diff --git a/main.ipynb b/main.ipynb
index ccff3640..bc9d80cf 100644
--- a/main.ipynb
+++ b/main.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 324,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 325,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,14 +34,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 326,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_28456\\3760256257.py:1: DtypeWarning: Columns (25) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_22072\\3760256257.py:1: DtypeWarning: Columns (25) have mixed types. Specify dtype option on import or set low_memory=False.\n",
       "  df_dofinansowanie = pd.read_csv(\n"
      ]
     }
@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 327,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 328,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 329,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -124,14 +124,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 330,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_28456\\1671418303.py:1: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_22072\\1671418303.py:1: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
       "  df_fina_1 = pd.read_csv(\n"
      ]
     },
@@ -424,7 +424,7 @@
        "[47083 rows x 11 columns]"
       ]
      },
-     "execution_count": 105,
+     "execution_count": 330,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -454,14 +454,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 331,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_28456\\2161929356.py:1: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_22072\\2161929356.py:1: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
       "  df_fina_2 = pd.read_csv(\n"
      ]
     },
@@ -829,7 +829,7 @@
        "[47083 rows x 14 columns]"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 331,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -862,7 +862,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 332,
    "metadata": {},
    "outputs": [
     {
@@ -1079,7 +1079,7 @@
        "[48611 rows x 8 columns]"
       ]
      },
-     "execution_count": 107,
+     "execution_count": 332,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1106,7 +1106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 333,
    "metadata": {},
    "outputs": [
     {
@@ -1349,7 +1349,7 @@
        "[48611 rows x 8 columns]"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 333,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1376,7 +1376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 334,
    "metadata": {},
    "outputs": [
     {
@@ -1606,7 +1606,7 @@
        "[48611 rows x 8 columns]"
       ]
      },
-     "execution_count": 109,
+     "execution_count": 334,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1631,6 +1631,1064 @@
     "df_ludn_3"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 335,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_ludn_4 = pd.read_csv(\n",
+    "    'LUDN_2425_CREL.csv',\n",
+    "    sep=';',\n",
+    "    converters={'Kod': str},\n",
+    "    decimal=',')\n",
+    "df_ludn_4 = df_ludn_4[['Kod', 'Wskaźniki', 'Rok', 'Wartosc']]\n",
+    "df_ludn_4 = df_ludn_4.dropna()\n",
+    "df_ludn_4 = df_ludn_4.pivot_table(index=['Kod', 'Rok'], columns='Wskaźniki', values='Wartosc').reset_index()\n",
+    "df_ludn_4 = df_ludn_4.rename(columns={\n",
+    "    'gęstość zaludnienia powierzchni zabudowanej i zurbanizowanej (osoby/km2)': 'Gestosc_zaludnienia',\n",
+    "    'ludność na 1 km2': 'Ludnosc_na_1_km2',\n",
+    "    'ludność w tysiącach': 'Ludnosc',\n",
+    "    'ludność w tysiącach kobiety': 'Ludnosc_kobiety',\n",
+    "    'ludność w tysiącach mężczyźni': 'Ludnosc_mezczyzni',\n",
+    "    'wskaźnik urbanizacji': 'Wskaznik_urbanizacji',\n",
+    "    'zmiana liczby ludności na 1000 mieszkańców': 'Zmiana_liczby_ludnosci'})\n",
+    "\n",
+    "df_ludn_4 = df_ludn_4[[\n",
+    "    'Kod',\n",
+    "    'Rok',\n",
+    "    # 'Gestosc_zaludnienia',\n",
+    "    'Ludnosc_na_1_km2',\n",
+    "    'Ludnosc',\n",
+    "    'Ludnosc_kobiety',\n",
+    "    'Ludnosc_mezczyzni',\n",
+    "    'Wskaznik_urbanizacji',\n",
+    "    'Zmiana_liczby_ludnosci']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 336,
+   "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>Kierunki migracji</th>\n",
+       "      <th>Kod</th>\n",
+       "      <th>Rok</th>\n",
+       "      <th>Saldo_migracji_na_1000_ludnosci</th>\n",
+       "      <th>Saldo_migracji</th>\n",
+       "      <th>Wymeldowania_do_miast_kobiety</th>\n",
+       "      <th>Wymeldowania_do_miast_mezczyzni</th>\n",
+       "      <th>Wymeldowania_do_miast_ogolem</th>\n",
+       "      <th>Wymeldowania_na_wies_kobiety</th>\n",
+       "      <th>Wymeldowania_na_wies_mezczyzni</th>\n",
+       "      <th>Wymeldowania_na_wies_ogolem</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Wymeldowania_za_granice_ogolem</th>\n",
+       "      <th>Zameldowania_kobiety</th>\n",
+       "      <th>Zameldowania_mezczyzni</th>\n",
+       "      <th>Zameldowania_ogolem</th>\n",
+       "      <th>Zameldowania_z_miast_kobiety</th>\n",
+       "      <th>Zameldowania_z_miast_mezczyzni</th>\n",
+       "      <th>Zameldowania_z_miast_ogolem</th>\n",
+       "      <th>Zameldowania_ze_wsi_kobiety</th>\n",
+       "      <th>Zameldowania_ze_wsi_mezczyzni</th>\n",
+       "      <th>Zameldowania_ze_wsi_ogolem</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0201011</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>-3.70</td>\n",
+       "      <td>-151.00</td>\n",
+       "      <td>108.00</td>\n",
+       "      <td>96.00</td>\n",
+       "      <td>204.00</td>\n",
+       "      <td>170.00</td>\n",
+       "      <td>177.00</td>\n",
+       "      <td>347.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>223.00</td>\n",
+       "      <td>177.00</td>\n",
+       "      <td>400.00</td>\n",
+       "      <td>70.00</td>\n",
+       "      <td>52.00</td>\n",
+       "      <td>122.00</td>\n",
+       "      <td>147.00</td>\n",
+       "      <td>118.00</td>\n",
+       "      <td>265.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0201011</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>-4.60</td>\n",
+       "      <td>-186.00</td>\n",
+       "      <td>111.00</td>\n",
+       "      <td>99.00</td>\n",
+       "      <td>210.00</td>\n",
+       "      <td>170.00</td>\n",
+       "      <td>157.00</td>\n",
+       "      <td>327.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>196.00</td>\n",
+       "      <td>156.00</td>\n",
+       "      <td>352.00</td>\n",
+       "      <td>67.00</td>\n",
+       "      <td>59.00</td>\n",
+       "      <td>126.00</td>\n",
+       "      <td>125.00</td>\n",
+       "      <td>94.00</td>\n",
+       "      <td>219.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0201011</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>-3.70</td>\n",
+       "      <td>-149.00</td>\n",
+       "      <td>100.00</td>\n",
+       "      <td>92.00</td>\n",
+       "      <td>192.00</td>\n",
+       "      <td>147.00</td>\n",
+       "      <td>153.00</td>\n",
+       "      <td>300.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>197.00</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>352.00</td>\n",
+       "      <td>78.00</td>\n",
+       "      <td>61.00</td>\n",
+       "      <td>139.00</td>\n",
+       "      <td>116.00</td>\n",
+       "      <td>92.00</td>\n",
+       "      <td>208.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0201011</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>-4.80</td>\n",
+       "      <td>-191.00</td>\n",
+       "      <td>115.00</td>\n",
+       "      <td>88.00</td>\n",
+       "      <td>203.00</td>\n",
+       "      <td>182.00</td>\n",
+       "      <td>158.00</td>\n",
+       "      <td>340.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.00</td>\n",
+       "      <td>211.00</td>\n",
+       "      <td>165.00</td>\n",
+       "      <td>376.00</td>\n",
+       "      <td>83.00</td>\n",
+       "      <td>58.00</td>\n",
+       "      <td>141.00</td>\n",
+       "      <td>128.00</td>\n",
+       "      <td>101.00</td>\n",
+       "      <td>229.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0201011</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>-4.20</td>\n",
+       "      <td>-167.00</td>\n",
+       "      <td>100.00</td>\n",
+       "      <td>86.00</td>\n",
+       "      <td>186.00</td>\n",
+       "      <td>168.00</td>\n",
+       "      <td>161.00</td>\n",
+       "      <td>329.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>41.00</td>\n",
+       "      <td>196.00</td>\n",
+       "      <td>193.00</td>\n",
+       "      <td>389.00</td>\n",
+       "      <td>71.00</td>\n",
+       "      <td>71.00</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>125.00</td>\n",
+       "      <td>121.00</td>\n",
+       "      <td>246.00</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>48606</th>\n",
+       "      <td>3263011</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>1.70</td>\n",
+       "      <td>71.00</td>\n",
+       "      <td>125.00</td>\n",
+       "      <td>152.00</td>\n",
+       "      <td>277.00</td>\n",
+       "      <td>40.00</td>\n",
+       "      <td>66.00</td>\n",
+       "      <td>106.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>245.00</td>\n",
+       "      <td>240.00</td>\n",
+       "      <td>485.00</td>\n",
+       "      <td>156.00</td>\n",
+       "      <td>138.00</td>\n",
+       "      <td>294.00</td>\n",
+       "      <td>73.00</td>\n",
+       "      <td>79.00</td>\n",
+       "      <td>152.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48607</th>\n",
+       "      <td>3263011</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>141.00</td>\n",
+       "      <td>151.00</td>\n",
+       "      <td>116.00</td>\n",
+       "      <td>267.00</td>\n",
+       "      <td>48.00</td>\n",
+       "      <td>53.00</td>\n",
+       "      <td>101.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>273.00</td>\n",
+       "      <td>259.00</td>\n",
+       "      <td>532.00</td>\n",
+       "      <td>179.00</td>\n",
+       "      <td>149.00</td>\n",
+       "      <td>328.00</td>\n",
+       "      <td>71.00</td>\n",
+       "      <td>90.00</td>\n",
+       "      <td>161.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48608</th>\n",
+       "      <td>3263011</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>3.20</td>\n",
+       "      <td>129.00</td>\n",
+       "      <td>98.00</td>\n",
+       "      <td>99.00</td>\n",
+       "      <td>197.00</td>\n",
+       "      <td>40.00</td>\n",
+       "      <td>44.00</td>\n",
+       "      <td>84.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>226.00</td>\n",
+       "      <td>203.00</td>\n",
+       "      <td>429.00</td>\n",
+       "      <td>159.00</td>\n",
+       "      <td>131.00</td>\n",
+       "      <td>290.00</td>\n",
+       "      <td>52.00</td>\n",
+       "      <td>53.00</td>\n",
+       "      <td>105.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48609</th>\n",
+       "      <td>3263011</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>-1.40</td>\n",
+       "      <td>-55.00</td>\n",
+       "      <td>122.00</td>\n",
+       "      <td>126.00</td>\n",
+       "      <td>248.00</td>\n",
+       "      <td>63.00</td>\n",
+       "      <td>50.00</td>\n",
+       "      <td>113.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>171.00</td>\n",
+       "      <td>168.00</td>\n",
+       "      <td>339.00</td>\n",
+       "      <td>109.00</td>\n",
+       "      <td>95.00</td>\n",
+       "      <td>204.00</td>\n",
+       "      <td>49.00</td>\n",
+       "      <td>46.00</td>\n",
+       "      <td>95.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48610</th>\n",
+       "      <td>3263011</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>-3.50</td>\n",
+       "      <td>-138.00</td>\n",
+       "      <td>116.00</td>\n",
+       "      <td>105.00</td>\n",
+       "      <td>221.00</td>\n",
+       "      <td>73.00</td>\n",
+       "      <td>69.00</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>141.00</td>\n",
+       "      <td>138.00</td>\n",
+       "      <td>279.00</td>\n",
+       "      <td>85.00</td>\n",
+       "      <td>71.00</td>\n",
+       "      <td>156.00</td>\n",
+       "      <td>38.00</td>\n",
+       "      <td>39.00</td>\n",
+       "      <td>77.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>48611 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Kierunki migracji      Kod   Rok  Saldo_migracji_na_1000_ludnosci   \n",
+       "0                  0201011  2010                            -3.70  \\\n",
+       "1                  0201011  2011                            -4.60   \n",
+       "2                  0201011  2012                            -3.70   \n",
+       "3                  0201011  2013                            -4.80   \n",
+       "4                  0201011  2014                            -4.20   \n",
+       "...                    ...   ...                              ...   \n",
+       "48606              3263011  2018                             1.70   \n",
+       "48607              3263011  2019                             3.40   \n",
+       "48608              3263011  2020                             3.20   \n",
+       "48609              3263011  2021                            -1.40   \n",
+       "48610              3263011  2022                            -3.50   \n",
+       "\n",
+       "Kierunki migracji  Saldo_migracji  Wymeldowania_do_miast_kobiety   \n",
+       "0                         -151.00                         108.00  \\\n",
+       "1                         -186.00                         111.00   \n",
+       "2                         -149.00                         100.00   \n",
+       "3                         -191.00                         115.00   \n",
+       "4                         -167.00                         100.00   \n",
+       "...                           ...                            ...   \n",
+       "48606                       71.00                         125.00   \n",
+       "48607                      141.00                         151.00   \n",
+       "48608                      129.00                          98.00   \n",
+       "48609                      -55.00                         122.00   \n",
+       "48610                     -138.00                         116.00   \n",
+       "\n",
+       "Kierunki migracji  Wymeldowania_do_miast_mezczyzni   \n",
+       "0                                            96.00  \\\n",
+       "1                                            99.00   \n",
+       "2                                            92.00   \n",
+       "3                                            88.00   \n",
+       "4                                            86.00   \n",
+       "...                                            ...   \n",
+       "48606                                       152.00   \n",
+       "48607                                       116.00   \n",
+       "48608                                        99.00   \n",
+       "48609                                       126.00   \n",
+       "48610                                       105.00   \n",
+       "\n",
+       "Kierunki migracji  Wymeldowania_do_miast_ogolem  Wymeldowania_na_wies_kobiety   \n",
+       "0                                        204.00                        170.00  \\\n",
+       "1                                        210.00                        170.00   \n",
+       "2                                        192.00                        147.00   \n",
+       "3                                        203.00                        182.00   \n",
+       "4                                        186.00                        168.00   \n",
+       "...                                         ...                           ...   \n",
+       "48606                                    277.00                         40.00   \n",
+       "48607                                    267.00                         48.00   \n",
+       "48608                                    197.00                         40.00   \n",
+       "48609                                    248.00                         63.00   \n",
+       "48610                                    221.00                         73.00   \n",
+       "\n",
+       "Kierunki migracji  Wymeldowania_na_wies_mezczyzni   \n",
+       "0                                          177.00  \\\n",
+       "1                                          157.00   \n",
+       "2                                          153.00   \n",
+       "3                                          158.00   \n",
+       "4                                          161.00   \n",
+       "...                                           ...   \n",
+       "48606                                       66.00   \n",
+       "48607                                       53.00   \n",
+       "48608                                       44.00   \n",
+       "48609                                       50.00   \n",
+       "48610                                       69.00   \n",
+       "\n",
+       "Kierunki migracji  Wymeldowania_na_wies_ogolem  ...   \n",
+       "0                                       347.00  ...  \\\n",
+       "1                                       327.00  ...   \n",
+       "2                                       300.00  ...   \n",
+       "3                                       340.00  ...   \n",
+       "4                                       329.00  ...   \n",
+       "...                                        ...  ...   \n",
+       "48606                                   106.00  ...   \n",
+       "48607                                   101.00  ...   \n",
+       "48608                                    84.00  ...   \n",
+       "48609                                   113.00  ...   \n",
+       "48610                                   142.00  ...   \n",
+       "\n",
+       "Kierunki migracji  Wymeldowania_za_granice_ogolem  Zameldowania_kobiety   \n",
+       "0                                            0.00                223.00  \\\n",
+       "1                                            1.00                196.00   \n",
+       "2                                            9.00                197.00   \n",
+       "3                                           24.00                211.00   \n",
+       "4                                           41.00                196.00   \n",
+       "...                                           ...                   ...   \n",
+       "48606                                         NaN                245.00   \n",
+       "48607                                         NaN                273.00   \n",
+       "48608                                         NaN                226.00   \n",
+       "48609                                         NaN                171.00   \n",
+       "48610                                         NaN                141.00   \n",
+       "\n",
+       "Kierunki migracji  Zameldowania_mezczyzni  Zameldowania_ogolem   \n",
+       "0                                  177.00               400.00  \\\n",
+       "1                                  156.00               352.00   \n",
+       "2                                  155.00               352.00   \n",
+       "3                                  165.00               376.00   \n",
+       "4                                  193.00               389.00   \n",
+       "...                                   ...                  ...   \n",
+       "48606                              240.00               485.00   \n",
+       "48607                              259.00               532.00   \n",
+       "48608                              203.00               429.00   \n",
+       "48609                              168.00               339.00   \n",
+       "48610                              138.00               279.00   \n",
+       "\n",
+       "Kierunki migracji  Zameldowania_z_miast_kobiety   \n",
+       "0                                         70.00  \\\n",
+       "1                                         67.00   \n",
+       "2                                         78.00   \n",
+       "3                                         83.00   \n",
+       "4                                         71.00   \n",
+       "...                                         ...   \n",
+       "48606                                    156.00   \n",
+       "48607                                    179.00   \n",
+       "48608                                    159.00   \n",
+       "48609                                    109.00   \n",
+       "48610                                     85.00   \n",
+       "\n",
+       "Kierunki migracji  Zameldowania_z_miast_mezczyzni   \n",
+       "0                                           52.00  \\\n",
+       "1                                           59.00   \n",
+       "2                                           61.00   \n",
+       "3                                           58.00   \n",
+       "4                                           71.00   \n",
+       "...                                           ...   \n",
+       "48606                                      138.00   \n",
+       "48607                                      149.00   \n",
+       "48608                                      131.00   \n",
+       "48609                                       95.00   \n",
+       "48610                                       71.00   \n",
+       "\n",
+       "Kierunki migracji  Zameldowania_z_miast_ogolem  Zameldowania_ze_wsi_kobiety   \n",
+       "0                                       122.00                       147.00  \\\n",
+       "1                                       126.00                       125.00   \n",
+       "2                                       139.00                       116.00   \n",
+       "3                                       141.00                       128.00   \n",
+       "4                                       142.00                       125.00   \n",
+       "...                                        ...                          ...   \n",
+       "48606                                   294.00                        73.00   \n",
+       "48607                                   328.00                        71.00   \n",
+       "48608                                   290.00                        52.00   \n",
+       "48609                                   204.00                        49.00   \n",
+       "48610                                   156.00                        38.00   \n",
+       "\n",
+       "Kierunki migracji  Zameldowania_ze_wsi_mezczyzni  Zameldowania_ze_wsi_ogolem  \n",
+       "0                                         118.00                      265.00  \n",
+       "1                                          94.00                      219.00  \n",
+       "2                                          92.00                      208.00  \n",
+       "3                                         101.00                      229.00  \n",
+       "4                                         121.00                      246.00  \n",
+       "...                                          ...                         ...  \n",
+       "48606                                      79.00                      152.00  \n",
+       "48607                                      90.00                      161.00  \n",
+       "48608                                      53.00                      105.00  \n",
+       "48609                                      46.00                       95.00  \n",
+       "48610                                      39.00                       77.00  \n",
+       "\n",
+       "[48611 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 336,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_ludn_5 = pd.read_csv(\n",
+    "    'LUDN_1355_CREL.csv',\n",
+    "    sep=';',\n",
+    "    converters={'Kod': str},\n",
+    "    decimal=',')\n",
+    "df_ludn_5['Kierunki migracji'] = df_ludn_5['Kierunki migracji'] + df_ludn_5['Płeć']\n",
+    "df_ludn_5 = df_ludn_5[['Kod', 'Kierunki migracji', 'Rok', 'Wartosc']]\n",
+    "df_ludn_5 = df_ludn_5.dropna()\n",
+    "df_ludn_5 = df_ludn_5.pivot_table(index=['Kod', 'Rok'], columns='Kierunki migracji', values='Wartosc').reset_index()\n",
+    "df_ludn_5 = df_ludn_5.rename(columns={\n",
+    "    'saldo migracji na 1000 ludnościogółem': 'Saldo_migracji_na_1000_ludnosci',\n",
+    "    'saldo migracjiogółem': 'Saldo_migracji',\n",
+    "    'wymeldowania do miastkobiety': 'Wymeldowania_do_miast_kobiety',\n",
+    "    'wymeldowania do miastmężczyźni': 'Wymeldowania_do_miast_mezczyzni',\n",
+    "    'wymeldowania do miastogółem': 'Wymeldowania_do_miast_ogolem',\n",
+    "    'wymeldowania na wieśkobiety': 'Wymeldowania_na_wies_kobiety',\n",
+    "    'wymeldowania na wieśmężczyźni': 'Wymeldowania_na_wies_mezczyzni',\n",
+    "    'wymeldowania na wieśogółem': 'Wymeldowania_na_wies_ogolem',\n",
+    "    'wymeldowania ogółemkobiety': 'Wymeldowania_kobiety',\n",
+    "    'wymeldowania ogółemmężczyźni': 'Wymeldowania_mezczyzni',\n",
+    "    'wymeldowania ogółemogółem': 'Wymeldowania_ogolem',\n",
+    "    'wymeldowania za granicękobiety': 'Wymeldowania_za_granice_kobiety',\n",
+    "    'wymeldowania za granicęmężczyźni': 'Wymeldowania_za_granice_mezczyzni',\n",
+    "    'wymeldowania za granicęogółem': 'Wymeldowania_za_granice_ogolem',\n",
+    "    'zameldowania ogółemkobiety': 'Zameldowania_kobiety',\n",
+    "    'zameldowania ogółemmężczyźni': 'Zameldowania_mezczyzni',\n",
+    "    'zameldowania ogółemogółem': 'Zameldowania_ogolem',\n",
+    "    'zameldowania z miastkobiety': 'Zameldowania_z_miast_kobiety',\n",
+    "    'zameldowania z miastmężczyźni': 'Zameldowania_z_miast_mezczyzni',\n",
+    "    'zameldowania z miastogółem': 'Zameldowania_z_miast_ogolem',\n",
+    "    'zameldowania ze wsikobiety': 'Zameldowania_ze_wsi_kobiety',\n",
+    "    'zameldowania ze wsimężczyźni': 'Zameldowania_ze_wsi_mezczyzni',\n",
+    "    'zameldowania ze wsiogółem': 'Zameldowania_ze_wsi_ogolem'})\n",
+    "\n",
+    "df_ludn_5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 337,
+   "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>Turystyczne obiekty noclegowe</th>\n",
+       "      <th>Kod</th>\n",
+       "      <th>Rok</th>\n",
+       "      <th>Miejsca_noclegowe_caloroczne</th>\n",
+       "      <th>Miejsca_noclegowe_ogolem</th>\n",
+       "      <th>Obiekty_caloroczne</th>\n",
+       "      <th>Obiekty_ogolem</th>\n",
+       "      <th>Turysci_ogolem</th>\n",
+       "      <th>Turysci_zagraniczni</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0201011</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>265.00</td>\n",
+       "      <td>265.00</td>\n",
+       "      <td>7.00</td>\n",
+       "      <td>7.00</td>\n",
+       "      <td>16427.00</td>\n",
+       "      <td>5173.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0201011</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>267.00</td>\n",
+       "      <td>267.00</td>\n",
+       "      <td>7.00</td>\n",
+       "      <td>7.00</td>\n",
+       "      <td>13134.00</td>\n",
+       "      <td>4486.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0201011</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>295.00</td>\n",
+       "      <td>295.00</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>13159.00</td>\n",
+       "      <td>4856.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0201011</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>293.00</td>\n",
+       "      <td>293.00</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>11914.00</td>\n",
+       "      <td>4701.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0201011</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>292.00</td>\n",
+       "      <td>292.00</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>12398.00</td>\n",
+       "      <td>3919.00</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34697</th>\n",
+       "      <td>3263011</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>9757.00</td>\n",
+       "      <td>11717.00</td>\n",
+       "      <td>76.00</td>\n",
+       "      <td>107.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34698</th>\n",
+       "      <td>3263011</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>9963.00</td>\n",
+       "      <td>11805.00</td>\n",
+       "      <td>74.00</td>\n",
+       "      <td>103.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34699</th>\n",
+       "      <td>3263011</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>9673.00</td>\n",
+       "      <td>11557.00</td>\n",
+       "      <td>68.00</td>\n",
+       "      <td>97.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34700</th>\n",
+       "      <td>3263011</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>8731.00</td>\n",
+       "      <td>10551.00</td>\n",
+       "      <td>66.00</td>\n",
+       "      <td>92.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34701</th>\n",
+       "      <td>3263011</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>8893.00</td>\n",
+       "      <td>10738.00</td>\n",
+       "      <td>68.00</td>\n",
+       "      <td>92.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>34702 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Turystyczne obiekty noclegowe      Kod   Rok  Miejsca_noclegowe_caloroczne   \n",
+       "0                              0201011  2010                        265.00  \\\n",
+       "1                              0201011  2011                        267.00   \n",
+       "2                              0201011  2012                        295.00   \n",
+       "3                              0201011  2013                        293.00   \n",
+       "4                              0201011  2014                        292.00   \n",
+       "...                                ...   ...                           ...   \n",
+       "34697                          3263011  2018                       9757.00   \n",
+       "34698                          3263011  2019                       9963.00   \n",
+       "34699                          3263011  2020                       9673.00   \n",
+       "34700                          3263011  2021                       8731.00   \n",
+       "34701                          3263011  2022                       8893.00   \n",
+       "\n",
+       "Turystyczne obiekty noclegowe  Miejsca_noclegowe_ogolem  Obiekty_caloroczne   \n",
+       "0                                                265.00                7.00  \\\n",
+       "1                                                267.00                7.00   \n",
+       "2                                                295.00                8.00   \n",
+       "3                                                293.00                8.00   \n",
+       "4                                                292.00                8.00   \n",
+       "...                                                 ...                 ...   \n",
+       "34697                                          11717.00               76.00   \n",
+       "34698                                          11805.00               74.00   \n",
+       "34699                                          11557.00               68.00   \n",
+       "34700                                          10551.00               66.00   \n",
+       "34701                                          10738.00               68.00   \n",
+       "\n",
+       "Turystyczne obiekty noclegowe  Obiekty_ogolem  Turysci_ogolem   \n",
+       "0                                        7.00        16427.00  \\\n",
+       "1                                        7.00        13134.00   \n",
+       "2                                        8.00        13159.00   \n",
+       "3                                        8.00        11914.00   \n",
+       "4                                        8.00        12398.00   \n",
+       "...                                       ...             ...   \n",
+       "34697                                  107.00             NaN   \n",
+       "34698                                  103.00             NaN   \n",
+       "34699                                   97.00             NaN   \n",
+       "34700                                   92.00             NaN   \n",
+       "34701                                   92.00             NaN   \n",
+       "\n",
+       "Turystyczne obiekty noclegowe  Turysci_zagraniczni  \n",
+       "0                                          5173.00  \n",
+       "1                                          4486.00  \n",
+       "2                                          4856.00  \n",
+       "3                                          4701.00  \n",
+       "4                                          3919.00  \n",
+       "...                                            ...  \n",
+       "34697                                          NaN  \n",
+       "34698                                          NaN  \n",
+       "34699                                          NaN  \n",
+       "34700                                          NaN  \n",
+       "34701                                          NaN  \n",
+       "\n",
+       "[34702 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 337,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_tury = pd.read_csv(\n",
+    "    'TURY_2017_CREL.csv',\n",
+    "    sep=';',\n",
+    "    converters={'Kod': str},\n",
+    "    decimal=',')\n",
+    "df_tury = df_tury[['Kod', 'Turystyczne obiekty noclegowe', 'Rok', 'Wartosc']]\n",
+    "df_tury = df_tury.dropna()\n",
+    "df_tury = df_tury.pivot_table(index=['Kod', 'Rok'], columns='Turystyczne obiekty noclegowe', values='Wartosc').reset_index()\n",
+    "df_tury = df_tury.rename(columns={\n",
+    "    'miejsca noclegowe całoroczne lipiec': 'Miejsca_noclegowe_caloroczne',\n",
+    "    'miejsca noclegowe ogółem lipiec': 'Miejsca_noclegowe_ogolem',\n",
+    "    'obiekty całoroczne lipiec': 'Obiekty_caloroczne',\n",
+    "    'obiekty ogółem lipiec': 'Obiekty_ogolem',\n",
+    "    'turyści (korzystający) ogółem styczeń-grudzień': 'Turysci_ogolem',\n",
+    "    'turyści zagraniczni (korzystający) - nierezydenci styczeń-grudzień': 'Turysci_zagraniczni'})\n",
+    "\n",
+    "df_tury"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 338,
+   "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>Bezrobotni</th>\n",
+       "      <th>Kod</th>\n",
+       "      <th>Rok</th>\n",
+       "      <th>Bezrobotni_do_25_roku_zycia</th>\n",
+       "      <th>Bezrobotni_do_30_roku_zycia</th>\n",
+       "      <th>Dlugotrwale_bezrobotni</th>\n",
+       "      <th>Bezrobotne_kobiety</th>\n",
+       "      <th>Bezrobotni_mezczyzni</th>\n",
+       "      <th>Bezrobotni_ogolem</th>\n",
+       "      <th>Bezrobotni_powyzej_50_roku_zycia</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0201011</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>284.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>819.50</td>\n",
+       "      <td>900.50</td>\n",
+       "      <td>818.00</td>\n",
+       "      <td>1718.50</td>\n",
+       "      <td>486.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0201011</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>293.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>756.50</td>\n",
+       "      <td>894.50</td>\n",
+       "      <td>888.00</td>\n",
+       "      <td>1782.50</td>\n",
+       "      <td>498.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0201011</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>253.50</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>788.00</td>\n",
+       "      <td>869.50</td>\n",
+       "      <td>874.00</td>\n",
+       "      <td>1743.50</td>\n",
+       "      <td>521.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0201011</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>172.50</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>651.50</td>\n",
+       "      <td>648.50</td>\n",
+       "      <td>667.50</td>\n",
+       "      <td>1316.00</td>\n",
+       "      <td>402.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0201011</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>107.50</td>\n",
+       "      <td>238.00</td>\n",
+       "      <td>434.50</td>\n",
+       "      <td>504.00</td>\n",
+       "      <td>518.50</td>\n",
+       "      <td>1022.50</td>\n",
+       "      <td>359.00</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>48530</th>\n",
+       "      <td>3263011</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>27.50</td>\n",
+       "      <td>66.00</td>\n",
+       "      <td>226.50</td>\n",
+       "      <td>272.50</td>\n",
+       "      <td>221.00</td>\n",
+       "      <td>493.50</td>\n",
+       "      <td>181.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48531</th>\n",
+       "      <td>3263011</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>56.00</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>239.50</td>\n",
+       "      <td>390.00</td>\n",
+       "      <td>361.50</td>\n",
+       "      <td>751.50</td>\n",
+       "      <td>250.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48532</th>\n",
+       "      <td>3263011</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>34.50</td>\n",
+       "      <td>88.00</td>\n",
+       "      <td>260.50</td>\n",
+       "      <td>295.00</td>\n",
+       "      <td>341.00</td>\n",
+       "      <td>636.00</td>\n",
+       "      <td>239.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48533</th>\n",
+       "      <td>3263011</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>31.50</td>\n",
+       "      <td>72.00</td>\n",
+       "      <td>199.00</td>\n",
+       "      <td>211.50</td>\n",
+       "      <td>270.50</td>\n",
+       "      <td>482.00</td>\n",
+       "      <td>182.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48534</th>\n",
+       "      <td>3263011</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>33.50</td>\n",
+       "      <td>81.00</td>\n",
+       "      <td>200.00</td>\n",
+       "      <td>241.00</td>\n",
+       "      <td>287.50</td>\n",
+       "      <td>528.50</td>\n",
+       "      <td>189.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>48535 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Bezrobotni      Kod   Rok  Bezrobotni_do_25_roku_zycia   \n",
+       "0           0201011  2011                       284.00  \\\n",
+       "1           0201011  2012                       293.00   \n",
+       "2           0201011  2013                       253.50   \n",
+       "3           0201011  2014                       172.50   \n",
+       "4           0201011  2015                       107.50   \n",
+       "...             ...   ...                          ...   \n",
+       "48530       3263011  2019                        27.50   \n",
+       "48531       3263011  2020                        56.00   \n",
+       "48532       3263011  2021                        34.50   \n",
+       "48533       3263011  2022                        31.50   \n",
+       "48534       3263011  2023                        33.50   \n",
+       "\n",
+       "Bezrobotni  Bezrobotni_do_30_roku_zycia  Dlugotrwale_bezrobotni   \n",
+       "0                                   NaN                  819.50  \\\n",
+       "1                                   NaN                  756.50   \n",
+       "2                                   NaN                  788.00   \n",
+       "3                                   NaN                  651.50   \n",
+       "4                                238.00                  434.50   \n",
+       "...                                 ...                     ...   \n",
+       "48530                             66.00                  226.50   \n",
+       "48531                            142.00                  239.50   \n",
+       "48532                             88.00                  260.50   \n",
+       "48533                             72.00                  199.00   \n",
+       "48534                             81.00                  200.00   \n",
+       "\n",
+       "Bezrobotni  Bezrobotne_kobiety  Bezrobotni_mezczyzni  Bezrobotni_ogolem   \n",
+       "0                       900.50                818.00            1718.50  \\\n",
+       "1                       894.50                888.00            1782.50   \n",
+       "2                       869.50                874.00            1743.50   \n",
+       "3                       648.50                667.50            1316.00   \n",
+       "4                       504.00                518.50            1022.50   \n",
+       "...                        ...                   ...                ...   \n",
+       "48530                   272.50                221.00             493.50   \n",
+       "48531                   390.00                361.50             751.50   \n",
+       "48532                   295.00                341.00             636.00   \n",
+       "48533                   211.50                270.50             482.00   \n",
+       "48534                   241.00                287.50             528.50   \n",
+       "\n",
+       "Bezrobotni  Bezrobotni_powyzej_50_roku_zycia  \n",
+       "0                                     486.50  \n",
+       "1                                     498.50  \n",
+       "2                                     521.00  \n",
+       "3                                     402.00  \n",
+       "4                                     359.00  \n",
+       "...                                      ...  \n",
+       "48530                                 181.00  \n",
+       "48531                                 250.00  \n",
+       "48532                                 239.50  \n",
+       "48533                                 182.50  \n",
+       "48534                                 189.00  \n",
+       "\n",
+       "[48535 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 338,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_ryne = pd.read_csv(\n",
+    "    'RYNE_3733_CREL.csv',\n",
+    "    sep=';',\n",
+    "    converters={'Kod': str},\n",
+    "    decimal=',')\n",
+    "df_ryne = df_ryne[['Kod', 'Bezrobotni', 'Rok', 'Wartosc']]\n",
+    "df_ryne = df_ryne.dropna()\n",
+    "df_ryne = df_ryne.pivot_table(index=['Kod', 'Rok'], columns='Bezrobotni', values='Wartosc').reset_index()\n",
+    "df_ryne = df_ryne.rename(columns={\n",
+    "    'do 25 roku życia': 'Bezrobotni_do_25_roku_zycia',\n",
+    "    'do 30 roku życia': 'Bezrobotni_do_30_roku_zycia',\n",
+    "    'długotrwale bezrobotni': 'Dlugotrwale_bezrobotni',\n",
+    "    'kobiety': 'Bezrobotne_kobiety',\n",
+    "    'mężczyźni': 'Bezrobotni_mezczyzni',\n",
+    "    'ogółem': 'Bezrobotni_ogolem',\n",
+    "    'powyżej 50 roku życia': 'Bezrobotni_powyzej_50_roku_zycia'})\n",
+    "\n",
+    "df_ryne"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1640,16 +2698,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 339,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9728\n"
+     ]
+    }
+   ],
    "source": [
-    "df_data = df_dofinansowanie_agg.copy()"
+    "df_data = df_dofinansowanie_agg.copy()\n",
+    "print(len(df_data))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 340,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1676,51 +2743,163 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 341,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9728\n"
+     ]
+    }
+   ],
    "source": [
     "df_data = df_data.merge(df_podz, left_on=[df_data['Kod'].str.slice(stop=-1), 'Rok'], right_on=[df_podz['Kod'].str.slice(stop=-1), 'Rok'], how='left', suffixes=(None, '_podz'))\n",
-    "df_data = df_data.drop(['key_0', 'Kod_podz'], axis=1)"
+    "df_data = df_data.drop(['key_0', 'Kod_podz'], axis=1)\n",
+    "print(len(df_data))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 342,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9728\n"
+     ]
+    }
+   ],
    "source": [
     "df_data = df_data.merge(df_wyna, left_on=[df_data['Kod'].str.slice(stop=-3), 'Rok'], right_on=[df_wyna['Kod'].str.slice(stop=-3), 'Rok'], how='left', suffixes=(None, '_wyna'))\n",
-    "df_data = df_data.drop(['key_0', 'Kod_wyna'], axis=1)"
+    "df_data = df_data.drop(['key_0', 'Kod_wyna'], axis=1)\n",
+    "print(len(df_data))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 343,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9728\n",
+      "9728\n"
+     ]
+    }
+   ],
    "source": [
     "df_data = df_data.merge(df_fina_1, left_on=[df_data['Kod'].str.slice(stop=-1), 'Rok'], right_on=[df_fina_1['Kod'].str.slice(stop=-1), 'Rok'], how='left', suffixes=(None, '_fina_1'))\n",
     "df_data = df_data.drop(['key_0', 'Kod_fina_1'], axis=1)\n",
+    "print(len(df_data))\n",
     "\n",
     "df_data = df_data.merge(df_fina_2, left_on=[df_data['Kod'].str.slice(stop=-1), 'Rok'], right_on=[df_fina_2['Kod'].str.slice(stop=-1), 'Rok'], how='left', suffixes=(None, '_fina_2'))\n",
-    "df_data = df_data.drop(['key_0', 'Kod_fina_2'], axis=1)\n"
+    "df_data = df_data.drop(['key_0', 'Kod_fina_2'], axis=1)\n",
+    "print(len(df_data))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 344,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9728\n",
+      "9728\n",
+      "9728\n",
+      "9728\n",
+      "9728\n"
+     ]
+    }
+   ],
    "source": [
-    "df_data = df_data.merge(df_ludn_1, left_on=[df_data['Kod'].str.slice(stop=-1), 'Rok'], right_on=[df_ludn_1['Kod'].str.slice(stop=-1), 'Rok'], how='left', suffixes=(None, '_ludn_1'))\n",
-    "df_data = df_data.drop(['key_0', 'Kod_ludn_1'], axis=1)\n",
+    "df_data = df_data.merge(df_ludn_1, left_on=['Kod', 'Rok'], right_on=['Kod', 'Rok'], how='left', suffixes=(None, '_ludn_1'))\n",
+    "# df_data = df_data.drop(['key_0', 'Kod_ludn_1'], axis=1)\n",
+    "print(len(df_data))\n",
     "\n",
-    "df_data = df_data.merge(df_ludn_2, left_on=[df_data['Kod'].str.slice(stop=-1), 'Rok'], right_on=[df_ludn_2['Kod'].str.slice(stop=-1), 'Rok'], how='left', suffixes=(None, '_ludn_2'))\n",
-    "df_data = df_data.drop(['key_0', 'Kod_ludn_2'], axis=1)\n",
+    "df_data = df_data.merge(df_ludn_2, left_on=['Kod', 'Rok'], right_on=['Kod', 'Rok'], how='left', suffixes=(None, '_ludn_2'))\n",
+    "# df_data = df_data.drop(['key_0', 'Kod_ludn_2'], axis=1)\n",
+    "print(len(df_data))\n",
     "\n",
-    "df_data = df_data.merge(df_ludn_3, left_on=[df_data['Kod'].str.slice(stop=-1), 'Rok'], right_on=[df_ludn_3['Kod'].str.slice(stop=-1), 'Rok'], how='left', suffixes=(None, '_ludn_3'))\n",
-    "df_data = df_data.drop(['key_0', 'Kod_ludn_3'], axis=1)"
+    "df_data = df_data.merge(df_ludn_3, left_on=['Kod', 'Rok'], right_on=['Kod', 'Rok'], how='left', suffixes=(None, '_ludn_3'))\n",
+    "# df_data = df_data.drop(['key_0', 'Kod_ludn_3'], axis=1)\n",
+    "print(len(df_data))\n",
+    "\n",
+    "df_data = df_data.merge(df_ludn_4, left_on=['Kod', 'Rok'], right_on=['Kod', 'Rok'], how='left', suffixes=(None, '_ludn_4'))\n",
+    "# df_data = df_data.drop(['key_0', 'Kod_ludn_4'], axis=1)\n",
+    "print(len(df_data))\n",
+    "\n",
+    "df_data = df_data.merge(df_ludn_5, left_on=['Kod', 'Rok'], right_on=['Kod', 'Rok'], how='left', suffixes=(None, '_ludn_5'))\n",
+    "# df_data = df_data.drop(['key_0', 'Kod_ludn_5'], axis=1)\n",
+    "print(len(df_data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 345,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9728\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_data = df_data.merge(df_tury, left_on=['Kod', 'Rok'], right_on=['Kod', 'Rok'], how='left', suffixes=(None, '_tury'))\n",
+    "# df_data = df_data.drop(['key_0', 'Kod_tury'], axis=1)\n",
+    "print(len(df_data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 346,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9728\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_data = df_data.merge(df_ryne, left_on=['Kod', 'Rok'], right_on=['Kod', 'Rok'], how='left', suffixes=(None, '_ryne'))\n",
+    "# df_data = df_data.drop(['key_0', 'Kod_ryne'], axis=1)\n",
+    "print(len(df_data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 347,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.3548088160423639"
+      ]
+     },
+     "execution_count": 347,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_data['Gestosc_zaludnienia'] = df_data['Ludnosc'] / df_data['Powierzchnia']\n",
+    "\n",
+    "df_data['Gestosc_zaludnienia'].mean()"
    ]
   },
   {
@@ -1732,68 +2911,475 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 348,
    "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>Kod</th>\n",
+       "      <th>Rok</th>\n",
+       "      <th>Suma</th>\n",
+       "      <th>Wojewodztwo_Dolnoslaskie</th>\n",
+       "      <th>Wojewodztwo_Kujawsko_Pomorskie</th>\n",
+       "      <th>Wojewodztwo_Lodzkie</th>\n",
+       "      <th>Wojewodztwo_Lubelskie</th>\n",
+       "      <th>Wojewodztwo_Lubuskie</th>\n",
+       "      <th>Wojewodztwo_Malopolskie</th>\n",
+       "      <th>Wojewodztwo_Mazowieckie</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Turysci_ogolem</th>\n",
+       "      <th>Turysci_zagraniczni</th>\n",
+       "      <th>Bezrobotni_do_25_roku_zycia</th>\n",
+       "      <th>Bezrobotni_do_30_roku_zycia</th>\n",
+       "      <th>Dlugotrwale_bezrobotni</th>\n",
+       "      <th>Bezrobotne_kobiety</th>\n",
+       "      <th>Bezrobotni_mezczyzni</th>\n",
+       "      <th>Bezrobotni_ogolem</th>\n",
+       "      <th>Bezrobotni_powyzej_50_roku_zycia</th>\n",
+       "      <th>Gestosc_zaludnienia</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0201011</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>2482706.36</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12398.00</td>\n",
+       "      <td>3919.00</td>\n",
+       "      <td>172.50</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>651.50</td>\n",
+       "      <td>648.50</td>\n",
+       "      <td>667.50</td>\n",
+       "      <td>1316.00</td>\n",
+       "      <td>402.00</td>\n",
+       "      <td>1.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0201011</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>1016449.03</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.00</td>\n",
+       "      <td>177.50</td>\n",
+       "      <td>282.50</td>\n",
+       "      <td>391.00</td>\n",
+       "      <td>378.00</td>\n",
+       "      <td>769.00</td>\n",
+       "      <td>264.00</td>\n",
+       "      <td>1.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0201011</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>1507540.44</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>63.50</td>\n",
+       "      <td>141.00</td>\n",
+       "      <td>215.00</td>\n",
+       "      <td>314.00</td>\n",
+       "      <td>290.00</td>\n",
+       "      <td>604.00</td>\n",
+       "      <td>197.00</td>\n",
+       "      <td>1.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0201011</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>1125087.75</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>55.50</td>\n",
+       "      <td>133.50</td>\n",
+       "      <td>169.00</td>\n",
+       "      <td>277.50</td>\n",
+       "      <td>217.50</td>\n",
+       "      <td>495.00</td>\n",
+       "      <td>151.50</td>\n",
+       "      <td>1.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0201011</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>9052911.45</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>74.00</td>\n",
+       "      <td>189.00</td>\n",
+       "      <td>239.00</td>\n",
+       "      <td>391.50</td>\n",
+       "      <td>334.50</td>\n",
+       "      <td>726.00</td>\n",
+       "      <td>180.50</td>\n",
+       "      <td>1.57</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>9723</th>\n",
+       "      <td>3263011</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>467956789.99</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>49.50</td>\n",
+       "      <td>109.00</td>\n",
+       "      <td>301.50</td>\n",
+       "      <td>374.00</td>\n",
+       "      <td>329.00</td>\n",
+       "      <td>703.00</td>\n",
+       "      <td>266.00</td>\n",
+       "      <td>0.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9724</th>\n",
+       "      <td>3263011</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>11972196.58</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>40.50</td>\n",
+       "      <td>91.00</td>\n",
+       "      <td>278.00</td>\n",
+       "      <td>328.00</td>\n",
+       "      <td>285.50</td>\n",
+       "      <td>613.50</td>\n",
+       "      <td>227.00</td>\n",
+       "      <td>0.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9725</th>\n",
+       "      <td>3263011</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>2074346.69</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>27.50</td>\n",
+       "      <td>66.00</td>\n",
+       "      <td>226.50</td>\n",
+       "      <td>272.50</td>\n",
+       "      <td>221.00</td>\n",
+       "      <td>493.50</td>\n",
+       "      <td>181.00</td>\n",
+       "      <td>0.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9726</th>\n",
+       "      <td>3263011</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>8314185.11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>56.00</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>239.50</td>\n",
+       "      <td>390.00</td>\n",
+       "      <td>361.50</td>\n",
+       "      <td>751.50</td>\n",
+       "      <td>250.00</td>\n",
+       "      <td>0.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9727</th>\n",
+       "      <td>3263011</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>752819.71</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>34.50</td>\n",
+       "      <td>88.00</td>\n",
+       "      <td>260.50</td>\n",
+       "      <td>295.00</td>\n",
+       "      <td>341.00</td>\n",
+       "      <td>636.00</td>\n",
+       "      <td>239.50</td>\n",
+       "      <td>0.20</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>9728 rows × 104 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "Index(['Kod', 'Rok', 'Suma', 'Wojewodztwo_Dolnoslaskie',\n",
-       "       'Wojewodztwo_Kujawsko_Pomorskie', 'Wojewodztwo_Lodzkie',\n",
-       "       'Wojewodztwo_Lubelskie', 'Wojewodztwo_Lubuskie',\n",
-       "       'Wojewodztwo_Malopolskie', 'Wojewodztwo_Mazowieckie',\n",
-       "       'Wojewodztwo_Opolskie', 'Wojewodztwo_Podkarpackie',\n",
-       "       'Wojewodztwo_Podlaskie', 'Wojewodztwo_Pomorskie', 'Wojewodztwo_Slaskie',\n",
-       "       'Wojewodztwo_Swietokrzyskie', 'Wojewodztwo_Warminsko_Mazurskie',\n",
-       "       'Wojewodztwo_Wielkopolskie', 'Wojewodztwo_Zachodniopomorskie',\n",
-       "       'Powierzchnia', 'Wynagrodzenie_ogolem',\n",
-       "       'Wynagrodzenie_w_relacji_do_sredniej', 'Dochody_podatek_lesny',\n",
-       "       'Dochody_podatek_PCC', 'Dochody_podatek_od_dzialalnosci_gospodarczej',\n",
-       "       'Dochody_podatek_od_nieruchomosci', 'Dochody_podatek_od_spadkow',\n",
-       "       'Dochody_podatek_od_srodkow_transportowych', 'Dochody_podatek_rolny',\n",
-       "       'Dochody_podatek_odrebne_ustawy', 'Dochody_razem', 'Dochody_z_majatku',\n",
-       "       'Dochody_z_najmu_i_dzierzawy', 'Dochody_z_uslug',\n",
-       "       'Dochody_dofinansowanie_inwestycyjne', 'Dochody_dofinansowanie_razem',\n",
-       "       'Udzialy_w_podatkach_dochodowych_od_osob_fizycznych',\n",
-       "       'Udzialy_w_podatkach_dochodowych_od_osob_prywatnych',\n",
-       "       'Udzialy_w_podatkach_dochodowych_razem',\n",
-       "       'Wplywy_z_innych_lokalnych_oplat', 'Wplywy_z_oplaty_eksploatacyjnej',\n",
-       "       'Wplywy_z_oplaty_skarbowej', 'Wplywy_z_oplaty_targowej',\n",
-       "       'Ludnosc_ogolem', 'Ludnosc_w_wieku_poprodukcyjnym',\n",
-       "       'Ludnosc_w_wieku_produkcyjnym', 'Ludnosc_w_wieku_produkcyjnym_mobilnym',\n",
-       "       'Ludnosc_w_wieku_produkcyjnym_niemobilnym',\n",
-       "       'Ludnosc_w_wieku_przedprodukcyjnym', 'Ludnosc_mezczyzni',\n",
-       "       'Ludnosc_mezczyzni_w_wieku_poprodukcyjnym',\n",
-       "       'Ludnosc_mezczyzni_w_wieku_produkcyjnym',\n",
-       "       'Ludnosc_mezczyzni_w_wieku_produkcyjnym_mobilnym',\n",
-       "       'Ludnosc_mezczyzni_w_wieku_produkcyjnym_niemobilnym',\n",
-       "       'Ludnosc_mezczyzni_w_wieku_przedprodukcyjnym', 'Ludnosc_kobiety',\n",
-       "       'Ludnosc_kobiety_w_wieku_poprodukcyjnym',\n",
-       "       'Ludnosc_kobiety_w_wieku_produkcyjnym',\n",
-       "       'Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym',\n",
-       "       'Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym',\n",
-       "       'Ludnosc_kobiety_w_wieku_przedprodukcyjnym'],\n",
-       "      dtype='object')"
+       "          Kod   Rok         Suma  Wojewodztwo_Dolnoslaskie   \n",
+       "0     0201011  2014   2482706.36                         1  \\\n",
+       "1     0201011  2016   1016449.03                         1   \n",
+       "2     0201011  2017   1507540.44                         1   \n",
+       "3     0201011  2018   1125087.75                         1   \n",
+       "4     0201011  2020   9052911.45                         1   \n",
+       "...       ...   ...          ...                       ...   \n",
+       "9723  3263011  2017 467956789.99                         0   \n",
+       "9724  3263011  2018  11972196.58                         0   \n",
+       "9725  3263011  2019   2074346.69                         0   \n",
+       "9726  3263011  2020   8314185.11                         0   \n",
+       "9727  3263011  2021    752819.71                         0   \n",
+       "\n",
+       "      Wojewodztwo_Kujawsko_Pomorskie  Wojewodztwo_Lodzkie   \n",
+       "0                                  0                    0  \\\n",
+       "1                                  0                    0   \n",
+       "2                                  0                    0   \n",
+       "3                                  0                    0   \n",
+       "4                                  0                    0   \n",
+       "...                              ...                  ...   \n",
+       "9723                               0                    0   \n",
+       "9724                               0                    0   \n",
+       "9725                               0                    0   \n",
+       "9726                               0                    0   \n",
+       "9727                               0                    0   \n",
+       "\n",
+       "      Wojewodztwo_Lubelskie  Wojewodztwo_Lubuskie  Wojewodztwo_Malopolskie   \n",
+       "0                         0                     0                        0  \\\n",
+       "1                         0                     0                        0   \n",
+       "2                         0                     0                        0   \n",
+       "3                         0                     0                        0   \n",
+       "4                         0                     0                        0   \n",
+       "...                     ...                   ...                      ...   \n",
+       "9723                      0                     0                        0   \n",
+       "9724                      0                     0                        0   \n",
+       "9725                      0                     0                        0   \n",
+       "9726                      0                     0                        0   \n",
+       "9727                      0                     0                        0   \n",
+       "\n",
+       "      Wojewodztwo_Mazowieckie  ...  Turysci_ogolem  Turysci_zagraniczni   \n",
+       "0                           0  ...        12398.00              3919.00  \\\n",
+       "1                           0  ...             NaN                  NaN   \n",
+       "2                           0  ...             NaN                  NaN   \n",
+       "3                           0  ...             NaN                  NaN   \n",
+       "4                           0  ...             NaN                  NaN   \n",
+       "...                       ...  ...             ...                  ...   \n",
+       "9723                        0  ...             NaN                  NaN   \n",
+       "9724                        0  ...             NaN                  NaN   \n",
+       "9725                        0  ...             NaN                  NaN   \n",
+       "9726                        0  ...             NaN                  NaN   \n",
+       "9727                        0  ...             NaN                  NaN   \n",
+       "\n",
+       "      Bezrobotni_do_25_roku_zycia  Bezrobotni_do_30_roku_zycia   \n",
+       "0                          172.50                          NaN  \\\n",
+       "1                           86.00                       177.50   \n",
+       "2                           63.50                       141.00   \n",
+       "3                           55.50                       133.50   \n",
+       "4                           74.00                       189.00   \n",
+       "...                           ...                          ...   \n",
+       "9723                        49.50                       109.00   \n",
+       "9724                        40.50                        91.00   \n",
+       "9725                        27.50                        66.00   \n",
+       "9726                        56.00                       142.00   \n",
+       "9727                        34.50                        88.00   \n",
+       "\n",
+       "      Dlugotrwale_bezrobotni  Bezrobotne_kobiety  Bezrobotni_mezczyzni   \n",
+       "0                     651.50              648.50                667.50  \\\n",
+       "1                     282.50              391.00                378.00   \n",
+       "2                     215.00              314.00                290.00   \n",
+       "3                     169.00              277.50                217.50   \n",
+       "4                     239.00              391.50                334.50   \n",
+       "...                      ...                 ...                   ...   \n",
+       "9723                  301.50              374.00                329.00   \n",
+       "9724                  278.00              328.00                285.50   \n",
+       "9725                  226.50              272.50                221.00   \n",
+       "9726                  239.50              390.00                361.50   \n",
+       "9727                  260.50              295.00                341.00   \n",
+       "\n",
+       "      Bezrobotni_ogolem  Bezrobotni_powyzej_50_roku_zycia  Gestosc_zaludnienia  \n",
+       "0               1316.00                            402.00                 1.64  \n",
+       "1                769.00                            264.00                 1.63  \n",
+       "2                604.00                            197.00                 1.63  \n",
+       "3                495.00                            151.50                 1.62  \n",
+       "4                726.00                            180.50                 1.57  \n",
+       "...                 ...                               ...                  ...  \n",
+       "9723             703.00                            266.00                 0.21  \n",
+       "9724             613.50                            227.00                 0.20  \n",
+       "9725             493.50                            181.00                 0.20  \n",
+       "9726             751.50                            250.00                 0.20  \n",
+       "9727             636.00                            239.50                 0.20  \n",
+       "\n",
+       "[9728 rows x 104 columns]"
       ]
      },
-     "execution_count": 116,
+     "execution_count": 348,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_data.columns"
+    "df_data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 349,
    "metadata": {},
    "outputs": [],
    "source": [
     "# df_data[df_data.isna().any(axis=1)]  # ['Rok'].drop_duplicates().reset_index(drop=True)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 350,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = df_data.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 351,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Wymeldowania_za_granice_kobiety      6196\n",
+       "Wymeldowania_za_granice_mezczyzni    6196\n",
+       "Wymeldowania_za_granice_ogolem       6196\n",
+       "Miejsca_noclegowe_caloroczne         2263\n",
+       "Miejsca_noclegowe_ogolem             2263\n",
+       "Obiekty_caloroczne                   2263\n",
+       "Obiekty_ogolem                       2263\n",
+       "Turysci_ogolem                       9177\n",
+       "Turysci_zagraniczni                  9177\n",
+       "Bezrobotni_do_30_roku_zycia           863\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 351,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[s > 330]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1803,24 +3389,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 352,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean Squared Error: 294627671837056.8\n"
+      "9728\n",
+      "9395\n",
+      "Mean Squared Error: 2091634764504252.2\n"
      ]
     }
    ],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.tree import DecisionTreeRegressor, plot_tree\n",
+    "from sklearn.tree import DecisionTreeRegressor, plot_tree, export_text\n",
     "from sklearn.metrics import mean_squared_error\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "df_data.dropna(inplace=True)\n",
+    "df_data[[\n",
+    "    'Miejsca_noclegowe_caloroczne',\n",
+    "    'Miejsca_noclegowe_ogolem',\n",
+    "    'Obiekty_caloroczne',\n",
+    "    'Obiekty_ogolem',\n",
+    "    'Turysci_ogolem',\n",
+    "    'Turysci_zagraniczni']] = df_data[[\n",
+    "        'Miejsca_noclegowe_caloroczne',\n",
+    "        'Miejsca_noclegowe_ogolem',\n",
+    "        'Obiekty_caloroczne',\n",
+    "        'Obiekty_ogolem',\n",
+    "        'Turysci_ogolem',\n",
+    "        'Turysci_zagraniczni']].fillna(0)\n",
     "\n",
     "feature_names = [\n",
     "    'Powierzchnia',                                        # 1\n",
@@ -1880,29 +3480,128 @@
     "    'Wojewodztwo_Swietokrzyskie',                          # 55\n",
     "    'Wojewodztwo_Warminsko_Mazurskie',                     # 56\n",
     "    'Wojewodztwo_Wielkopolskie',                           # 57\n",
-    "    'Wojewodztwo_Zachodniopomorskie']                      # 58\n",
+    "    'Wojewodztwo_Zachodniopomorskie',                      # 58\n",
+    "    'Gestosc_zaludnienia',                                 # 59\n",
+    "    'Ludnosc_na_1_km2',                                    # 60\n",
+    "    'Ludnosc',                                             # 61\n",
+    "    'Ludnosc_kobiety',                                     # 62\n",
+    "    'Ludnosc_mezczyzni',                                   # 63\n",
+    "    'Wskaznik_urbanizacji',                                # 64\n",
+    "    'Zmiana_liczby_ludnosci',                              # 65\n",
+    "    'Saldo_migracji_na_1000_ludnosci',                     # 66\n",
+    "    'Saldo_migracji',                                      # 67\n",
+    "    'Wymeldowania_do_miast_kobiety',                       # 68\n",
+    "    'Wymeldowania_do_miast_mezczyzni',                     # 69\n",
+    "    'Wymeldowania_do_miast_ogolem',                        # 70\n",
+    "    'Wymeldowania_na_wies_kobiety',                        # 71\n",
+    "    'Wymeldowania_na_wies_mezczyzni',                      # 72\n",
+    "    'Wymeldowania_na_wies_ogolem',                         # 73\n",
+    "    'Wymeldowania_kobiety',                                # 74\n",
+    "    'Wymeldowania_mezczyzni',                              # 75\n",
+    "    'Wymeldowania_ogolem',                                 # 76\n",
+    "    'Zameldowania_kobiety',                                # 77\n",
+    "    'Zameldowania_mezczyzni',                              # 78\n",
+    "    'Zameldowania_ogolem',                                 # 79\n",
+    "    'Zameldowania_z_miast_kobiety',                        # 80\n",
+    "    'Zameldowania_z_miast_mezczyzni',                      # 81\n",
+    "    'Zameldowania_z_miast_ogolem',                         # 82\n",
+    "    'Zameldowania_ze_wsi_kobiety',                         # 83\n",
+    "    'Zameldowania_ze_wsi_mezczyzni',                       # 84\n",
+    "    'Zameldowania_ze_wsi_ogolem',                          # 85\n",
+    "    'Miejsca_noclegowe_caloroczne',                        # 86\n",
+    "    'Miejsca_noclegowe_ogolem',                            # 87\n",
+    "    'Obiekty_caloroczne',                                  # 88\n",
+    "    'Obiekty_ogolem',                                      # 89\n",
+    "    'Turysci_ogolem',                                      # 90\n",
+    "    'Turysci_zagraniczni',                                 # 91\n",
+    "    'Bezrobotni_do_25_roku_zycia',                         # 92\n",
+    "    'Dlugotrwale_bezrobotni',                              # 93\n",
+    "    'Bezrobotne_kobiety',                                  # 94\n",
+    "    'Bezrobotni_mezczyzni',                                # 95\n",
+    "    'Bezrobotni_ogolem',                                   # 96\n",
+    "    'Bezrobotni_powyzej_50_roku_zycia']                    # 97\n",
+    "\n",
+    "df_data.drop(columns=[\n",
+    "    'Wymeldowania_za_granice_kobiety',\n",
+    "    'Wymeldowania_za_granice_mezczyzni',\n",
+    "    'Wymeldowania_za_granice_ogolem',\n",
+    "    'Bezrobotni_do_30_roku_zycia'], inplace=True, errors='ignore')\n",
+    "\n",
+    "print(len(df_data))\n",
+    "df_data.dropna(inplace=True)\n",
+    "df_data = df_data[df_data['Suma'] > 0]\n",
+    "print(len(df_data))\n",
     "\n",
     "X = df_data[feature_names]\n",
     "y = df_data['Suma']\n",
     "\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n",
+    "color_column = df_data['Gestosc_zaludnienia']\n",
+    "color_column = (df_data['Gestosc_zaludnienia'] > 1.5).astype(int)\n",
+    "\n",
+    "X_train, X_test, y_train, y_test, color_column_train, color_column_test = train_test_split(X, y, color_column, test_size=0.2, random_state=1)\n",
     "\n",
     "model = DecisionTreeRegressor(random_state=1)\n",
     "model.fit(X_train, y_train)\n",
     "\n",
     "y_pred = model.predict(X_test)\n",
     "mse = mean_squared_error(y_test, y_pred)\n",
-    "print(\"Mean Squared Error:\", mse)"
+    "print('Mean Squared Error:', mse)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 353,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bezrobotni_powyzej_50_roku_zycia                     0.69\n",
+      "Bezrobotni_mezczyzni                                 0.68\n",
+      "Bezrobotni_ogolem                                    0.67\n",
+      "Bezrobotne_kobiety                                   0.66\n",
+      "Dochody_z_majatku                                    0.65\n",
+      "Dlugotrwale_bezrobotni                               0.65\n",
+      "Turysci_ogolem                                       0.62\n",
+      "Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym     0.62\n",
+      "Ludnosc_w_wieku_produkcyjnym_niemobilnym             0.62\n",
+      "Ludnosc_mezczyzni_w_wieku_produkcyjnym_niemobilnym   0.62\n",
+      "Ludnosc_kobiety_w_wieku_produkcyjnym                 0.62\n",
+      "Ludnosc_w_wieku_produkcyjnym                         0.62\n",
+      "Ludnosc_mezczyzni_w_wieku_produkcyjnym               0.62\n",
+      "Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym        0.62\n",
+      "Dochody_z_uslug                                      0.62\n",
+      "Ludnosc_w_wieku_produkcyjnym_mobilnym                0.62\n",
+      "Zameldowania_z_miast_kobiety                         0.61\n",
+      "Ludnosc_kobiety_ludn_4                               0.61\n",
+      "Ludnosc_kobiety                                      0.61\n",
+      "Ludnosc_mezczyzni_w_wieku_produkcyjnym_mobilnym      0.61\n",
+      "Ludnosc_ogolem                                       0.61\n",
+      "Ludnosc                                              0.61\n",
+      "Ludnosc_kobiety_w_wieku_poprodukcyjnym               0.61\n",
+      "Ludnosc_mezczyzni_ludn_4                             0.61\n",
+      "Ludnosc_mezczyzni                                    0.61\n",
+      "Zameldowania_z_miast_ogolem                          0.61\n",
+      "Ludnosc_w_wieku_poprodukcyjnym                       0.60\n",
+      "Ludnosc_kobiety_w_wieku_przedprodukcyjnym            0.60\n",
+      "Name: Suma, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "correlation_matrix = df_data.corr()\n",
+    "print(correlation_matrix['Suma'].sort_values(ascending=False)[1:29])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 354,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1912,88 +3611,650 @@
     }
    ],
    "source": [
-    "plt.scatter(y_test, y_pred, alpha=0.5)\n",
+    "plt.scatter(y_test, y_pred, alpha=0.5, c=color_column_test, cmap='viridis')\n",
     "plt.xlabel('Actual')\n",
     "plt.ylabel('Predicted')\n",
     "plt.title('Actual vs Predicted')\n",
+    "\n",
+    "plt.xlim(0, max(max(y_test), max(y_pred)))\n",
+    "plt.ylim(0, max(max(y_test), max(y_pred)))\n",
+    "\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 355,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(y_test, y_pred, alpha=0.5, c=color_column_test, cmap='viridis')\n",
+    "plt.xlabel('Actual')\n",
+    "plt.ylabel('Predicted')\n",
+    "plt.title('Actual vs Predicted')\n",
+    "\n",
+    "plt.xlim(0, 3*10**7)\n",
+    "plt.ylim(0, 3*10**7)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 356,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.39277 — Dochody_z_majatku\n",
-      "0.09099 — Ludnosc_mezczyzni_w_wieku_produkcyjnym_mobilnym\n",
-      "0.06227 — Wynagrodzenie_w_relacji_do_sredniej\n",
-      "0.05967 — Ludnosc_w_wieku_produkcyjnym_niemobilnym\n",
-      "0.04757 — Wynagrodzenie_ogolem\n",
-      "0.02963 — Dochody_podatek_od_nieruchomosci\n",
-      "0.02897 — Dochody_podatek_rolny\n",
-      "0.02743 — Udzialy_w_podatkach_dochodowych_razem\n",
-      "0.02328 — Dochody_z_uslug\n",
-      "0.02201 — Wplywy_z_oplaty_skarbowej\n",
-      "0.02172 — Wplywy_z_oplaty_targowej\n",
-      "0.02024 — Dochody_z_najmu_i_dzierzawy\n",
-      "0.02010 — Udzialy_w_podatkach_dochodowych_od_osob_fizycznych\n",
-      "0.01977 — Wplywy_z_oplaty_eksploatacyjnej\n",
-      "0.01961 — Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym\n",
-      "0.01413 — Ludnosc_mezczyzni_w_wieku_przedprodukcyjnym\n",
-      "0.01349 — Dochody_podatek_od_dzialalnosci_gospodarczej\n",
-      "0.01158 — Wojewodztwo_Lubelskie\n",
-      "0.00762 — Dochody_podatek_od_srodkow_transportowych\n",
-      "0.00673 — Dochody_dofinansowanie_inwestycyjne\n",
-      "0.00610 — Wplywy_z_innych_lokalnych_oplat\n",
-      "0.00588 — Dochody_podatek_lesny\n",
-      "0.00567 — Dochody_podatek_PCC\n",
-      "0.00555 — Udzialy_w_podatkach_dochodowych_od_osob_prywatnych\n",
-      "0.00449 — Dochody_podatek_od_spadkow\n",
-      "0.00406 — Dochody_razem\n",
-      "0.00401 — Dochody_dofinansowanie_razem\n",
-      "0.00381 — Ludnosc_mezczyzni_w_wieku_poprodukcyjnym\n",
-      "0.00297 — Powierzchnia\n",
-      "0.00270 — Wojewodztwo_Dolnoslaskie\n",
-      "0.00267 — Dochody_podatek_odrebne_ustawy\n",
-      "0.00177 — Wojewodztwo_Swietokrzyskie\n",
-      "0.00123 — Ludnosc_kobiety_w_wieku_przedprodukcyjnym\n",
-      "0.00115 — Ludnosc_mezczyzni_w_wieku_produkcyjnym_niemobilnym\n",
-      "0.00101 — Ludnosc_mezczyzni_w_wieku_produkcyjnym\n",
-      "0.00096 — Wojewodztwo_Podkarpackie\n",
-      "0.00092 — Wojewodztwo_Wielkopolskie\n",
-      "0.00091 — Ludnosc_w_wieku_przedprodukcyjnym\n",
-      "0.00089 — Wojewodztwo_Slaskie\n",
-      "0.00075 — Wojewodztwo_Opolskie\n",
-      "0.00072 — Wojewodztwo_Warminsko_Mazurskie\n",
-      "0.00061 — Wojewodztwo_Podlaskie\n",
-      "0.00051 — Wojewodztwo_Malopolskie\n",
-      "0.00030 — Ludnosc_w_wieku_produkcyjnym_mobilnym\n",
+      "|--- Bezrobotni_powyzej_50_roku_zycia <= 13525.75\n",
+      "|   |--- Zameldowania_mezczyzni <= 1265.00\n",
+      "|   |   |--- Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym <= 32794.00\n",
+      "|   |   |   |--- Turysci_ogolem <= 6009.50\n",
+      "|   |   |   |   |--- Ludnosc_kobiety_w_wieku_poprodukcyjnym <= 6520.50\n",
+      "|   |   |   |   |   |--- Turysci_ogolem <= 3.00\n",
+      "|   |   |   |   |   |   |--- Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym <= 2868.00\n",
+      "|   |   |   |   |   |   |   |--- Dochody_podatek_lesny <= 5.00\n",
+      "|   |   |   |   |   |   |   |   |--- Dochody_podatek_rolny <= 2018743.00\n",
+      "|   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_ogolem <= 3946.51\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Saldo_migracji_na_1000_ludnosci <= -0.95\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Saldo_migracji_na_1000_ludnosci >  -0.95\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
+      "|   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_ogolem >  3946.51\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Ludnosc_na_1_km2 <= 2311.40\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Ludnosc_na_1_km2 >  2311.40\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [4604400.65]\n",
+      "|   |   |   |   |   |   |   |   |--- Dochody_podatek_rolny >  2018743.00\n",
+      "|   |   |   |   |   |   |   |   |   |--- value: [481929545.58]\n",
+      "|   |   |   |   |   |   |   |--- Dochody_podatek_lesny >  5.00\n",
+      "|   |   |   |   |   |   |   |   |--- Wynagrodzenie_w_relacji_do_sredniej <= 167.35\n",
+      "|   |   |   |   |   |   |   |   |   |--- Wplywy_z_oplaty_targowej <= 1425012.25\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_ogolem <= 3648.14\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 40\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_ogolem >  3648.14\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 52\n",
+      "|   |   |   |   |   |   |   |   |   |--- Wplywy_z_oplaty_targowej >  1425012.25\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Wojewodztwo_Lodzkie <= 0.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [83037656.42]\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Wojewodztwo_Lodzkie >  0.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [6196535.59]\n",
+      "|   |   |   |   |   |   |   |   |--- Wynagrodzenie_w_relacji_do_sredniej >  167.35\n",
+      "|   |   |   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_razem <= 6796306.25\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [89747833.80]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_razem >  6796306.25\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [18873808.95]\n",
+      "|   |   |   |   |   |   |--- Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym >  2868.00\n",
+      "|   |   |   |   |   |   |   |--- Dochody_z_majatku <= 29869035.00\n",
+      "|   |   |   |   |   |   |   |   |--- Dochody_podatek_rolny <= 3991218.50\n",
+      "|   |   |   |   |   |   |   |   |   |--- Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym <= 2871.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Zmiana_liczby_ludnosci <= 4.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Zmiana_liczby_ludnosci >  4.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
+      "|   |   |   |   |   |   |   |   |   |--- Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym >  2871.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Gestosc_zaludnienia <= 0.23\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 36\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Gestosc_zaludnienia >  0.23\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 39\n",
+      "|   |   |   |   |   |   |   |   |--- Dochody_podatek_rolny >  3991218.50\n",
+      "|   |   |   |   |   |   |   |   |   |--- Ludnosc_mezczyzni_w_wieku_przedprodukcyjnym <= 1809.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Ludnosc_kobiety_w_wieku_produkcyjnym <= 5795.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [4470689.32]\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Ludnosc_kobiety_w_wieku_produkcyjnym >  5795.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [13232.57]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Ludnosc_mezczyzni_w_wieku_przedprodukcyjnym >  1809.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [164709707.63]\n",
+      "|   |   |   |   |   |   |   |--- Dochody_z_majatku >  29869035.00\n",
+      "|   |   |   |   |   |   |   |   |--- Wojewodztwo_Podkarpackie <= 0.50\n",
+      "|   |   |   |   |   |   |   |   |   |--- Dochody_podatek_PCC <= 4539613.88\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Ludnosc_kobiety <= 13425.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [11622668.77]\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Ludnosc_kobiety >  13425.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
+      "|   |   |   |   |   |   |   |   |   |--- Dochody_podatek_PCC >  4539613.88\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [29177611.36]\n",
+      "|   |   |   |   |   |   |   |   |--- Wojewodztwo_Podkarpackie >  0.50\n",
+      "|   |   |   |   |   |   |   |   |   |--- Wymeldowania_do_miast_mezczyzni <= 57.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [119972268.58]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Wymeldowania_do_miast_mezczyzni >  57.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [93902447.15]\n",
+      "|   |   |   |   |   |--- Turysci_ogolem >  3.00\n",
+      "|   |   |   |   |   |   |--- Wymeldowania_na_wies_ogolem <= 13.50\n",
+      "|   |   |   |   |   |   |   |--- Ludnosc_na_1_km2 <= 73.45\n",
+      "|   |   |   |   |   |   |   |   |--- Turysci_ogolem <= 60.00\n",
+      "|   |   |   |   |   |   |   |   |   |--- value: [160629826.38]\n",
+      "|   |   |   |   |   |   |   |   |--- Turysci_ogolem >  60.00\n",
+      "|   |   |   |   |   |   |   |   |   |--- Ludnosc_kobiety_w_wieku_przedprodukcyjnym <= 425.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [171383125.19]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Ludnosc_kobiety_w_wieku_przedprodukcyjnym >  425.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [171383125.19]\n",
+      "|   |   |   |   |   |   |   |--- Ludnosc_na_1_km2 >  73.45\n",
+      "|   |   |   |   |   |   |   |   |--- value: [386451.19]\n",
+      "|   |   |   |   |   |   |--- Wymeldowania_na_wies_ogolem >  13.50\n",
+      "|   |   |   |   |   |   |   |--- Wynagrodzenie_ogolem <= 2697.03\n",
+      "|   |   |   |   |   |   |   |   |--- value: [118036329.69]\n",
+      "|   |   |   |   |   |   |   |--- Wynagrodzenie_ogolem >  2697.03\n",
+      "|   |   |   |   |   |   |   |   |--- Wymeldowania_na_wies_ogolem <= 19.50\n",
+      "|   |   |   |   |   |   |   |   |   |--- Dochody_podatek_od_srodkow_transportowych <= 93764.41\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_podatek_od_spadkow <= 25398.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_podatek_od_spadkow >  25398.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [29271376.16]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Dochody_podatek_od_srodkow_transportowych >  93764.41\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_dofinansowanie_inwestycyjne <= 29700.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_dofinansowanie_inwestycyjne >  29700.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
+      "|   |   |   |   |   |   |   |   |--- Wymeldowania_na_wies_ogolem >  19.50\n",
+      "|   |   |   |   |   |   |   |   |   |--- Dlugotrwale_bezrobotni <= 1395.25\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Wojewodztwo_Opolskie <= 0.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 20\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Wojewodztwo_Opolskie >  0.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
+      "|   |   |   |   |   |   |   |   |   |--- Dlugotrwale_bezrobotni >  1395.25\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Ludnosc_kobiety_w_wieku_przedprodukcyjnym <= 3088.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [150089223.44]\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Ludnosc_kobiety_w_wieku_przedprodukcyjnym >  3088.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [2311554.06]\n",
+      "|   |   |   |   |--- Ludnosc_kobiety_w_wieku_poprodukcyjnym >  6520.50\n",
+      "|   |   |   |   |   |--- Ludnosc_mezczyzni_w_wieku_przedprodukcyjnym <= 2975.00\n",
+      "|   |   |   |   |   |   |--- Ludnosc_w_wieku_przedprodukcyjnym <= 5223.50\n",
+      "|   |   |   |   |   |   |   |--- Zmiana_liczby_ludnosci <= -10.75\n",
+      "|   |   |   |   |   |   |   |   |--- Wymeldowania_na_wies_kobiety <= 46.50\n",
+      "|   |   |   |   |   |   |   |   |   |--- value: [8812724.45]\n",
+      "|   |   |   |   |   |   |   |   |--- Wymeldowania_na_wies_kobiety >  46.50\n",
+      "|   |   |   |   |   |   |   |   |   |--- Dochody_razem <= 235494656.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [1078443.09]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Dochody_razem >  235494656.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [4704073.28]\n",
+      "|   |   |   |   |   |   |   |--- Zmiana_liczby_ludnosci >  -10.75\n",
+      "|   |   |   |   |   |   |   |   |--- Wynagrodzenie_w_relacji_do_sredniej <= 118.00\n",
+      "|   |   |   |   |   |   |   |   |   |--- value: [58310839.60]\n",
+      "|   |   |   |   |   |   |   |   |--- Wynagrodzenie_w_relacji_do_sredniej >  118.00\n",
+      "|   |   |   |   |   |   |   |   |   |--- value: [33777678.29]\n",
+      "|   |   |   |   |   |   |--- Ludnosc_w_wieku_przedprodukcyjnym >  5223.50\n",
+      "|   |   |   |   |   |   |   |--- value: [467956789.99]\n",
+      "|   |   |   |   |   |--- Ludnosc_mezczyzni_w_wieku_przedprodukcyjnym >  2975.00\n",
+      "|   |   |   |   |   |   |--- Dochody_dofinansowanie_inwestycyjne <= 45246016.00\n",
+      "|   |   |   |   |   |   |   |--- Ludnosc_mezczyzni_w_wieku_produkcyjnym_mobilnym <= 21738.50\n",
+      "|   |   |   |   |   |   |   |   |--- Wynagrodzenie_ogolem <= 3167.90\n",
+      "|   |   |   |   |   |   |   |   |   |--- Powierzchnia <= 24.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [135056683.01]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Powierzchnia >  24.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [75231116.98]\n",
+      "|   |   |   |   |   |   |   |   |--- Wynagrodzenie_ogolem >  3167.90\n",
+      "|   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_ogolem <= 4982.40\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Wplywy_z_innych_lokalnych_oplat <= 260.68\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Wplywy_z_innych_lokalnych_oplat >  260.68\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 29\n",
+      "|   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_ogolem >  4982.40\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Zameldowania_z_miast_kobiety <= 39.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Zameldowania_z_miast_kobiety >  39.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17\n",
+      "|   |   |   |   |   |   |   |--- Ludnosc_mezczyzni_w_wieku_produkcyjnym_mobilnym >  21738.50\n",
+      "|   |   |   |   |   |   |   |   |--- Wplywy_z_oplaty_eksploatacyjnej <= 861942.31\n",
+      "|   |   |   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_od_osob_fizycznych <= 355838512.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_od_osob_fizycznych <= 131902492.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_od_osob_fizycznych >  131902492.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14\n",
+      "|   |   |   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_od_osob_fizycznych >  355838512.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [132180510.14]\n",
+      "|   |   |   |   |   |   |   |   |--- Wplywy_z_oplaty_eksploatacyjnej >  861942.31\n",
+      "|   |   |   |   |   |   |   |   |   |--- value: [147872438.55]\n",
+      "|   |   |   |   |   |   |--- Dochody_dofinansowanie_inwestycyjne >  45246016.00\n",
+      "|   |   |   |   |   |   |   |--- value: [183812381.68]\n",
+      "|   |   |   |--- Turysci_ogolem >  6009.50\n",
+      "|   |   |   |   |--- Turysci_ogolem <= 6055.00\n",
+      "|   |   |   |   |   |--- Ludnosc <= 15.54\n",
+      "|   |   |   |   |   |   |--- value: [350203527.33]\n",
+      "|   |   |   |   |   |--- Ludnosc >  15.54\n",
+      "|   |   |   |   |   |   |--- value: [374147379.96]\n",
+      "|   |   |   |   |--- Turysci_ogolem >  6055.00\n",
+      "|   |   |   |   |   |--- Zmiana_liczby_ludnosci <= 23.40\n",
+      "|   |   |   |   |   |   |--- Wplywy_z_oplaty_eksploatacyjnej <= 157595.28\n",
+      "|   |   |   |   |   |   |   |--- Ludnosc_mezczyzni <= 51898.50\n",
+      "|   |   |   |   |   |   |   |   |--- Dochody_z_majatku <= 84208.02\n",
+      "|   |   |   |   |   |   |   |   |   |--- value: [122764445.52]\n",
+      "|   |   |   |   |   |   |   |   |--- Dochody_z_majatku >  84208.02\n",
+      "|   |   |   |   |   |   |   |   |   |--- Powierzchnia <= 14.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Obiekty_ogolem <= 7.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Obiekty_ogolem >  7.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [286243234.59]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Powierzchnia >  14.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Wojewodztwo_Lubuskie <= 0.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 20\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Wojewodztwo_Lubuskie >  0.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
+      "|   |   |   |   |   |   |   |--- Ludnosc_mezczyzni >  51898.50\n",
+      "|   |   |   |   |   |   |   |   |--- Dochody_podatek_od_dzialalnosci_gospodarczej <= 197814.20\n",
+      "|   |   |   |   |   |   |   |   |   |--- Miejsca_noclegowe_ogolem <= 596.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [6639219.07]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Miejsca_noclegowe_ogolem >  596.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [58823370.96]\n",
+      "|   |   |   |   |   |   |   |   |--- Dochody_podatek_od_dzialalnosci_gospodarczej >  197814.20\n",
+      "|   |   |   |   |   |   |   |   |   |--- Bezrobotni_mezczyzni <= 1910.25\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Ludnosc_w_wieku_produkcyjnym_niemobilnym <= 33678.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Ludnosc_w_wieku_produkcyjnym_niemobilnym >  33678.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [118788666.24]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Bezrobotni_mezczyzni >  1910.25\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Zameldowania_z_miast_mezczyzni <= 550.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Zameldowania_z_miast_mezczyzni >  550.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [91773045.43]\n",
+      "|   |   |   |   |   |   |--- Wplywy_z_oplaty_eksploatacyjnej >  157595.28\n",
+      "|   |   |   |   |   |   |   |--- Dochody_dofinansowanie_inwestycyjne <= 3634991.00\n",
+      "|   |   |   |   |   |   |   |   |--- Dlugotrwale_bezrobotni <= 1539.50\n",
+      "|   |   |   |   |   |   |   |   |   |--- Dochody_podatek_od_spadkow <= 234245.18\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Wplywy_z_oplaty_targowej <= 1411.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [87692501.12]\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Wplywy_z_oplaty_targowej >  1411.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
+      "|   |   |   |   |   |   |   |   |   |--- Dochody_podatek_od_spadkow >  234245.18\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_od_osob_fizycznych <= 27334711.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [158370335.96]\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_od_osob_fizycznych >  27334711.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
+      "|   |   |   |   |   |   |   |   |--- Dlugotrwale_bezrobotni >  1539.50\n",
+      "|   |   |   |   |   |   |   |   |   |--- Dochody_podatek_lesny <= 23532.44\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [292699374.69]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Dochody_podatek_lesny >  23532.44\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [279411324.25]\n",
+      "|   |   |   |   |   |   |   |--- Dochody_dofinansowanie_inwestycyjne >  3634991.00\n",
+      "|   |   |   |   |   |   |   |   |--- value: [675624217.52]\n",
+      "|   |   |   |   |   |--- Zmiana_liczby_ludnosci >  23.40\n",
+      "|   |   |   |   |   |   |--- Zameldowania_mezczyzni <= 176.00\n",
+      "|   |   |   |   |   |   |   |--- value: [819103731.56]\n",
+      "|   |   |   |   |   |   |--- Zameldowania_mezczyzni >  176.00\n",
+      "|   |   |   |   |   |   |   |--- Ludnosc_kobiety_w_wieku_produkcyjnym <= 6849.00\n",
+      "|   |   |   |   |   |   |   |   |--- Ludnosc_w_wieku_przedprodukcyjnym <= 3168.50\n",
+      "|   |   |   |   |   |   |   |   |   |--- value: [14511954.52]\n",
+      "|   |   |   |   |   |   |   |   |--- Ludnosc_w_wieku_przedprodukcyjnym >  3168.50\n",
+      "|   |   |   |   |   |   |   |   |   |--- value: [9361480.07]\n",
+      "|   |   |   |   |   |   |   |--- Ludnosc_kobiety_w_wieku_produkcyjnym >  6849.00\n",
+      "|   |   |   |   |   |   |   |   |--- value: [123148.63]\n",
+      "|   |   |--- Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym >  32794.00\n",
+      "|   |   |   |--- Ludnosc_mezczyzni_w_wieku_poprodukcyjnym <= 11830.50\n",
+      "|   |   |   |   |--- Wymeldowania_do_miast_ogolem <= 657.00\n",
+      "|   |   |   |   |   |--- Ludnosc_na_1_km2 <= 1778.05\n",
+      "|   |   |   |   |   |   |--- value: [259959198.51]\n",
+      "|   |   |   |   |   |--- Ludnosc_na_1_km2 >  1778.05\n",
+      "|   |   |   |   |   |   |--- value: [123909283.41]\n",
+      "|   |   |   |   |--- Wymeldowania_do_miast_ogolem >  657.00\n",
+      "|   |   |   |   |   |--- Ludnosc_mezczyzni_w_wieku_produkcyjnym <= 56920.00\n",
+      "|   |   |   |   |   |   |--- value: [512361631.15]\n",
+      "|   |   |   |   |   |--- Ludnosc_mezczyzni_w_wieku_produkcyjnym >  56920.00\n",
+      "|   |   |   |   |   |   |--- value: [398540337.28]\n",
+      "|   |   |   |--- Ludnosc_mezczyzni_w_wieku_poprodukcyjnym >  11830.50\n",
+      "|   |   |   |   |--- Wplywy_z_oplaty_targowej <= 2084361.06\n",
+      "|   |   |   |   |   |--- Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym <= 33308.00\n",
+      "|   |   |   |   |   |   |--- Zameldowania_kobiety <= 921.00\n",
+      "|   |   |   |   |   |   |   |--- value: [360347448.51]\n",
+      "|   |   |   |   |   |   |--- Zameldowania_kobiety >  921.00\n",
+      "|   |   |   |   |   |   |   |--- value: [292058544.30]\n",
+      "|   |   |   |   |   |--- Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym >  33308.00\n",
+      "|   |   |   |   |   |   |--- Turysci_zagraniczni <= 5907.00\n",
+      "|   |   |   |   |   |   |   |--- Dochody_podatek_lesny <= 47997.95\n",
+      "|   |   |   |   |   |   |   |   |--- Dochody_podatek_lesny <= 12500.82\n",
+      "|   |   |   |   |   |   |   |   |   |--- value: [165098317.48]\n",
+      "|   |   |   |   |   |   |   |   |--- Dochody_podatek_lesny >  12500.82\n",
+      "|   |   |   |   |   |   |   |   |   |--- Dochody_podatek_rolny <= 116828.12\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [117349953.24]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Dochody_podatek_rolny >  116828.12\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_od_osob_prywatnych <= 6458233.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_od_osob_prywatnych >  6458233.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
+      "|   |   |   |   |   |   |   |--- Dochody_podatek_lesny >  47997.95\n",
+      "|   |   |   |   |   |   |   |   |--- Miejsca_noclegowe_ogolem <= 3446.00\n",
+      "|   |   |   |   |   |   |   |   |   |--- Wymeldowania_do_miast_kobiety <= 565.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_dofinansowanie_razem <= 4328410.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_dofinansowanie_razem >  4328410.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
+      "|   |   |   |   |   |   |   |   |   |--- Wymeldowania_do_miast_kobiety >  565.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Bezrobotne_kobiety <= 2300.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [120567151.23]\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Bezrobotne_kobiety >  2300.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
+      "|   |   |   |   |   |   |   |   |--- Miejsca_noclegowe_ogolem >  3446.00\n",
+      "|   |   |   |   |   |   |   |   |   |--- Bezrobotni_do_25_roku_zycia <= 275.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_razem <= 706544576.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [34560334.11]\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_razem >  706544576.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
+      "|   |   |   |   |   |   |   |   |   |--- Bezrobotni_do_25_roku_zycia >  275.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_w_relacji_do_sredniej <= 124.85\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_w_relacji_do_sredniej >  124.85\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [3978361.07]\n",
+      "|   |   |   |   |   |   |--- Turysci_zagraniczni >  5907.00\n",
+      "|   |   |   |   |   |   |   |--- Dochody_dofinansowanie_razem <= 1652968.06\n",
+      "|   |   |   |   |   |   |   |   |--- Ludnosc_w_wieku_produkcyjnym_niemobilnym <= 52302.50\n",
+      "|   |   |   |   |   |   |   |   |   |--- value: [61446000.66]\n",
+      "|   |   |   |   |   |   |   |   |--- Ludnosc_w_wieku_produkcyjnym_niemobilnym >  52302.50\n",
+      "|   |   |   |   |   |   |   |   |   |--- value: [70210346.90]\n",
+      "|   |   |   |   |   |   |   |--- Dochody_dofinansowanie_razem >  1652968.06\n",
+      "|   |   |   |   |   |   |   |   |--- Wplywy_z_oplaty_eksploatacyjnej <= 16106.75\n",
+      "|   |   |   |   |   |   |   |   |   |--- Ludnosc_mezczyzni <= 128514.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [257797653.62]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Ludnosc_mezczyzni >  128514.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [218682371.69]\n",
+      "|   |   |   |   |   |   |   |   |--- Wplywy_z_oplaty_eksploatacyjnej >  16106.75\n",
+      "|   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_w_relacji_do_sredniej <= 92.85\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [190652253.27]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_w_relacji_do_sredniej >  92.85\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [171162684.72]\n",
+      "|   |   |   |   |--- Wplywy_z_oplaty_targowej >  2084361.06\n",
+      "|   |   |   |   |   |--- Zmiana_liczby_ludnosci <= -3.30\n",
+      "|   |   |   |   |   |   |--- value: [278636941.63]\n",
+      "|   |   |   |   |   |--- Zmiana_liczby_ludnosci >  -3.30\n",
+      "|   |   |   |   |   |   |--- value: [397408737.72]\n",
+      "|   |--- Zameldowania_mezczyzni >  1265.00\n",
+      "|   |   |--- Wynagrodzenie_w_relacji_do_sredniej <= 133.45\n",
+      "|   |   |   |--- Bezrobotne_kobiety <= 3540.75\n",
+      "|   |   |   |   |--- Dochody_podatek_od_dzialalnosci_gospodarczej <= 5928072.50\n",
+      "|   |   |   |   |   |--- Wynagrodzenie_ogolem <= 6725.34\n",
+      "|   |   |   |   |   |   |--- Bezrobotne_kobiety <= 3348.75\n",
+      "|   |   |   |   |   |   |   |--- Obiekty_caloroczne <= 29.00\n",
+      "|   |   |   |   |   |   |   |   |--- Wplywy_z_innych_lokalnych_oplat <= 58960390.00\n",
+      "|   |   |   |   |   |   |   |   |   |--- Ludnosc_kobiety_w_wieku_przedprodukcyjnym <= 19505.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [170265122.35]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Ludnosc_kobiety_w_wieku_przedprodukcyjnym >  19505.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [169956996.35]\n",
+      "|   |   |   |   |   |   |   |   |--- Wplywy_z_innych_lokalnych_oplat >  58960390.00\n",
+      "|   |   |   |   |   |   |   |   |   |--- value: [210308526.53]\n",
+      "|   |   |   |   |   |   |   |--- Obiekty_caloroczne >  29.00\n",
+      "|   |   |   |   |   |   |   |   |--- Dlugotrwale_bezrobotni <= 1692.75\n",
+      "|   |   |   |   |   |   |   |   |   |--- Dochody_z_najmu_i_dzierzawy <= 63677126.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_dofinansowanie_inwestycyjne <= 61285.23\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [36212527.68]\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_dofinansowanie_inwestycyjne >  61285.23\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [41194483.33]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Dochody_z_najmu_i_dzierzawy >  63677126.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_ogolem <= 5792.35\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [49036212.87]\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_ogolem >  5792.35\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [60701715.89]\n",
+      "|   |   |   |   |   |   |   |   |--- Dlugotrwale_bezrobotni >  1692.75\n",
+      "|   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_w_relacji_do_sredniej <= 108.15\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Wplywy_z_oplaty_targowej <= 249883.05\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Wplywy_z_oplaty_targowej >  249883.05\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
+      "|   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_w_relacji_do_sredniej >  108.15\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_w_relacji_do_sredniej <= 114.85\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [160689378.99]\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_w_relacji_do_sredniej >  114.85\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [154781987.47]\n",
+      "|   |   |   |   |   |   |--- Bezrobotne_kobiety >  3348.75\n",
+      "|   |   |   |   |   |   |   |--- Bezrobotne_kobiety <= 3459.75\n",
+      "|   |   |   |   |   |   |   |   |--- Zameldowania_z_miast_mezczyzni <= 1035.50\n",
+      "|   |   |   |   |   |   |   |   |   |--- value: [258640027.26]\n",
+      "|   |   |   |   |   |   |   |   |--- Zameldowania_z_miast_mezczyzni >  1035.50\n",
+      "|   |   |   |   |   |   |   |   |   |--- value: [311347808.39]\n",
+      "|   |   |   |   |   |   |   |--- Bezrobotne_kobiety >  3459.75\n",
+      "|   |   |   |   |   |   |   |   |--- Dochody_podatek_od_srodkow_transportowych <= 9519707.50\n",
+      "|   |   |   |   |   |   |   |   |   |--- Bezrobotne_kobiety <= 3495.75\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [175142819.65]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Bezrobotne_kobiety >  3495.75\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [178199385.64]\n",
+      "|   |   |   |   |   |   |   |   |--- Dochody_podatek_od_srodkow_transportowych >  9519707.50\n",
+      "|   |   |   |   |   |   |   |   |   |--- value: [136488465.37]\n",
+      "|   |   |   |   |   |--- Wynagrodzenie_ogolem >  6725.34\n",
+      "|   |   |   |   |   |   |--- Dochody_podatek_PCC <= 44497146.00\n",
+      "|   |   |   |   |   |   |   |--- value: [72794568.87]\n",
+      "|   |   |   |   |   |   |--- Dochody_podatek_PCC >  44497146.00\n",
+      "|   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_od_osob_prywatnych <= 205810792.00\n",
+      "|   |   |   |   |   |   |   |   |--- Dochody_podatek_lesny <= 215557.38\n",
+      "|   |   |   |   |   |   |   |   |   |--- Wymeldowania_na_wies_kobiety <= 1870.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [16029835.87]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Wymeldowania_na_wies_kobiety >  1870.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [14592470.05]\n",
+      "|   |   |   |   |   |   |   |   |--- Dochody_podatek_lesny >  215557.38\n",
+      "|   |   |   |   |   |   |   |   |   |--- Ludnosc_mezczyzni_w_wieku_produkcyjnym <= 102266.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [6335068.75]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Ludnosc_mezczyzni_w_wieku_produkcyjnym >  102266.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [2570182.79]\n",
+      "|   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_od_osob_prywatnych >  205810792.00\n",
+      "|   |   |   |   |   |   |   |   |--- value: [31782949.68]\n",
+      "|   |   |   |   |--- Dochody_podatek_od_dzialalnosci_gospodarczej >  5928072.50\n",
+      "|   |   |   |   |   |--- value: [486634417.85]\n",
+      "|   |   |   |--- Bezrobotne_kobiety >  3540.75\n",
+      "|   |   |   |   |--- Zameldowania_ze_wsi_mezczyzni <= 595.50\n",
+      "|   |   |   |   |   |--- Zameldowania_z_miast_mezczyzni <= 986.00\n",
+      "|   |   |   |   |   |   |--- Ludnosc_w_wieku_produkcyjnym_mobilnym <= 246973.00\n",
+      "|   |   |   |   |   |   |   |--- value: [226429554.13]\n",
+      "|   |   |   |   |   |   |--- Ludnosc_w_wieku_produkcyjnym_mobilnym >  246973.00\n",
+      "|   |   |   |   |   |   |   |--- value: [299234598.97]\n",
+      "|   |   |   |   |   |--- Zameldowania_z_miast_mezczyzni >  986.00\n",
+      "|   |   |   |   |   |   |--- Zameldowania_ze_wsi_kobiety <= 628.00\n",
+      "|   |   |   |   |   |   |   |--- Wymeldowania_mezczyzni <= 1642.00\n",
+      "|   |   |   |   |   |   |   |   |--- value: [697207249.86]\n",
+      "|   |   |   |   |   |   |   |--- Wymeldowania_mezczyzni >  1642.00\n",
+      "|   |   |   |   |   |   |   |   |--- value: [773787942.19]\n",
+      "|   |   |   |   |   |   |--- Zameldowania_ze_wsi_kobiety >  628.00\n",
+      "|   |   |   |   |   |   |   |--- value: [1029458518.72]\n",
+      "|   |   |   |   |--- Zameldowania_ze_wsi_mezczyzni >  595.50\n",
+      "|   |   |   |   |   |--- Dochody_podatek_rolny <= 2382003.62\n",
+      "|   |   |   |   |   |   |--- Dlugotrwale_bezrobotni <= 10208.50\n",
+      "|   |   |   |   |   |   |   |--- Bezrobotni_do_25_roku_zycia <= 956.00\n",
+      "|   |   |   |   |   |   |   |   |--- Wymeldowania_na_wies_kobiety <= 2371.50\n",
+      "|   |   |   |   |   |   |   |   |   |--- Gestosc_zaludnienia <= 1.57\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Wymeldowania_do_miast_kobiety <= 506.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [101454834.75]\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Wymeldowania_do_miast_kobiety >  506.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [146572830.94]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Gestosc_zaludnienia >  1.57\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_podatek_odrebne_ustawy <= 698383488.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_podatek_odrebne_ustawy >  698383488.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
+      "|   |   |   |   |   |   |   |   |--- Wymeldowania_na_wies_kobiety >  2371.50\n",
+      "|   |   |   |   |   |   |   |   |   |--- Zmiana_liczby_ludnosci <= -3.95\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [561559712.26]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Zmiana_liczby_ludnosci >  -3.95\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_podatek_od_dzialalnosci_gospodarczej <= 1665681.25\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [449470386.73]\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_podatek_od_dzialalnosci_gospodarczej >  1665681.25\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
+      "|   |   |   |   |   |   |   |--- Bezrobotni_do_25_roku_zycia >  956.00\n",
+      "|   |   |   |   |   |   |   |   |--- Dochody_podatek_lesny <= 65387.26\n",
+      "|   |   |   |   |   |   |   |   |   |--- Turysci_zagraniczni <= 132817.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_podatek_PCC <= 96171684.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [525003920.12]\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_podatek_PCC >  96171684.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [512681952.25]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Turysci_zagraniczni >  132817.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [570198401.95]\n",
+      "|   |   |   |   |   |   |   |   |--- Dochody_podatek_lesny >  65387.26\n",
+      "|   |   |   |   |   |   |   |   |   |--- Turysci_zagraniczni <= 68721.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Ludnosc_mezczyzni_w_wieku_produkcyjnym <= 331145.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [402181004.03]\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Ludnosc_mezczyzni_w_wieku_produkcyjnym >  331145.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [393784496.80]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Turysci_zagraniczni >  68721.50\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [336825560.70]\n",
+      "|   |   |   |   |   |   |--- Dlugotrwale_bezrobotni >  10208.50\n",
+      "|   |   |   |   |   |   |   |--- Dochody_dofinansowanie_inwestycyjne <= 8016522.75\n",
+      "|   |   |   |   |   |   |   |   |--- Wymeldowania_na_wies_ogolem <= 2801.00\n",
+      "|   |   |   |   |   |   |   |   |   |--- value: [343376510.79]\n",
+      "|   |   |   |   |   |   |   |   |--- Wymeldowania_na_wies_ogolem >  2801.00\n",
+      "|   |   |   |   |   |   |   |   |   |--- Powierzchnia <= 405.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Ludnosc_w_wieku_produkcyjnym_niemobilnym <= 144979.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [181961465.97]\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- Ludnosc_w_wieku_produkcyjnym_niemobilnym >  144979.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [148846494.63]\n",
+      "|   |   |   |   |   |   |   |   |   |--- Powierzchnia >  405.00\n",
+      "|   |   |   |   |   |   |   |   |   |   |--- value: [238904839.22]\n",
+      "|   |   |   |   |   |   |   |--- Dochody_dofinansowanie_inwestycyjne >  8016522.75\n",
+      "|   |   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_od_osob_prywatnych <= 749905256.00\n",
+      "|   |   |   |   |   |   |   |   |   |--- value: [16359845.01]\n",
+      "|   |   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_od_osob_prywatnych >  749905256.00\n",
+      "|   |   |   |   |   |   |   |   |   |--- value: [49321083.34]\n",
+      "|   |   |   |   |   |--- Dochody_podatek_rolny >  2382003.62\n",
+      "|   |   |   |   |   |   |--- Bezrobotni_ogolem <= 19139.25\n",
+      "|   |   |   |   |   |   |   |--- value: [630168440.25]\n",
+      "|   |   |   |   |   |   |--- Bezrobotni_ogolem >  19139.25\n",
+      "|   |   |   |   |   |   |   |--- value: [546788438.22]\n",
+      "|   |   |--- Wynagrodzenie_w_relacji_do_sredniej >  133.45\n",
+      "|   |   |   |--- Zameldowania_mezczyzni <= 8867.50\n",
+      "|   |   |   |   |--- value: [718045029.74]\n",
+      "|   |   |   |--- Zameldowania_mezczyzni >  8867.50\n",
+      "|   |   |   |   |--- value: [1880272790.67]\n",
+      "|--- Bezrobotni_powyzej_50_roku_zycia >  13525.75\n",
+      "|   |--- Dochody_podatek_od_spadkow <= 51075938.00\n",
+      "|   |   |--- Zameldowania_ze_wsi_mezczyzni <= 1711.50\n",
+      "|   |   |   |--- value: [1636234649.62]\n",
+      "|   |   |--- Zameldowania_ze_wsi_mezczyzni >  1711.50\n",
+      "|   |   |   |--- value: [2317085372.95]\n",
+      "|   |--- Dochody_podatek_od_spadkow >  51075938.00\n",
+      "|   |   |--- value: [4888827044.14]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(export_text(model, feature_names=feature_names))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 357,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.53063 — Bezrobotni_powyzej_50_roku_zycia\n",
+      "0.15004 — Zameldowania_mezczyzni\n",
+      "0.11683 — Dochody_podatek_od_spadkow\n",
+      "0.04385 — Wynagrodzenie_w_relacji_do_sredniej\n",
+      "0.01884 — Bezrobotne_kobiety\n",
+      "0.01772 — Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym\n",
+      "0.01252 — Zameldowania_ze_wsi_mezczyzni\n",
+      "0.00942 — Dochody_dofinansowanie_inwestycyjne\n",
+      "0.00925 — Turysci_ogolem\n",
+      "0.00823 — Zameldowania_z_miast_mezczyzni\n",
+      "0.00817 — Dochody_podatek_rolny\n",
+      "0.00541 — Dlugotrwale_bezrobotni\n",
+      "0.00442 — Zmiana_liczby_ludnosci\n",
+      "0.00426 — Ludnosc_mezczyzni_w_wieku_poprodukcyjnym\n",
+      "0.00379 — Wplywy_z_oplaty_eksploatacyjnej\n",
+      "0.00352 — Bezrobotni_do_25_roku_zycia\n",
+      "0.00343 — Ludnosc_w_wieku_przedprodukcyjnym\n",
+      "0.00328 — Dochody_podatek_od_dzialalnosci_gospodarczej\n",
+      "0.00321 — Wplywy_z_oplaty_targowej\n",
+      "0.00311 — Dochody_podatek_lesny\n",
+      "0.00253 — Ludnosc_kobiety_w_wieku_poprodukcyjnym\n",
+      "0.00227 — Wynagrodzenie_ogolem\n",
+      "0.00213 — Wymeldowania_na_wies_kobiety\n",
+      "0.00191 — Dochody_dofinansowanie_razem\n",
+      "0.00180 — Wplywy_z_innych_lokalnych_oplat\n",
+      "0.00172 — Wymeldowania_do_miast_ogolem\n",
+      "0.00160 — Wymeldowania_na_wies_ogolem\n",
+      "0.00159 — Dochody_podatek_odrebne_ustawy\n",
+      "0.00152 — Miejsca_noclegowe_ogolem\n",
+      "0.00129 — Turysci_zagraniczni\n",
+      "0.00129 — Obiekty_ogolem\n",
+      "0.00127 — Ludnosc_mezczyzni_w_wieku_przedprodukcyjnym\n",
+      "0.00124 — Zameldowania_ze_wsi_kobiety\n",
+      "0.00121 — Wojewodztwo_Slaskie\n",
+      "0.00109 — Gestosc_zaludnienia\n",
+      "0.00097 — Dochody_z_majatku\n",
+      "0.00081 — Saldo_migracji\n",
+      "0.00074 — Obiekty_caloroczne\n",
+      "0.00072 — Ludnosc_na_1_km2\n",
+      "0.00070 — Dochody_z_uslug\n",
+      "0.00068 — Powierzchnia\n",
+      "0.00065 — Dochody_podatek_od_srodkow_transportowych\n",
+      "0.00058 — Udzialy_w_podatkach_dochodowych_od_osob_prywatnych\n",
+      "0.00055 — Udzialy_w_podatkach_dochodowych_razem\n",
+      "0.00052 — Dochody_podatek_PCC\n",
+      "0.00050 — Ludnosc_mezczyzni_w_wieku_produkcyjnym_mobilnym\n",
+      "0.00045 — Udzialy_w_podatkach_dochodowych_od_osob_fizycznych\n",
+      "0.00040 — Dochody_podatek_od_nieruchomosci\n",
+      "0.00038 — Wymeldowania_do_miast_kobiety\n",
+      "0.00038 — Dochody_z_najmu_i_dzierzawy\n",
+      "0.00037 — Ludnosc_kobiety_w_wieku_przedprodukcyjnym\n",
+      "0.00035 — Wymeldowania_do_miast_mezczyzni\n",
+      "0.00032 — Ludnosc_mezczyzni_w_wieku_produkcyjnym\n",
+      "0.00032 — Wplywy_z_oplaty_skarbowej\n",
+      "0.00030 — Bezrobotni_mezczyzni\n",
+      "0.00026 — Wojewodztwo_Podkarpackie\n",
+      "0.00025 — Saldo_migracji_na_1000_ludnosci\n",
+      "0.00023 — Wojewodztwo_Lubuskie\n",
+      "0.00022 — Wskaznik_urbanizacji\n",
+      "0.00021 — Zameldowania_kobiety\n",
+      "0.00020 — Wymeldowania_kobiety\n",
+      "0.00020 — Zameldowania_ze_wsi_ogolem\n",
+      "0.00019 — Wojewodztwo_Opolskie\n",
+      "0.00019 — Ludnosc_mezczyzni\n",
+      "0.00018 — Wymeldowania_na_wies_mezczyzni\n",
+      "0.00017 — Bezrobotni_ogolem\n",
+      "0.00017 — Ludnosc\n",
       "0.00017 — Ludnosc_kobiety_w_wieku_produkcyjnym\n",
-      "0.00017 — Ludnosc_kobiety_w_wieku_poprodukcyjnym\n",
-      "0.00009 — Ludnosc_kobiety\n",
-      "0.00005 — Ludnosc_ogolem\n",
-      "0.00005 — Wojewodztwo_Lubuskie\n",
-      "0.00004 — Ludnosc_mezczyzni\n",
-      "0.00004 — Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym\n",
-      "0.00004 — Wojewodztwo_Kujawsko_Pomorskie\n",
-      "0.00004 — Wojewodztwo_Lodzkie\n",
+      "0.00016 — Ludnosc_w_wieku_produkcyjnym_mobilnym\n",
+      "0.00015 — Wojewodztwo_Podlaskie\n",
+      "0.00012 — Zameldowania_z_miast_ogolem\n",
+      "0.00012 — Zameldowania_ogolem\n",
+      "0.00011 — Wymeldowania_mezczyzni\n",
+      "0.00011 — Dochody_razem\n",
+      "0.00010 — Miejsca_noclegowe_caloroczne\n",
+      "0.00010 — Zameldowania_z_miast_kobiety\n",
+      "0.00008 — Wojewodztwo_Mazowieckie\n",
+      "0.00008 — Wojewodztwo_Dolnoslaskie\n",
+      "0.00007 — Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym\n",
+      "0.00006 — Wymeldowania_ogolem\n",
+      "0.00006 — Wojewodztwo_Lodzkie\n",
+      "0.00005 — Ludnosc_w_wieku_produkcyjnym\n",
+      "0.00004 — Ludnosc_mezczyzni_w_wieku_produkcyjnym_niemobilnym\n",
       "0.00003 — Ludnosc_w_wieku_poprodukcyjnym\n",
-      "0.00002 — Ludnosc_w_wieku_produkcyjnym\n",
-      "0.00002 — Wojewodztwo_Zachodniopomorskie\n",
-      "0.00001 — Wojewodztwo_Pomorskie\n",
-      "0.00001 — Wojewodztwo_Mazowieckie\n"
+      "0.00003 — Wojewodztwo_Swietokrzyskie\n",
+      "0.00003 — Ludnosc_kobiety\n",
+      "0.00003 — Ludnosc_w_wieku_produkcyjnym_niemobilnym\n",
+      "0.00002 — Wojewodztwo_Kujawsko_Pomorskie\n",
+      "0.00001 — Wojewodztwo_Lubelskie\n",
+      "0.00001 — Wojewodztwo_Warminsko_Mazurskie\n",
+      "0.00001 — Wojewodztwo_Zachodniopomorskie\n",
+      "0.00001 — Ludnosc_ogolem\n",
+      "0.00000 — Wojewodztwo_Pomorskie\n",
+      "0.00000 — Wojewodztwo_Malopolskie\n",
+      "0.00000 — Wojewodztwo_Wielkopolskie\n"
      ]
     }
    ],
    "source": [
     "feature_importance = dict(zip(feature_names, model.feature_importances_))\n",
     "for feature, importance in sorted(feature_importance.items(), key=lambda x: x[1], reverse=True):\n",
-    "    print(f\"{importance:.5f} \\u2014 {feature}\")"
+    "    print(f'{importance:.5f} \\u2014 {feature}')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {