\n",
    "Interestingly, there are more winners who start sixth on the grid than those who start fifth."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Who was the driver that won a Grand Prix starting on 22nd position?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The driver who won starting from the 22nd grid position is John Watson.\n"
     ]
    }
   ],
   "source": [
    "# Filter data for positionOrder equal to 1 (winning position)\n",
    "winning_positions_data = final_merged_data[\n",
    "    (final_merged_data['positionOrder'] == 1) &\n",
    "    (final_merged_data['grid'] == 22)\n",
    "]\n",
    "\n",
    "# Get the driver's information\n",
    "if not winning_positions_data.empty:\n",
    "    winning_driver = winning_positions_data.iloc[0]\n",
    "    driver_name = f\"{winning_driver['forename']} {winning_driver['surname']}\"\n",
    "    print(f\"The driver who won starting from the 22nd grid position is {driver_name}.\")\n",
    "else:\n",
    "    print(\"No driver won from the 22nd grid position.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "John Watson is a British former racing driver and current commentator from Northern Ireland. He competed in Formula One, winning five Grands Prix and was third in the 1982 championship.\n",
    "John Watson holds the record, after starting 22nd at the historic 1983 Long Beach Grand Prix in the US.\n",
    "Find [here](https://www.youtube.com/watch?v=fl0pFVqShas) more information about this event.\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fernando Alonso's Numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
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   "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",
    "fig = px.histogram(fernando_alonso_wins, x='grid', nbins=20, color_discrete_sequence=[\"#00A08B\"], text_auto='.0f', title='Distribution of Wins Based on Starting Position for Fernando Alonso')\n",
    "\n",
    "fig.update_xaxes(title_text='Starting Position', title_font=dict(size=18))  # Increase title size\n",
    "fig.update_yaxes(title_text='Number of Wins', title_font=dict(size=18))  # Increase title size\n",
    "\n",
    "x_labels = fernando_alonso_wins['grid'].unique()\n",
    "fig.update_xaxes(tickmode='array', tickvals=x_labels, ticktext=x_labels)\n",
    "\n",
    "fig.update_traces(\n",
    "    marker=dict(line=dict(width=1, color='black')),  # Add black borders between bars\n",
    "    textangle=0,  # Rotate text labels to be horizontal\n",
    ")\n",
    "\n",
    "fig.update_layout(\n",
    "    title_font=dict(size=24),  # Increase title size\n",
    "    bargap=0.1,  # Adjust gap between bars\n",
    "    bargroupgap=0.1,  # Adjust gap between groups of bars\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fernando Alonso won a Grand Prix for 32 times. Of these 32 victories, 14 came from pole positions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lance Stroll's Numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
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   "source": [
    "lance_stroll_data = final_merged_data[\n",
    "    (final_merged_data['forename'] == 'Lance') &\n",
    "    (final_merged_data['surname'] == 'Stroll') &\n",
    "    (final_merged_data['positionOrder'] >= 1) &\n",
    "    (final_merged_data['positionOrder'] <= 10)\n",
    "]\n",
    "\n",
    "# Create a histogram\n",
    "fig = px.histogram(\n",
    "    lance_stroll_data,\n",
    "    x='positionOrder',  # Ending Position Order on x-axis\n",
    "    color_discrete_sequence=[\"#00A08B\"],  # Set the color for all bars\n",
    "    barmode='group',    # Grouped histogram\n",
    "    title='Distribution of Ending Positions for Lance Stroll ',\n",
    "    text_auto='.0f',\n",
    "    labels={'positionOrder': 'Ending Position'},\n",
    "    nbins=10,  # Set the number of bins\n",
    ")\n",
    "\n",
    "fig.update_xaxes(title_text='Ending Position', title_font=dict(size=18))  # Increase title size\n",
    "fig.update_yaxes(title_text='Number of Races', title_font=dict(size=18))  # Increase title size\n",
    "\n",
    "# Adjust y-axis range and ticks\n",
    "fig.update_layout(\n",
    "    title_font=dict(size=24),  # Increase title size\n",
    "    bargap=0.1,  # Adjust gap between bars\n",
    "    bargroupgap=0.1,  # Adjust gap between groups of bars\n",
    "    yaxis=dict(range=[0,12], tickvals=list(range(12))),  # Set y-axis range and tick values\n",
    ")\n",
    "\n",
    "fig.update_traces(\n",
    "    marker=dict(line=dict(width=1, color='black')),  # Add black borders between bars\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lance Stroll, the second Aston Martin driver, never won a Grand Prix and his best position was third place."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Monaco's Numbers\n",
    "\n",
    "Drivers loves the romance, the tradition and the history of the place. They love the idea of driving on the edge, knowing a single mistake can ruin a weekend's work. They love the pressure of knowing that their abilities behind the wheel can make all the difference.\n",
    "\n",
    "Indeed, there is a lot to admire about Monaco from a driving perspective—but what about those of us who don't have that privilege?\n",
    "\n",
    "For spectators, the Monte Carlo weekend can rank among the most boring of the season, to the point where it crosses your mind whether F1 is beginning to outgrow its most famous race.\n",
    "\n",
    "But why is so boring? \n",
    "\n",
    "Because there are almost no opportunities to overtaking. So, Pole positions is the key in this circuit.\n",
    "\n",
    "Let's have a look in the distribution of wins based on starting position.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "title='Tyre Type Distribution on Street Circuits 2012-2023<br><sup>Street Circuits: Monaco, Australia, Singapore, Russia, Azerbaijan, Saudi Arabia, Miami and Canada</sup>',"
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  },
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        },
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   "source": [
    "winning_positions_data_monaco = final_merged_data[\n",
    "    (final_merged_data['positionOrder'] == 1) &\n",
    "    (final_merged_data['country'] == 'Monaco')\n",
    "]\n",
    "\n",
    "fig = px.histogram(winning_positions_data_monaco, x='grid', nbins=20, color_discrete_sequence=[\"#00A08B\"], text_auto='.0f', title=\"Distribution of Wins Based on Starting Position (Monaco) from 1950\")\n",
    "\n",
    "# Manually set the x-axis tick positions and labels for each bar\n",
    "x_labels = winning_positions_data_monaco['grid'].unique()\n",
    "fig.update_xaxes(tickmode='array', tickvals=x_labels, ticktext=x_labels)\n",
    "fig.update_xaxes(title_text='Starting Position', title_font=dict(size=18))  # Increase title size\n",
    "fig.update_yaxes(title_text='Number of Wins', title_font=dict(size=18))  # Increase title size\n",
    "\n",
    "fig.update_traces(\n",
    "    marker=dict(line=dict(width=1, color='black')),  # Add black borders between bars\n",
    "    textangle=0,  # Rotate text labels to be vertical\n",
    ")\n",
    "\n",
    "fig.update_layout(\n",
    "    title_font=dict(size=24),  # Increase title size\n",
    "    bargap=0.1,  # Adjust gap between bars\n",
    "    bargroupgap=0.1,  # Adjust gap between groups of bars\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of Monaco's 68 grand prix, only one driver managed to win, having started on the grid beyond the first 10 qualifiers.\n",
    "\n",
    "Let's find out who did it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The driver who won starting from the 14th grid position is Olivier Panis.\n"
     ]
    }
   ],
   "source": [
    " #Filter data for positionOrder equal to 1 (winning position)\n",
    "winning_positions_data = final_merged_data[\n",
    "    (final_merged_data['positionOrder'] == 1) &\n",
    "    (final_merged_data['grid'] == 14)\n",
    "]\n",
    "\n",
    "# Get the driver's information\n",
    "if not winning_positions_data.empty:\n",
    "    winning_driver = winning_positions_data.iloc[0]\n",
    "    driver_name = f\"{winning_driver['forename']} {winning_driver['surname']}\"\n",
    "    print(f\"The driver who won starting from the 14th grid position is {driver_name}.\")\n",
    "else:\n",
    "    print(\"No driver won from the 22nd grid position.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Olivier Jean Denis Marie Panis is a French former racing driver. Panis raced in Formula One for ten seasons, earning his first and only win at the 1996 Monaco Grand Prix for the Ligier team.\n",
    "\n",
    "For more information about this win, click [here](https://www.youtube.com/watch?v=dBRm5iCqQBk)"
   ]
  }
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