api_weather_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "circuit_df = pd.read_csv(\"data/circuits.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         time  temp  dwpt  rhum  prcp  snow   wdir  wspd  wpgt    pres  tsun   \n",
      "0    15:00:00  11.8  10.7  93.0   NaN   NaN  310.0  13.0   NaN  1022.7   NaN  \\\n",
      "1    16:00:00  11.8  11.0  95.0   NaN   NaN  320.0  13.0   NaN  1022.7   NaN   \n",
      "2    15:00:00  12.2   9.9  86.0   NaN   NaN  190.0   5.4   NaN  1023.1   NaN   \n",
      "3    16:00:00  11.4   9.5  88.0   NaN   NaN  200.0   9.4   NaN  1022.9   NaN   \n",
      "4    15:00:00   9.7   8.3  91.0   NaN   NaN   80.0  13.0   NaN  1017.2   NaN   \n",
      "..        ...   ...   ...   ...   ...   ...    ...   ...   ...     ...   ...   \n",
      "106  16:00:00  26.1  17.7  60.0   0.0   NaN  360.0  20.5   NaN  1019.3   NaN   \n",
      "107  17:00:00  27.2  18.2  58.0   0.0   NaN   40.0  22.3   NaN  1018.9   NaN   \n",
      "108  15:00:00  18.3  12.3  68.0   0.0   NaN  310.0   9.4   NaN  1021.0   NaN   \n",
      "109  16:00:00  20.6  12.3  59.0   0.0   NaN  320.0  16.6   NaN  1021.2   NaN   \n",
      "110  17:00:00  21.7  12.3  55.0   0.0   NaN    NaN  11.2   NaN  1020.7   NaN   \n",
      "\n",
      "     coco                            name date hour  \n",
      "0     NaN  Albert Park Grand Prix Circuit  NaN  NaN  \n",
      "1     NaN  Albert Park Grand Prix Circuit  NaN  NaN  \n",
      "2     NaN  Albert Park Grand Prix Circuit  NaN  NaN  \n",
      "3     NaN  Albert Park Grand Prix Circuit  NaN  NaN  \n",
      "4     NaN  Albert Park Grand Prix Circuit  NaN  NaN  \n",
      "..    ...                             ...  ...  ...  \n",
      "106   NaN   Miami International Autodrome  NaN  NaN  \n",
      "107   NaN   Miami International Autodrome  NaN  NaN  \n",
      "108   NaN   Miami International Autodrome  NaN  NaN  \n",
      "109   NaN   Miami International Autodrome  NaN  NaN  \n",
      "110   NaN   Miami International Autodrome  NaN  NaN  \n",
      "\n",
      "[5808 rows x 15 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime, time\n",
    "from meteostat import Hourly, Stations\n",
    "from concurrent.futures import ThreadPoolExecutor\n",
    "\n",
    "# List of circuit information with special times\n",
    "special_circuits = {\n",
    "    \"bahrain\": {\"time_range\": (time(17, 0), time(23, 0))},\n",
    "    \"marina_bay\": {\"time_range\": (time(17, 0), time(23, 0))},\n",
    "    \"las_vegas\": {\"time_range\": (time(17, 0), time(23, 0))},\n",
    "    \"yas_marina\": {\"time_range\": (time(17, 0), time(23, 0))},\n",
    "    \"jeddah\": {\"time_range\": (time(17, 0), time(23, 0))},\n",
    "}\n",
    "\n",
    "\n",
    "# Set time periods\n",
    "start_2012 = datetime(2012, 3, 15)\n",
    "end_2012 = datetime(2012, 11, 30, 23, 59)\n",
    "start_2022 = datetime(2022, 3, 15)\n",
    "end_2022 = datetime(2022, 11, 30, 23, 59)\n",
    "\n",
    "# Create a DataFrame to store aggregated data\n",
    "all_data = pd.DataFrame()\n",
    "\n",
    "def fetch_data(circuit_info):\n",
    "    station = Stations().nearby(circuit_info[\"lat\"], circuit_info[\"lng\"]).fetch(1)\n",
    "    if station.empty:\n",
    "        return circuit_info[\"name\"], pd.DataFrame()\n",
    "\n",
    "    data = Hourly(station.index[0], start_2012, end_2012)\n",
    "    return circuit_info[\"name\"], data.fetch()\n",
    "\n",
    "with ThreadPoolExecutor(max_workers=5) as executor:\n",
    "    futures = [executor.submit(fetch_data, circuit) for _, circuit in circuit_df.iterrows()]\n",
    "\n",
    "for future in futures:\n",
    "    try:\n",
    "        circuit_name, data = future.result()\n",
    "\n",
    "        # Fetch the circuit's information from the CSV\n",
    "        circuit_info = circuit_df[circuit_df['name'] == circuit_name].iloc[0]\n",
    "\n",
    "        if circuit_name in special_circuits:\n",
    "            start_time, end_time = special_circuits[circuit_name][\"time_range\"]\n",
    "            filtered_data = data[(data.index.time >= start_time) & (data.index.time <= end_time)]\n",
    "        else:\n",
    "            # Convert index to DatetimeIndex\n",
    "            data.index = pd.to_datetime(data.index)\n",
    "            filtered_data = data[(data.index.dayofweek == 6) & (data.index.time >= time(14, 30)) & (data.index.time <= time(17, 0))]\n",
    "        \n",
    "        # Reset the index and create a new \"time\" and \"name\" column\n",
    "        filtered_data = filtered_data.reset_index()\n",
    "        filtered_data[\"time\"] = filtered_data[\"time\"].apply(lambda x: x.time())\n",
    "        filtered_data[\"name\"] = circuit_name\n",
    "\n",
    "\n",
    "\n",
    "        all_data = pd.concat([all_data, filtered_data])\n",
    "    except Exception as e:\n",
    "        print(f\"Error fetching data for circuit {circuit_name}: {e}\")\n",
    "\n",
    "# Print the aggregated data for all circuits\n",
    "print(all_data)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               name      time  temp  dwpt  rhum  prcp  snow   \n",
      "0    Albert Park Grand Prix Circuit  15:00:00  11.8  10.7  93.0   NaN   NaN  \\\n",
      "1    Albert Park Grand Prix Circuit  16:00:00  11.8  11.0  95.0   NaN   NaN   \n",
      "2    Albert Park Grand Prix Circuit  15:00:00  12.2   9.9  86.0   NaN   NaN   \n",
      "3    Albert Park Grand Prix Circuit  16:00:00  11.4   9.5  88.0   NaN   NaN   \n",
      "4    Albert Park Grand Prix Circuit  15:00:00   9.7   8.3  91.0   NaN   NaN   \n",
      "..                              ...       ...   ...   ...   ...   ...   ...   \n",
      "106   Miami International Autodrome  16:00:00  26.1  17.7  60.0   0.0   NaN   \n",
      "107   Miami International Autodrome  17:00:00  27.2  18.2  58.0   0.0   NaN   \n",
      "108   Miami International Autodrome  15:00:00  18.3  12.3  68.0   0.0   NaN   \n",
      "109   Miami International Autodrome  16:00:00  20.6  12.3  59.0   0.0   NaN   \n",
      "110   Miami International Autodrome  17:00:00  21.7  12.3  55.0   0.0   NaN   \n",
      "\n",
      "      wdir  wspd  wpgt    pres  tsun  coco date hour  \n",
      "0    310.0  13.0   NaN  1022.7   NaN   NaN  NaN  NaN  \n",
      "1    320.0  13.0   NaN  1022.7   NaN   NaN  NaN  NaN  \n",
      "2    190.0   5.4   NaN  1023.1   NaN   NaN  NaN  NaN  \n",
      "3    200.0   9.4   NaN  1022.9   NaN   NaN  NaN  NaN  \n",
      "4     80.0  13.0   NaN  1017.2   NaN   NaN  NaN  NaN  \n",
      "..     ...   ...   ...     ...   ...   ...  ...  ...  \n",
      "106  360.0  20.5   NaN  1019.3   NaN   NaN  NaN  NaN  \n",
      "107   40.0  22.3   NaN  1018.9   NaN   NaN  NaN  NaN  \n",
      "108  310.0   9.4   NaN  1021.0   NaN   NaN  NaN  NaN  \n",
      "109  320.0  16.6   NaN  1021.2   NaN   NaN  NaN  NaN  \n",
      "110    NaN  11.2   NaN  1020.7   NaN   NaN  NaN  NaN  \n",
      "\n",
      "[5808 rows x 15 columns]\n"
     ]
    }
   ],
   "source": [
    "# Specify the column to move\n",
    "column_to_move = 'name'\n",
    "\n",
    "# Extract the column to be moved\n",
    "moved_column = all_data[column_to_move]\n",
    "\n",
    "# Drop the column from its original position\n",
    "df_f1_circuit_weather = all_data.drop(column_to_move, axis=1)\n",
    "\n",
    "# Insert the column at the beginning of the DataFrame\n",
    "df_f1_circuit_weather.insert(0, column_to_move, moved_column)\n",
    "\n",
    "print(df_f1_circuit_weather)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.   nan  0.5  0.3  2.3  0.8  9.9  1.   2.5  2.   3.3  1.8  5.3  3.6\n",
      "  1.3  2.8  4.8  5.8  1.5 13.2  4.6  4.3  8.4  3.  15.5 20.6  4.1  6.4\n",
      " 18.  10.4 15.7  9.7  5.1  7.6  8.1  5.6 12.2 27.7 17.5  8.9 13.5  9.1\n",
      " 17.3 19.1  6.1  6.6 16.3  7.1 11.7  7.4 20.1 13.   9.4  3.8 31.2]\n"
     ]
    }
   ],
   "source": [
    "unique_prcp_values = data['prcp'].unique()\n",
    "print(unique_prcp_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering\n",
    "\n",
    "Let's create a new column \"recommended_tyres\" based on precipitation values.\n",
    "- Intermediate tyres are recommended from 1 to 4mm of precipitation.\n",
    "- Full wet tyres are recommended if the water on track exceeds 4mm.\n",
    "- For 0.0 values we assign \" dry tyres \".\n",
    "- For this, we will create bins : \n",
    "\n",
    "\n",
    "```[-1, 1)``` (left-closed, right-open): This bin includes precipitation values from greater than -1 mm (since there can't be negative precipitation) to less than 1 mm. The label assigned to this bin is \"dry tyres.\"\n",
    "\n",
    "```[1, 4]```: This bin includes precipitation values from 1 mm (inclusive) to 4 mm (inclusive). The label assigned to this bin is \"intermediate tyres.\"\n",
    "\n",
    "```(4, inf)```: This bin includes precipitation values greater than 4 mm. The label assigned to this bin is \"full wet tyres.\"\n",
    "\n",
    "This classification was based on the website [simracingsetup](https://simracingsetup.com/f1-manager/how-to-manage-your-tyres-in-f1-manager-22-complete-tyre-management-guide/#:~:text=The%20full%20wet%20tyre%20should,on%20track%20exceeds%204%2Bmm])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         time  temp  dwpt  rhum  prcp  snow   wdir  wspd  wpgt    pres  tsun   \n",
      "0    15:00:00  11.8  10.7  93.0   NaN   NaN  310.0  13.0   NaN  1022.7   NaN  \\\n",
      "1    16:00:00  11.8  11.0  95.0   NaN   NaN  320.0  13.0   NaN  1022.7   NaN   \n",
      "2    15:00:00  12.2   9.9  86.0   NaN   NaN  190.0   5.4   NaN  1023.1   NaN   \n",
      "3    16:00:00  11.4   9.5  88.0   NaN   NaN  200.0   9.4   NaN  1022.9   NaN   \n",
      "4    15:00:00   9.7   8.3  91.0   NaN   NaN   80.0  13.0   NaN  1017.2   NaN   \n",
      "..        ...   ...   ...   ...   ...   ...    ...   ...   ...     ...   ...   \n",
      "106  16:00:00  26.1  17.7  60.0   0.0   NaN  360.0  20.5   NaN  1019.3   NaN   \n",
      "107  17:00:00  27.2  18.2  58.0   0.0   NaN   40.0  22.3   NaN  1018.9   NaN   \n",
      "108  15:00:00  18.3  12.3  68.0   0.0   NaN  310.0   9.4   NaN  1021.0   NaN   \n",
      "109  16:00:00  20.6  12.3  59.0   0.0   NaN  320.0  16.6   NaN  1021.2   NaN   \n",
      "110  17:00:00  21.7  12.3  55.0   0.0   NaN    NaN  11.2   NaN  1020.7   NaN   \n",
      "\n",
      "     coco                            name date hour recommended_tyres  \n",
      "0     NaN  Albert Park Grand Prix Circuit  NaN  NaN               NaN  \n",
      "1     NaN  Albert Park Grand Prix Circuit  NaN  NaN               NaN  \n",
      "2     NaN  Albert Park Grand Prix Circuit  NaN  NaN               NaN  \n",
      "3     NaN  Albert Park Grand Prix Circuit  NaN  NaN               NaN  \n",
      "4     NaN  Albert Park Grand Prix Circuit  NaN  NaN               NaN  \n",
      "..    ...                             ...  ...  ...               ...  \n",
      "106   NaN   Miami International Autodrome  NaN  NaN         dry tyres  \n",
      "107   NaN   Miami International Autodrome  NaN  NaN         dry tyres  \n",
      "108   NaN   Miami International Autodrome  NaN  NaN         dry tyres  \n",
      "109   NaN   Miami International Autodrome  NaN  NaN         dry tyres  \n",
      "110   NaN   Miami International Autodrome  NaN  NaN         dry tyres  \n",
      "\n",
      "[5808 rows x 16 columns]\n"
     ]
    }
   ],
   "source": [
    "# Create a new DataFrame with recommended_tyres column\n",
    "recommended_tyres_data = all_data.copy()\n",
    "\n",
    "# Define the labels for the bins\n",
    "bin_labels = [\"dry tyres\", \"intermediate tyres\", \"full wet tyres\"]\n",
    "\n",
    "# Add the recommended_tyres column based on prcp values\n",
    "recommended_tyres_data[\"recommended_tyres\"] = pd.cut(\n",
    "    recommended_tyres_data[\"prcp\"],\n",
    "    bins=[-1, 1, 4, float('inf')],\n",
    "    labels=bin_labels,\n",
    "    right=False  # Use right=False to exclude the right bin edge\n",
    ")\n",
    "\n",
    "# Print the aggregated data with the new recommended_tyres column\n",
    "print(recommended_tyres_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look on our table on the new created column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>time</th>\n",
       "      <th>temp</th>\n",
       "      <th>dwpt</th>\n",
       "      <th>rhum</th>\n",
       "      <th>prcp</th>\n",
       "      <th>snow</th>\n",
       "      <th>wdir</th>\n",
       "      <th>wspd</th>\n",
       "      <th>wpgt</th>\n",
       "      <th>pres</th>\n",
       "      <th>tsun</th>\n",
       "      <th>coco</th>\n",
       "      <th>name</th>\n",
       "      <th>date</th>\n",
       "      <th>hour</th>\n",
       "      <th>recommended_tyres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15:00:00</td>\n",
       "      <td>11.8</td>\n",
       "      <td>10.7</td>\n",
       "      <td>93.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>310.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1022.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albert Park Grand Prix Circuit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16:00:00</td>\n",
       "      <td>11.8</td>\n",
       "      <td>11.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>320.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1022.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albert Park Grand Prix Circuit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15:00:00</td>\n",
       "      <td>12.2</td>\n",
       "      <td>9.9</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>190.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1023.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albert Park Grand Prix Circuit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16:00:00</td>\n",
       "      <td>11.4</td>\n",
       "      <td>9.5</td>\n",
       "      <td>88.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1022.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albert Park Grand Prix Circuit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15:00:00</td>\n",
       "      <td>9.7</td>\n",
       "      <td>8.3</td>\n",
       "      <td>91.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1017.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albert Park Grand Prix Circuit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16:00:00</td>\n",
       "      <td>24.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahrain International Circuit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17:00:00</td>\n",
       "      <td>24.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahrain International Circuit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15:00:00</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14.1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>14.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahrain International Circuit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16:00:00</td>\n",
       "      <td>24.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahrain International Circuit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>17:00:00</td>\n",
       "      <td>24.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110.0</td>\n",
       "      <td>22.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahrain International Circuit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        time  temp  dwpt  rhum  prcp  snow   wdir  wspd  wpgt    pres  tsun   \n",
       "0   15:00:00  11.8  10.7  93.0   NaN   NaN  310.0  13.0   NaN  1022.7   NaN  \\\n",
       "1   16:00:00  11.8  11.0  95.0   NaN   NaN  320.0  13.0   NaN  1022.7   NaN   \n",
       "2   15:00:00  12.2   9.9  86.0   NaN   NaN  190.0   5.4   NaN  1023.1   NaN   \n",
       "3   16:00:00  11.4   9.5  88.0   NaN   NaN  200.0   9.4   NaN  1022.9   NaN   \n",
       "4   15:00:00   9.7   8.3  91.0   NaN   NaN   80.0  13.0   NaN  1017.2   NaN   \n",
       "..       ...   ...   ...   ...   ...   ...    ...   ...   ...     ...   ...   \n",
       "10  16:00:00  24.0  16.0  61.0   NaN   NaN   90.0   7.6   NaN     NaN   NaN   \n",
       "11  17:00:00  24.0  17.0  65.0   NaN   NaN  120.0   9.4   NaN     NaN   NaN   \n",
       "12  15:00:00  24.0  14.1  54.0   NaN   NaN   70.0  14.8   NaN     NaN   NaN   \n",
       "13  16:00:00  24.0  15.0  57.0   NaN   NaN   70.0  13.0   NaN     NaN   NaN   \n",
       "14  17:00:00  24.0  15.0  57.0   NaN   NaN  110.0  22.3   NaN     NaN   NaN   \n",
       "\n",
       "    coco                            name date hour recommended_tyres  \n",
       "0    NaN  Albert Park Grand Prix Circuit  NaN  NaN               NaN  \n",
       "1    NaN  Albert Park Grand Prix Circuit  NaN  NaN               NaN  \n",
       "2    NaN  Albert Park Grand Prix Circuit  NaN  NaN               NaN  \n",
       "3    NaN  Albert Park Grand Prix Circuit  NaN  NaN               NaN  \n",
       "4    NaN  Albert Park Grand Prix Circuit  NaN  NaN               NaN  \n",
       "..   ...                             ...  ...  ...               ...  \n",
       "10   NaN   Bahrain International Circuit  NaN  NaN               NaN  \n",
       "11   NaN   Bahrain International Circuit  NaN  NaN               NaN  \n",
       "12   NaN   Bahrain International Circuit  NaN  NaN               NaN  \n",
       "13   NaN   Bahrain International Circuit  NaN  NaN               NaN  \n",
       "14   NaN   Bahrain International Circuit  NaN  NaN               NaN  \n",
       "\n",
       "[200 rows x 16 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommended_tyres_data.head(200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's check the unique values of our new column \"recommended_tyres\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[NaN, 'dry tyres', 'intermediate tyres', 'full wet tyres']\n",
       "Categories (3, object): ['dry tyres' < 'intermediate tyres' < 'full wet tyres']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommended_tyres_data.recommended_tyres.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems correct, although we have NaN values. These NaN values come from the NaN values in the precipitation column.\n",
    "\n",
    "Let's check how many NaN we have in this table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time                    0\n",
       "temp                  229\n",
       "dwpt                  237\n",
       "rhum                  237\n",
       "prcp                 4247\n",
       "snow                 5808\n",
       "wdir                  844\n",
       "wspd                  232\n",
       "wpgt                 5808\n",
       "pres                 2722\n",
       "tsun                 5697\n",
       "coco                 5808\n",
       "name                    0\n",
       "date                 5808\n",
       "hour                 5808\n",
       "recommended_tyres    4247\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommended_tyres_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import missingno\n",
    "import missingno as msno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2500x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting percentage of missing values per column\n",
    "msno.bar(recommended_tyres_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numbers of rows : 5808\n",
      "missing values in temp : 3.94 %\n",
      "missing values in dwpt : 4.08 %\n",
      "missing values in rhum : 4.08 %\n",
      "missing values in prcp : 73.12 %\n",
      "missing values in snow : 100.0 %\n",
      "missing values in wdir : 14.53 %\n",
      "missing values in wspd : 3.99 %\n",
      "missing values in wpgt : 100.0 %\n",
      "missing values in pres : 46.87 %\n",
      "missing values in tsun : 98.09 %\n",
      "missing values in coco : 100.0 %\n",
      "missing values in date : 100.0 %\n",
      "missing values in hour : 100.0 %\n",
      "missing values in recommended tyres : 73.12 %\n",
      "missing values in data frame : 51.36 %\n"
     ]
    }
   ],
   "source": [
    "print(f\"numbers of rows : {recommended_tyres_data.shape[0]}\")\n",
    "print(f\"missing values in temp : {round(recommended_tyres_data.temp.isna().sum()/recommended_tyres_data.shape[0]*100,2)} %\")\n",
    "print(f\"missing values in dwpt : {round(recommended_tyres_data.dwpt.isna().sum()/recommended_tyres_data.shape[0]*100,2)} %\")\n",
    "print(f\"missing values in rhum : {round(recommended_tyres_data.rhum.isna().sum()/recommended_tyres_data.shape[0]*100,2)} %\")\n",
    "print(f\"missing values in prcp : {round(recommended_tyres_data.prcp.isna().sum()/recommended_tyres_data.shape[0]*100,2)} %\")\n",
    "print(f\"missing values in snow : {round(recommended_tyres_data.snow.isna().sum()/recommended_tyres_data.shape[0]*100,2)} %\")\n",
    "print(f\"missing values in wdir : {round(recommended_tyres_data.wdir.isna().sum()/recommended_tyres_data.shape[0]*100,2)} %\")\n",
    "print(f\"missing values in wspd : {round(recommended_tyres_data.wspd.isna().sum()/recommended_tyres_data.shape[0]*100,2)} %\")\n",
    "print(f\"missing values in wpgt : {round(recommended_tyres_data.wpgt.isna().sum()/recommended_tyres_data.shape[0]*100,2)} %\")\n",
    "print(f\"missing values in pres : {round(recommended_tyres_data.pres.isna().sum()/recommended_tyres_data.shape[0]*100,2)} %\")\n",
    "print(f\"missing values in tsun : {round(recommended_tyres_data.tsun.isna().sum()/recommended_tyres_data.shape[0]*100,2)} %\")\n",
    "print(f\"missing values in coco : {round(recommended_tyres_data.coco.isna().sum()/recommended_tyres_data.shape[0]*100,2)} %\")\n",
    "print(f\"missing values in date : {round(recommended_tyres_data.date.isna().sum()/recommended_tyres_data.shape[0]*100,2)} %\")\n",
    "print(f\"missing values in hour : {round(recommended_tyres_data.hour.isna().sum()/recommended_tyres_data.shape[0]*100,2)} %\")\n",
    "print(f\"missing values in recommended tyres : {round(recommended_tyres_data.recommended_tyres.isna().sum()/recommended_tyres_data.shape[0]*100,2)} %\")\n",
    "print(f\"missing values in data frame : {round(recommended_tyres_data.isna().sum().sum()/(recommended_tyres_data.shape[0]*recommended_tyres_data.shape[1])*100,2)} %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Precipitation and recommend tyres columns have both the same missing values. 73% is a lot. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 5808 entries, 0 to 110\n",
      "Data columns (total 16 columns):\n",
      " #   Column             Non-Null Count  Dtype   \n",
      "---  ------             --------------  -----   \n",
      " 0   time               5808 non-null   object  \n",
      " 1   temp               5579 non-null   float64 \n",
      " 2   dwpt               5571 non-null   float64 \n",
      " 3   rhum               5571 non-null   float64 \n",
      " 4   prcp               1561 non-null   float64 \n",
      " 5   snow               0 non-null      float64 \n",
      " 6   wdir               4964 non-null   float64 \n",
      " 7   wspd               5576 non-null   float64 \n",
      " 8   wpgt               0 non-null      float64 \n",
      " 9   pres               3086 non-null   float64 \n",
      " 10  tsun               111 non-null    float64 \n",
      " 11  coco               0 non-null      float64 \n",
      " 12  name               5808 non-null   object  \n",
      " 13  date               0 non-null      object  \n",
      " 14  hour               0 non-null      object  \n",
      " 15  recommended_tyres  1561 non-null   category\n",
      "dtypes: category(1), float64(11), object(4)\n",
      "memory usage: 731.8+ KB\n"
     ]
    }
   ],
   "source": [
    "recommended_tyres_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Filter data for circuits with precipitation greater than 0.0\n",
    "circuits_with_precipitation = recommended_tyres_data[recommended_tyres_data[\"prcp\"] > 0.0]\n",
    "\n",
    "# Create a bar plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(circuits_with_precipitation[\"name\"], circuits_with_precipitation[\"prcp\"], color='blue')\n",
    "plt.xlabel(\"Circuit Name\")\n",
    "plt.ylabel(\"Precipitation (mm)\")\n",
    "plt.title(\"Circuits with Precipitation Greater Than 0.0 mm\")\n",
    "plt.xticks(rotation=45, ha=\"right\")\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     temp  dwpt  rhum  prcp  snow  wdir  wspd  wpgt    pres   \n",
      "time                                                                          \n",
      "2012-03-15 00:00:00  23.3  15.6  62.0   0.0   NaN  40.0  14.8   NaN  1021.2  \\\n",
      "2012-03-15 01:00:00  22.8  14.9  61.0   0.0   NaN  40.0  16.6   NaN  1021.9   \n",
      "2012-03-15 02:00:00  22.8  16.1  66.0   0.0   NaN  60.0  20.5   NaN  1022.4   \n",
      "2012-03-15 03:00:00  22.8  16.1  66.0   0.0   NaN  50.0  18.4   NaN  1022.5   \n",
      "2012-03-15 04:00:00  22.8  17.7  73.0   NaN   NaN  60.0  18.4   NaN  1022.5   \n",
      "\n",
      "                     tsun  coco  \n",
      "time                             \n",
      "2012-03-15 00:00:00   NaN   NaN  \n",
      "2012-03-15 01:00:00   NaN   NaN  \n",
      "2012-03-15 02:00:00   NaN   NaN  \n",
      "2012-03-15 03:00:00   NaN   NaN  \n",
      "2012-03-15 04:00:00   NaN   NaN  \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 6259 entries, 2012-03-15 00:00:00 to 2012-11-30 23:00:00\n",
      "Data columns (total 11 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   temp    6259 non-null   float64\n",
      " 1   dwpt    6254 non-null   float64\n",
      " 2   rhum    6254 non-null   float64\n",
      " 3   prcp    5801 non-null   float64\n",
      " 4   snow    0 non-null      float64\n",
      " 5   wdir    5030 non-null   float64\n",
      " 6   wspd    6259 non-null   float64\n",
      " 7   wpgt    0 non-null      float64\n",
      " 8   pres    6181 non-null   float64\n",
      " 9   tsun    0 non-null      float64\n",
      " 10  coco    0 non-null      float64\n",
      "dtypes: float64(11)\n",
      "memory usage: 586.8 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(data.head())  # Print the first few rows of the data\n",
    "print(data.info())  # Print information about the data (e.g., column data types, missing values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      NaN\n",
      "1      NaN\n",
      "2      NaN\n",
      "3      NaN\n",
      "4      NaN\n",
      "      ... \n",
      "106    0.0\n",
      "107    0.0\n",
      "108    0.0\n",
      "109    0.0\n",
      "110    0.0\n",
      "Name: prcp, Length: 5808, dtype: float64\n",
      "[nan 0.2 0.  2.  0.4 9.  1.  0.5 0.8 0.1 0.3 3.3 5.1 3.6 0.6 4.1 1.3 3.\n",
      " 0.9 4.6 8.4 1.8 2.3 1.5]\n"
     ]
    }
   ],
   "source": [
    "# Print the values in the 'prcp' column\n",
    "print(recommended_tyres_data['prcp'])\n",
    "\n",
    "# Print unique values in the 'prcp' column\n",
    "print(recommended_tyres_data['prcp'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Index' object has no attribute 'date'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[31], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39m# Assuming you have the 'data' DataFrame with the weather data\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m nan_counts_by_day \u001b[39m=\u001b[39m recommended_tyres_data[\u001b[39m'\u001b[39m\u001b[39mprcp\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mgroupby(recommended_tyres_data\u001b[39m.\u001b[39;49mindex\u001b[39m.\u001b[39;49mdate)\u001b[39m.\u001b[39mapply(\u001b[39mlambda\u001b[39;00m x: x\u001b[39m.\u001b[39misna()\u001b[39m.\u001b[39msum())\n\u001b[1;32m      4\u001b[0m \u001b[39mprint\u001b[39m(nan_counts_by_day)\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Index' object has no attribute 'date'"
     ]
    }
   ],
   "source": [
    "# Assuming you have the 'data' DataFrame with the weather data\n",
    "nan_counts_by_day = recommended_tyres_data['prcp'].groupby(recommended_tyres_data.index.date).apply(lambda x: x.isna().sum())\n",
    "\n",
    "print(nan_counts_by_day)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
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 },
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