linear-regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.ticker as mtick\n",
    "import datetime\n",
    "from datetime import timedelta\n",
    "from datetime import datetime\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "import numpy as np\n",
    "from numpy import NaN\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "CURRENT_LOCATION = \"rov\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_to_file = 'APPA_data_merged.csv'\n",
    "df = pd.read_csv(path_to_file, parse_dates=[\"Data\"])\n",
    "df_bolz_pm10 = df[(df.Stazione == \"Rovereto\") & (df.Inquinante == \"PM10\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A22 (Avio)': ['Biossido di Azoto', 'Ossido di Carbonio', 'PM10'],\n",
       " 'Borgo Valsugana': ['Biossido di Azoto', 'Ozono', 'PM10', 'PM2.5'],\n",
       " 'Monte Gaza': ['Biossido di Azoto', 'Ozono', 'PM10'],\n",
       " 'Parco S. Chiara': ['Biossido Zolfo',\n",
       "  'Biossido di Azoto',\n",
       "  'Ozono',\n",
       "  'PM10',\n",
       "  'PM2.5'],\n",
       " 'Piana Rotaliana': ['Biossido di Azoto', 'Ozono', 'PM10'],\n",
       " 'Riva del Garda': ['Biossido di Azoto', 'Ozono', 'PM10'],\n",
       " 'Rovereto': ['Biossido di Azoto', 'Ozono', 'PM10', 'PM2.5'],\n",
       " 'Via Bolzano': ['Biossido di Azoto', 'Ossido di Carbonio', 'PM10']}"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station_list = df.Stazione.unique()\n",
    "pollutants_per_station = {}\n",
    "for st in station_list:\n",
    "    pollutants_per_station[st] = df[df.Stazione == st].Inquinante.unique().tolist()\n",
    "\n",
    "pollutants_per_station"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Makes the daily mean from the hour values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_daily = df_bolz_pm10.resample(\"D\", on='Data').mean()\n",
    "df_daily = df_daily.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data</th>\n",
       "      <th>Valore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>38.521739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>39.791667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>19.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>23.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>19.958333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Data     Valore\n",
       "0 2012-01-01  38.521739\n",
       "1 2012-01-02  39.791667\n",
       "2 2012-01-03  19.166667\n",
       "3 2012-01-04  23.875000\n",
       "4 2012-01-05  19.958333"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_daily.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_file_path = \"weather_data.csv\"\n",
    "weather_df = pd.read_csv(weather_file_path, parse_dates=[\"Data\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['borgo', 'rg', 'avio', 'bolz', 'mgazza', 'parco', 'piana', 'rov']"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locations = list(set(weather_df.columns[1:].map(lambda x: x.split()[0])))\n",
    "locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thoma\\AppData\\Local\\Temp\\ipykernel_2228\\2564517573.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  location_df[\"Location\"] = location\n",
      "C:\\Users\\thoma\\AppData\\Local\\Temp\\ipykernel_2228\\2564517573.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  location_df[\"Location\"] = location\n",
      "C:\\Users\\thoma\\AppData\\Local\\Temp\\ipykernel_2228\\2564517573.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  location_df[\"Location\"] = location\n",
      "C:\\Users\\thoma\\AppData\\Local\\Temp\\ipykernel_2228\\2564517573.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  location_df[\"Location\"] = location\n",
      "C:\\Users\\thoma\\AppData\\Local\\Temp\\ipykernel_2228\\2564517573.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  location_df[\"Location\"] = location\n",
      "C:\\Users\\thoma\\AppData\\Local\\Temp\\ipykernel_2228\\2564517573.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  location_df[\"Location\"] = location\n",
      "C:\\Users\\thoma\\AppData\\Local\\Temp\\ipykernel_2228\\2564517573.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  location_df[\"Location\"] = location\n",
      "C:\\Users\\thoma\\AppData\\Local\\Temp\\ipykernel_2228\\2564517573.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  location_df[\"Location\"] = location\n"
     ]
    }
   ],
   "source": [
    "weather_loc = {}\n",
    "\n",
    "for location in locations:\n",
    "    location_columns = weather_df.columns[\n",
    "        weather_df.columns.map(lambda x: x.split()[0] == location)\n",
    "    ]\n",
    "\n",
    "    location_df = weather_df[[\"Data\"] + location_columns.tolist()]\n",
    "    location_df.columns = location_df.columns.str.replace(location, \"\").str.strip()\n",
    "    location_df[\"Location\"] = location\n",
    "    \n",
    "    weather_loc[location] = location_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_weather_long = pd.concat(weather_loc.values())\n",
    "# df_weather_long.to_csv(\"./weather_long_format.csv\", index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\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>Data</th>\n",
       "      <th>Valore</th>\n",
       "      <th>rain</th>\n",
       "      <th>temp</th>\n",
       "      <th>Location</th>\n",
       "      <th>hum</th>\n",
       "      <th>w_dir</th>\n",
       "      <th>w_sp</th>\n",
       "      <th>press</th>\n",
       "      <th>sol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>38.521739</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>rov</td>\n",
       "      <td>73.5</td>\n",
       "      <td>183.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>997.1</td>\n",
       "      <td>3615.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>39.791667</td>\n",
       "      <td>17.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>rov</td>\n",
       "      <td>88.5</td>\n",
       "      <td>194.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>996.0</td>\n",
       "      <td>998.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>19.166667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>rov</td>\n",
       "      <td>93.7</td>\n",
       "      <td>169.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>998.9</td>\n",
       "      <td>4112.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>23.875000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>rov</td>\n",
       "      <td>90.3</td>\n",
       "      <td>174.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>995.2</td>\n",
       "      <td>2201.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>19.958333</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>rov</td>\n",
       "      <td>75.6</td>\n",
       "      <td>179.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>981.9</td>\n",
       "      <td>3931.4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3837</th>\n",
       "      <td>2022-07-04</td>\n",
       "      <td>14.250000</td>\n",
       "      <td>2.8</td>\n",
       "      <td>24.5</td>\n",
       "      <td>rov</td>\n",
       "      <td>63.1</td>\n",
       "      <td>320.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>992.4</td>\n",
       "      <td>18361.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3838</th>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>8.125000</td>\n",
       "      <td>0.8</td>\n",
       "      <td>25.3</td>\n",
       "      <td>rov</td>\n",
       "      <td>59.3</td>\n",
       "      <td>331.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>990.5</td>\n",
       "      <td>25412.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3839</th>\n",
       "      <td>2022-07-06</td>\n",
       "      <td>10.875000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>25.7</td>\n",
       "      <td>rov</td>\n",
       "      <td>57.6</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>992.5</td>\n",
       "      <td>25734.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3840</th>\n",
       "      <td>2022-07-07</td>\n",
       "      <td>14.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.7</td>\n",
       "      <td>rov</td>\n",
       "      <td>52.7</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>991.1</td>\n",
       "      <td>23376.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3841</th>\n",
       "      <td>2022-07-08</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.1</td>\n",
       "      <td>rov</td>\n",
       "      <td>40.2</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>994.7</td>\n",
       "      <td>29978.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3842 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Data     Valore  rain  temp Location   hum  w_dir  w_sp  press  \\\n",
       "0    2012-01-01  38.521739   0.0   0.8      rov  73.5  183.0   0.9  997.1   \n",
       "1    2012-01-02  39.791667  17.4   0.8      rov  88.5  194.0   1.0  996.0   \n",
       "2    2012-01-03  19.166667   1.0   3.3      rov  93.7  169.0   1.0  998.9   \n",
       "3    2012-01-04  23.875000   0.0   1.1      rov  90.3  174.0   1.0  995.2   \n",
       "4    2012-01-05  19.958333   0.4   1.6      rov  75.6  179.0   1.4  981.9   \n",
       "...         ...        ...   ...   ...      ...   ...    ...   ...    ...   \n",
       "3837 2022-07-04  14.250000   2.8  24.5      rov  63.1  320.0   2.0  992.4   \n",
       "3838 2022-07-05   8.125000   0.8  25.3      rov  59.3  331.0   1.3  990.5   \n",
       "3839 2022-07-06  10.875000   0.2  25.7      rov  57.6  115.0   1.4  992.5   \n",
       "3840 2022-07-07  14.833333   0.0  25.7      rov  52.7   32.0   1.6  991.1   \n",
       "3841 2022-07-08  16.000000   0.0  25.1      rov  40.2  349.0   1.6  994.7   \n",
       "\n",
       "          sol  \n",
       "0      3615.3  \n",
       "1       998.3  \n",
       "2      4112.6  \n",
       "3      2201.1  \n",
       "4      3931.4  \n",
       "...       ...  \n",
       "3837  18361.3  \n",
       "3838  25412.8  \n",
       "3839  25734.4  \n",
       "3840  23376.6  \n",
       "3841  29978.0  \n",
       "\n",
       "[3842 rows x 10 columns]"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df = df_daily.join(\n",
    "    df_weather_long[df_weather_long.Location == CURRENT_LOCATION].set_index(\"Data\"), \n",
    "    on=\"Data\"\n",
    ")\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add column for the value of the previous day\n",
    "weather_parameters = ['rain', 'temp', 'press', 'hum', 'w_dir', 'w_sp', 'sol']\n",
    "prev_day_values = {}\n",
    "for par in weather_parameters:\n",
    "    for i in range(1, 8):\n",
    "        prev_day_values[f\"{par} {i} day/s earlier\"]  = []\n",
    "        for date in merged_df[\"Data\"]:\n",
    "            prev_day = date - timedelta(hours=int(24 * i))\n",
    "            series = merged_df[merged_df.Data == prev_day][par].values.tolist()\n",
    "            #print(series)\n",
    "            if len(series) > 0:\n",
    "                value = series[0]\n",
    "            else:\n",
    "                value = NaN  # takes the current day, not the previous\n",
    "            prev_day_values[f\"{par} {i} day/s earlier\"].append(value)\n",
    "\n",
    "        merged_df[f\"{par} {i} day/s earlier\"] = prev_day_values[f'{par} {i} day/s earlier']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = merged_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data                     0\n",
       "Valore                   0\n",
       "rain                     0\n",
       "temp                     0\n",
       "Location                 0\n",
       "hum                      0\n",
       "w_dir                    0\n",
       "w_sp                     0\n",
       "press                    0\n",
       "sol                      0\n",
       "rain 1 day/s earlier     0\n",
       "rain 2 day/s earlier     0\n",
       "rain 3 day/s earlier     0\n",
       "rain 4 day/s earlier     0\n",
       "rain 5 day/s earlier     0\n",
       "rain 6 day/s earlier     0\n",
       "rain 7 day/s earlier     0\n",
       "temp 1 day/s earlier     0\n",
       "temp 2 day/s earlier     0\n",
       "temp 3 day/s earlier     0\n",
       "temp 4 day/s earlier     0\n",
       "temp 5 day/s earlier     0\n",
       "temp 6 day/s earlier     0\n",
       "temp 7 day/s earlier     0\n",
       "press 1 day/s earlier    0\n",
       "press 2 day/s earlier    0\n",
       "press 3 day/s earlier    0\n",
       "press 4 day/s earlier    0\n",
       "press 5 day/s earlier    0\n",
       "press 6 day/s earlier    0\n",
       "press 7 day/s earlier    0\n",
       "hum 1 day/s earlier      0\n",
       "hum 2 day/s earlier      0\n",
       "hum 3 day/s earlier      0\n",
       "hum 4 day/s earlier      0\n",
       "hum 5 day/s earlier      0\n",
       "hum 6 day/s earlier      0\n",
       "hum 7 day/s earlier      0\n",
       "w_dir 1 day/s earlier    0\n",
       "w_dir 2 day/s earlier    0\n",
       "w_dir 3 day/s earlier    0\n",
       "w_dir 4 day/s earlier    0\n",
       "w_dir 5 day/s earlier    0\n",
       "w_dir 6 day/s earlier    0\n",
       "w_dir 7 day/s earlier    0\n",
       "w_sp 1 day/s earlier     0\n",
       "w_sp 2 day/s earlier     0\n",
       "w_sp 3 day/s earlier     0\n",
       "w_sp 4 day/s earlier     0\n",
       "w_sp 5 day/s earlier     0\n",
       "w_sp 6 day/s earlier     0\n",
       "w_sp 7 day/s earlier     0\n",
       "sol 1 day/s earlier      0\n",
       "sol 2 day/s earlier      0\n",
       "sol 3 day/s earlier      0\n",
       "sol 4 day/s earlier      0\n",
       "sol 5 day/s earlier      0\n",
       "sol 6 day/s earlier      0\n",
       "sol 7 day/s earlier      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merged_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "\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>Valore</th>\n",
       "      <th>rain</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>w_dir</th>\n",
       "      <th>w_sp</th>\n",
       "      <th>press</th>\n",
       "      <th>sol</th>\n",
       "      <th>rain 1 day/s earlier</th>\n",
       "      <th>rain 2 day/s earlier</th>\n",
       "      <th>...</th>\n",
       "      <th>w_sp 5 day/s earlier</th>\n",
       "      <th>w_sp 6 day/s earlier</th>\n",
       "      <th>w_sp 7 day/s earlier</th>\n",
       "      <th>sol 1 day/s earlier</th>\n",
       "      <th>sol 2 day/s earlier</th>\n",
       "      <th>sol 3 day/s earlier</th>\n",
       "      <th>sol 4 day/s earlier</th>\n",
       "      <th>sol 5 day/s earlier</th>\n",
       "      <th>sol 6 day/s earlier</th>\n",
       "      <th>sol 7 day/s earlier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3779.000000</td>\n",
       "      <td>3779.000000</td>\n",
       "      <td>3779.000000</td>\n",
       "      <td>3779.000000</td>\n",
       "      <td>3779.000000</td>\n",
       "      <td>3779.000000</td>\n",
       "      <td>3779.000000</td>\n",
       "      <td>3779.000000</td>\n",
       "      <td>3779.00000</td>\n",
       "      <td>3779.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3779.000000</td>\n",
       "      <td>3779.000000</td>\n",
       "      <td>3779.000000</td>\n",
       "      <td>3779.000000</td>\n",
       "      <td>3779.000000</td>\n",
       "      <td>3779.00000</td>\n",
       "      <td>3779.000000</td>\n",
       "      <td>3779.000000</td>\n",
       "      <td>3779.000000</td>\n",
       "      <td>3779.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19.287404</td>\n",
       "      <td>2.794337</td>\n",
       "      <td>13.560598</td>\n",
       "      <td>68.879545</td>\n",
       "      <td>138.719238</td>\n",
       "      <td>1.165917</td>\n",
       "      <td>991.147393</td>\n",
       "      <td>12161.459487</td>\n",
       "      <td>2.76163</td>\n",
       "      <td>2.752898</td>\n",
       "      <td>...</td>\n",
       "      <td>1.164753</td>\n",
       "      <td>1.164620</td>\n",
       "      <td>1.164938</td>\n",
       "      <td>12158.962450</td>\n",
       "      <td>12160.596322</td>\n",
       "      <td>12155.13014</td>\n",
       "      <td>12156.120852</td>\n",
       "      <td>12161.331596</td>\n",
       "      <td>12154.959116</td>\n",
       "      <td>12143.338132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.727569</td>\n",
       "      <td>7.763969</td>\n",
       "      <td>8.038128</td>\n",
       "      <td>14.917297</td>\n",
       "      <td>92.491161</td>\n",
       "      <td>0.417329</td>\n",
       "      <td>7.227854</td>\n",
       "      <td>8212.060559</td>\n",
       "      <td>7.58160</td>\n",
       "      <td>7.575330</td>\n",
       "      <td>...</td>\n",
       "      <td>0.417866</td>\n",
       "      <td>0.416837</td>\n",
       "      <td>0.419649</td>\n",
       "      <td>8209.694215</td>\n",
       "      <td>8212.971951</td>\n",
       "      <td>8212.13727</td>\n",
       "      <td>8212.440840</td>\n",
       "      <td>8214.472853</td>\n",
       "      <td>8207.620121</td>\n",
       "      <td>8197.899856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4.600000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>954.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>58.350000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>987.000000</td>\n",
       "      <td>4806.600000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>4806.600000</td>\n",
       "      <td>4811.100000</td>\n",
       "      <td>4806.60000</td>\n",
       "      <td>4816.250000</td>\n",
       "      <td>4811.100000</td>\n",
       "      <td>4806.600000</td>\n",
       "      <td>4806.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.700000</td>\n",
       "      <td>68.800000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>991.200000</td>\n",
       "      <td>10517.100000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>10517.100000</td>\n",
       "      <td>10517.100000</td>\n",
       "      <td>10517.10000</td>\n",
       "      <td>10517.100000</td>\n",
       "      <td>10523.900000</td>\n",
       "      <td>10523.900000</td>\n",
       "      <td>10523.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24.270833</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>20.400000</td>\n",
       "      <td>80.100000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>995.300000</td>\n",
       "      <td>19065.600000</td>\n",
       "      <td>0.80000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>19049.100000</td>\n",
       "      <td>19023.600000</td>\n",
       "      <td>19005.40000</td>\n",
       "      <td>18995.300000</td>\n",
       "      <td>19023.600000</td>\n",
       "      <td>19005.400000</td>\n",
       "      <td>18981.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>149.583333</td>\n",
       "      <td>105.200000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>100.200000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>1016.500000</td>\n",
       "      <td>30444.200000</td>\n",
       "      <td>82.60000</td>\n",
       "      <td>82.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>30444.200000</td>\n",
       "      <td>30444.200000</td>\n",
       "      <td>30444.20000</td>\n",
       "      <td>30444.200000</td>\n",
       "      <td>30444.200000</td>\n",
       "      <td>30444.200000</td>\n",
       "      <td>30444.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Valore         rain         temp          hum        w_dir  \\\n",
       "count  3779.000000  3779.000000  3779.000000  3779.000000  3779.000000   \n",
       "mean     19.287404     2.794337    13.560598    68.879545   138.719238   \n",
       "std      11.727569     7.763969     8.038128    14.917297    92.491161   \n",
       "min       0.200000     0.000000    -4.600000     1.700000     0.000000   \n",
       "25%      11.500000     0.000000     6.600000    58.350000    66.000000   \n",
       "50%      17.083333     0.000000    13.700000    68.800000   139.000000   \n",
       "75%      24.270833     0.800000    20.400000    80.100000   175.000000   \n",
       "max     149.583333   105.200000    31.000000   100.200000   360.000000   \n",
       "\n",
       "              w_sp        press           sol  rain 1 day/s earlier  \\\n",
       "count  3779.000000  3779.000000   3779.000000            3779.00000   \n",
       "mean      1.165917   991.147393  12161.459487               2.76163   \n",
       "std       0.417329     7.227854   8212.060559               7.58160   \n",
       "min       0.000000   954.900000      0.000000               0.00000   \n",
       "25%       0.900000   987.000000   4806.600000               0.00000   \n",
       "50%       1.100000   991.200000  10517.100000               0.00000   \n",
       "75%       1.400000   995.300000  19065.600000               0.80000   \n",
       "max       3.900000  1016.500000  30444.200000              82.60000   \n",
       "\n",
       "       rain 2 day/s earlier  ...  w_sp 5 day/s earlier  w_sp 6 day/s earlier  \\\n",
       "count           3779.000000  ...           3779.000000           3779.000000   \n",
       "mean               2.752898  ...              1.164753              1.164620   \n",
       "std                7.575330  ...              0.417866              0.416837   \n",
       "min                0.000000  ...              0.000000              0.000000   \n",
       "25%                0.000000  ...              0.900000              0.900000   \n",
       "50%                0.000000  ...              1.100000              1.100000   \n",
       "75%                0.800000  ...              1.400000              1.400000   \n",
       "max               82.600000  ...              3.900000              3.900000   \n",
       "\n",
       "       w_sp 7 day/s earlier  sol 1 day/s earlier  sol 2 day/s earlier  \\\n",
       "count           3779.000000          3779.000000          3779.000000   \n",
       "mean               1.164938         12158.962450         12160.596322   \n",
       "std                0.419649          8209.694215          8212.971951   \n",
       "min                0.000000             0.000000             0.000000   \n",
       "25%                0.900000          4806.600000          4811.100000   \n",
       "50%                1.100000         10517.100000         10517.100000   \n",
       "75%                1.400000         19049.100000         19023.600000   \n",
       "max                4.200000         30444.200000         30444.200000   \n",
       "\n",
       "       sol 3 day/s earlier  sol 4 day/s earlier  sol 5 day/s earlier  \\\n",
       "count           3779.00000          3779.000000          3779.000000   \n",
       "mean           12155.13014         12156.120852         12161.331596   \n",
       "std             8212.13727          8212.440840          8214.472853   \n",
       "min                0.00000             0.000000             0.000000   \n",
       "25%             4806.60000          4816.250000          4811.100000   \n",
       "50%            10517.10000         10517.100000         10523.900000   \n",
       "75%            19005.40000         18995.300000         19023.600000   \n",
       "max            30444.20000         30444.200000         30444.200000   \n",
       "\n",
       "       sol 6 day/s earlier  sol 7 day/s earlier  \n",
       "count          3779.000000          3779.000000  \n",
       "mean          12154.959116         12143.338132  \n",
       "std            8207.620121          8197.899856  \n",
       "min               0.000000             0.000000  \n",
       "25%            4806.600000          4806.600000  \n",
       "50%           10523.900000         10523.000000  \n",
       "75%           19005.400000         18981.150000  \n",
       "max           30444.200000         30444.200000  \n",
       "\n",
       "[8 rows x 57 columns]"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data</th>\n",
       "      <th>Valore</th>\n",
       "      <th>rain</th>\n",
       "      <th>temp</th>\n",
       "      <th>Location</th>\n",
       "      <th>hum</th>\n",
       "      <th>w_dir</th>\n",
       "      <th>w_sp</th>\n",
       "      <th>press</th>\n",
       "      <th>sol</th>\n",
       "      <th>...</th>\n",
       "      <th>w_sp 5 day/s earlier</th>\n",
       "      <th>w_sp 6 day/s earlier</th>\n",
       "      <th>w_sp 7 day/s earlier</th>\n",
       "      <th>sol 1 day/s earlier</th>\n",
       "      <th>sol 2 day/s earlier</th>\n",
       "      <th>sol 3 day/s earlier</th>\n",
       "      <th>sol 4 day/s earlier</th>\n",
       "      <th>sol 5 day/s earlier</th>\n",
       "      <th>sol 6 day/s earlier</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2012-01-08</td>\n",
       "      <td>16.416667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>rov</td>\n",
       "      <td>62.1</td>\n",
       "      <td>165.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>991.8</td>\n",
       "      <td>4613.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3558.7</td>\n",
       "      <td>4402.5</td>\n",
       "      <td>3931.4</td>\n",
       "      <td>2201.1</td>\n",
       "      <td>4112.6</td>\n",
       "      <td>998.3</td>\n",
       "      <td>3615.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2012-01-09</td>\n",
       "      <td>17.875000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>rov</td>\n",
       "      <td>62.7</td>\n",
       "      <td>148.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>998.8</td>\n",
       "      <td>4541.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4613.5</td>\n",
       "      <td>3558.7</td>\n",
       "      <td>4402.5</td>\n",
       "      <td>3931.4</td>\n",
       "      <td>2201.1</td>\n",
       "      <td>4112.6</td>\n",
       "      <td>998.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012-01-10</td>\n",
       "      <td>24.458333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>rov</td>\n",
       "      <td>59.1</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1002.5</td>\n",
       "      <td>4511.3</td>\n",
       "      <td>...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4541.6</td>\n",
       "      <td>4613.5</td>\n",
       "      <td>3558.7</td>\n",
       "      <td>4402.5</td>\n",
       "      <td>3931.4</td>\n",
       "      <td>2201.1</td>\n",
       "      <td>4112.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012-01-11</td>\n",
       "      <td>22.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>rov</td>\n",
       "      <td>62.4</td>\n",
       "      <td>179.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1006.6</td>\n",
       "      <td>4585.7</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4511.3</td>\n",
       "      <td>4541.6</td>\n",
       "      <td>4613.5</td>\n",
       "      <td>3558.7</td>\n",
       "      <td>4402.5</td>\n",
       "      <td>3931.4</td>\n",
       "      <td>2201.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2012-01-12</td>\n",
       "      <td>28.041667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>rov</td>\n",
       "      <td>73.5</td>\n",
       "      <td>184.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1002.8</td>\n",
       "      <td>4701.3</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>4585.7</td>\n",
       "      <td>4511.3</td>\n",
       "      <td>4541.6</td>\n",
       "      <td>4613.5</td>\n",
       "      <td>3558.7</td>\n",
       "      <td>4402.5</td>\n",
       "      <td>3931.4</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3837</th>\n",
       "      <td>2022-07-04</td>\n",
       "      <td>14.250000</td>\n",
       "      <td>2.8</td>\n",
       "      <td>24.5</td>\n",
       "      <td>rov</td>\n",
       "      <td>63.1</td>\n",
       "      <td>320.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>992.4</td>\n",
       "      <td>18361.3</td>\n",
       "      <td>...</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>27961.8</td>\n",
       "      <td>29056.3</td>\n",
       "      <td>20648.2</td>\n",
       "      <td>28212.6</td>\n",
       "      <td>16730.9</td>\n",
       "      <td>10409.2</td>\n",
       "      <td>26379.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3838</th>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>8.125000</td>\n",
       "      <td>0.8</td>\n",
       "      <td>25.3</td>\n",
       "      <td>rov</td>\n",
       "      <td>59.3</td>\n",
       "      <td>331.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>990.5</td>\n",
       "      <td>25412.8</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>18361.3</td>\n",
       "      <td>27961.8</td>\n",
       "      <td>29056.3</td>\n",
       "      <td>20648.2</td>\n",
       "      <td>28212.6</td>\n",
       "      <td>16730.9</td>\n",
       "      <td>10409.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3839</th>\n",
       "      <td>2022-07-06</td>\n",
       "      <td>10.875000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>25.7</td>\n",
       "      <td>rov</td>\n",
       "      <td>57.6</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>992.5</td>\n",
       "      <td>25734.4</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.1</td>\n",
       "      <td>25412.8</td>\n",
       "      <td>18361.3</td>\n",
       "      <td>27961.8</td>\n",
       "      <td>29056.3</td>\n",
       "      <td>20648.2</td>\n",
       "      <td>28212.6</td>\n",
       "      <td>16730.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3840</th>\n",
       "      <td>2022-07-07</td>\n",
       "      <td>14.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.7</td>\n",
       "      <td>rov</td>\n",
       "      <td>52.7</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>991.1</td>\n",
       "      <td>23376.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>25734.4</td>\n",
       "      <td>25412.8</td>\n",
       "      <td>18361.3</td>\n",
       "      <td>27961.8</td>\n",
       "      <td>29056.3</td>\n",
       "      <td>20648.2</td>\n",
       "      <td>28212.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3841</th>\n",
       "      <td>2022-07-08</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.1</td>\n",
       "      <td>rov</td>\n",
       "      <td>40.2</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>994.7</td>\n",
       "      <td>29978.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>23376.6</td>\n",
       "      <td>25734.4</td>\n",
       "      <td>25412.8</td>\n",
       "      <td>18361.3</td>\n",
       "      <td>27961.8</td>\n",
       "      <td>29056.3</td>\n",
       "      <td>20648.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3779 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Data     Valore  rain  temp Location   hum  w_dir  w_sp   press  \\\n",
       "7    2012-01-08  16.416667   0.0   3.7      rov  62.1  165.0   1.3   991.8   \n",
       "8    2012-01-09  17.875000   0.0   3.2      rov  62.7  148.0   1.1   998.8   \n",
       "9    2012-01-10  24.458333   0.0   3.9      rov  59.1  150.0   1.1  1002.5   \n",
       "10   2012-01-11  22.333333   0.0   3.1      rov  62.4  179.0   0.9  1006.6   \n",
       "11   2012-01-12  28.041667   0.0   1.9      rov  73.5  184.0   0.9  1002.8   \n",
       "...         ...        ...   ...   ...      ...   ...    ...   ...     ...   \n",
       "3837 2022-07-04  14.250000   2.8  24.5      rov  63.1  320.0   2.0   992.4   \n",
       "3838 2022-07-05   8.125000   0.8  25.3      rov  59.3  331.0   1.3   990.5   \n",
       "3839 2022-07-06  10.875000   0.2  25.7      rov  57.6  115.0   1.4   992.5   \n",
       "3840 2022-07-07  14.833333   0.0  25.7      rov  52.7   32.0   1.6   991.1   \n",
       "3841 2022-07-08  16.000000   0.0  25.1      rov  40.2  349.0   1.6   994.7   \n",
       "\n",
       "          sol  ...  w_sp 5 day/s earlier  w_sp 6 day/s earlier  \\\n",
       "7      4613.5  ...                   1.0                   1.0   \n",
       "8      4541.6  ...                   1.0                   1.0   \n",
       "9      4511.3  ...                   1.4                   1.0   \n",
       "10     4585.7  ...                   1.6                   1.4   \n",
       "11     4701.3  ...                   1.0                   1.6   \n",
       "...       ...  ...                   ...                   ...   \n",
       "3837  18361.3  ...                   1.1                   1.5   \n",
       "3838  25412.8  ...                   1.6                   1.1   \n",
       "3839  25734.4  ...                   1.2                   1.6   \n",
       "3840  23376.6  ...                   1.6                   1.2   \n",
       "3841  29978.0  ...                   1.6                   1.6   \n",
       "\n",
       "      w_sp 7 day/s earlier  sol 1 day/s earlier  sol 2 day/s earlier  \\\n",
       "7                      0.9               3558.7               4402.5   \n",
       "8                      1.0               4613.5               3558.7   \n",
       "9                      1.0               4541.6               4613.5   \n",
       "10                     1.0               4511.3               4541.6   \n",
       "11                     1.4               4585.7               4511.3   \n",
       "...                    ...                  ...                  ...   \n",
       "3837                   2.1              27961.8              29056.3   \n",
       "3838                   1.5              18361.3              27961.8   \n",
       "3839                   1.1              25412.8              18361.3   \n",
       "3840                   1.6              25734.4              25412.8   \n",
       "3841                   1.2              23376.6              25734.4   \n",
       "\n",
       "      sol 3 day/s earlier  sol 4 day/s earlier  sol 5 day/s earlier  \\\n",
       "7                  3931.4               2201.1               4112.6   \n",
       "8                  4402.5               3931.4               2201.1   \n",
       "9                  3558.7               4402.5               3931.4   \n",
       "10                 4613.5               3558.7               4402.5   \n",
       "11                 4541.6               4613.5               3558.7   \n",
       "...                   ...                  ...                  ...   \n",
       "3837              20648.2              28212.6              16730.9   \n",
       "3838              29056.3              20648.2              28212.6   \n",
       "3839              27961.8              29056.3              20648.2   \n",
       "3840              18361.3              27961.8              29056.3   \n",
       "3841              25412.8              18361.3              27961.8   \n",
       "\n",
       "      sol 6 day/s earlier  sol 7 day/s earlier  \n",
       "7                   998.3               3615.3  \n",
       "8                  4112.6                998.3  \n",
       "9                  2201.1               4112.6  \n",
       "10                 3931.4               2201.1  \n",
       "11                 4402.5               3931.4  \n",
       "...                   ...                  ...  \n",
       "3837              10409.2              26379.2  \n",
       "3838              16730.9              10409.2  \n",
       "3839              28212.6              16730.9  \n",
       "3840              20648.2              28212.6  \n",
       "3841              29056.3              20648.2  \n",
       "\n",
       "[3779 rows x 59 columns]"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df = merged_df.dropna()\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_columns = [\n",
    "    'rain', \n",
    "    'temp', \n",
    "    'hum', \n",
    "    'w_dir', \n",
    "    'w_sp', \n",
    "    'press', \n",
    "    'sol'\n",
    "]\n",
    "\n",
    "for i in range (1, 8):\n",
    "    for par in weather_parameters:\n",
    "        input_columns.append(f\"{par} {i} day/s earlier\")\n",
    "\n",
    "X = merged_df[input_columns]\n",
    "y = merged_df['Valore']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test_years = [2015, 2017]\n",
    "#train_years = [2012, 2013, 2014, 2016, 2018, 2019, 2020, 2021, 2022]\n",
    "\n",
    "test_years = [2019]\n",
    "train_years = [2018, 2020, 2021, 2022]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "#                                                     test_size=0.2, \n",
    "#                                                     random_state=SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train, X_test, y_test = (\n",
    "    X[merged_df.Data.dt.year.isin(train_years)],\n",
    "    y[merged_df.Data.dt.year.isin(train_years)],\n",
    "    X[merged_df.Data.dt.year.isin(test_years)],\n",
    "    y[merged_df.Data.dt.year.isin(test_years)]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 56)"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ---- START OF RANDOM FOREST ----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "SEED = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1280, 56)"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(random_state=42)"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = RandomForestRegressor(n_estimators = 100, random_state = SEED)\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.9332856384558452\n"
     ]
    }
   ],
   "source": [
    "score = regressor.score(X_train, y_train)\n",
    "print(\"R2:\", score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rain</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>w_dir</th>\n",
       "      <th>w_sp</th>\n",
       "      <th>press</th>\n",
       "      <th>sol</th>\n",
       "      <th>rain 1 day/s earlier</th>\n",
       "      <th>temp 1 day/s earlier</th>\n",
       "      <th>press 1 day/s earlier</th>\n",
       "      <th>...</th>\n",
       "      <th>w_dir 6 day/s earlier</th>\n",
       "      <th>w_sp 6 day/s earlier</th>\n",
       "      <th>sol 6 day/s earlier</th>\n",
       "      <th>rain 7 day/s earlier</th>\n",
       "      <th>temp 7 day/s earlier</th>\n",
       "      <th>press 7 day/s earlier</th>\n",
       "      <th>hum 7 day/s earlier</th>\n",
       "      <th>w_dir 7 day/s earlier</th>\n",
       "      <th>w_sp 7 day/s earlier</th>\n",
       "      <th>sol 7 day/s earlier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2557</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>87.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1001.1</td>\n",
       "      <td>1378.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1000.7</td>\n",
       "      <td>...</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3957.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1004.1</td>\n",
       "      <td>63.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>3647.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2558</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>56.7</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>994.9</td>\n",
       "      <td>4525.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1001.1</td>\n",
       "      <td>...</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3971.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>67.8</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3957.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2559</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>24.2</td>\n",
       "      <td>297.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1000.6</td>\n",
       "      <td>4403.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>994.9</td>\n",
       "      <td>...</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3839.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1002.6</td>\n",
       "      <td>68.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3971.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>46.8</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1002.5</td>\n",
       "      <td>3800.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1000.6</td>\n",
       "      <td>...</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3926.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>999.9</td>\n",
       "      <td>66.2</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3839.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>59.2</td>\n",
       "      <td>348.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>995.8</td>\n",
       "      <td>4268.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1002.5</td>\n",
       "      <td>...</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3837.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1001.3</td>\n",
       "      <td>75.1</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3926.1</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>78.8</td>\n",
       "      <td>357.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>998.3</td>\n",
       "      <td>3953.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>997.7</td>\n",
       "      <td>...</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1156.8</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>983.2</td>\n",
       "      <td>99.9</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>191.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>84.5</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1005.4</td>\n",
       "      <td>3879.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>998.3</td>\n",
       "      <td>...</td>\n",
       "      <td>348.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2326.3</td>\n",
       "      <td>31.6</td>\n",
       "      <td>6.6</td>\n",
       "      <td>971.0</td>\n",
       "      <td>97.1</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1156.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2919</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>82.7</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1008.6</td>\n",
       "      <td>4117.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1005.4</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3942.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>5.8</td>\n",
       "      <td>966.5</td>\n",
       "      <td>92.6</td>\n",
       "      <td>348.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2326.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2920</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>86.2</td>\n",
       "      <td>350.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>3966.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1008.6</td>\n",
       "      <td>...</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2369.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>980.0</td>\n",
       "      <td>92.3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3942.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>76.3</td>\n",
       "      <td>136.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1005.8</td>\n",
       "      <td>4235.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>...</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3990.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>990.8</td>\n",
       "      <td>87.6</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2369.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      rain  temp   hum  w_dir  w_sp   press     sol  rain 1 day/s earlier  \\\n",
       "2557   0.0   1.3  87.4    1.0   0.3  1001.1  1378.9                   0.0   \n",
       "2558   0.0   3.5  56.7   20.0   1.6   994.9  4525.2                   0.0   \n",
       "2559   0.0   4.8  24.2  297.0   2.1  1000.6  4403.8                   0.0   \n",
       "2560   0.0   1.2  46.8   67.0   0.7  1002.5  3800.9                   0.0   \n",
       "2561   0.0   2.7  59.2  348.0   0.8   995.8  4268.9                   0.0   \n",
       "...    ...   ...   ...    ...   ...     ...     ...                   ...   \n",
       "2917   0.0   3.0  78.8  357.0   0.5   998.3  3953.2                   0.0   \n",
       "2918   0.0   2.2  84.5   46.0   0.6  1005.4  3879.8                   0.0   \n",
       "2919   0.0   0.6  82.7   19.0   0.4  1008.6  4117.7                   0.0   \n",
       "2920   0.0   0.2  86.2  350.0   0.3  1010.6  3966.7                   0.0   \n",
       "2921   0.0   2.6  76.3  136.0   0.9  1005.8  4235.8                   0.0   \n",
       "\n",
       "      temp 1 day/s earlier  press 1 day/s earlier  ...  w_dir 6 day/s earlier  \\\n",
       "2557                   4.8                 1000.7  ...                   52.0   \n",
       "2558                   1.3                 1001.1  ...                   73.0   \n",
       "2559                   3.5                  994.9  ...                  154.0   \n",
       "2560                   4.8                 1000.6  ...                  156.0   \n",
       "2561                   1.2                 1002.5  ...                   65.0   \n",
       "...                    ...                    ...  ...                    ...   \n",
       "2917                   4.0                  997.7  ...                  349.0   \n",
       "2918                   3.0                  998.3  ...                  348.0   \n",
       "2919                   2.2                 1005.4  ...                   26.0   \n",
       "2920                   0.6                 1008.6  ...                   65.0   \n",
       "2921                   0.2                 1010.6  ...                   46.0   \n",
       "\n",
       "      w_sp 6 day/s earlier  sol 6 day/s earlier  rain 7 day/s earlier  \\\n",
       "2557                   0.5               3957.7                   0.0   \n",
       "2558                   0.6               3971.0                   0.0   \n",
       "2559                   0.8               3839.4                   0.0   \n",
       "2560                   0.5               3926.1                   0.0   \n",
       "2561                   1.5               3837.7                   0.0   \n",
       "...                    ...                  ...                   ...   \n",
       "2917                   1.1               1156.8                  21.0   \n",
       "2918                   0.9               2326.3                  31.6   \n",
       "2919                   0.4               3942.0                   8.2   \n",
       "2920                   0.5               2369.2                   0.0   \n",
       "2921                   0.6               3990.9                   0.0   \n",
       "\n",
       "      temp 7 day/s earlier  press 7 day/s earlier  hum 7 day/s earlier  \\\n",
       "2557                   2.5                 1004.1                 63.0   \n",
       "2558                   1.3                 1005.0                 67.8   \n",
       "2559                   1.4                 1002.6                 68.0   \n",
       "2560                   3.0                  999.9                 66.2   \n",
       "2561                   3.0                 1001.3                 75.1   \n",
       "...                    ...                    ...                  ...   \n",
       "2917                   7.9                  983.2                 99.9   \n",
       "2918                   6.6                  971.0                 97.1   \n",
       "2919                   5.8                  966.5                 92.6   \n",
       "2920                   4.7                  980.0                 92.3   \n",
       "2921                   3.4                  990.8                 87.6   \n",
       "\n",
       "      w_dir 7 day/s earlier  w_sp 7 day/s earlier  sol 7 day/s earlier  \n",
       "2557                   61.0                   0.7               3647.9  \n",
       "2558                   52.0                   0.5               3957.7  \n",
       "2559                   73.0                   0.6               3971.0  \n",
       "2560                  154.0                   0.8               3839.4  \n",
       "2561                  156.0                   0.5               3926.1  \n",
       "...                     ...                   ...                  ...  \n",
       "2917                   12.0                   0.2                191.6  \n",
       "2918                  349.0                   1.1               1156.8  \n",
       "2919                  348.0                   0.9               2326.3  \n",
       "2920                   26.0                   0.4               3942.0  \n",
       "2921                   65.0                   0.5               2369.2  \n",
       "\n",
       "[365 rows x 56 columns]"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Actual  Predicted\n",
      "2557  64.625000  27.880195\n",
      "2558  19.666667  20.124452\n",
      "2559   5.708333  10.006391\n",
      "2560  11.458333  12.031707\n",
      "2561  21.166667  25.839274\n",
      "...         ...        ...\n",
      "2917  14.250000  24.655853\n",
      "2918  19.958333  26.186232\n",
      "2919  19.708333  25.784959\n",
      "2920  32.833333  27.747636\n",
      "2921  20.375000  28.668140\n",
      "\n",
      "[365 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "results = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.696897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predicted</th>\n",
       "      <td>0.696897</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Actual  Predicted\n",
       "Actual     1.000000   0.696897\n",
       "Predicted  0.696897   1.000000"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thoma\\AppData\\Local\\Temp\\ipykernel_2228\\643691821.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_chart_data[\"pred\"] = regressor.predict(test_chart_data[input_columns])\n"
     ]
    }
   ],
   "source": [
    "test_chart_data = merged_df[\n",
    "    merged_df.Data.dt.year.isin(test_years)\n",
    "]\n",
    "test_chart_data[\"pred\"] = regressor.predict(test_chart_data[input_columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rain</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>w_dir</th>\n",
       "      <th>w_sp</th>\n",
       "      <th>press</th>\n",
       "      <th>sol</th>\n",
       "      <th>rain 1 day/s earlier</th>\n",
       "      <th>temp 1 day/s earlier</th>\n",
       "      <th>press 1 day/s earlier</th>\n",
       "      <th>...</th>\n",
       "      <th>w_dir 6 day/s earlier</th>\n",
       "      <th>w_sp 6 day/s earlier</th>\n",
       "      <th>sol 6 day/s earlier</th>\n",
       "      <th>rain 7 day/s earlier</th>\n",
       "      <th>temp 7 day/s earlier</th>\n",
       "      <th>press 7 day/s earlier</th>\n",
       "      <th>hum 7 day/s earlier</th>\n",
       "      <th>w_dir 7 day/s earlier</th>\n",
       "      <th>w_sp 7 day/s earlier</th>\n",
       "      <th>sol 7 day/s earlier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2192</th>\n",
       "      <td>14.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>92.8</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>987.5</td>\n",
       "      <td>762.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>995.1</td>\n",
       "      <td>...</td>\n",
       "      <td>275.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1883.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1000.7</td>\n",
       "      <td>77.6</td>\n",
       "      <td>322.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3953.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2193</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>87.2</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>988.7</td>\n",
       "      <td>4158.9</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>987.5</td>\n",
       "      <td>...</td>\n",
       "      <td>313.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>146.3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.9</td>\n",
       "      <td>991.5</td>\n",
       "      <td>83.7</td>\n",
       "      <td>275.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1883.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2194</th>\n",
       "      <td>0.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>83.8</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>986.1</td>\n",
       "      <td>2104.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>988.7</td>\n",
       "      <td>...</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1037.5</td>\n",
       "      <td>31.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>971.7</td>\n",
       "      <td>97.3</td>\n",
       "      <td>313.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>146.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2195</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>84.3</td>\n",
       "      <td>345.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>983.6</td>\n",
       "      <td>4445.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>986.1</td>\n",
       "      <td>...</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>4210.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>968.0</td>\n",
       "      <td>67.7</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1037.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2196</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>84.9</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>985.1</td>\n",
       "      <td>3350.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>983.6</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4086.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>985.6</td>\n",
       "      <td>48.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>4210.9</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3837</th>\n",
       "      <td>2.8</td>\n",
       "      <td>24.5</td>\n",
       "      <td>63.1</td>\n",
       "      <td>320.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>992.4</td>\n",
       "      <td>18361.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.8</td>\n",
       "      <td>992.0</td>\n",
       "      <td>...</td>\n",
       "      <td>331.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>10409.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.4</td>\n",
       "      <td>989.9</td>\n",
       "      <td>47.7</td>\n",
       "      <td>138.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>26379.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3838</th>\n",
       "      <td>0.8</td>\n",
       "      <td>25.3</td>\n",
       "      <td>59.3</td>\n",
       "      <td>331.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>990.5</td>\n",
       "      <td>25412.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>24.5</td>\n",
       "      <td>992.4</td>\n",
       "      <td>...</td>\n",
       "      <td>335.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>16730.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>24.4</td>\n",
       "      <td>989.9</td>\n",
       "      <td>61.9</td>\n",
       "      <td>331.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>10409.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3839</th>\n",
       "      <td>0.2</td>\n",
       "      <td>25.7</td>\n",
       "      <td>57.6</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>992.5</td>\n",
       "      <td>25734.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>25.3</td>\n",
       "      <td>990.5</td>\n",
       "      <td>...</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>28212.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>986.9</td>\n",
       "      <td>73.0</td>\n",
       "      <td>335.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>16730.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3840</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25.7</td>\n",
       "      <td>52.7</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>991.1</td>\n",
       "      <td>23376.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>25.7</td>\n",
       "      <td>992.5</td>\n",
       "      <td>...</td>\n",
       "      <td>183.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>20648.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>987.1</td>\n",
       "      <td>59.9</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>28212.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3841</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25.1</td>\n",
       "      <td>40.2</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>994.7</td>\n",
       "      <td>29978.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.7</td>\n",
       "      <td>991.1</td>\n",
       "      <td>...</td>\n",
       "      <td>161.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>29056.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.8</td>\n",
       "      <td>988.2</td>\n",
       "      <td>53.5</td>\n",
       "      <td>183.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>20648.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1280 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      rain  temp   hum  w_dir  w_sp  press      sol  rain 1 day/s earlier  \\\n",
       "2192  14.0   1.6  92.8   12.0   0.8  987.5    762.8                   0.0   \n",
       "2193   0.0   2.6  87.2   36.0   0.8  988.7   4158.9                  14.0   \n",
       "2194   0.8   1.2  83.8   37.0   1.0  986.1   2104.0                   0.0   \n",
       "2195   0.0   3.3  84.3  345.0   1.1  983.6   4445.6                   0.8   \n",
       "2196   0.0   3.1  84.9   32.0   0.8  985.1   3350.7                   0.0   \n",
       "...    ...   ...   ...    ...   ...    ...      ...                   ...   \n",
       "3837   2.8  24.5  63.1  320.0   2.0  992.4  18361.3                   0.0   \n",
       "3838   0.8  25.3  59.3  331.0   1.3  990.5  25412.8                   2.8   \n",
       "3839   0.2  25.7  57.6  115.0   1.4  992.5  25734.4                   0.8   \n",
       "3840   0.0  25.7  52.7   32.0   1.6  991.1  23376.6                   0.2   \n",
       "3841   0.0  25.1  40.2  349.0   1.6  994.7  29978.0                   0.0   \n",
       "\n",
       "      temp 1 day/s earlier  press 1 day/s earlier  ...  w_dir 6 day/s earlier  \\\n",
       "2192                   0.8                  995.1  ...                  275.0   \n",
       "2193                   1.6                  987.5  ...                  313.0   \n",
       "2194                   2.6                  988.7  ...                   53.0   \n",
       "2195                   1.2                  986.1  ...                   74.0   \n",
       "2196                   3.3                  983.6  ...                    8.0   \n",
       "...                    ...                    ...  ...                    ...   \n",
       "3837                  28.8                  992.0  ...                  331.0   \n",
       "3838                  24.5                  992.4  ...                  335.0   \n",
       "3839                  25.3                  990.5  ...                  140.0   \n",
       "3840                  25.7                  992.5  ...                  183.0   \n",
       "3841                  25.7                  991.1  ...                  161.0   \n",
       "\n",
       "      w_sp 6 day/s earlier  sol 6 day/s earlier  rain 7 day/s earlier  \\\n",
       "2192                   0.4               1883.4                   0.0   \n",
       "2193                   0.7                146.3                   4.2   \n",
       "2194                   1.3               1037.5                  31.2   \n",
       "2195                   1.2               4210.9                   0.4   \n",
       "2196                   1.1               4086.1                   0.0   \n",
       "...                    ...                  ...                   ...   \n",
       "3837                   1.5              10409.2                   0.0   \n",
       "3838                   1.1              16730.9                   3.6   \n",
       "3839                   1.6              28212.6                   6.0   \n",
       "3840                   1.2              20648.2                   0.0   \n",
       "3841                   1.6              29056.3                   0.0   \n",
       "\n",
       "      temp 7 day/s earlier  press 7 day/s earlier  hum 7 day/s earlier  \\\n",
       "2192                   1.3                 1000.7                 77.6   \n",
       "2193                   1.9                  991.5                 83.7   \n",
       "2194                   2.0                  971.7                 97.3   \n",
       "2195                   4.7                  968.0                 67.7   \n",
       "2196                   3.2                  985.6                 48.0   \n",
       "...                    ...                    ...                  ...   \n",
       "3837                  28.4                  989.9                 47.7   \n",
       "3838                  24.4                  989.9                 61.9   \n",
       "3839                  23.5                  986.9                 73.0   \n",
       "3840                  26.6                  987.1                 59.9   \n",
       "3841                  26.8                  988.2                 53.5   \n",
       "\n",
       "      w_dir 7 day/s earlier  w_sp 7 day/s earlier  sol 7 day/s earlier  \n",
       "2192                  322.0                   0.4               3953.6  \n",
       "2193                  275.0                   0.4               1883.4  \n",
       "2194                  313.0                   0.7                146.3  \n",
       "2195                   53.0                   1.3               1037.5  \n",
       "2196                   74.0                   1.2               4210.9  \n",
       "...                     ...                   ...                  ...  \n",
       "3837                  138.0                   2.1              26379.2  \n",
       "3838                  331.0                   1.5              10409.2  \n",
       "3839                  335.0                   1.1              16730.9  \n",
       "3840                  140.0                   1.6              28212.6  \n",
       "3841                  183.0                   1.2              20648.2  \n",
       "\n",
       "[1280 rows x 56 columns]"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Data'>"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "test_chart_data.plot(\"Data\", \"Valore\", ax=ax)\n",
    "test_chart_data.plot(\"Data\", \"pred\", ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-5.0, 105.0)"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "test_chart_data.plot.scatter(\"Valore\", \"pred\", alpha=0.3, ax=ax)\n",
    "ax.set_xlim(-5, 105)\n",
    "ax.set_ylim(-5, 105)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 4.81\n",
      "Mean squared error: 40.50\n",
      "Root mean squared error: 6.36\n"
     ]
    }
   ],
   "source": [
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "\n",
    "print(f'Mean absolute error: {mae:.2f}')\n",
    "print(f'Mean squared error: {mse:.2f}')\n",
    "print(f'Root mean squared error: {rmse:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4546586808835571"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R²: 0.45465868088355677\n"
     ]
    }
   ],
   "source": [
    "actual_minus_predicted = sum((y_test - y_pred)**2)\n",
    "actual_minus_actual_mean = sum((y_test - y_test.mean())**2)\n",
    "r2 = 1 - actual_minus_predicted/actual_minus_actual_mean\n",
    "print('R²:', r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "importance_features = X_test.columns.tolist()\n",
    "importance_values = regressor.feature_importances_.tolist()\n",
    "\n",
    "importance_features_sorted = [x for _,x in sorted(zip(importance_values, importance_features))]\n",
    "importance_values_sorted = sorted(importance_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot()\n",
    "ax.barh(X_test.columns,regressor.feature_importances_)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot()\n",
    "ax.barh(importance_features_sorted, importance_values_sorted)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rain': 0.07661459030666505,\n",
       " 'temp': 0.2464956536594612,\n",
       " 'hum': 0.1739541397984941,\n",
       " 'w_dir': 0.08137351083443732,\n",
       " 'w_sp': 0.07367651416906384,\n",
       " 'press': 0.2319076321042254,\n",
       " 'sol': 0.11597795912765313}"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_feat_sum = {\n",
    "    \"rain\" : 0,\n",
    "    \"temp\" : 0,\n",
    "    \"hum\" : 0,\n",
    "    \"w_dir\" : 0,\n",
    "    \"w_sp\" : 0,\n",
    "    \"press\" : 0,\n",
    "    \"sol\" : 0\n",
    "}\n",
    "feat_sum = {\n",
    "    \"Precipitation\" : 0,\n",
    "    \"Temperature\" : 0,\n",
    "    \"Humidity\" : 0,\n",
    "    \"Wind direction\" : 0,\n",
    "    \"Wind speed\" : 0,\n",
    "    \"Atmospheric pressure\" : 0,\n",
    "    \"Solar radiation\" : 0\n",
    "}\n",
    "\n",
    "for feat, val in zip(importance_features_sorted, importance_values_sorted):\n",
    "    old_feat_sum[feat.split()[0]] += val\n",
    "\n",
    "old_feat_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Precipitation': 0.07661459030666505,\n",
       " 'Temperature': 0.2464956536594612,\n",
       " 'Humidity': 0.1739541397984941,\n",
       " 'Wind direction': 0.08137351083443732,\n",
       " 'Wind speed': 0.07367651416906384,\n",
       " 'Atmospheric pressure': 0.2319076321042254,\n",
       " 'Solar radiation': 0.11597795912765313}"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "feat_sum[\"Precipitation\"] = old_feat_sum[\"rain\"]\n",
    "feat_sum[\"Temperature\"] = old_feat_sum[\"temp\"]\n",
    "feat_sum[\"Humidity\"] = old_feat_sum[\"hum\"]\n",
    "feat_sum[\"Wind direction\"] = old_feat_sum[\"w_dir\"]\n",
    "feat_sum[\"Wind speed\"] = old_feat_sum[\"w_sp\"]\n",
    "feat_sum[\"Atmospheric pressure\"] = old_feat_sum[\"press\"]\n",
    "feat_sum[\"Solar radiation\"] = old_feat_sum[\"sol\"]\n",
    "\n",
    "feat_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.07661459030666505,\n",
       " 0.2464956536594612,\n",
       " 0.1739541397984941,\n",
       " 0.08137351083443732,\n",
       " 0.07367651416906384,\n",
       " 0.2319076321042254,\n",
       " 0.11597795912765313]"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_list = feat_sum.keys()\n",
    "values_list = feat_sum.values()\n",
    "list(features_list)\n",
    "list(values_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_list_sorted = [x for _,x in sorted(zip(values_list, features_list))]\n",
    "values_list_sorted = sorted(values_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Wind speed',\n",
       " 'Precipitation',\n",
       " 'Wind direction',\n",
       " 'Solar radiation',\n",
       " 'Humidity',\n",
       " 'Atmospheric pressure',\n",
       " 'Temperature']"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_list_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7.3676514169063845,\n",
       " 7.661459030666505,\n",
       " 8.137351083443733,\n",
       " 11.597795912765314,\n",
       " 17.395413979849412,\n",
       " 23.190763210422542,\n",
       " 24.64956536594612]"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values_list_sorted_mult_by_100 = list(map(lambda x: x*100, values_list_sorted))\n",
    "values_list_sorted_mult_by_100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot()\n",
    "ax.barh(features_list_sorted, values_list_sorted_mult_by_100)\n",
    "\n",
    "plt.title(\"PM10 Rovereto\", fontsize=35)\n",
    "plt.yticks(fontsize=25)\n",
    "plt.xticks(fontsize=25)\n",
    "\n",
    "fmt = \"%.0f%%\"\n",
    "xticks = mtick.FormatStrFormatter(fmt)\n",
    "ax.xaxis.set_major_formatter(xticks)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'temp 3 day/s earlier'"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_columns[22]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1cb515f36a0>]"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(test_chart_data[\"press\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['rain',\n",
       " 'temp',\n",
       " 'hum',\n",
       " 'w_dir',\n",
       " 'w_sp',\n",
       " 'press',\n",
       " 'sol',\n",
       " 'rain 1 day/s earlier',\n",
       " 'temp 1 day/s earlier',\n",
       " 'press 1 day/s earlier',\n",
       " 'hum 1 day/s earlier',\n",
       " 'w_dir 1 day/s earlier',\n",
       " 'w_sp 1 day/s earlier',\n",
       " 'sol 1 day/s earlier',\n",
       " 'rain 2 day/s earlier',\n",
       " 'temp 2 day/s earlier',\n",
       " 'press 2 day/s earlier',\n",
       " 'hum 2 day/s earlier',\n",
       " 'w_dir 2 day/s earlier',\n",
       " 'w_sp 2 day/s earlier',\n",
       " 'sol 2 day/s earlier',\n",
       " 'rain 3 day/s earlier',\n",
       " 'temp 3 day/s earlier',\n",
       " 'press 3 day/s earlier',\n",
       " 'hum 3 day/s earlier',\n",
       " 'w_dir 3 day/s earlier',\n",
       " 'w_sp 3 day/s earlier',\n",
       " 'sol 3 day/s earlier',\n",
       " 'rain 4 day/s earlier',\n",
       " 'temp 4 day/s earlier',\n",
       " 'press 4 day/s earlier',\n",
       " 'hum 4 day/s earlier',\n",
       " 'w_dir 4 day/s earlier',\n",
       " 'w_sp 4 day/s earlier',\n",
       " 'sol 4 day/s earlier',\n",
       " 'rain 5 day/s earlier',\n",
       " 'temp 5 day/s earlier',\n",
       " 'press 5 day/s earlier',\n",
       " 'hum 5 day/s earlier',\n",
       " 'w_dir 5 day/s earlier',\n",
       " 'w_sp 5 day/s earlier',\n",
       " 'sol 5 day/s earlier',\n",
       " 'rain 6 day/s earlier',\n",
       " 'temp 6 day/s earlier',\n",
       " 'press 6 day/s earlier',\n",
       " 'hum 6 day/s earlier',\n",
       " 'w_dir 6 day/s earlier',\n",
       " 'w_sp 6 day/s earlier',\n",
       " 'sol 6 day/s earlier',\n",
       " 'rain 7 day/s earlier',\n",
       " 'temp 7 day/s earlier',\n",
       " 'press 7 day/s earlier',\n",
       " 'hum 7 day/s earlier',\n",
       " 'w_dir 7 day/s earlier',\n",
       " 'w_sp 7 day/s earlier',\n",
       " 'sol 7 day/s earlier']"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulazione"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thoma\\AppData\\Local\\Temp\\ipykernel_2228\\3115695229.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_chart_data.loc[:, \"doy\"] = test_chart_data.Data.dt.dayofyear\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data</th>\n",
       "      <th>Valore</th>\n",
       "      <th>rain</th>\n",
       "      <th>temp</th>\n",
       "      <th>Location</th>\n",
       "      <th>hum</th>\n",
       "      <th>w_dir</th>\n",
       "      <th>w_sp</th>\n",
       "      <th>press</th>\n",
       "      <th>sol</th>\n",
       "      <th>...</th>\n",
       "      <th>w_sp 7 day/s earlier</th>\n",
       "      <th>sol 1 day/s earlier</th>\n",
       "      <th>sol 2 day/s earlier</th>\n",
       "      <th>sol 3 day/s earlier</th>\n",
       "      <th>sol 4 day/s earlier</th>\n",
       "      <th>sol 5 day/s earlier</th>\n",
       "      <th>sol 6 day/s earlier</th>\n",
       "      <th>sol 7 day/s earlier</th>\n",
       "      <th>pred</th>\n",
       "      <th>doy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2557</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>64.625000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>rov</td>\n",
       "      <td>87.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1001.1</td>\n",
       "      <td>1378.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.7</td>\n",
       "      <td>4082.8</td>\n",
       "      <td>3837.7</td>\n",
       "      <td>3926.1</td>\n",
       "      <td>3839.4</td>\n",
       "      <td>3971.0</td>\n",
       "      <td>3957.7</td>\n",
       "      <td>3647.9</td>\n",
       "      <td>27.880195</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2558</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>19.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>rov</td>\n",
       "      <td>56.7</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>994.9</td>\n",
       "      <td>4525.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1378.9</td>\n",
       "      <td>4082.8</td>\n",
       "      <td>3837.7</td>\n",
       "      <td>3926.1</td>\n",
       "      <td>3839.4</td>\n",
       "      <td>3971.0</td>\n",
       "      <td>3957.7</td>\n",
       "      <td>20.124452</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2559</th>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>5.708333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>rov</td>\n",
       "      <td>24.2</td>\n",
       "      <td>297.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1000.6</td>\n",
       "      <td>4403.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4525.2</td>\n",
       "      <td>1378.9</td>\n",
       "      <td>4082.8</td>\n",
       "      <td>3837.7</td>\n",
       "      <td>3926.1</td>\n",
       "      <td>3839.4</td>\n",
       "      <td>3971.0</td>\n",
       "      <td>10.006391</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>11.458333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>rov</td>\n",
       "      <td>46.8</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1002.5</td>\n",
       "      <td>3800.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>4403.8</td>\n",
       "      <td>4525.2</td>\n",
       "      <td>1378.9</td>\n",
       "      <td>4082.8</td>\n",
       "      <td>3837.7</td>\n",
       "      <td>3926.1</td>\n",
       "      <td>3839.4</td>\n",
       "      <td>12.031707</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>21.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>rov</td>\n",
       "      <td>59.2</td>\n",
       "      <td>348.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>995.8</td>\n",
       "      <td>4268.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3800.9</td>\n",
       "      <td>4403.8</td>\n",
       "      <td>4525.2</td>\n",
       "      <td>1378.9</td>\n",
       "      <td>4082.8</td>\n",
       "      <td>3837.7</td>\n",
       "      <td>3926.1</td>\n",
       "      <td>25.839274</td>\n",
       "      <td>5</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>2019-12-27</td>\n",
       "      <td>14.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>rov</td>\n",
       "      <td>78.8</td>\n",
       "      <td>357.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>998.3</td>\n",
       "      <td>3953.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>3796.4</td>\n",
       "      <td>3990.9</td>\n",
       "      <td>2369.2</td>\n",
       "      <td>3942.0</td>\n",
       "      <td>2326.3</td>\n",
       "      <td>1156.8</td>\n",
       "      <td>191.6</td>\n",
       "      <td>24.655853</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>2019-12-28</td>\n",
       "      <td>19.958333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>rov</td>\n",
       "      <td>84.5</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1005.4</td>\n",
       "      <td>3879.8</td>\n",
       "      <td>...</td>\n",
       "      <td>1.1</td>\n",
       "      <td>3953.2</td>\n",
       "      <td>3796.4</td>\n",
       "      <td>3990.9</td>\n",
       "      <td>2369.2</td>\n",
       "      <td>3942.0</td>\n",
       "      <td>2326.3</td>\n",
       "      <td>1156.8</td>\n",
       "      <td>26.186232</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2919</th>\n",
       "      <td>2019-12-29</td>\n",
       "      <td>19.708333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>rov</td>\n",
       "      <td>82.7</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1008.6</td>\n",
       "      <td>4117.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3879.8</td>\n",
       "      <td>3953.2</td>\n",
       "      <td>3796.4</td>\n",
       "      <td>3990.9</td>\n",
       "      <td>2369.2</td>\n",
       "      <td>3942.0</td>\n",
       "      <td>2326.3</td>\n",
       "      <td>25.784959</td>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2920</th>\n",
       "      <td>2019-12-30</td>\n",
       "      <td>32.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>rov</td>\n",
       "      <td>86.2</td>\n",
       "      <td>350.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>3966.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4</td>\n",
       "      <td>4117.7</td>\n",
       "      <td>3879.8</td>\n",
       "      <td>3953.2</td>\n",
       "      <td>3796.4</td>\n",
       "      <td>3990.9</td>\n",
       "      <td>2369.2</td>\n",
       "      <td>3942.0</td>\n",
       "      <td>27.747636</td>\n",
       "      <td>364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>20.375000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>rov</td>\n",
       "      <td>76.3</td>\n",
       "      <td>136.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1005.8</td>\n",
       "      <td>4235.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3966.7</td>\n",
       "      <td>4117.7</td>\n",
       "      <td>3879.8</td>\n",
       "      <td>3953.2</td>\n",
       "      <td>3796.4</td>\n",
       "      <td>3990.9</td>\n",
       "      <td>2369.2</td>\n",
       "      <td>28.668140</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Data     Valore  rain  temp Location   hum  w_dir  w_sp   press  \\\n",
       "2557 2019-01-01  64.625000   0.0   1.3      rov  87.4    1.0   0.3  1001.1   \n",
       "2558 2019-01-02  19.666667   0.0   3.5      rov  56.7   20.0   1.6   994.9   \n",
       "2559 2019-01-03   5.708333   0.0   4.8      rov  24.2  297.0   2.1  1000.6   \n",
       "2560 2019-01-04  11.458333   0.0   1.2      rov  46.8   67.0   0.7  1002.5   \n",
       "2561 2019-01-05  21.166667   0.0   2.7      rov  59.2  348.0   0.8   995.8   \n",
       "...         ...        ...   ...   ...      ...   ...    ...   ...     ...   \n",
       "2917 2019-12-27  14.250000   0.0   3.0      rov  78.8  357.0   0.5   998.3   \n",
       "2918 2019-12-28  19.958333   0.0   2.2      rov  84.5   46.0   0.6  1005.4   \n",
       "2919 2019-12-29  19.708333   0.0   0.6      rov  82.7   19.0   0.4  1008.6   \n",
       "2920 2019-12-30  32.833333   0.0   0.2      rov  86.2  350.0   0.3  1010.6   \n",
       "2921 2019-12-31  20.375000   0.0   2.6      rov  76.3  136.0   0.9  1005.8   \n",
       "\n",
       "         sol  ...  w_sp 7 day/s earlier  sol 1 day/s earlier  \\\n",
       "2557  1378.9  ...                   0.7               4082.8   \n",
       "2558  4525.2  ...                   0.5               1378.9   \n",
       "2559  4403.8  ...                   0.6               4525.2   \n",
       "2560  3800.9  ...                   0.8               4403.8   \n",
       "2561  4268.9  ...                   0.5               3800.9   \n",
       "...      ...  ...                   ...                  ...   \n",
       "2917  3953.2  ...                   0.2               3796.4   \n",
       "2918  3879.8  ...                   1.1               3953.2   \n",
       "2919  4117.7  ...                   0.9               3879.8   \n",
       "2920  3966.7  ...                   0.4               4117.7   \n",
       "2921  4235.8  ...                   0.5               3966.7   \n",
       "\n",
       "      sol 2 day/s earlier  sol 3 day/s earlier  sol 4 day/s earlier  \\\n",
       "2557               3837.7               3926.1               3839.4   \n",
       "2558               4082.8               3837.7               3926.1   \n",
       "2559               1378.9               4082.8               3837.7   \n",
       "2560               4525.2               1378.9               4082.8   \n",
       "2561               4403.8               4525.2               1378.9   \n",
       "...                   ...                  ...                  ...   \n",
       "2917               3990.9               2369.2               3942.0   \n",
       "2918               3796.4               3990.9               2369.2   \n",
       "2919               3953.2               3796.4               3990.9   \n",
       "2920               3879.8               3953.2               3796.4   \n",
       "2921               4117.7               3879.8               3953.2   \n",
       "\n",
       "      sol 5 day/s earlier  sol 6 day/s earlier  sol 7 day/s earlier  \\\n",
       "2557               3971.0               3957.7               3647.9   \n",
       "2558               3839.4               3971.0               3957.7   \n",
       "2559               3926.1               3839.4               3971.0   \n",
       "2560               3837.7               3926.1               3839.4   \n",
       "2561               4082.8               3837.7               3926.1   \n",
       "...                   ...                  ...                  ...   \n",
       "2917               2326.3               1156.8                191.6   \n",
       "2918               3942.0               2326.3               1156.8   \n",
       "2919               2369.2               3942.0               2326.3   \n",
       "2920               3990.9               2369.2               3942.0   \n",
       "2921               3796.4               3990.9               2369.2   \n",
       "\n",
       "           pred  doy  \n",
       "2557  27.880195    1  \n",
       "2558  20.124452    2  \n",
       "2559  10.006391    3  \n",
       "2560  12.031707    4  \n",
       "2561  25.839274    5  \n",
       "...         ...  ...  \n",
       "2917  24.655853  361  \n",
       "2918  26.186232  362  \n",
       "2919  25.784959  363  \n",
       "2920  27.747636  364  \n",
       "2921  28.668140  365  \n",
       "\n",
       "[365 rows x 61 columns]"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_chart_data.loc[:, \"doy\"] = test_chart_data.Data.dt.dayofyear\n",
    "test_chart_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_season(doy):\n",
    "    if doy < datetime(2022, 3, 21).timetuple().tm_yday:\n",
    "        return \"winter\"\n",
    "    elif doy < datetime(2022, 6, 21).timetuple().tm_yday:\n",
    "        return \"spring\"\n",
    "    elif doy < datetime(2022, 9, 21).timetuple().tm_yday:\n",
    "        return \"summer\"\n",
    "    elif doy < datetime(2022, 12, 21).timetuple().tm_yday:\n",
    "        return \"fall\"\n",
    "    else:\n",
    "        return \"winter\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thoma\\AppData\\Local\\Temp\\ipykernel_2228\\3540010652.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_chart_data[\"season\"] = test_chart_data.doy.map(get_season)\n"
     ]
    }
   ],
   "source": [
    "test_chart_data[\"season\"] = test_chart_data.doy.map(get_season)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5134.186813186812"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_chart_data[test_chart_data.season == \"fall\"].sol.mean()\n",
    "#15600 - 18000 - 5887 - 8000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['rain',\n",
       " 'temp',\n",
       " 'hum',\n",
       " 'w_dir',\n",
       " 'w_sp',\n",
       " 'press',\n",
       " 'sol',\n",
       " 'rain 1 day/s earlier',\n",
       " 'temp 1 day/s earlier',\n",
       " 'press 1 day/s earlier',\n",
       " 'hum 1 day/s earlier',\n",
       " 'w_dir 1 day/s earlier',\n",
       " 'w_sp 1 day/s earlier',\n",
       " 'sol 1 day/s earlier',\n",
       " 'rain 2 day/s earlier',\n",
       " 'temp 2 day/s earlier',\n",
       " 'press 2 day/s earlier',\n",
       " 'hum 2 day/s earlier',\n",
       " 'w_dir 2 day/s earlier',\n",
       " 'w_sp 2 day/s earlier',\n",
       " 'sol 2 day/s earlier',\n",
       " 'rain 3 day/s earlier',\n",
       " 'temp 3 day/s earlier',\n",
       " 'press 3 day/s earlier',\n",
       " 'hum 3 day/s earlier',\n",
       " 'w_dir 3 day/s earlier',\n",
       " 'w_sp 3 day/s earlier',\n",
       " 'sol 3 day/s earlier',\n",
       " 'rain 4 day/s earlier',\n",
       " 'temp 4 day/s earlier',\n",
       " 'press 4 day/s earlier',\n",
       " 'hum 4 day/s earlier',\n",
       " 'w_dir 4 day/s earlier',\n",
       " 'w_sp 4 day/s earlier',\n",
       " 'sol 4 day/s earlier',\n",
       " 'rain 5 day/s earlier',\n",
       " 'temp 5 day/s earlier',\n",
       " 'press 5 day/s earlier',\n",
       " 'hum 5 day/s earlier',\n",
       " 'w_dir 5 day/s earlier',\n",
       " 'w_sp 5 day/s earlier',\n",
       " 'sol 5 day/s earlier',\n",
       " 'rain 6 day/s earlier',\n",
       " 'temp 6 day/s earlier',\n",
       " 'press 6 day/s earlier',\n",
       " 'hum 6 day/s earlier',\n",
       " 'w_dir 6 day/s earlier',\n",
       " 'w_sp 6 day/s earlier',\n",
       " 'sol 6 day/s earlier',\n",
       " 'rain 7 day/s earlier',\n",
       " 'temp 7 day/s earlier',\n",
       " 'press 7 day/s earlier',\n",
       " 'hum 7 day/s earlier',\n",
       " 'w_dir 7 day/s earlier',\n",
       " 'w_sp 7 day/s earlier',\n",
       " 'sol 7 day/s earlier']"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_simulation(\n",
    "    X,\n",
    "    simulation_variable, \n",
    "    change, \n",
    "    change_as_perc,\n",
    "    need_threshold,\n",
    "    threshold,\n",
    "):\n",
    "    X_simulation = X[input_columns].copy()\n",
    "    # ['rain','temp','hum','w_dir','w_sp','press','sol']\n",
    "\n",
    "    simulation_results = {}\n",
    "\n",
    "    sim_var_columns = X_simulation.columns[\n",
    "        X_simulation.columns.str.contains(f\"^{simulation_variable}\")\n",
    "    ]\n",
    "\n",
    "    X_simulation_high = X_simulation.copy()\n",
    "    if change_as_perc:\n",
    "        X_simulation_high.loc[:, sim_var_columns] *= (1 + change)\n",
    "    else:\n",
    "        X_simulation_high.loc[:, sim_var_columns] += change\n",
    "    if need_threshold:\n",
    "        X_simulation_high.loc[:, sim_var_columns] = X_simulation_high[sim_var_columns].clip(upper=threshold)\n",
    "\n",
    "\n",
    "    X_simulation_low = X_simulation.copy()\n",
    "    if change_as_perc:\n",
    "        X_simulation_low.loc[:, sim_var_columns] *= (1 - change)\n",
    "    else:\n",
    "        X_simulation_low.loc[:, sim_var_columns] -= change\n",
    "\n",
    "    simulation_results[\"ref\"] = regressor.predict(X_simulation)  # takes the predicted value\n",
    "    simulation_results[\"low\"] = regressor.predict(X_simulation_low)  # takes the predicted value\n",
    "    simulation_results[\"high\"] = regressor.predict(X_simulation_high)  # takes the predicted value\n",
    "\n",
    "    simulation_results = pd.DataFrame(simulation_results)\n",
    "\n",
    "    return simulation_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05 True\n",
      "10 False\n",
      "1 False\n",
      "1 False\n",
      "1 False\n",
      "1500 False\n"
     ]
    }
   ],
   "source": [
    "year = 2019\n",
    "test_chart_data_year = test_chart_data[test_chart_data.Data.dt.year == year]\n",
    "\n",
    "exps = [{\n",
    "    \"simulation_variable\": \"hum\",\n",
    "    \"change\": 0.05,\n",
    "    \"change_as_perc\": True,\n",
    "    \"need_threshold\": True,\n",
    "    \"threshold\": 100\n",
    "}, {\n",
    "    \"simulation_variable\": \"press\",\n",
    "    \"change\": 10,\n",
    "    \"change_as_perc\": False,\n",
    "    \"need_threshold\": False,\n",
    "    \"threshold\": None\n",
    "}, {\n",
    "    \"simulation_variable\": \"rain\",\n",
    "    \"change\": 1,\n",
    "    \"change_as_perc\": False,\n",
    "    \"need_threshold\": True,\n",
    "    \"threshold\": 1000\n",
    "}, {\n",
    "    \"simulation_variable\": \"temp\",\n",
    "    \"change\": 1,\n",
    "    \"change_as_perc\": False,\n",
    "    \"need_threshold\": False,\n",
    "    \"threshold\": None\n",
    "}, {\n",
    "    \"simulation_variable\": \"w_sp\",\n",
    "    \"change\": 1,\n",
    "    \"change_as_perc\": False,\n",
    "    \"need_threshold\": True,\n",
    "    \"threshold\": 1000\n",
    "}, {\n",
    "    \"simulation_variable\": \"sol\",\n",
    "    \"change\": 1500,\n",
    "    \"change_as_perc\": False,\n",
    "    \"need_threshold\": False,\n",
    "    \"threshold\": None\n",
    "}]\n",
    "\n",
    "simulation_results = {}\n",
    "\n",
    "for exp in exps:\n",
    "    simulation_result = run_simulation(\n",
    "        test_chart_data_year,\n",
    "        **exp\n",
    "    )\n",
    "\n",
    "    print(exp[\"change\"], exp[\"change_as_perc\"])\n",
    "\n",
    "\n",
    "    simulation_result[\"season\"] = test_chart_data_year.season.values\n",
    "\n",
    "\n",
    "    simulation_result = (\n",
    "        simulation_result\n",
    "        .melt(id_vars=[\"season\"])\n",
    "        .set_index(\"variable\")\n",
    "        .loc[[\"low\", \"ref\", \"high\"]]\n",
    "        .reset_index()\n",
    "    )\n",
    "    simulation_results[exp[\"simulation_variable\"]] = simulation_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "def do_box_plot(param, title, ylab):\n",
    "    df_box = simulation_results[param].copy()\n",
    "    df_box = df_box[df_box.season.isin([\"winter\", \"summer\"])]\n",
    "\n",
    "    # Need to find the dict that contains the \"param\" parameter in the list exps\n",
    "    exp_needed = None\n",
    "    for exp in exps:\n",
    "        if exp[\"simulation_variable\"] == param:\n",
    "            exp_needed = exp\n",
    "    \n",
    "    if exp == None:\n",
    "        print(\"------------------------ERROR--------------------------------\")\n",
    "\n",
    "    condition_to_unit = {\"rain\": \"mm\", \"temp\": \"°C\", \"hum\": \"%\", \"w_sp\": \"m/s\", \"press\": \"hPa\", \"sol\": \"kJ/m2\"}\n",
    "    condition_to_description = {\n",
    "        \"rain\": \"precipitation\",\n",
    "        \"temp\": \"temperature\",\n",
    "        \"hum\" : \"relative humidity\",\n",
    "        \"w_sp\": \"wind speed\",\n",
    "        \"press\": \"atmospheric pressure\",\n",
    "        \"sol\": \"solar radiation\"\n",
    "    }\n",
    "\n",
    "    low_value = f'-{int(float(exp_needed[\"change\"])*100) if exp_needed[\"change_as_perc\"] else exp_needed[\"change\"]}{\"%\" if exp_needed[\"change_as_perc\"] else \"\"} {condition_to_unit[exp_needed[\"simulation_variable\"]]}'\n",
    "    reference = \"Reference\"\n",
    "    high_value = f'+{int(float(exp_needed[\"change\"])*100) if exp_needed[\"change_as_perc\"] else exp_needed[\"change\"]}{\"%\" if exp_needed[\"change_as_perc\"] else \"\"} {condition_to_unit[exp_needed[\"simulation_variable\"]]}'\n",
    "    \n",
    "    df_box[\"variable\"] = df_box.variable.map({\n",
    "        \"low\": low_value, \n",
    "        \"ref\": reference,\n",
    "        \"high\": high_value\n",
    "    })\n",
    "\n",
    "    df_box = df_box.rename({\"variable\": \"Values\"}, axis=1)\n",
    "\n",
    "    ax = sns.boxplot(\n",
    "        x=\"season\",\n",
    "        y=\"value\",\n",
    "        hue=\"Values\",\n",
    "        data=df_box\n",
    "    )\n",
    "    \n",
    "    ax.set_title(title)\n",
    "    ax.set_xlabel(\"Average \" + condition_to_description[param])\n",
    "    ax.set_ylabel(ylab + \" (μg/m3)\")\n",
    "    ax.grid(visible=True)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "do_box_plot(\"temp\", \"Rovereto\", \"PM10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "do_box_plot(\"sol\", \"Monte Gazza\", \"O3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.1 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.1"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "369f2c481f4da34e4445cda3fffd2e751bd1c4d706f27375911949ba6bb62e1c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}