hypothesis_2_tyre_surface.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 138,
   "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": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "tyre_data_df = pd.read_csv(\"data/tyre_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Driver</th>\n",
       "      <th>GP</th>\n",
       "      <th>Tyres</th>\n",
       "      <th>From</th>\n",
       "      <th>To</th>\n",
       "      <th>#Laps</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Jenson Button</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Soft Used</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Jenson Button</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Medium New</td>\n",
       "      <td>17</td>\n",
       "      <td>36</td>\n",
       "      <td>20</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Jenson Button</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Medium New</td>\n",
       "      <td>37</td>\n",
       "      <td>58</td>\n",
       "      <td>22</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Sebastian Vettel</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Soft Used</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Sebastian Vettel</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Soft Used</td>\n",
       "      <td>17</td>\n",
       "      <td>37</td>\n",
       "      <td>21</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0            Driver         GP       Tyres  From  To  #Laps  year\n",
       "0           0     Jenson Button  Australia   Soft Used     1  16     16  2012\n",
       "1           1     Jenson Button  Australia  Medium New    17  36     20  2012\n",
       "2           2     Jenson Button  Australia  Medium New    37  58     22  2012\n",
       "3           3  Sebastian Vettel  Australia   Soft Used     1  16     16  2012\n",
       "4           4  Sebastian Vettel  Australia   Soft Used    17  37     21  2012"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tyre_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13302 entries, 0 to 13301\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   Unnamed: 0  13302 non-null  int64 \n",
      " 1   Driver      13297 non-null  object\n",
      " 2   GP          13302 non-null  object\n",
      " 3   Tyres       13302 non-null  object\n",
      " 4   From        13302 non-null  int64 \n",
      " 5   To          13302 non-null  int64 \n",
      " 6   #Laps       13302 non-null  int64 \n",
      " 7   year        13302 non-null  int64 \n",
      "dtypes: int64(5), object(3)\n",
      "memory usage: 831.5+ KB\n"
     ]
    }
   ],
   "source": [
    "tyre_data_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0    0\n",
       "Driver        5\n",
       "GP            0\n",
       "Tyres         0\n",
       "From          0\n",
       "To            0\n",
       "#Laps         0\n",
       "year          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tyre_data_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022,\n",
       "       2023])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tyre_data_df.year.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "tyre_data_df = tyre_data_df.drop(\"Unnamed: 0\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "tyre_data_df.columns = tyre_data_df.columns.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>driver</th>\n",
       "      <th>gp</th>\n",
       "      <th>tyres</th>\n",
       "      <th>from</th>\n",
       "      <th>to</th>\n",
       "      <th>#laps</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jenson Button</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Soft Used</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jenson Button</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Medium New</td>\n",
       "      <td>17</td>\n",
       "      <td>36</td>\n",
       "      <td>20</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jenson Button</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Medium New</td>\n",
       "      <td>37</td>\n",
       "      <td>58</td>\n",
       "      <td>22</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sebastian Vettel</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Soft Used</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sebastian Vettel</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Soft Used</td>\n",
       "      <td>17</td>\n",
       "      <td>37</td>\n",
       "      <td>21</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             driver         gp       tyres  from  to  #laps  year\n",
       "0     Jenson Button  Australia   Soft Used     1  16     16  2012\n",
       "1     Jenson Button  Australia  Medium New    17  36     20  2012\n",
       "2     Jenson Button  Australia  Medium New    37  58     22  2012\n",
       "3  Sebastian Vettel  Australia   Soft Used     1  16     16  2012\n",
       "4  Sebastian Vettel  Australia   Soft Used    17  37     21  2012"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tyre_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Soft Used', 'Medium New', 'Soft New', 'Medium Used',\n",
       "       'Intermediate New', 'Wet New', 'Hard New', 'Intermediate Used',\n",
       "       'Lluvia Extrema Usado', 'Hard Used', 'Supersoft Used',\n",
       "       'Supersoft New', 'Ultrasoft New', 'Ultrasoft Used',\n",
       "       'Hiper Blando Usado', 'Hiper Blando Nuevo'], dtype=object)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tyre_data_df.tyres.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 driver           gp         tyres  from  to  #laps  year\n",
      "0         Jenson Button    Australia          Soft     1  16     16  2012\n",
      "1         Jenson Button    Australia        Medium    17  36     20  2012\n",
      "2         Jenson Button    Australia        Medium    37  58     22  2012\n",
      "3      Sebastian Vettel    Australia          Soft     1  16     16  2012\n",
      "4      Sebastian Vettel    Australia          Soft    17  37     21  2012\n",
      "...                 ...          ...           ...   ...  ..    ...   ...\n",
      "13297       Zhou Guanyu  Netherlands          Soft    38  60     23  2023\n",
      "13298    Logan Sargeant  Netherlands          Soft     1  14     14  2023\n",
      "13299    Max Verstappen  Netherlands  Intermediate    65  72      8  2023\n",
      "13300      Pierre Gasly  Netherlands          Soft    47  60     14  2023\n",
      "13301   Nico Hulkenberg  Netherlands        Medium    17  48     32  2023\n",
      "\n",
      "[13302 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "# Remove \" Used\" and \" New\" from the 'tyres' column\n",
    "tyre_data_df['tyres'] = tyre_data_df['tyres'].str.replace(' Used', '').str.replace(' New', '')\n",
    "\n",
    "# Print the DataFrame to verify the changes\n",
    "print(tyre_data_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Soft', 'Medium', 'Intermediate', 'Wet', 'Hard',\n",
       "       'Lluvia Extrema Usado', 'Supersoft', 'Ultrasoft',\n",
       "       'Hiper Blando Usado', 'Hiper Blando Nuevo'], dtype=object)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tyre_data_df.tyres.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a dictionary to map old values to new values\n",
    "tyres_mapping = {\n",
    "    'Hiper Blando Usado': 'Hyper Soft',\n",
    "    'Hiper Blando Nuevo': 'Hyper Soft',\n",
    "    'Lluvia Extrema Usado': 'Full Wet'\n",
    "}\n",
    "\n",
    "# Use the replace() method to update the values in the \"tyres\" column\n",
    "tyre_data_df['tyres'] = tyre_data_df['tyres'].replace(tyres_mapping)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "tyred_data_df = tyre_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Soft', 'Medium', 'Intermediate', 'Wet', 'Hard', 'Full Wet',\n",
       "       'Supersoft', 'Ultrasoft', 'Hyper Soft'], dtype=object)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tyre_data_df.tyres.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Australia', 'Malaysia', 'China', 'Bahrain', 'Spain', 'Monaco',\n",
       "       'Canada', 'Europa', 'Great Britain', 'Germany', 'Hungary',\n",
       "       'Belgium', 'Italy', 'Singapore', 'Japan', 'Korea', 'India',\n",
       "       'Abu Dhabi', 'United States', 'Brazil', 'Austria', 'Russia',\n",
       "       'Mexico', 'Europe', 'Azerbaijan', 'France', 'Styria',\n",
       "       '70TH Anniversary', 'Tuscany', 'Russian', 'Portugal', 'Imola',\n",
       "       'Turkey', 'Sakhir', 'Netherlands', 'Qatar', 'Saudi Arabia',\n",
       "       'Italia', 'Miami', 'Francia'], dtype=object)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tyre_data_df.gp.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tyre_data_df.gp.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "USA Grand Prix is in Austin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               driver      gp      tyres  from  to  #laps  year\n",
      "530   Fernando Alonso  Europa       Soft     1  15     15  2012\n",
      "531   Fernando Alonso  Europa       Soft    16  28     13  2012\n",
      "532   Fernando Alonso  Europa     Medium    29  57     29  2012\n",
      "533    Kimi Räikkönen  Europa       Soft     1  14     14  2012\n",
      "534    Kimi Räikkönen  Europa       Soft    15  28     14  2012\n",
      "...               ...     ...        ...   ...  ..    ...   ...\n",
      "5538  Pascal Wehrlein  Europe       Soft     1  29     29  2016\n",
      "5539  Pascal Wehrlein  Europe     Medium    30  39     10  2016\n",
      "5540     Carlos Sainz  Europe       Soft     5  27     23  2016\n",
      "5541     Carlos Sainz  Europe  Supersoft    28  31      4  2016\n",
      "5542  Marcus Ericsson  Europe  Supersoft    17  25      9  2016\n",
      "\n",
      "[125 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "desired_values = ['Europa', 'Europe']\n",
    "\n",
    "# Create a boolean mask to filter rows where the 'gp' column matches the desired values\n",
    "mask = tyre_data_df['gp'].isin(desired_values)\n",
    "\n",
    "# Use the mask to select the rows and print them\n",
    "filtered_rows = tyre_data_df[mask]\n",
    "print(filtered_rows)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Year 2012:\n",
      "['Australia' 'Malaysia' 'China' 'Bahrain' 'Spain' 'Monaco' 'Canada'\n",
      " 'Europa' 'Great Britain' 'Germany' 'Hungary' 'Belgium' 'Italy'\n",
      " 'Singapore' 'Japan' 'Korea' 'India' 'Abu Dhabi' 'United States' 'Brazil']\n",
      "\n",
      "Year 2013:\n",
      "['Australia' 'Malaysia' 'China' 'Bahrain' 'Spain' 'Monaco' 'Canada'\n",
      " 'Great Britain' 'Germany' 'Hungary' 'Belgium' 'Italy' 'Singapore' 'Korea'\n",
      " 'Japan' 'India' 'Abu Dhabi' 'United States' 'Brazil']\n",
      "\n",
      "Year 2014:\n",
      "['Australia' 'Malaysia' 'Bahrain' 'China' 'Spain' 'Monaco' 'Canada'\n",
      " 'Austria' 'Great Britain' 'Germany' 'Hungary' 'Belgium' 'Italy'\n",
      " 'Singapore' 'Japan' 'Russia' 'United States' 'Brazil' 'Abu Dhabi']\n",
      "\n",
      "Year 2015:\n",
      "['Australia' 'Malaysia' 'China' 'Bahrain' 'Spain' 'Monaco' 'Canada'\n",
      " 'Austria' 'Great Britain' 'Hungary' 'Belgium' 'Italy' 'Singapore' 'Japan'\n",
      " 'Russia' 'United States' 'Mexico' 'Brazil' 'Abu Dhabi']\n",
      "\n",
      "Year 2016:\n",
      "['Australia' 'Bahrain' 'China' 'Russia' 'Spain' 'Monaco' 'Canada' 'Europe'\n",
      " 'Austria' 'Great Britain' 'Hungary' 'Germany' 'Belgium' 'Italy'\n",
      " 'Singapore' 'Japan' 'Malaysia' 'United States' 'Mexico' 'Brazil'\n",
      " 'Abu Dhabi']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# List of years from 2012 to 2016\n",
    "years = [2012, 2013, 2014, 2015, 2016]\n",
    "\n",
    "# Loop through the years and print unique values for each year\n",
    "for year in years:\n",
    "    # Filter the DataFrame for the current year\n",
    "    tyre_data_year = tyre_data_df[tyre_data_df['year'] == year]\n",
    "    \n",
    "    # Get the unique values from the \"gp\" column for the current year\n",
    "    unique_gp_values = tyre_data_year['gp'].unique()\n",
    "    \n",
    "    # Print the year and its unique values\n",
    "    print(f\"Year {year}:\")\n",
    "    print(unique_gp_values)\n",
    "    print()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Click [here](https://en.wikipedia.org/wiki/European_Grand_Prix) If you want to know more about the Grand Prix of \"Europe\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Click [here](https://en.wikipedia.org/wiki/70th_Anniversary_Grand_Prix) to know more about the '70TH Anniversary' Grand Prix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "bingroup": "x",
         "hovertemplate": "Street Circuit=Australia<br>Tyre Types=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "Australia",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Australia",
         "offsetgroup": "Australia",
         "orientation": "v",
         "showlegend": true,
         "type": "histogram",
         "x": [
          "Soft",
          "Medium",
          "Medium",
          "Soft",
          "Soft",
          "Medium",
          "Soft",
          "Medium",
          "Medium",
          "Soft",
          "Medium",
          "Medium",
          "Medium",
          "Medium",
          "Soft",
          "Soft",
          "Medium",
          "Soft",
          "Soft",
          "Soft",
          "Medium",
          "Medium",
          "Soft",
          "Soft",
          "Medium",
          "Soft",
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    "import plotly.express as px\n",
    "\n",
    "# List of street circuit names\n",
    "street_circuits = ['Monaco', 'Australia', 'Singapore', 'Russia', 'Azerbaijan','Miami', 'Saudi Arabia', 'Canada']\n",
    "\n",
    "# Filter the DataFrame to select rows where the \"gp\" column is in the list of street circuits\n",
    "street_circuit_rows = tyre_data_df[tyre_data_df['gp'].isin(street_circuits)]\n",
    "\n",
    "# Create a non-stacked histogram to visualize the distribution of updated tire types\n",
    "fig = px.histogram(street_circuit_rows, x='tyres', color='gp', title='Tire Type Distribution on Street Circuits',\n",
    "                   labels={'tyres': 'Tyre Types', 'gp': 'Street Circuit'},\n",
    "                   barmode='group')  # Set barmode to 'group' for non-stacked bars\n",
    "\n",
    "# Customize the layout\n",
    "fig.update_layout(xaxis_tickangle=-45, xaxis_title=None, yaxis_title='Count')\n",
    "\n",
    "# Show the plot\n",
    "fig.show()\n"
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    "import plotly.express as px\n",
    "\n",
    "# List of street circuit names\n",
    "street_circuits = ['Monaco', 'Australia', 'Singapore', 'Russia', 'Azerbaijan','Miami', 'Saudi Arabia','Canada']\n",
    "\n",
    "# Filter the DataFrame to select rows where the \"gp\" column is in the list of street circuits\n",
    "street_circuit_rows = tyre_data_df[tyre_data_df['gp'].isin(street_circuits)]\n",
    "\n",
    "# Calculate the percentage distribution of tire types\n",
    "percentage_distribution = street_circuit_rows['tyres'].value_counts(normalize=True) * 100\n",
    "\n",
    "# Create a DataFrame with the calculated percentages\n",
    "percentage_df = percentage_distribution.reset_index()\n",
    "percentage_df.columns = ['Tyre Types', 'Percentage (%)']\n",
    "\n",
    "# Create a bar chart to visualize the percentage distribution of updated tire types\n",
    "fig = px.bar(percentage_df, x='Tyre Types', y='Percentage (%)',\n",
    "             title='Tyre Type Distribution on Street Circuits 2012-2023<br><sup>Street Circuits: Monaco, Australia, Singapore, Russia, Azerbaijan, Saudi Arabia, Miami and Canada</sup>',\n",
    "             color_discrete_sequence=[\"#00A08B\"],\n",
    "             text=percentage_df['Percentage (%)'].apply(lambda x: f'{x:.0f}%'))\n",
    "\n",
    "# Update marker properties for the bars\n",
    "fig.update_traces(\n",
    "    marker=dict(line=dict(width=1, color='black'))\n",
    ")\n",
    "\n",
    "# Increase title font size\n",
    "fig.update_layout(\n",
    "    title_font=dict(size=24),\n",
    "    xaxis_title_font=dict(size=18),  # Increase x-axis label font size\n",
    "    yaxis_title_font=dict(size=18)   # Increase y-axis label font size\n",
    ")\n",
    "\n",
    "# Show the plot\n",
    "fig.show()\n"
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            "ticks": "",
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           },
           "yaxis": {
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            "gridwidth": 2,
            "linecolor": "white",
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           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "font": {
          "size": 24
         },
         "text": "Tyre Type Distribution on Race Circuits 2012-2023"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "font": {
           "size": 18
          },
          "text": "Tyre Types"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "font": {
           "size": 18
          },
          "text": "Percentage (%)"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "race_circuits = ['Malaysia', 'China', 'Bahrain', 'Spain',\n",
    "       'Europa', 'Great Britain', 'Germany', 'Hungary',\n",
    "       'Belgium', 'Italy','Japan', 'Korea', 'India',\n",
    "       'Abu Dhabi', 'United States', 'Brazil', 'Austria',\n",
    "       'Mexico', 'Europe','France', 'Styria',\n",
    "       '70TH Anniversary', 'Tuscany', 'Russian', 'Portugal', 'Imola',\n",
    "       'Turkey', 'Sakhir', 'Netherlands', 'Qatar',\n",
    "       'Italia', 'Francia']\n",
    "\n",
    "# Filter the DataFrame to select rows where the \"gp\" column is in the list of street circuits\n",
    "race_circuit_rows = tyre_data_df[tyre_data_df['gp'].isin(race_circuits)]\n",
    "\n",
    "# Calculate the percentage distribution of tire types\n",
    "percentage_distribution = race_circuit_rows['tyres'].value_counts(normalize=True) * 100\n",
    "\n",
    "# Create a DataFrame with the calculated percentages\n",
    "percentage_df = percentage_distribution.reset_index()\n",
    "percentage_df.columns = ['Tyre Types', 'Percentage (%)']\n",
    "\n",
    "# Create a bar chart to visualize the percentage distribution of updated tire types\n",
    "fig = px.bar(percentage_df, x='Tyre Types', y='Percentage (%)',\n",
    "             title='Tyre Type Distribution on Race Circuits 2012-2023',\n",
    "             color_discrete_sequence=[\"#00A08B\"],\n",
    "             text=percentage_df['Percentage (%)'].apply(lambda x: f'{x:.0f}%'))\n",
    "\n",
    "# Update marker properties for the bars\n",
    "fig.update_traces(\n",
    "    marker=dict(line=dict(width=1, color='black'))\n",
    ")\n",
    "\n",
    "# Increase title font size\n",
    "fig.update_layout(\n",
    "    title_font=dict(size=24),\n",
    "    xaxis_title_font=dict(size=18),  # Increase x-axis label font size\n",
    "    yaxis_title_font=dict(size=18)   # Increase y-axis label font size\n",
    ")\n",
    "\n",
    "# Show the plot\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export the cleaned DataFrame to a CSV file\n",
    "tyre_data_df.to_csv('tyre_data_df_cleaned.csv', index=False)\n",
    "\n",
    "# The 'index=False' argument ensures that the index column is not included in the CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>driver</th>\n",
       "      <th>gp</th>\n",
       "      <th>tyres</th>\n",
       "      <th>from</th>\n",
       "      <th>to</th>\n",
       "      <th>#laps</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jenson Button</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Soft</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jenson Button</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Medium</td>\n",
       "      <td>17</td>\n",
       "      <td>36</td>\n",
       "      <td>20</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jenson Button</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Medium</td>\n",
       "      <td>37</td>\n",
       "      <td>58</td>\n",
       "      <td>22</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sebastian Vettel</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Soft</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sebastian Vettel</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Soft</td>\n",
       "      <td>17</td>\n",
       "      <td>37</td>\n",
       "      <td>21</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             driver         gp   tyres  from  to  #laps  year\n",
       "0     Jenson Button  Australia    Soft     1  16     16  2012\n",
       "1     Jenson Button  Australia  Medium    17  36     20  2012\n",
       "2     Jenson Button  Australia  Medium    37  58     22  2012\n",
       "3  Sebastian Vettel  Australia    Soft     1  16     16  2012\n",
       "4  Sebastian Vettel  Australia    Soft    17  37     21  2012"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tyre_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                gp         tyres  from  to  #laps  year   forename     surname\n",
      "0        Australia          Soft     1  16     16  2012     Jenson      Button\n",
      "1        Australia        Medium    17  36     20  2012     Jenson      Button\n",
      "2        Australia        Medium    37  58     22  2012     Jenson      Button\n",
      "3        Australia          Soft     1  16     16  2012  Sebastian      Vettel\n",
      "4        Australia          Soft    17  37     21  2012  Sebastian      Vettel\n",
      "...            ...           ...   ...  ..    ...   ...        ...         ...\n",
      "13297  Netherlands          Soft    38  60     23  2023       Zhou      Guanyu\n",
      "13298  Netherlands          Soft     1  14     14  2023      Logan    Sargeant\n",
      "13299  Netherlands  Intermediate    65  72      8  2023        Max  Verstappen\n",
      "13300  Netherlands          Soft    47  60     14  2023     Pierre       Gasly\n",
      "13301  Netherlands        Medium    17  48     32  2023       Nico  Hulkenberg\n",
      "\n",
      "[13302 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "# Split the \"Driver\" column into \"Forename\" and \"Surname\"\n",
    "tyre_data_df[['forename', 'surname']] = tyre_data_df['driver'].str.split(' ', n=1, expand=True)\n",
    "\n",
    "# Drop the original \"Driver\" column\n",
    "tyre_data_df.drop('driver', axis=1, inplace=True)\n",
    "\n",
    "# Print the updated DataFrame\n",
    "print(tyre_data_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>gp</th>\n",
       "      <th>tyres</th>\n",
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       "      <th>surname</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Soft</td>\n",
       "      <td>1</td>\n",
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       "      <td>2012</td>\n",
       "      <td>Jenson</td>\n",
       "      <td>Button</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Medium</td>\n",
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       "    </tr>\n",
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       "      <td>22</td>\n",
       "      <td>2012</td>\n",
       "      <td>Jenson</td>\n",
       "      <td>Button</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Soft</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>2012</td>\n",
       "      <td>Sebastian</td>\n",
       "      <td>Vettel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Soft</td>\n",
       "      <td>17</td>\n",
       "      <td>37</td>\n",
       "      <td>21</td>\n",
       "      <td>2012</td>\n",
       "      <td>Sebastian</td>\n",
       "      <td>Vettel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          gp   tyres  from  to  #laps  year   forename surname\n",
       "0  Australia    Soft     1  16     16  2012     Jenson  Button\n",
       "1  Australia  Medium    17  36     20  2012     Jenson  Button\n",
       "2  Australia  Medium    37  58     22  2012     Jenson  Button\n",
       "3  Australia    Soft     1  16     16  2012  Sebastian  Vettel\n",
       "4  Australia    Soft    17  37     21  2012  Sebastian  Vettel"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tyre_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export the cleaned DataFrame to a CSV file\n",
    "tyre_data_df.to_csv('tyre_data_df_cleaned.csv', index=False)\n",
    "\n",
    "# The 'index=False' argument ensures that the index column is not included in the CSV file"
   ]
  }
 ],
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