inflation_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "03eae8bb",
   "metadata": {},
   "source": [
    "Warunki zadania:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eae6fb9a",
   "metadata": {},
   "source": [
    "Projekt w Pythonie lub R."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aad2a2e",
   "metadata": {},
   "source": [
    "Projekty w pliku powinny zawierać następujące sekcje:\n",
    "\n",
    "* nazwa zespołu;\n",
    "* imiona i nazwiska członków zespołu;\n",
    "* jasno wskazana prognoza punktowa procentowego wzrostu cen towarów i usług konsumpcyjnych według szybkiego szacunku GUS w maju 2023 r. w porównaniu z analogicznym miesiącem ub. roku;\n",
    "* krótki opis zastosowanej metody: na czym polega model i wykorzystane zmienne;\n",
    "* kod, który prowadzi do wyniku podanego w pkcie 3 wraz z danymi (jeśli uruchomię kod, to musi mi on dać wskazany wynik).\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "99e8c705",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Nazwa zespołu: InflaVision\n",
    "#Autorka: Aleksandra Gomolka\n",
    "#Indeks: 123034"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d6c83e6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from datetime import date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "821f7c4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "today = date.today()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "0de6e505",
   "metadata": {},
   "outputs": [],
   "source": [
    "inflation = pd.read_csv(\"data/inflacja.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "37d4ea23",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>'label'</th>\n",
       "      <th>'2021'</th>\n",
       "      <th>'2022'</th>\n",
       "      <th>'2023'</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Styczeń</td>\n",
       "      <td>2.6</td>\n",
       "      <td>9.4</td>\n",
       "      <td>16.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Luty</td>\n",
       "      <td>2.4</td>\n",
       "      <td>8.5</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Marzec</td>\n",
       "      <td>3.2</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kwiecień</td>\n",
       "      <td>4.3</td>\n",
       "      <td>12.4</td>\n",
       "      <td>14.7</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Maj</td>\n",
       "      <td>4.7</td>\n",
       "      <td>13.9</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Czerwiec</td>\n",
       "      <td>4.4</td>\n",
       "      <td>15.5</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Lipiec</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sierpień</td>\n",
       "      <td>5.5</td>\n",
       "      <td>16.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Wrzesień</td>\n",
       "      <td>5.9</td>\n",
       "      <td>17.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Październik</td>\n",
       "      <td>6.8</td>\n",
       "      <td>17.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Listopad</td>\n",
       "      <td>7.8</td>\n",
       "      <td>17.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Grudzień</td>\n",
       "      <td>8.6</td>\n",
       "      <td>16.6</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "        'label'  '2021'  '2022'  '2023'\n",
       "0       Styczeń     2.6     9.4    16.6\n",
       "1          Luty     2.4     8.5    18.4\n",
       "2        Marzec     3.2    11.0    16.1\n",
       "3      Kwiecień     4.3    12.4    14.7\n",
       "4           Maj     4.7    13.9     NaN\n",
       "5      Czerwiec     4.4    15.5     NaN\n",
       "6        Lipiec     5.0    15.6     NaN\n",
       "7      Sierpień     5.5    16.1     NaN\n",
       "8      Wrzesień     5.9    17.2     NaN\n",
       "9   Październik     6.8    17.9     NaN\n",
       "10     Listopad     7.8    17.5     NaN\n",
       "11     Grudzień     8.6    16.6     NaN"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inflation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "dc836138",
   "metadata": {},
   "outputs": [],
   "source": [
    "inflation = inflation.rename(columns=lambda x: x[1:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3beb4ab3",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'Year': [], 'Month': [], 'Inflation': []}\n",
    "df = pd.DataFrame(data=d)\n",
    "\n",
    "today = date.today()\n",
    "mth = int(today.strftime(\"%m\"))\n",
    "mth\n",
    "\n",
    "for year in inflation.columns[1::].tolist():\n",
    "    for mthn in range(5): \n",
    "        df = pd.concat([df, pd.DataFrame({\"Year\":[year], \"Month\":[inflation.label[:mth].tolist()[mthn]], \"Inflation\":[inflation[year][mthn]]})], ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "aa1429ca",
   "metadata": {},
   "outputs": [
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       "      <td>Wrzesień</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021</td>\n",
       "      <td>Październik</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021</td>\n",
       "      <td>Listopad</td>\n",
       "      <td>7.8</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021</td>\n",
       "      <td>Grudzień</td>\n",
       "      <td>8.6</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
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       "      <td>9.4</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
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       "      <td>8.5</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2022</td>\n",
       "      <td>Marzec</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2022</td>\n",
       "      <td>Kwiecień</td>\n",
       "      <td>12.4</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2022</td>\n",
       "      <td>Maj</td>\n",
       "      <td>13.9</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2022</td>\n",
       "      <td>Czerwiec</td>\n",
       "      <td>15.5</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2022</td>\n",
       "      <td>Lipiec</td>\n",
       "      <td>15.6</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2022</td>\n",
       "      <td>Sierpień</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2022</td>\n",
       "      <td>Wrzesień</td>\n",
       "      <td>17.2</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2022</td>\n",
       "      <td>Październik</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2022</td>\n",
       "      <td>Listopad</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2022</td>\n",
       "      <td>Grudzień</td>\n",
       "      <td>16.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2023</td>\n",
       "      <td>Styczeń</td>\n",
       "      <td>16.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2023</td>\n",
       "      <td>Luty</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2023</td>\n",
       "      <td>Marzec</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2023</td>\n",
       "      <td>Kwiecień</td>\n",
       "      <td>14.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2023</td>\n",
       "      <td>Maj</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "    Year        Month  Inflation\n",
       "0   2021      Styczeń        2.6\n",
       "1   2021         Luty        2.4\n",
       "2   2021       Marzec        3.2\n",
       "3   2021     Kwiecień        4.3\n",
       "4   2021          Maj        4.7\n",
       "5   2021     Czerwiec        4.4\n",
       "6   2021       Lipiec        5.0\n",
       "7   2021     Sierpień        5.5\n",
       "8   2021     Wrzesień        5.9\n",
       "9   2021  Październik        6.8\n",
       "10  2021     Listopad        7.8\n",
       "11  2021     Grudzień        8.6\n",
       "12  2022      Styczeń        9.4\n",
       "13  2022         Luty        8.5\n",
       "14  2022       Marzec       11.0\n",
       "15  2022     Kwiecień       12.4\n",
       "16  2022          Maj       13.9\n",
       "17  2022     Czerwiec       15.5\n",
       "18  2022       Lipiec       15.6\n",
       "19  2022     Sierpień       16.1\n",
       "20  2022     Wrzesień       17.2\n",
       "21  2022  Październik       17.9\n",
       "22  2022     Listopad       17.5\n",
       "23  2022     Grudzień       16.6\n",
       "24  2023      Styczeń       16.6\n",
       "25  2023         Luty       18.4\n",
       "26  2023       Marzec       16.1\n",
       "27  2023     Kwiecień       14.7\n",
       "28  2023          Maj        NaN"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inflation = pd.read_csv(\"data/inflacja.csv\", sep=\";\")\n",
    "inflation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "ccade367",
   "metadata": {},
   "outputs": [],
   "source": [
    "miesieczne_wskazniki_cen_towarow = pd.read_csv(\"data/miesieczne_wskazniki_cen_towarow_i_uslug_konsumpcyjnych_od_1982_roku-2.csv\", encoding=\"cp1250\", sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "0118f80e",
   "metadata": {},
   "outputs": [
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       "      <td>Polska</td>\n",
       "      <td>Grudzień poprzedniego roku = 100</td>\n",
       "      <td>2023</td>\n",
       "      <td>4</td>\n",
       "      <td>105,7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Grudzień poprzedniego roku = 100</td>\n",
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       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Wskaźnik cen towarów i usług konsumpcyjnych</td>\n",
       "      <td>Polska</td>\n",
       "      <td>Rok poprzedni = 100</td>\n",
       "      <td>2021</td>\n",
       "      <td>8</td>\n",
       "      <td>105,3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Wskaźnik cen towarów i usług konsumpcyjnych</td>\n",
       "      <td>Polska</td>\n",
       "      <td>Rok poprzedni = 100</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>106,0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Wskaźnik cen towarów i usług konsumpcyjnych</td>\n",
       "      <td>Polska</td>\n",
       "      <td>Rok poprzedni = 100</td>\n",
       "      <td>2021</td>\n",
       "      <td>10</td>\n",
       "      <td>107,1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Wskaźnik cen towarów i usług konsumpcyjnych</td>\n",
       "      <td>Polska</td>\n",
       "      <td>Rok poprzedni = 100</td>\n",
       "      <td>2021</td>\n",
       "      <td>11</td>\n",
       "      <td>108,2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Wskaźnik cen towarów i usług konsumpcyjnych</td>\n",
       "      <td>Polska</td>\n",
       "      <td>Rok poprzedni = 100</td>\n",
       "      <td>2021</td>\n",
       "      <td>12</td>\n",
       "      <td>109,2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Nazwa zmiennej Jednostka terytorialna   \n",
       "0    Wskaźnik cen towarów i usług konsumpcyjnych                 Polska  \\\n",
       "1    Wskaźnik cen towarów i usług konsumpcyjnych                 Polska   \n",
       "2    Wskaźnik cen towarów i usług konsumpcyjnych                 Polska   \n",
       "3    Wskaźnik cen towarów i usług konsumpcyjnych                 Polska   \n",
       "4    Wskaźnik cen towarów i usług konsumpcyjnych                 Polska   \n",
       "..                                           ...                    ...   \n",
       "175  Wskaźnik cen towarów i usług konsumpcyjnych                 Polska   \n",
       "176  Wskaźnik cen towarów i usług konsumpcyjnych                 Polska   \n",
       "177  Wskaźnik cen towarów i usług konsumpcyjnych                 Polska   \n",
       "178  Wskaźnik cen towarów i usług konsumpcyjnych                 Polska   \n",
       "179  Wskaźnik cen towarów i usług konsumpcyjnych                 Polska   \n",
       "\n",
       "                   Sposób prezentacji   Rok  Miesiąc Wartość Flaga   \n",
       "0    Grudzień poprzedniego roku = 100  2023        1   102,5   NaN  \\\n",
       "1    Grudzień poprzedniego roku = 100  2023        2   103,8   NaN   \n",
       "2    Grudzień poprzedniego roku = 100  2023        3   104,9   NaN   \n",
       "3    Grudzień poprzedniego roku = 100  2023        4   105,7   NaN   \n",
       "4    Grudzień poprzedniego roku = 100  2023        5     NaN   NaN   \n",
       "..                                ...   ...      ...     ...   ...   \n",
       "175               Rok poprzedni = 100  2021        8   105,3   NaN   \n",
       "176               Rok poprzedni = 100  2021        9   106,0   NaN   \n",
       "177               Rok poprzedni = 100  2021       10   107,1   NaN   \n",
       "178               Rok poprzedni = 100  2021       11   108,2   NaN   \n",
       "179               Rok poprzedni = 100  2021       12   109,2   NaN   \n",
       "\n",
       "     Unnamed: 7  Unnamed: 8  \n",
       "0           NaN         NaN  \n",
       "1           NaN         NaN  \n",
       "2           NaN         NaN  \n",
       "3           NaN         NaN  \n",
       "4           NaN         NaN  \n",
       "..          ...         ...  \n",
       "175         NaN         NaN  \n",
       "176         NaN         NaN  \n",
       "177         NaN         NaN  \n",
       "178         NaN         NaN  \n",
       "179         NaN         NaN  \n",
       "\n",
       "[180 rows x 9 columns]"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "miesieczne_wskazniki_cen_towarow = miesieczne_wskazniki_cen_towarow[miesieczne_wskazniki_cen_towarow[\"Rok\"] >= 2021]\n",
    "miesieczne_wskazniki_cen_towarow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "b5940661",
   "metadata": {},
   "outputs": [],
   "source": [
    "miesieczne_wskazniki_cen_towarow['Wartość'] = miesieczne_wskazniki_cen_towarow['Wartość'].str.replace(',', '.')\n",
    "miesieczne_wskazniki_cen_towarow['Wartość'] = miesieczne_wskazniki_cen_towarow['Wartość'].astype(float)\n",
    "inflation[\"Month_No\"] = 0\n",
    "mth_list = [\"Styczeń\", \"Luty\", \"Marzec\", \"Kwiecień\", \"Maj\", \"Czerwiec\", \"Lipiec\", \"Sierpień\", \"Wrzesień\", \"Październik\", \"Listopad\", \"Grudzień\"]\n",
    "\n",
    "for index, row in inflation.iterrows():\n",
    "    inflation.at[index,'Month_No'] = mth_list.index(row[\"Month\"])+1\n",
    "    if((int(mth_list.index(row[\"Month\"])+1))<10):\n",
    "        inflation.at[index,'Yr_Mnth'] = str(row[\"Year\"]) + \"-0\" + str(mth_list.index(row[\"Month\"])+1)\n",
    "    elif((int(mth_list.index(row[\"Month\"])+1))>=10):\n",
    "         inflation.at[index,'Yr_Mnth'] = str(row[\"Year\"]) + \"-\" + str(mth_list.index(row[\"Month\"])+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "a8018718",
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in inflation.iterrows():\n",
    "    df_wskazniki = miesieczne_wskazniki_cen_towarow.loc[miesieczne_wskazniki_cen_towarow[\"Rok\"] == int(row[\"Year\"])]\n",
    "    df_wskazniki = df_wskazniki[df_wskazniki[\"Miesiąc\"] == int(row[\"Month_No\"])]\n",
    "    mean_wartosc= (df_wskazniki[\"Wartość\"]).mean()\n",
    "    inflation.at[index,'Mies_wsk_cen_towarow'] = mean_wartosc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "000302ed",
   "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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Inflation</th>\n",
       "      <th>Month_No</th>\n",
       "      <th>Yr_Mnth</th>\n",
       "      <th>Mies_wsk_cen_towarow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021</td>\n",
       "      <td>Styczeń</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-01</td>\n",
       "      <td>101.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021</td>\n",
       "      <td>Luty</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-02</td>\n",
       "      <td>101.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021</td>\n",
       "      <td>Marzec</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-03</td>\n",
       "      <td>102.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021</td>\n",
       "      <td>Kwiecień</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4</td>\n",
       "      <td>2021-04</td>\n",
       "      <td>103.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021</td>\n",
       "      <td>Maj</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5</td>\n",
       "      <td>2021-05</td>\n",
       "      <td>103.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021</td>\n",
       "      <td>Czerwiec</td>\n",
       "      <td>4.4</td>\n",
       "      <td>6</td>\n",
       "      <td>2021-06</td>\n",
       "      <td>103.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021</td>\n",
       "      <td>Lipiec</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2021-07</td>\n",
       "      <td>103.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021</td>\n",
       "      <td>Sierpień</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8</td>\n",
       "      <td>2021-08</td>\n",
       "      <td>103.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021</td>\n",
       "      <td>Wrzesień</td>\n",
       "      <td>5.9</td>\n",
       "      <td>9</td>\n",
       "      <td>2021-09</td>\n",
       "      <td>104.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021</td>\n",
       "      <td>Październik</td>\n",
       "      <td>6.8</td>\n",
       "      <td>10</td>\n",
       "      <td>2021-10</td>\n",
       "      <td>105.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021</td>\n",
       "      <td>Listopad</td>\n",
       "      <td>7.8</td>\n",
       "      <td>11</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>105.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021</td>\n",
       "      <td>Grudzień</td>\n",
       "      <td>8.6</td>\n",
       "      <td>12</td>\n",
       "      <td>2021-12</td>\n",
       "      <td>106.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2022</td>\n",
       "      <td>Styczeń</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-01</td>\n",
       "      <td>105.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2022</td>\n",
       "      <td>Luty</td>\n",
       "      <td>8.5</td>\n",
       "      <td>2</td>\n",
       "      <td>2022-02</td>\n",
       "      <td>104.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2022</td>\n",
       "      <td>Marzec</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2022-03</td>\n",
       "      <td>107.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2022</td>\n",
       "      <td>Kwiecień</td>\n",
       "      <td>12.4</td>\n",
       "      <td>4</td>\n",
       "      <td>2022-04</td>\n",
       "      <td>108.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2022</td>\n",
       "      <td>Maj</td>\n",
       "      <td>13.9</td>\n",
       "      <td>5</td>\n",
       "      <td>2022-05</td>\n",
       "      <td>109.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2022</td>\n",
       "      <td>Czerwiec</td>\n",
       "      <td>15.5</td>\n",
       "      <td>6</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>110.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2022</td>\n",
       "      <td>Lipiec</td>\n",
       "      <td>15.6</td>\n",
       "      <td>7</td>\n",
       "      <td>2022-07</td>\n",
       "      <td>110.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2022</td>\n",
       "      <td>Sierpień</td>\n",
       "      <td>16.1</td>\n",
       "      <td>8</td>\n",
       "      <td>2022-08</td>\n",
       "      <td>111.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2022</td>\n",
       "      <td>Wrzesień</td>\n",
       "      <td>17.2</td>\n",
       "      <td>9</td>\n",
       "      <td>2022-09</td>\n",
       "      <td>112.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2022</td>\n",
       "      <td>Październik</td>\n",
       "      <td>17.9</td>\n",
       "      <td>10</td>\n",
       "      <td>2022-10</td>\n",
       "      <td>113.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2022</td>\n",
       "      <td>Listopad</td>\n",
       "      <td>17.5</td>\n",
       "      <td>11</td>\n",
       "      <td>2022-11</td>\n",
       "      <td>113.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2022</td>\n",
       "      <td>Grudzień</td>\n",
       "      <td>16.6</td>\n",
       "      <td>12</td>\n",
       "      <td>2022-12</td>\n",
       "      <td>113.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2023</td>\n",
       "      <td>Styczeń</td>\n",
       "      <td>16.6</td>\n",
       "      <td>1</td>\n",
       "      <td>2023-01</td>\n",
       "      <td>109.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2023</td>\n",
       "      <td>Luty</td>\n",
       "      <td>18.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2023-02</td>\n",
       "      <td>110.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2023</td>\n",
       "      <td>Marzec</td>\n",
       "      <td>16.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2023-03</td>\n",
       "      <td>110.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2023</td>\n",
       "      <td>Kwiecień</td>\n",
       "      <td>14.7</td>\n",
       "      <td>4</td>\n",
       "      <td>2023-04</td>\n",
       "      <td>109.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2023</td>\n",
       "      <td>Maj</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2023-05</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year        Month  Inflation  Month_No  Yr_Mnth  Mies_wsk_cen_towarow\n",
       "0   2021      Styczeń        2.6         1  2021-01                101.92\n",
       "1   2021         Luty        2.4         2  2021-02                101.90\n",
       "2   2021       Marzec        3.2         3  2021-03                102.60\n",
       "3   2021     Kwiecień        4.3         4  2021-04                103.18\n",
       "4   2021          Maj        4.7         5  2021-05                103.36\n",
       "5   2021     Czerwiec        4.4         6  2021-06                103.32\n",
       "6   2021       Lipiec        5.0         7  2021-07                103.74\n",
       "7   2021     Sierpień        5.5         8  2021-08                103.98\n",
       "8   2021     Wrzesień        5.9         9  2021-09                104.46\n",
       "9   2021  Październik        6.8        10  2021-10                105.22\n",
       "10  2021     Listopad        7.8        11  2021-11                105.88\n",
       "11  2021     Grudzień        8.6        12  2021-12                106.48\n",
       "12  2022      Styczeń        9.4         1  2022-01                105.72\n",
       "13  2022         Luty        8.5         2  2022-02                104.88\n",
       "14  2022       Marzec       11.0         3  2022-03                107.60\n",
       "15  2022     Kwiecień       12.4         4  2022-04                108.60\n",
       "16  2022          Maj       13.9         5  2022-05                109.72\n",
       "17  2022     Czerwiec       15.5         6  2022-06                110.80\n",
       "18  2022       Lipiec       15.6         7  2022-07                110.96\n",
       "19  2022     Sierpień       16.1         8  2022-08                111.56\n",
       "20  2022     Wrzesień       17.2         9  2022-09                112.76\n",
       "21  2022  Październik       17.9        10  2022-10                113.86\n",
       "22  2022     Listopad       17.5        11  2022-11                113.96\n",
       "23  2022     Grudzień       16.6        12  2022-12                113.78\n",
       "24  2023      Styczeń       16.6         1  2023-01                109.36\n",
       "25  2023         Luty       18.4         2  2023-02                110.16\n",
       "26  2023       Marzec       16.1         3  2023-03                110.06\n",
       "27  2023     Kwiecień       14.7         4  2023-04                109.88\n",
       "28  2023          Maj        NaN         5  2023-05                   NaN"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inflation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "6af6a33b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# wig20 = pd.read_csv(\"data/wig20.csv\", parse_dates=['Date'], index_col='Date')\n",
    "# eur_pln = pd.read_csv(\"data/Currency_EUR_PLN.csv\", parse_dates=['Date'], index_col='Date')\n",
    "# pln_eur = pd.read_csv(\"data/Currency_PLN_EUR.csv\", parse_dates=['Date'], index_col='Date')\n",
    "# pln_usd = pd.read_csv(\"data/currency_pln_usd.csv\", parse_dates=['Date'], index_col='Date')\n",
    "# usd_eur = pd.read_csv(\"data/currency_usd_eur.csv\", parse_dates=['Date'], index_col='Date')\n",
    "# usd_pln = pd.read_csv(\"data/currency_usd_pln.csv\", parse_dates=['Date'], index_col='Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "b6d988b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# wig20['Close_wig20'] = wig20['Close'].str.replace(',', '.').astype(float)\n",
    "# wig20['Yr_Mnth']  = wig20.index.to_period('M')\n",
    "# wig20=wig20.groupby(wig20['Yr_Mnth']).Close_wig20.mean().to_frame()\n",
    "# wig20['y_m'] = wig20.index.astype(str)\n",
    "# inflation = pd.merge(inflation, wig20,  how='left', left_on=['Yr_Mnth'], right_on = ['y_m'])\n",
    "# inflation = inflation.drop(columns=[\"y_m\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "38d51561",
   "metadata": {},
   "outputs": [],
   "source": [
    "# eur_pln['Close_eur_pln'] = eur_pln['Close'].str.replace(',', '.').astype(float)\n",
    "# eur_pln['Yr_Mnth']  = eur_pln.index.to_period('M')\n",
    "# eur_pln = eur_pln.groupby(eur_pln['Yr_Mnth']).Close_eur_pln.mean().to_frame()\n",
    "# eur_pln['y_m'] = eur_pln.index.astype(str)\n",
    "# inflation = pd.merge(inflation, eur_pln,  how='left', left_on=['Yr_Mnth'], right_on = ['y_m'])\n",
    "# inflation = inflation.drop(columns=[\"y_m\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "48d230e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pln_eur['Close_pln_eur'] = pln_eur['Close'].str.replace(',', '.').astype(float)\n",
    "# pln_eur['Yr_Mnth']  = pln_eur.index.to_period('M')\n",
    "# pln_eur = pln_eur.groupby(pln_eur['Yr_Mnth']).Close_pln_eur.mean().to_frame()\n",
    "# pln_eur['y_m'] = pln_eur.index.astype(str)\n",
    "# inflation = pd.merge(inflation, pln_eur,  how='left', left_on=['Yr_Mnth'], right_on = ['y_m'])\n",
    "# inflation = inflation.drop(columns=[\"y_m\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "f7e3acb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pln_usd['Close_pln_usd'] = pln_usd['Close'].str.replace(',', '.').astype(float)\n",
    "# pln_usd['Yr_Mnth']  = pln_usd.index.to_period('M')\n",
    "# pln_usd = pln_usd.groupby(pln_usd['Yr_Mnth']).Close_pln_usd.mean().to_frame()\n",
    "# pln_usd['y_m'] = pln_usd.index.astype(str)\n",
    "# inflation = pd.merge(inflation, pln_usd,  how='left', left_on=['Yr_Mnth'], right_on = ['y_m'])\n",
    "# inflation = inflation.drop(columns=[\"y_m\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "b3561be0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# usd_eur['Close_usd_eur'] = usd_eur['Close'].str.replace(',', '.').astype(float)\n",
    "# usd_eur['Yr_Mnth']  = usd_eur.index.to_period('M')\n",
    "# usd_eur = usd_eur.groupby(usd_eur['Yr_Mnth']).Close_usd_eur.mean().to_frame()\n",
    "# usd_eur['y_m'] = usd_eur.index.astype(str)\n",
    "# inflation = pd.merge(inflation, usd_eur,  how='left', left_on=['Yr_Mnth'], right_on = ['y_m'])\n",
    "# inflation = inflation.drop(columns=[\"y_m\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "d928ba67",
   "metadata": {},
   "outputs": [],
   "source": [
    "# usd_pln['Close_usd_pln'] = usd_pln['Close'].str.replace(',', '.').astype(float)\n",
    "# usd_pln['Yr_Mnth']  = usd_pln.index.to_period('M')\n",
    "# usd_pln = usd_pln.groupby(usd_pln['Yr_Mnth']).Close_usd_pln.mean().to_frame()\n",
    "# usd_pln['y_m'] = usd_pln.index.astype(str)\n",
    "# inflation = pd.merge(inflation, usd_pln,  how='left', left_on=['Yr_Mnth'], right_on = ['y_m'])\n",
    "# inflation = inflation.drop(columns=[\"y_m\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "9c0c1b8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gd/kyzdlh5x2bj6z9mv_q_r_mx40000gn/T/ipykernel_1108/2436744023.py:1: UserWarning: Parsing dates in %d/%m/%Y format when dayfirst=False (the default) was specified. Pass `dayfirst=True` or specify a format to silence this warning.\n",
      "  eurodisel = pd.read_csv(\"data/Olej_napedowy_EURODIESEL.csv\", encoding=\"cp1250\", sep=\";\", parse_dates=['Data'], index_col='Data')\n"
     ]
    }
   ],
   "source": [
    "eurodisel = pd.read_csv(\"data/Olej_napedowy_EURODIESEL.csv\", encoding=\"cp1250\", sep=\";\", parse_dates=['Data'], index_col='Data')\n",
    "eurodisel['PRICE_EURODISEL'] = eurodisel[\"Cena\"].str.replace(' ', '').str.replace(',', '.').astype(float)\n",
    "eurodisel['Yr_Mnth']  = eurodisel.index.to_period('M')\n",
    "eurodisel = eurodisel.groupby(eurodisel['Yr_Mnth']).PRICE_EURODISEL.mean().to_frame()\n",
    "eurodisel['y_m'] = eurodisel.index.astype(str)\n",
    "inflation = pd.merge(inflation, eurodisel,  how='left', left_on=['Yr_Mnth'], right_on = ['y_m'])\n",
    "inflation = inflation.drop(columns=[\"y_m\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "8a99b61e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gd/kyzdlh5x2bj6z9mv_q_r_mx40000gn/T/ipykernel_1108/3876782903.py:1: UserWarning: Parsing dates in %d/%m/%Y format when dayfirst=False (the default) was specified. Pass `dayfirst=True` or specify a format to silence this warning.\n",
      "  eurodisel = pd.read_csv(\"data/Olej_napedowy_EURODIESEL.csv\", encoding=\"cp1250\", sep=\";\", parse_dates=['Data'], index_col='Data')\n"
     ]
    }
   ],
   "source": [
    "eurodisel = pd.read_csv(\"data/Olej_napedowy_EURODIESEL.csv\", encoding=\"cp1250\", sep=\";\", parse_dates=['Data'], index_col='Data')\n",
    "eurodisel['COST_EURODISEL'] = eurodisel[\"Oplata paliwowa\"].str.replace(' ', '').str.replace(',', '.').astype(float)\n",
    "eurodisel['Yr_Mnth']  = eurodisel.index.to_period('M')\n",
    "eurodisel = eurodisel.groupby(eurodisel['Yr_Mnth']).COST_EURODISEL.mean().to_frame()\n",
    "eurodisel['y_m'] = eurodisel.index.astype(str)\n",
    "inflation = pd.merge(inflation, eurodisel,  how='left', left_on=['Yr_Mnth'], right_on = ['y_m'])\n",
    "inflation = inflation.drop(columns=[\"y_m\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "0abd2433",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gd/kyzdlh5x2bj6z9mv_q_r_mx40000gn/T/ipykernel_1108/3876782903.py:1: UserWarning: Parsing dates in %d/%m/%Y format when dayfirst=False (the default) was specified. Pass `dayfirst=True` or specify a format to silence this warning.\n",
      "  eurodisel = pd.read_csv(\"data/Olej_napedowy_EURODIESEL.csv\", encoding=\"cp1250\", sep=\";\", parse_dates=['Data'], index_col='Data')\n"
     ]
    }
   ],
   "source": [
    "eurodisel = pd.read_csv(\"data/Olej_napedowy_EURODIESEL.csv\", encoding=\"cp1250\", sep=\";\", parse_dates=['Data'], index_col='Data')\n",
    "eurodisel['COST_EURODISEL'] = eurodisel[\"Oplata paliwowa\"].str.replace(' ', '').str.replace(',', '.').astype(float)\n",
    "eurodisel['Yr_Mnth']  = eurodisel.index.to_period('M')\n",
    "eurodisel = eurodisel.groupby(eurodisel['Yr_Mnth']).COST_EURODISEL.mean().to_frame()\n",
    "eurodisel['y_m'] = eurodisel.index.astype(str)\n",
    "inflation = pd.merge(inflation, eurodisel,  how='left', left_on=['Yr_Mnth'], right_on = ['y_m'])\n",
    "inflation = inflation.drop(columns=[\"y_m\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "e03ac807",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gd/kyzdlh5x2bj6z9mv_q_r_mx40000gn/T/ipykernel_1108/2638529615.py:1: UserWarning: Parsing dates in %d/%m/%Y format when dayfirst=False (the default) was specified. Pass `dayfirst=True` or specify a format to silence this warning.\n",
      "  gasoline_95 = pd.read_csv(\"data/benzyna_bezolowiowa_95.csv\", sep=\";\", parse_dates=['Data'], index_col='Data')\n"
     ]
    }
   ],
   "source": [
    "gasoline_95 = pd.read_csv(\"data/benzyna_bezolowiowa_95.csv\", sep=\";\", parse_dates=['Data'], index_col='Data')\n",
    "gasoline_95['COST_95'] = gasoline_95[\"Oplata_paliwowa\"].str.replace(' ', '').str.replace(',', '.').astype(float)\n",
    "gasoline_95['Yr_Mnth']  = gasoline_95.index.to_period('M')\n",
    "gasoline_95 = gasoline_95.groupby(gasoline_95['Yr_Mnth']).COST_95.mean().to_frame()\n",
    "gasoline_95['y_m'] = gasoline_95.index.astype(str)\n",
    "inflation = pd.merge(inflation, gasoline_95,  how='left', left_on=['Yr_Mnth'], right_on = ['y_m'])\n",
    "inflation = inflation.drop(columns=[\"y_m\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "73fb70d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gd/kyzdlh5x2bj6z9mv_q_r_mx40000gn/T/ipykernel_1108/1231648091.py:1: UserWarning: Parsing dates in %d/%m/%Y format when dayfirst=False (the default) was specified. Pass `dayfirst=True` or specify a format to silence this warning.\n",
      "  gasoline_95 = pd.read_csv(\"data/benzyna_bezolowiowa_95.csv\", sep=\";\", parse_dates=['Data'], index_col='Data')\n"
     ]
    }
   ],
   "source": [
    "gasoline_95 = pd.read_csv(\"data/benzyna_bezolowiowa_95.csv\", sep=\";\", parse_dates=['Data'], index_col='Data')\n",
    "gasoline_95['PRICE_95'] = gasoline_95[\"Cena\"].str.replace(' ', '').str.replace(',', '.').astype(float)\n",
    "gasoline_95['Yr_Mnth']  = gasoline_95.index.to_period('M')\n",
    "gasoline_95 = gasoline_95.groupby(gasoline_95['Yr_Mnth']).PRICE_95.mean().to_frame()\n",
    "gasoline_95['y_m'] = gasoline_95.index.astype(str)\n",
    "inflation = pd.merge(inflation, gasoline_95,  how='left', left_on=['Yr_Mnth'], right_on = ['y_m'])\n",
    "inflation = inflation.drop(columns=[\"y_m\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "4e7ed692",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gd/kyzdlh5x2bj6z9mv_q_r_mx40000gn/T/ipykernel_1108/4088838089.py:1: UserWarning: Parsing dates in %d/%m/%Y format when dayfirst=False (the default) was specified. Pass `dayfirst=True` or specify a format to silence this warning.\n",
      "  diesel_iz40 = pd.read_csv(\"data/Olej_napedowy_IZ-40.csv\", sep=\";\", parse_dates=['Data'], index_col='Data')\n"
     ]
    }
   ],
   "source": [
    "diesel_iz40 = pd.read_csv(\"data/Olej_napedowy_IZ-40.csv\", sep=\";\", parse_dates=['Data'], index_col='Data')\n",
    "diesel_iz40['PRICE_diesel'] = diesel_iz40[\"Cena\"].str.replace(' ', '').str.replace(',', '.').astype(float)\n",
    "diesel_iz40['Yr_Mnth']  = diesel_iz40.index.to_period('M')\n",
    "diesel_iz40 = diesel_iz40.groupby(diesel_iz40['Yr_Mnth']).PRICE_diesel.mean().to_frame()\n",
    "diesel_iz40['y_m'] = diesel_iz40.index.astype(str)\n",
    "inflation = pd.merge(inflation, diesel_iz40,  how='left', left_on=['Yr_Mnth'], right_on = ['y_m'])\n",
    "inflation = inflation.drop(columns=[\"y_m\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "2a29ea34",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gd/kyzdlh5x2bj6z9mv_q_r_mx40000gn/T/ipykernel_1108/687624417.py:1: UserWarning: Parsing dates in %d/%m/%Y format when dayfirst=False (the default) was specified. Pass `dayfirst=True` or specify a format to silence this warning.\n",
      "  diesel_iz40 = pd.read_csv(\"data/Olej_napedowy_IZ-40.csv\", sep=\";\", parse_dates=['Data'], index_col='Data')\n"
     ]
    }
   ],
   "source": [
    "diesel_iz40 = pd.read_csv(\"data/Olej_napedowy_IZ-40.csv\", sep=\";\", parse_dates=['Data'], index_col='Data')\n",
    "diesel_iz40['COST_diesel'] = diesel_iz40[\"Oplata_paliwowa\"].str.replace(' ', '').str.replace(',', '.').astype(float)\n",
    "diesel_iz40['Yr_Mnth']  = diesel_iz40.index.to_period('M')\n",
    "diesel_iz40 = diesel_iz40.groupby(diesel_iz40['Yr_Mnth']).COST_diesel.mean().to_frame()\n",
    "diesel_iz40['y_m'] = diesel_iz40.index.astype(str)\n",
    "inflation = pd.merge(inflation, diesel_iz40,  how='left', left_on=['Yr_Mnth'], right_on = ['y_m'])\n",
    "inflation = inflation.drop(columns=[\"y_m\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "e67d52d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gd/kyzdlh5x2bj6z9mv_q_r_mx40000gn/T/ipykernel_1108/2236549205.py:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  oze = pd.read_csv(\"data/oze.csv\", sep=\";\", parse_dates=['Data'], index_col='Data')\n"
     ]
    }
   ],
   "source": [
    "oze = pd.read_csv(\"data/oze.csv\", sep=\";\", parse_dates=['Data'], index_col='Data')\n",
    "oze['Cena_PMOZE'] = oze[\"Cena_PMOZE\"].str.replace(' ', '').str.replace(',', '.').astype(float)\n",
    "oze['Yr_Mnth']  = oze.index.to_period('M')\n",
    "oze = oze.groupby(oze['Yr_Mnth']).Cena_PMOZE.mean().to_frame()\n",
    "oze['y_m'] = oze.index.astype(str)\n",
    "inflation = pd.merge(inflation, oze,  how='left', left_on=['Yr_Mnth'], right_on = ['y_m'])\n",
    "inflation = inflation.drop(columns=[\"y_m\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "a74c315b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gd/kyzdlh5x2bj6z9mv_q_r_mx40000gn/T/ipykernel_1108/577346994.py:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  oze = pd.read_csv(\"data/oze.csv\", sep=\";\", parse_dates=['Data'], index_col='Data')\n"
     ]
    }
   ],
   "source": [
    "oze = pd.read_csv(\"data/oze.csv\", sep=\";\", parse_dates=['Data'], index_col='Data')\n",
    "oze['CenaPMOZEBIO'] = oze[\"CenaPMOZE-BIO\"].str.replace(' ', '').str.replace(',', '.').astype(float)\n",
    "oze['Yr_Mnth']  = oze.index.to_period('M')\n",
    "oze = oze.groupby(oze['Yr_Mnth']).CenaPMOZEBIO.mean().to_frame()\n",
    "oze['y_m'] = oze.index.astype(str)\n",
    "inflation = pd.merge(inflation, oze,  how='left', left_on=['Yr_Mnth'], right_on = ['y_m'])\n",
    "inflation = inflation.drop(columns=[\"y_m\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "93996f02",
   "metadata": {},
   "outputs": [],
   "source": [
    "meat = pd.read_csv(\"data/mieso.csv\", sep=\";\", encoding=\"cp1250\", parse_dates=['Data'], index_col='Data')\n",
    "meat['PRICE_bull'] = meat[\"ceny_bykow\"].str.replace(' ', '').str.replace(',', '.').astype(float)\n",
    "meat['Yr_Mnth']  = meat.index.to_period('M')\n",
    "meat = meat.groupby(meat['Yr_Mnth']).PRICE_bull.mean().to_frame()\n",
    "meat['y_m'] = oze.index.astype(str)\n",
    "inflation = pd.merge(inflation, meat,  how='left', left_on=['Yr_Mnth'], right_on = ['y_m'])\n",
    "inflation = inflation.drop(columns=[\"y_m\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "e54faf71",
   "metadata": {},
   "outputs": [],
   "source": [
    "meat = pd.read_csv(\"data/mieso.csv\", sep=\";\", encoding=\"cp1250\", index_col='Data')\n",
    "meat['PRICE_bull'] = meat[\"ceny_bykow\"].str.replace(' ', '').str.replace(',', '.').astype(float)\n",
    "meat['PRICE_heifer'] = meat[\"ceny_jalowek\"].str.replace(' ', '').str.replace(',', '.').astype(float)\n",
    "meat['PRICE_cow'] = meat[\"ceny_krow\"].str.replace(' ', '').str.replace(',', '.').astype(float)\n",
    "meat['PRICE_bull_wbc'] = meat[\"ceny_bykow_wbc\"].str.replace(' ', '').str.replace(',', '.').astype(float)\n",
    "meat['PRICE_heifer_wbc'] = meat[\"ceny_jalowek_wbc\"].str.replace(' ', '').str.replace(',', '.').astype(float)\n",
    "meat['PRICE_cow_wbc'] = meat[\"ceny_krow_wbc\"].str.replace(' ', '').str.replace(',', '.').astype(float)\n",
    "meat = meat.drop(columns=[\"ceny_bykow\", \"ceny_jalowek\", \"ceny_krow\", \"ceny_bykow_wbc\", \"ceny_jalowek_wbc\", \"ceny_krow_wbc\", \"Month\", \"Year\"])\n",
    "# meat['Yr_Mnth']  = meat.index.to_period('M')\n",
    "# meat = meat.groupby(meat['Yr_Mnth']).PRICE_bull.mean().to_frame()\n",
    "meat['y_m'] = meat.index.astype(str)\n",
    "inflation = pd.merge(inflation, meat,  how='left', left_on=['Yr_Mnth'], right_on = ['y_m'])\n",
    "inflation = inflation.drop(columns=[\"y_m\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "d2158f5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "alco = pd.read_csv(\"data/alcohol.csv\", sep=\";\", encoding=\"cp1250\", index_col='Data')\n",
    "alco['PRICE_Zubrowka'] = alco[\"Zubrowka\"].str.replace(' ', '').str.replace(',', '.').astype(float)\n",
    "alco['PRICE_Soplica'] = alco[\"Soplica\"].str.replace(' ', '').str.replace(',', '.').astype(float)\n",
    "alco['PRICE_Stock'] = alco[\"Stock\"].str.replace(' ', '').str.replace(',', '.').astype(float)\n",
    "alco['PRICE_Bacardi'] = alco[\"Bacardi\"].str.replace(' ', '').str.replace(',', '.').astype(float)\n",
    "alco['PRICE_rice'] = alco[\"Ryz\"].str.replace(' ', '').str.replace(',', '.').astype(float)\n",
    "alco = alco.drop(columns=[\"Zubrowka\", \"Soplica\", \"Stock\", \"Bacardi\", \"Ryz\", \"Month\", \"Year\"])\n",
    "alco['y_m'] = alco.index.astype(str)\n",
    "inflation = pd.merge(inflation, alco,  how='left', left_on=['Yr_Mnth'], right_on = ['y_m'])\n",
    "inflation = inflation.drop(columns=[\"y_m\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "d38b978f",
   "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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Inflation</th>\n",
       "      <th>Month_No</th>\n",
       "      <th>Yr_Mnth</th>\n",
       "      <th>Mies_wsk_cen_towarow</th>\n",
       "      <th>Close_wig20</th>\n",
       "      <th>Close_eur_pln</th>\n",
       "      <th>Close_pln_eur</th>\n",
       "      <th>Close_pln_usd</th>\n",
       "      <th>...</th>\n",
       "      <th>PRICE_heifer</th>\n",
       "      <th>PRICE_cow</th>\n",
       "      <th>PRICE_bull_wbc</th>\n",
       "      <th>PRICE_heifer_wbc</th>\n",
       "      <th>PRICE_cow_wbc</th>\n",
       "      <th>PRICE_Zubrowka</th>\n",
       "      <th>PRICE_Soplica</th>\n",
       "      <th>PRICE_Stock</th>\n",
       "      <th>PRICE_Bacardi</th>\n",
       "      <th>PRICE_rice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021</td>\n",
       "      <td>Styczeń</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-01</td>\n",
       "      <td>101.92</td>\n",
       "      <td>1989.7750</td>\n",
       "      <td>4.528772</td>\n",
       "      <td>0.220715</td>\n",
       "      <td>0.266085</td>\n",
       "      <td>...</td>\n",
       "      <td>6.86</td>\n",
       "      <td>4.82</td>\n",
       "      <td>13.70</td>\n",
       "      <td>13.12</td>\n",
       "      <td>10.83</td>\n",
       "      <td>21.99</td>\n",
       "      <td>30.99</td>\n",
       "      <td>42.99</td>\n",
       "      <td>49.99</td>\n",
       "      <td>3.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021</td>\n",
       "      <td>Luty</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-02</td>\n",
       "      <td>101.90</td>\n",
       "      <td>1946.8475</td>\n",
       "      <td>4.497018</td>\n",
       "      <td>0.222353</td>\n",
       "      <td>0.268647</td>\n",
       "      <td>...</td>\n",
       "      <td>6.89</td>\n",
       "      <td>4.94</td>\n",
       "      <td>13.68</td>\n",
       "      <td>13.26</td>\n",
       "      <td>11.11</td>\n",
       "      <td>21.99</td>\n",
       "      <td>30.99</td>\n",
       "      <td>42.99</td>\n",
       "      <td>49.99</td>\n",
       "      <td>3.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021</td>\n",
       "      <td>Marzec</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-03</td>\n",
       "      <td>102.60</td>\n",
       "      <td>1947.9925</td>\n",
       "      <td>4.613775</td>\n",
       "      <td>0.216739</td>\n",
       "      <td>0.257932</td>\n",
       "      <td>...</td>\n",
       "      <td>6.85</td>\n",
       "      <td>4.96</td>\n",
       "      <td>13.55</td>\n",
       "      <td>13.27</td>\n",
       "      <td>11.27</td>\n",
       "      <td>21.99</td>\n",
       "      <td>30.99</td>\n",
       "      <td>42.99</td>\n",
       "      <td>49.99</td>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021</td>\n",
       "      <td>Kwiecień</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4</td>\n",
       "      <td>2021-04</td>\n",
       "      <td>103.18</td>\n",
       "      <td>1993.1960</td>\n",
       "      <td>4.558950</td>\n",
       "      <td>0.219282</td>\n",
       "      <td>0.261165</td>\n",
       "      <td>...</td>\n",
       "      <td>6.89</td>\n",
       "      <td>4.94</td>\n",
       "      <td>13.68</td>\n",
       "      <td>13.26</td>\n",
       "      <td>11.11</td>\n",
       "      <td>21.99</td>\n",
       "      <td>34.49</td>\n",
       "      <td>42.99</td>\n",
       "      <td>49.99</td>\n",
       "      <td>4.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021</td>\n",
       "      <td>Maj</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5</td>\n",
       "      <td>2021-05</td>\n",
       "      <td>103.36</td>\n",
       "      <td>2141.3700</td>\n",
       "      <td>4.514852</td>\n",
       "      <td>0.221446</td>\n",
       "      <td>0.267940</td>\n",
       "      <td>...</td>\n",
       "      <td>6.86</td>\n",
       "      <td>4.97</td>\n",
       "      <td>13.60</td>\n",
       "      <td>13.30</td>\n",
       "      <td>11.38</td>\n",
       "      <td>21.99</td>\n",
       "      <td>34.49</td>\n",
       "      <td>42.99</td>\n",
       "      <td>49.99</td>\n",
       "      <td>4.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021</td>\n",
       "      <td>Czerwiec</td>\n",
       "      <td>4.4</td>\n",
       "      <td>6</td>\n",
       "      <td>2021-06</td>\n",
       "      <td>103.32</td>\n",
       "      <td>2245.6000</td>\n",
       "      <td>4.507108</td>\n",
       "      <td>0.221912</td>\n",
       "      <td>0.266663</td>\n",
       "      <td>...</td>\n",
       "      <td>7.18</td>\n",
       "      <td>5.23</td>\n",
       "      <td>14.61</td>\n",
       "      <td>13.96</td>\n",
       "      <td>12.17</td>\n",
       "      <td>21.99</td>\n",
       "      <td>34.49</td>\n",
       "      <td>42.99</td>\n",
       "      <td>49.99</td>\n",
       "      <td>4.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021</td>\n",
       "      <td>Lipiec</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2021-07</td>\n",
       "      <td>103.74</td>\n",
       "      <td>2251.5840</td>\n",
       "      <td>4.557290</td>\n",
       "      <td>0.219491</td>\n",
       "      <td>0.258179</td>\n",
       "      <td>...</td>\n",
       "      <td>7.22</td>\n",
       "      <td>5.26</td>\n",
       "      <td>14.69</td>\n",
       "      <td>14.08</td>\n",
       "      <td>12.39</td>\n",
       "      <td>21.99</td>\n",
       "      <td>34.49</td>\n",
       "      <td>42.99</td>\n",
       "      <td>49.99</td>\n",
       "      <td>4.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021</td>\n",
       "      <td>Sierpień</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8</td>\n",
       "      <td>2021-08</td>\n",
       "      <td>103.98</td>\n",
       "      <td>2288.8125</td>\n",
       "      <td>4.574954</td>\n",
       "      <td>0.218821</td>\n",
       "      <td>0.256306</td>\n",
       "      <td>...</td>\n",
       "      <td>7.29</td>\n",
       "      <td>5.40</td>\n",
       "      <td>15.06</td>\n",
       "      <td>14.25</td>\n",
       "      <td>12.51</td>\n",
       "      <td>21.99</td>\n",
       "      <td>34.49</td>\n",
       "      <td>42.99</td>\n",
       "      <td>49.99</td>\n",
       "      <td>4.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021</td>\n",
       "      <td>Wrzesień</td>\n",
       "      <td>5.9</td>\n",
       "      <td>9</td>\n",
       "      <td>2021-09</td>\n",
       "      <td>104.46</td>\n",
       "      <td>2343.4275</td>\n",
       "      <td>4.561944</td>\n",
       "      <td>0.219202</td>\n",
       "      <td>0.257659</td>\n",
       "      <td>...</td>\n",
       "      <td>7.85</td>\n",
       "      <td>5.87</td>\n",
       "      <td>16.27</td>\n",
       "      <td>15.41</td>\n",
       "      <td>13.38</td>\n",
       "      <td>21.99</td>\n",
       "      <td>34.49</td>\n",
       "      <td>42.99</td>\n",
       "      <td>49.99</td>\n",
       "      <td>4.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021</td>\n",
       "      <td>Październik</td>\n",
       "      <td>6.8</td>\n",
       "      <td>10</td>\n",
       "      <td>2021-10</td>\n",
       "      <td>105.22</td>\n",
       "      <td>2403.0240</td>\n",
       "      <td>4.592937</td>\n",
       "      <td>0.217610</td>\n",
       "      <td>0.251540</td>\n",
       "      <td>...</td>\n",
       "      <td>8.19</td>\n",
       "      <td>6.26</td>\n",
       "      <td>17.10</td>\n",
       "      <td>16.27</td>\n",
       "      <td>13.99</td>\n",
       "      <td>21.99</td>\n",
       "      <td>34.49</td>\n",
       "      <td>48.49</td>\n",
       "      <td>49.99</td>\n",
       "      <td>4.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021</td>\n",
       "      <td>Listopad</td>\n",
       "      <td>7.8</td>\n",
       "      <td>11</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>105.88</td>\n",
       "      <td>2297.1750</td>\n",
       "      <td>4.659459</td>\n",
       "      <td>0.214562</td>\n",
       "      <td>0.244586</td>\n",
       "      <td>...</td>\n",
       "      <td>8.96</td>\n",
       "      <td>6.65</td>\n",
       "      <td>18.91</td>\n",
       "      <td>17.85</td>\n",
       "      <td>14.50</td>\n",
       "      <td>21.99</td>\n",
       "      <td>34.49</td>\n",
       "      <td>48.49</td>\n",
       "      <td>49.99</td>\n",
       "      <td>4.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021</td>\n",
       "      <td>Grudzień</td>\n",
       "      <td>8.6</td>\n",
       "      <td>12</td>\n",
       "      <td>2021-12</td>\n",
       "      <td>106.48</td>\n",
       "      <td>2216.1540</td>\n",
       "      <td>4.611308</td>\n",
       "      <td>0.216950</td>\n",
       "      <td>0.245438</td>\n",
       "      <td>...</td>\n",
       "      <td>8.74</td>\n",
       "      <td>6.49</td>\n",
       "      <td>18.01</td>\n",
       "      <td>17.33</td>\n",
       "      <td>14.89</td>\n",
       "      <td>22.50</td>\n",
       "      <td>34.49</td>\n",
       "      <td>48.49</td>\n",
       "      <td>49.99</td>\n",
       "      <td>4.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2022</td>\n",
       "      <td>Styczeń</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-01</td>\n",
       "      <td>105.72</td>\n",
       "      <td>2288.1500</td>\n",
       "      <td>4.548619</td>\n",
       "      <td>0.219796</td>\n",
       "      <td>0.248768</td>\n",
       "      <td>...</td>\n",
       "      <td>8.74</td>\n",
       "      <td>6.64</td>\n",
       "      <td>18.52</td>\n",
       "      <td>18.14</td>\n",
       "      <td>15.34</td>\n",
       "      <td>22.50</td>\n",
       "      <td>34.49</td>\n",
       "      <td>48.49</td>\n",
       "      <td>50.24</td>\n",
       "      <td>4.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2022</td>\n",
       "      <td>Luty</td>\n",
       "      <td>8.5</td>\n",
       "      <td>2</td>\n",
       "      <td>2022-02</td>\n",
       "      <td>104.88</td>\n",
       "      <td>2122.9400</td>\n",
       "      <td>4.576087</td>\n",
       "      <td>0.218606</td>\n",
       "      <td>0.248791</td>\n",
       "      <td>...</td>\n",
       "      <td>9.06</td>\n",
       "      <td>6.79</td>\n",
       "      <td>19.03</td>\n",
       "      <td>18.14</td>\n",
       "      <td>15.79</td>\n",
       "      <td>22.50</td>\n",
       "      <td>34.49</td>\n",
       "      <td>48.49</td>\n",
       "      <td>50.49</td>\n",
       "      <td>4.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2022</td>\n",
       "      <td>Marzec</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2022-03</td>\n",
       "      <td>107.60</td>\n",
       "      <td>2041.7700</td>\n",
       "      <td>4.771586</td>\n",
       "      <td>0.209640</td>\n",
       "      <td>0.229275</td>\n",
       "      <td>...</td>\n",
       "      <td>10.33</td>\n",
       "      <td>8.48</td>\n",
       "      <td>21.36</td>\n",
       "      <td>20.12</td>\n",
       "      <td>18.60</td>\n",
       "      <td>22.50</td>\n",
       "      <td>34.49</td>\n",
       "      <td>48.49</td>\n",
       "      <td>50.99</td>\n",
       "      <td>4.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2022</td>\n",
       "      <td>Kwiecień</td>\n",
       "      <td>12.4</td>\n",
       "      <td>4</td>\n",
       "      <td>2022-04</td>\n",
       "      <td>108.60</td>\n",
       "      <td>2034.8960</td>\n",
       "      <td>4.646829</td>\n",
       "      <td>0.215227</td>\n",
       "      <td>0.232751</td>\n",
       "      <td>...</td>\n",
       "      <td>10.49</td>\n",
       "      <td>8.73</td>\n",
       "      <td>21.84</td>\n",
       "      <td>20.85</td>\n",
       "      <td>19.41</td>\n",
       "      <td>24.49</td>\n",
       "      <td>34.49</td>\n",
       "      <td>48.49</td>\n",
       "      <td>51.99</td>\n",
       "      <td>4.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2022</td>\n",
       "      <td>Maj</td>\n",
       "      <td>13.9</td>\n",
       "      <td>5</td>\n",
       "      <td>2022-05</td>\n",
       "      <td>109.72</td>\n",
       "      <td>1781.0275</td>\n",
       "      <td>4.648433</td>\n",
       "      <td>0.215078</td>\n",
       "      <td>0.227239</td>\n",
       "      <td>...</td>\n",
       "      <td>10.82</td>\n",
       "      <td>9.36</td>\n",
       "      <td>21.73</td>\n",
       "      <td>21.78</td>\n",
       "      <td>20.14</td>\n",
       "      <td>24.49</td>\n",
       "      <td>34.49</td>\n",
       "      <td>48.49</td>\n",
       "      <td>53.99</td>\n",
       "      <td>4.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2022</td>\n",
       "      <td>Czerwiec</td>\n",
       "      <td>15.5</td>\n",
       "      <td>6</td>\n",
       "      <td>2022-06</td>\n",
       "      <td>110.80</td>\n",
       "      <td>1726.0425</td>\n",
       "      <td>4.642323</td>\n",
       "      <td>0.215445</td>\n",
       "      <td>0.227813</td>\n",
       "      <td>...</td>\n",
       "      <td>10.81</td>\n",
       "      <td>9.31</td>\n",
       "      <td>21.51</td>\n",
       "      <td>21.56</td>\n",
       "      <td>19.68</td>\n",
       "      <td>24.49</td>\n",
       "      <td>34.49</td>\n",
       "      <td>48.49</td>\n",
       "      <td>50.62</td>\n",
       "      <td>5.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2022</td>\n",
       "      <td>Lipiec</td>\n",
       "      <td>15.6</td>\n",
       "      <td>7</td>\n",
       "      <td>2022-07</td>\n",
       "      <td>110.96</td>\n",
       "      <td>1694.4560</td>\n",
       "      <td>4.745683</td>\n",
       "      <td>0.210726</td>\n",
       "      <td>0.215486</td>\n",
       "      <td>...</td>\n",
       "      <td>10.80</td>\n",
       "      <td>9.10</td>\n",
       "      <td>21.14</td>\n",
       "      <td>21.19</td>\n",
       "      <td>19.11</td>\n",
       "      <td>24.49</td>\n",
       "      <td>34.49</td>\n",
       "      <td>48.49</td>\n",
       "      <td>50.99</td>\n",
       "      <td>5.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2022</td>\n",
       "      <td>Sierpień</td>\n",
       "      <td>16.1</td>\n",
       "      <td>8</td>\n",
       "      <td>2022-08</td>\n",
       "      <td>111.56</td>\n",
       "      <td>1665.8550</td>\n",
       "      <td>4.712650</td>\n",
       "      <td>0.212182</td>\n",
       "      <td>0.214608</td>\n",
       "      <td>...</td>\n",
       "      <td>11.06</td>\n",
       "      <td>9.36</td>\n",
       "      <td>22.01</td>\n",
       "      <td>21.67</td>\n",
       "      <td>19.63</td>\n",
       "      <td>24.99</td>\n",
       "      <td>34.49</td>\n",
       "      <td>53.99</td>\n",
       "      <td>51.49</td>\n",
       "      <td>5.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2022</td>\n",
       "      <td>Wrzesień</td>\n",
       "      <td>17.2</td>\n",
       "      <td>9</td>\n",
       "      <td>2022-09</td>\n",
       "      <td>112.76</td>\n",
       "      <td>1475.6060</td>\n",
       "      <td>4.758280</td>\n",
       "      <td>0.210492</td>\n",
       "      <td>0.208898</td>\n",
       "      <td>...</td>\n",
       "      <td>11.11</td>\n",
       "      <td>9.22</td>\n",
       "      <td>21.61</td>\n",
       "      <td>21.76</td>\n",
       "      <td>19.52</td>\n",
       "      <td>24.99</td>\n",
       "      <td>34.49</td>\n",
       "      <td>53.99</td>\n",
       "      <td>51.99</td>\n",
       "      <td>5.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2022</td>\n",
       "      <td>Październik</td>\n",
       "      <td>17.9</td>\n",
       "      <td>10</td>\n",
       "      <td>2022-10</td>\n",
       "      <td>113.86</td>\n",
       "      <td>1421.0075</td>\n",
       "      <td>4.788652</td>\n",
       "      <td>0.208671</td>\n",
       "      <td>0.204995</td>\n",
       "      <td>...</td>\n",
       "      <td>11.24</td>\n",
       "      <td>9.38</td>\n",
       "      <td>21.49</td>\n",
       "      <td>21.62</td>\n",
       "      <td>18.99</td>\n",
       "      <td>24.99</td>\n",
       "      <td>34.49</td>\n",
       "      <td>53.99</td>\n",
       "      <td>51.99</td>\n",
       "      <td>5.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2022</td>\n",
       "      <td>Listopad</td>\n",
       "      <td>17.5</td>\n",
       "      <td>11</td>\n",
       "      <td>2022-11</td>\n",
       "      <td>113.96</td>\n",
       "      <td>1688.6425</td>\n",
       "      <td>4.697200</td>\n",
       "      <td>0.213120</td>\n",
       "      <td>0.219012</td>\n",
       "      <td>...</td>\n",
       "      <td>11.28</td>\n",
       "      <td>9.25</td>\n",
       "      <td>21.80</td>\n",
       "      <td>21.73</td>\n",
       "      <td>19.07</td>\n",
       "      <td>24.99</td>\n",
       "      <td>34.49</td>\n",
       "      <td>53.99</td>\n",
       "      <td>49.99</td>\n",
       "      <td>5.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2022</td>\n",
       "      <td>Grudzień</td>\n",
       "      <td>16.6</td>\n",
       "      <td>12</td>\n",
       "      <td>2022-12</td>\n",
       "      <td>113.78</td>\n",
       "      <td>1755.4400</td>\n",
       "      <td>4.684830</td>\n",
       "      <td>0.213516</td>\n",
       "      <td>0.226670</td>\n",
       "      <td>...</td>\n",
       "      <td>11.17</td>\n",
       "      <td>8.94</td>\n",
       "      <td>21.42</td>\n",
       "      <td>21.52</td>\n",
       "      <td>18.48</td>\n",
       "      <td>25.99</td>\n",
       "      <td>34.49</td>\n",
       "      <td>55.00</td>\n",
       "      <td>49.99</td>\n",
       "      <td>5.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2023</td>\n",
       "      <td>Styczeń</td>\n",
       "      <td>16.6</td>\n",
       "      <td>1</td>\n",
       "      <td>2023-01</td>\n",
       "      <td>109.36</td>\n",
       "      <td>1895.8275</td>\n",
       "      <td>4.591275</td>\n",
       "      <td>0.218163</td>\n",
       "      <td>0.236073</td>\n",
       "      <td>...</td>\n",
       "      <td>11.13</td>\n",
       "      <td>8.95</td>\n",
       "      <td>21.40</td>\n",
       "      <td>21.52</td>\n",
       "      <td>17.99</td>\n",
       "      <td>25.99</td>\n",
       "      <td>34.49</td>\n",
       "      <td>55.40</td>\n",
       "      <td>49.99</td>\n",
       "      <td>5.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2023</td>\n",
       "      <td>Luty</td>\n",
       "      <td>18.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2023-02</td>\n",
       "      <td>110.16</td>\n",
       "      <td>1855.1850</td>\n",
       "      <td>4.751362</td>\n",
       "      <td>0.210626</td>\n",
       "      <td>0.226136</td>\n",
       "      <td>...</td>\n",
       "      <td>11.18</td>\n",
       "      <td>8.60</td>\n",
       "      <td>21.25</td>\n",
       "      <td>21.52</td>\n",
       "      <td>17.79</td>\n",
       "      <td>25.99</td>\n",
       "      <td>34.49</td>\n",
       "      <td>57.00</td>\n",
       "      <td>57.49</td>\n",
       "      <td>5.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2023</td>\n",
       "      <td>Marzec</td>\n",
       "      <td>16.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2023-03</td>\n",
       "      <td>110.06</td>\n",
       "      <td>1749.6660</td>\n",
       "      <td>4.707950</td>\n",
       "      <td>0.212888</td>\n",
       "      <td>0.228761</td>\n",
       "      <td>...</td>\n",
       "      <td>11.14</td>\n",
       "      <td>8.65</td>\n",
       "      <td>21.59</td>\n",
       "      <td>21.63</td>\n",
       "      <td>18.07</td>\n",
       "      <td>25.99</td>\n",
       "      <td>37.99</td>\n",
       "      <td>57.00</td>\n",
       "      <td>64.99</td>\n",
       "      <td>5.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2023</td>\n",
       "      <td>Kwiecień</td>\n",
       "      <td>14.7</td>\n",
       "      <td>4</td>\n",
       "      <td>2023-04</td>\n",
       "      <td>109.88</td>\n",
       "      <td>1858.0575</td>\n",
       "      <td>4.673725</td>\n",
       "      <td>0.214503</td>\n",
       "      <td>0.238554</td>\n",
       "      <td>...</td>\n",
       "      <td>11.36</td>\n",
       "      <td>8.64</td>\n",
       "      <td>21.40</td>\n",
       "      <td>21.54</td>\n",
       "      <td>17.82</td>\n",
       "      <td>26.50</td>\n",
       "      <td>40.00</td>\n",
       "      <td>65.98</td>\n",
       "      <td>64.99</td>\n",
       "      <td>5.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2023</td>\n",
       "      <td>Maj</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2023-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1946.2175</td>\n",
       "      <td>4.571813</td>\n",
       "      <td>0.219374</td>\n",
       "      <td>0.239451</td>\n",
       "      <td>...</td>\n",
       "      <td>11.30</td>\n",
       "      <td>8.76</td>\n",
       "      <td>21.48</td>\n",
       "      <td>21.64</td>\n",
       "      <td>17.95</td>\n",
       "      <td>27.99</td>\n",
       "      <td>48.99</td>\n",
       "      <td>62.99</td>\n",
       "      <td>54.99</td>\n",
       "      <td>5.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year        Month  Inflation  Month_No  Yr_Mnth  Mies_wsk_cen_towarow   \n",
       "0   2021      Styczeń        2.6         1  2021-01                101.92  \\\n",
       "1   2021         Luty        2.4         2  2021-02                101.90   \n",
       "2   2021       Marzec        3.2         3  2021-03                102.60   \n",
       "3   2021     Kwiecień        4.3         4  2021-04                103.18   \n",
       "4   2021          Maj        4.7         5  2021-05                103.36   \n",
       "5   2021     Czerwiec        4.4         6  2021-06                103.32   \n",
       "6   2021       Lipiec        5.0         7  2021-07                103.74   \n",
       "7   2021     Sierpień        5.5         8  2021-08                103.98   \n",
       "8   2021     Wrzesień        5.9         9  2021-09                104.46   \n",
       "9   2021  Październik        6.8        10  2021-10                105.22   \n",
       "10  2021     Listopad        7.8        11  2021-11                105.88   \n",
       "11  2021     Grudzień        8.6        12  2021-12                106.48   \n",
       "12  2022      Styczeń        9.4         1  2022-01                105.72   \n",
       "13  2022         Luty        8.5         2  2022-02                104.88   \n",
       "14  2022       Marzec       11.0         3  2022-03                107.60   \n",
       "15  2022     Kwiecień       12.4         4  2022-04                108.60   \n",
       "16  2022          Maj       13.9         5  2022-05                109.72   \n",
       "17  2022     Czerwiec       15.5         6  2022-06                110.80   \n",
       "18  2022       Lipiec       15.6         7  2022-07                110.96   \n",
       "19  2022     Sierpień       16.1         8  2022-08                111.56   \n",
       "20  2022     Wrzesień       17.2         9  2022-09                112.76   \n",
       "21  2022  Październik       17.9        10  2022-10                113.86   \n",
       "22  2022     Listopad       17.5        11  2022-11                113.96   \n",
       "23  2022     Grudzień       16.6        12  2022-12                113.78   \n",
       "24  2023      Styczeń       16.6         1  2023-01                109.36   \n",
       "25  2023         Luty       18.4         2  2023-02                110.16   \n",
       "26  2023       Marzec       16.1         3  2023-03                110.06   \n",
       "27  2023     Kwiecień       14.7         4  2023-04                109.88   \n",
       "28  2023          Maj        NaN         5  2023-05                   NaN   \n",
       "\n",
       "    Close_wig20  Close_eur_pln  Close_pln_eur  Close_pln_usd  ...   \n",
       "0     1989.7750       4.528772       0.220715       0.266085  ...  \\\n",
       "1     1946.8475       4.497018       0.222353       0.268647  ...   \n",
       "2     1947.9925       4.613775       0.216739       0.257932  ...   \n",
       "3     1993.1960       4.558950       0.219282       0.261165  ...   \n",
       "4     2141.3700       4.514852       0.221446       0.267940  ...   \n",
       "5     2245.6000       4.507108       0.221912       0.266663  ...   \n",
       "6     2251.5840       4.557290       0.219491       0.258179  ...   \n",
       "7     2288.8125       4.574954       0.218821       0.256306  ...   \n",
       "8     2343.4275       4.561944       0.219202       0.257659  ...   \n",
       "9     2403.0240       4.592937       0.217610       0.251540  ...   \n",
       "10    2297.1750       4.659459       0.214562       0.244586  ...   \n",
       "11    2216.1540       4.611308       0.216950       0.245438  ...   \n",
       "12    2288.1500       4.548619       0.219796       0.248768  ...   \n",
       "13    2122.9400       4.576087       0.218606       0.248791  ...   \n",
       "14    2041.7700       4.771586       0.209640       0.229275  ...   \n",
       "15    2034.8960       4.646829       0.215227       0.232751  ...   \n",
       "16    1781.0275       4.648433       0.215078       0.227239  ...   \n",
       "17    1726.0425       4.642323       0.215445       0.227813  ...   \n",
       "18    1694.4560       4.745683       0.210726       0.215486  ...   \n",
       "19    1665.8550       4.712650       0.212182       0.214608  ...   \n",
       "20    1475.6060       4.758280       0.210492       0.208898  ...   \n",
       "21    1421.0075       4.788652       0.208671       0.204995  ...   \n",
       "22    1688.6425       4.697200       0.213120       0.219012  ...   \n",
       "23    1755.4400       4.684830       0.213516       0.226670  ...   \n",
       "24    1895.8275       4.591275       0.218163       0.236073  ...   \n",
       "25    1855.1850       4.751362       0.210626       0.226136  ...   \n",
       "26    1749.6660       4.707950       0.212888       0.228761  ...   \n",
       "27    1858.0575       4.673725       0.214503       0.238554  ...   \n",
       "28    1946.2175       4.571813       0.219374       0.239451  ...   \n",
       "\n",
       "    PRICE_heifer  PRICE_cow  PRICE_bull_wbc  PRICE_heifer_wbc  PRICE_cow_wbc   \n",
       "0           6.86       4.82           13.70             13.12          10.83  \\\n",
       "1           6.89       4.94           13.68             13.26          11.11   \n",
       "2           6.85       4.96           13.55             13.27          11.27   \n",
       "3           6.89       4.94           13.68             13.26          11.11   \n",
       "4           6.86       4.97           13.60             13.30          11.38   \n",
       "5           7.18       5.23           14.61             13.96          12.17   \n",
       "6           7.22       5.26           14.69             14.08          12.39   \n",
       "7           7.29       5.40           15.06             14.25          12.51   \n",
       "8           7.85       5.87           16.27             15.41          13.38   \n",
       "9           8.19       6.26           17.10             16.27          13.99   \n",
       "10          8.96       6.65           18.91             17.85          14.50   \n",
       "11          8.74       6.49           18.01             17.33          14.89   \n",
       "12          8.74       6.64           18.52             18.14          15.34   \n",
       "13          9.06       6.79           19.03             18.14          15.79   \n",
       "14         10.33       8.48           21.36             20.12          18.60   \n",
       "15         10.49       8.73           21.84             20.85          19.41   \n",
       "16         10.82       9.36           21.73             21.78          20.14   \n",
       "17         10.81       9.31           21.51             21.56          19.68   \n",
       "18         10.80       9.10           21.14             21.19          19.11   \n",
       "19         11.06       9.36           22.01             21.67          19.63   \n",
       "20         11.11       9.22           21.61             21.76          19.52   \n",
       "21         11.24       9.38           21.49             21.62          18.99   \n",
       "22         11.28       9.25           21.80             21.73          19.07   \n",
       "23         11.17       8.94           21.42             21.52          18.48   \n",
       "24         11.13       8.95           21.40             21.52          17.99   \n",
       "25         11.18       8.60           21.25             21.52          17.79   \n",
       "26         11.14       8.65           21.59             21.63          18.07   \n",
       "27         11.36       8.64           21.40             21.54          17.82   \n",
       "28         11.30       8.76           21.48             21.64          17.95   \n",
       "\n",
       "    PRICE_Zubrowka  PRICE_Soplica  PRICE_Stock  PRICE_Bacardi  PRICE_rice  \n",
       "0            21.99          30.99        42.99          49.99        3.43  \n",
       "1            21.99          30.99        42.99          49.99        3.97  \n",
       "2            21.99          30.99        42.99          49.99        4.30  \n",
       "3            21.99          34.49        42.99          49.99        4.46  \n",
       "4            21.99          34.49        42.99          49.99        4.54  \n",
       "5            21.99          34.49        42.99          49.99        4.59  \n",
       "6            21.99          34.49        42.99          49.99        4.51  \n",
       "7            21.99          34.49        42.99          49.99        4.63  \n",
       "8            21.99          34.49        42.99          49.99        4.63  \n",
       "9            21.99          34.49        48.49          49.99        4.63  \n",
       "10           21.99          34.49        48.49          49.99        4.63  \n",
       "11           22.50          34.49        48.49          49.99        4.63  \n",
       "12           22.50          34.49        48.49          50.24        4.63  \n",
       "13           22.50          34.49        48.49          50.49        4.63  \n",
       "14           22.50          34.49        48.49          50.99        4.63  \n",
       "15           24.49          34.49        48.49          51.99        4.63  \n",
       "16           24.49          34.49        48.49          53.99        4.74  \n",
       "17           24.49          34.49        48.49          50.62        5.23  \n",
       "18           24.49          34.49        48.49          50.99        5.23  \n",
       "19           24.99          34.49        53.99          51.49        5.23  \n",
       "20           24.99          34.49        53.99          51.99        5.23  \n",
       "21           24.99          34.49        53.99          51.99        5.23  \n",
       "22           24.99          34.49        53.99          49.99        5.23  \n",
       "23           25.99          34.49        55.00          49.99        5.23  \n",
       "24           25.99          34.49        55.40          49.99        5.23  \n",
       "25           25.99          34.49        57.00          57.49        5.23  \n",
       "26           25.99          37.99        57.00          64.99        5.23  \n",
       "27           26.50          40.00        65.98          64.99        5.23  \n",
       "28           27.99          48.99        62.99          54.99        5.72  \n",
       "\n",
       "[29 rows x 33 columns]"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inflation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "18dfbd01",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train =  inflation.iloc[:-1].drop(columns=[\"Inflation\", \"Month\", \"Yr_Mnth\", \"Mies_wsk_cen_towarow\"])\n",
    "X_test = inflation.iloc[-1].drop(labels=[\"Inflation\", \"Month\", \"Yr_Mnth\", \"Mies_wsk_cen_towarow\"])\n",
    "X_test = pd.DataFrame(X_test).transpose()\n",
    "y_train = inflation.iloc[:-1][\"Inflation\"]\n",
    "y_test = inflation.iloc[-1][\"Inflation\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "a01a9b6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "linear_regressor = LinearRegression()  # create object for the class\n",
    "linear_regressor.fit(X_train, y_train)  # perform linear regression\n",
    "y_pred = linear_regressor.predict(X_test)  # make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "346e7221",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14.60906431])"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "5f314ce1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13.79591233])"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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