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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"..."
]
},
{
"cell_type": "code",
"execution_count": 411,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 412,
"metadata": {},
"outputs": [],
"source": [
"pd.options.display.float_format = '{:.2f}'.format"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"..."
]
},
{
"cell_type": "code",
"execution_count": 413,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_7800\\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"
]
}
],
"source": [
"df_dofinansowanie = pd.read_csv(\n",
" 'umowy_pelna_lista_krajowe.csv',\n",
" encoding='ISO-8859-2',\n",
" converters={'TERYT pe?ny': str},\n",
" thousands=',')\n",
"\n",
"df_dofinansowanie = df_dofinansowanie.loc[df_dofinansowanie['TERYT pe?ny'] != ''].reset_index(drop=True)\n",
"\n",
"df_dofinansowanie['Dofinansowanie UE (PLN)'] = \\\n",
" df_dofinansowanie['Dofinansowanie UE (PLN)'].apply(pd.to_numeric)\n",
"\n",
"df_dofinansowanie['Data rozpocz?cia realizacji'] = pd.to_datetime(df_dofinansowanie['Data rozpocz?cia realizacji'])\n",
"df_dofinansowanie['Rok rozpocz?cia realizacji'] = df_dofinansowanie['Data rozpocz?cia realizacji'].dt.year\n",
"\n",
"df_dofinansowanie['Data podpisania umowy pierwotnej'] = pd.to_datetime(df_dofinansowanie['Data podpisania umowy pierwotnej'])\n",
"df_dofinansowanie['Rok podpisania umowy pierwotnej'] = df_dofinansowanie['Data podpisania umowy pierwotnej'].dt.year"
]
},
{
"cell_type": "code",
"execution_count": 414,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Program Operacyjny Inteligentny Rozwój'\n",
" 'Program Operacyjny Infrastruktura i ?rodowisko 2014-2020'\n",
" 'Program Operacyjny Polska Cyfrowa'\n",
" 'Program Operacyjny Pomoc Techniczna 2014-2020'\n",
" 'Program Operacyjny Polska Wschodnia'\n",
" 'Program Operacyjny Wiedza Edukacja Rozwój']\n"
]
}
],
"source": [
"print(df_dofinansowanie['Program operacyjny'].drop_duplicates().values)"
]
},
{
"cell_type": "code",
"execution_count": 415,
"metadata": {},
"outputs": [],
"source": [
"# Wybór programu operacyjnego...\n",
"df_dofinansowanie = df_dofinansowanie.loc[df_dofinansowanie['Program operacyjny'] == 'Program Operacyjny Infrastruktura i ?rodowisko 2014-2020'].reset_index(drop=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 416,
"metadata": {},
"outputs": [],
"source": [
"df_dofinansowanie_agg = df_dofinansowanie \\\n",
" .groupby(['TERYT pe?ny', 'Rok rozpocz?cia realizacji'])['Dofinansowanie UE (PLN)'].sum().reset_index()\n",
"df_dofinansowanie_agg = df_dofinansowanie_agg \\\n",
" .rename(columns={'TERYT pe?ny': 'Kod', 'Rok rozpocz?cia realizacji': 'Rok', 'Dofinansowanie UE (PLN)': 'Suma'})\n",
"df_dofinansowanie_agg = df_dofinansowanie_agg \\\n",
" .loc[df_dofinansowanie_agg['Kod'].str.len() == 7].reset_index(drop=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"..."
]
},
{
"cell_type": "code",
"execution_count": 417,
"metadata": {},
"outputs": [],
"source": [
"df_podz = pd.read_csv(\n",
" 'PODZ_1410_CREL.csv',\n",
" sep=';',\n",
" converters={'Kod': str})\n",
"df_podz = df_podz[['Kod', 'Rok', 'Wartosc']]\n",
"df_podz = df_podz.loc[df_podz['Kod'].str.endswith(('1', '2', '3'))]\n",
"df_podz = df_podz.dropna()\n",
"df_podz = df_podz.rename(columns={\n",
" 'Wartosc': 'Powierzchnia'})"
]
},
{
"cell_type": "code",
"execution_count": 418,
"metadata": {},
"outputs": [],
"source": [
"df_wyna = pd.read_csv(\n",
" 'WYNA_2497_CREL.csv',\n",
" sep=';',\n",
" converters={'Kod': str},\n",
" decimal=',')\n",
"df_wyna = df_wyna[['Kod', 'Wyszczególnienie', 'Rok', 'Wartosc']]\n",
"df_wyna = df_wyna.dropna()\n",
"df_wyna = df_wyna.pivot_table(index=['Kod', 'Rok'], columns='Wyszczególnienie', values='Wartosc').reset_index()\n",
"df_wyna = df_wyna.rename(columns={\n",
" 'ogółem': 'Wynagrodzenie_ogolem',\n",
" 'przeciętne miesięczne wynagrodzenia brutto w relacji do średniej krajowej (Polska=100)': 'Wynagrodzenie_w_relacji_do_sredniej'})"
]
},
{
"cell_type": "code",
"execution_count": 419,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_7800\\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"
]
},
{
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Rodzaje dochodów</th>\n",
" <th>Kod</th>\n",
" <th>Rok</th>\n",
" <th>Dochody_podatek_lesny</th>\n",
" <th>Dochody_podatek_PCC</th>\n",
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" <td>108107448.79</td>\n",
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],
"text/plain": [
"Rodzaje dochodów Kod Rok Dochody_podatek_lesny Dochody_podatek_PCC \n",
"0 0201011 2004 NaN 549608.00 \\\n",
"1 0201011 2005 NaN 609855.00 \n",
"2 0201011 2006 NaN 844223.65 \n",
"3 0201011 2007 NaN 1344365.01 \n",
"4 0201011 2008 6799.55 1790135.40 \n",
"... ... ... ... ... \n",
"47078 3263011 2018 154462.39 5361951.37 \n",
"47079 3263011 2019 150329.31 6088184.20 \n",
"47080 3263011 2020 156556.52 5125090.74 \n",
"47081 3263011 2021 163778.36 9082482.28 \n",
"47082 3263011 2022 174823.49 7474079.65 \n",
"\n",
"Rodzaje dochodów Dochody_podatek_od_dzialalnosci_gospodarczej \n",
"0 NaN \\\n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"... ... \n",
"47078 572868.36 \n",
"47079 468411.51 \n",
"47080 329522.12 \n",
"47081 492045.28 \n",
"47082 1019054.56 \n",
"\n",
"Rodzaje dochodów Dochody_podatek_od_nieruchomosci \n",
"0 13532989.00 \\\n",
"1 13667398.00 \n",
"2 14633962.72 \n",
"3 14944781.74 \n",
"4 16089534.56 \n",
"... ... \n",
"47078 108107448.79 \n",
"47079 38527846.59 \n",
"47080 78767466.83 \n",
"47081 78491368.35 \n",
"47082 84996948.99 \n",
"\n",
"Rodzaje dochodów Dochody_podatek_od_spadkow \n",
"0 NaN \\\n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"... ... \n",
"47078 437144.83 \n",
"47079 228886.23 \n",
"47080 552009.16 \n",
"47081 947992.83 \n",
"47082 593315.54 \n",
"\n",
"Rodzaje dochodów Dochody_podatek_od_srodkow_transportowych \n",
"0 625159.00 \\\n",
"1 700134.00 \n",
"2 747182.64 \n",
"3 777345.52 \n",
"4 836441.10 \n",
"... ... \n",
"47078 589658.88 \n",
"47079 608637.40 \n",
"47080 558925.68 \n",
"47081 602586.14 \n",
"47082 627169.86 \n",
"\n",
"Rodzaje dochodów Dochody_podatek_rolny Dochody_podatek_odrebne_ustawy \n",
"0 23687.00 NaN \\\n",
"1 26634.00 15438121.00 \n",
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"47081 59824.46 89840077.70 \n",
"47082 50987.00 94936379.09 \n",
"\n",
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"1 43417443.00 \n",
"2 50319253.08 \n",
"3 62025513.24 \n",
"4 80755930.93 \n",
"... ... \n",
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"47079 167638796.15 \n",
"47080 263006955.07 \n",
"47081 252345800.93 \n",
"47082 259310641.60 \n",
"\n",
"[47083 rows x 11 columns]"
]
},
"execution_count": 419,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_fina_1 = pd.read_csv(\n",
" 'FINA_2622_CREL_1.csv',\n",
" sep=';',\n",
" converters={'Kod': str},\n",
" decimal=',')\n",
"df_fina_1 = df_fina_1[['Kod', 'Rodzaje dochodów', 'Rok', 'Wartosc']]\n",
"df_fina_1 = df_fina_1.dropna()\n",
"df_fina_1 = df_fina_1.pivot_table(index=['Kod', 'Rok'], columns='Rodzaje dochodów', values='Wartosc').reset_index()\n",
"df_fina_1 = df_fina_1.rename(columns={\n",
" 'dochody podatkowe - podatek leśny': 'Dochody_podatek_lesny',\n",
" 'dochody podatkowe - podatek od czynności cywilnoprawnych': 'Dochody_podatek_PCC',\n",
" 'dochody podatkowe - podatek od działalności gospodarczej osób fizycznych, opłacany w formie karty podatkowej': 'Dochody_podatek_od_dzialalnosci_gospodarczej',\n",
" 'dochody podatkowe - podatek od nieruchomości': 'Dochody_podatek_od_nieruchomosci',\n",
" 'dochody podatkowe - podatek od spadków i darowizn': 'Dochody_podatek_od_spadkow',\n",
" 'dochody podatkowe - podatek od środków transportowych': 'Dochody_podatek_od_srodkow_transportowych',\n",
" 'dochody podatkowe - podatek rolny': 'Dochody_podatek_rolny',\n",
" 'dochody podatkowe - ustalone i pobierane na podstawie odrębnych ustaw': 'Dochody_podatek_odrebne_ustawy',\n",
" 'razem': 'Dochody_razem'})\n",
"\n",
"df_fina_1"
]
},
{
"cell_type": "code",
"execution_count": 420,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_7800\\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"
]
},
{
"data": {
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" <th>Rodzaje dochodów</th>\n",
" <th>Kod</th>\n",
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" <th>Dochody_z_majatku</th>\n",
" <th>Dochody_z_najmu_i_dzierzawy</th>\n",
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" <td>231470.96</td>\n",
" <td>8752288.98</td>\n",
" <td>8864860.57</td>\n",
" <td>18101668.00</td>\n",
" <td>1048115.83</td>\n",
" <td>19149783.83</td>\n",
" <td>37365.00</td>\n",
" <td>NaN</td>\n",
" <td>1203990.73</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0201011</td>\n",
" <td>2007</td>\n",
" <td>15042480.34</td>\n",
" <td>9219682.12</td>\n",
" <td>339654.15</td>\n",
" <td>18153240.30</td>\n",
" <td>18438743.21</td>\n",
" <td>21785308.00</td>\n",
" <td>1336702.02</td>\n",
" <td>23122010.02</td>\n",
" <td>78798.51</td>\n",
" <td>NaN</td>\n",
" <td>1228704.53</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0201011</td>\n",
" <td>2008</td>\n",
" <td>22797881.07</td>\n",
" <td>9546379.31</td>\n",
" <td>787256.69</td>\n",
" <td>5046691.69</td>\n",
" <td>5182137.79</td>\n",
" <td>23974587.00</td>\n",
" <td>1532633.44</td>\n",
" <td>25507220.44</td>\n",
" <td>83882.94</td>\n",
" <td>NaN</td>\n",
" <td>1364245.93</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47078</th>\n",
" <td>3263011</td>\n",
" <td>2018</td>\n",
" <td>16419859.31</td>\n",
" <td>4261374.83</td>\n",
" <td>1996824.80</td>\n",
" <td>25285.92</td>\n",
" <td>237485.34</td>\n",
" <td>52799183.00</td>\n",
" <td>2690098.17</td>\n",
" <td>55489281.17</td>\n",
" <td>10458871.30</td>\n",
" <td>4684.54</td>\n",
" <td>434077.88</td>\n",
" <td>608625.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47079</th>\n",
" <td>3263011</td>\n",
" <td>2019</td>\n",
" <td>8844350.07</td>\n",
" <td>4324758.68</td>\n",
" <td>2187576.47</td>\n",
" <td>0.00</td>\n",
" <td>225831.84</td>\n",
" <td>55319040.00</td>\n",
" <td>2770684.17</td>\n",
" <td>58089724.17</td>\n",
" <td>11369287.11</td>\n",
" <td>3456.95</td>\n",
" <td>415686.53</td>\n",
" <td>610059.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47080</th>\n",
" <td>3263011</td>\n",
" <td>2020</td>\n",
" <td>13485033.97</td>\n",
" <td>6159923.01</td>\n",
" <td>1917372.55</td>\n",
" <td>21002107.00</td>\n",
" <td>21192313.05</td>\n",
" <td>53739656.00</td>\n",
" <td>3144444.38</td>\n",
" <td>56884100.38</td>\n",
" <td>12281916.71</td>\n",
" <td>5157.50</td>\n",
" <td>355201.29</td>\n",
" <td>507341.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47081</th>\n",
" <td>3263011</td>\n",
" <td>2021</td>\n",
" <td>16928500.75</td>\n",
" <td>7582499.62</td>\n",
" <td>4110105.72</td>\n",
" <td>888293.63</td>\n",
" <td>1072910.83</td>\n",
" <td>63936763.00</td>\n",
" <td>3975531.95</td>\n",
" <td>67912294.95</td>\n",
" <td>17127683.55</td>\n",
" <td>27746.70</td>\n",
" <td>416473.03</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47082</th>\n",
" <td>3263011</td>\n",
" <td>2022</td>\n",
" <td>30415536.99</td>\n",
" <td>8651170.05</td>\n",
" <td>4117086.30</td>\n",
" <td>207597.50</td>\n",
" <td>800347.63</td>\n",
" <td>64657287.40</td>\n",
" <td>4082611.64</td>\n",
" <td>68739899.04</td>\n",
" <td>19150342.25</td>\n",
" <td>5035.87</td>\n",
" <td>421424.91</td>\n",
" <td>1233266.30</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>47083 rows × 14 columns</p>\n",
"</div>"
],
"text/plain": [
"Rodzaje dochodów Kod Rok Dochody_z_majatku \n",
"0 0201011 2004 5344205.00 \\\n",
"1 0201011 2005 4560489.00 \n",
"2 0201011 2006 8528727.69 \n",
"3 0201011 2007 15042480.34 \n",
"4 0201011 2008 22797881.07 \n",
"... ... ... ... \n",
"47078 3263011 2018 16419859.31 \n",
"47079 3263011 2019 8844350.07 \n",
"47080 3263011 2020 13485033.97 \n",
"47081 3263011 2021 16928500.75 \n",
"47082 3263011 2022 30415536.99 \n",
"\n",
"Rodzaje dochodów Dochody_z_najmu_i_dzierzawy Dochody_z_uslug \n",
"0 NaN 184307.00 \\\n",
"1 NaN 96462.00 \n",
"2 NaN 231470.96 \n",
"3 9219682.12 339654.15 \n",
"4 9546379.31 787256.69 \n",
"... ... ... \n",
"47078 4261374.83 1996824.80 \n",
"47079 4324758.68 2187576.47 \n",
"47080 6159923.01 1917372.55 \n",
"47081 7582499.62 4110105.72 \n",
"47082 8651170.05 4117086.30 \n",
"\n",
"Rodzaje dochodów Dochody_dofinansowanie_inwestycyjne \n",
"0 NaN \\\n",
"1 NaN \n",
"2 8752288.98 \n",
"3 18153240.30 \n",
"4 5046691.69 \n",
"... ... \n",
"47078 25285.92 \n",
"47079 0.00 \n",
"47080 21002107.00 \n",
"47081 888293.63 \n",
"47082 207597.50 \n",
"\n",
"Rodzaje dochodów Dochody_dofinansowanie_razem \n",
"0 519209.00 \\\n",
"1 9024183.00 \n",
"2 8864860.57 \n",
"3 18438743.21 \n",
"4 5182137.79 \n",
"... ... \n",
"47078 237485.34 \n",
"47079 225831.84 \n",
"47080 21192313.05 \n",
"47081 1072910.83 \n",
"47082 800347.63 \n",
"\n",
"Rodzaje dochodów Udzialy_w_podatkach_dochodowych_od_osob_fizycznych \n",
"0 13285456.00 \\\n",
"1 15985331.00 \n",
"2 18101668.00 \n",
"3 21785308.00 \n",
"4 23974587.00 \n",
"... ... \n",
"47078 52799183.00 \n",
"47079 55319040.00 \n",
"47080 53739656.00 \n",
"47081 63936763.00 \n",
"47082 64657287.40 \n",
"\n",
"Rodzaje dochodów Udzialy_w_podatkach_dochodowych_od_osob_prywatnych \n",
"0 1065169.00 \\\n",
"1 1170863.00 \n",
"2 1048115.83 \n",
"3 1336702.02 \n",
"4 1532633.44 \n",
"... ... \n",
"47078 2690098.17 \n",
"47079 2770684.17 \n",
"47080 3144444.38 \n",
"47081 3975531.95 \n",
"47082 4082611.64 \n",
"\n",
"Rodzaje dochodów Udzialy_w_podatkach_dochodowych_razem \n",
"0 14350625.00 \\\n",
"1 17156194.00 \n",
"2 19149783.83 \n",
"3 23122010.02 \n",
"4 25507220.44 \n",
"... ... \n",
"47078 55489281.17 \n",
"47079 58089724.17 \n",
"47080 56884100.38 \n",
"47081 67912294.95 \n",
"47082 68739899.04 \n",
"\n",
"Rodzaje dochodów Wplywy_z_innych_lokalnych_oplat \n",
"0 44200.00 \\\n",
"1 42840.00 \n",
"2 37365.00 \n",
"3 78798.51 \n",
"4 83882.94 \n",
"... ... \n",
"47078 10458871.30 \n",
"47079 11369287.11 \n",
"47080 12281916.71 \n",
"47081 17127683.55 \n",
"47082 19150342.25 \n",
"\n",
"Rodzaje dochodów Wplywy_z_oplaty_eksploatacyjnej Wplywy_z_oplaty_skarbowej \n",
"0 NaN 1209998.00 \\\n",
"1 NaN 1282943.00 \n",
"2 NaN 1203990.73 \n",
"3 NaN 1228704.53 \n",
"4 NaN 1364245.93 \n",
"... ... ... \n",
"47078 4684.54 434077.88 \n",
"47079 3456.95 415686.53 \n",
"47080 5157.50 355201.29 \n",
"47081 27746.70 416473.03 \n",
"47082 5035.87 421424.91 \n",
"\n",
"Rodzaje dochodów Wplywy_z_oplaty_targowej \n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"... ... \n",
"47078 608625.90 \n",
"47079 610059.50 \n",
"47080 507341.00 \n",
"47081 0.00 \n",
"47082 1233266.30 \n",
"\n",
"[47083 rows x 14 columns]"
]
},
"execution_count": 420,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_fina_2 = pd.read_csv(\n",
" 'FINA_2622_CREL_2.csv',\n",
" sep=';',\n",
" converters={'Kod': str},\n",
" decimal=',')\n",
"df_fina_2 = df_fina_2[['Kod', 'Rodzaje dochodów', 'Rok', 'Wartosc']]\n",
"df_fina_2 = df_fina_2.dropna()\n",
"df_fina_2 = df_fina_2.pivot_table(index=['Kod', 'Rok'], columns='Rodzaje dochodów', values='Wartosc').reset_index()\n",
"df_fina_2 = df_fina_2.rename(columns={\n",
" 'dochody z majątku': 'Dochody_z_majatku',\n",
" 'dochody z majątku - dochody z najmu i dzierżawy składników majątkowych JST oraz innych umów o podobnym charakterze': 'Dochody_z_najmu_i_dzierzawy',\n",
" 'pozostałe dochody - wpływy z usług': 'Dochody_z_uslug',\n",
" 'pozostałe dochody - środki na dofinansowanie własnych zadań pozyskane z innych źródeł - inwestycyjne': 'Dochody_dofinansowanie_inwestycyjne',\n",
" 'pozostałe dochody - środki na dofinansowanie własnych zadań pozyskane z innych źródeł - razem': 'Dochody_dofinansowanie_razem',\n",
" 'udziały w podatkach stanowiących dochody budżetu państwa podatek dochodowy od osób fizycznych': 'Udzialy_w_podatkach_dochodowych_od_osob_fizycznych',\n",
" 'udziały w podatkach stanowiących dochody budżetu państwa podatek dochodowy od osób prawnych': 'Udzialy_w_podatkach_dochodowych_od_osob_prywatnych',\n",
" 'udziały w podatkach stanowiących dochody budżetu państwa razem': 'Udzialy_w_podatkach_dochodowych_razem',\n",
" 'wpływy z innych lokalnych opłat pobieranych przez jednostki samorządu terytorialnego na podstawie odrębnych ustaw': 'Wplywy_z_innych_lokalnych_oplat',\n",
" 'wpływy z opłaty eksploatacyjnej': 'Wplywy_z_oplaty_eksploatacyjnej',\n",
" 'wpływy z opłaty skarbowej': 'Wplywy_z_oplaty_skarbowej',\n",
" 'wpływy z opłaty targowej': 'Wplywy_z_oplaty_targowej'})\n",
"\n",
"df_fina_2"
]
},
{
"cell_type": "code",
"execution_count": 421,
"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>Wiek</th>\n",
" <th>Kod</th>\n",
" <th>Rok</th>\n",
" <th>Ludnosc_ogolem</th>\n",
" <th>Ludnosc_w_wieku_poprodukcyjnym</th>\n",
" <th>Ludnosc_w_wieku_produkcyjnym</th>\n",
" <th>Ludnosc_w_wieku_produkcyjnym_mobilnym</th>\n",
" <th>Ludnosc_w_wieku_produkcyjnym_niemobilnym</th>\n",
" <th>Ludnosc_w_wieku_przedprodukcyjnym</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0201011</td>\n",
" <td>2010</td>\n",
" <td>40309.00</td>\n",
" <td>7683.00</td>\n",
" <td>26085.00</td>\n",
" <td>15183.00</td>\n",
" <td>10902.00</td>\n",
" <td>6541.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0201011</td>\n",
" <td>2011</td>\n",
" <td>40119.00</td>\n",
" <td>8020.00</td>\n",
" <td>25647.00</td>\n",
" <td>15047.00</td>\n",
" <td>10600.00</td>\n",
" <td>6452.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0201011</td>\n",
" <td>2012</td>\n",
" <td>39851.00</td>\n",
" <td>8392.00</td>\n",
" <td>25160.00</td>\n",
" <td>14932.00</td>\n",
" <td>10228.00</td>\n",
" <td>6299.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0201011</td>\n",
" <td>2013</td>\n",
" <td>39603.00</td>\n",
" <td>8678.00</td>\n",
" <td>24720.00</td>\n",
" <td>14784.00</td>\n",
" <td>9936.00</td>\n",
" <td>6205.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0201011</td>\n",
" <td>2014</td>\n",
" <td>39464.00</td>\n",
" <td>8971.00</td>\n",
" <td>24307.00</td>\n",
" <td>14645.00</td>\n",
" <td>9662.00</td>\n",
" <td>6186.00</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>48606</th>\n",
" <td>3263011</td>\n",
" <td>2018</td>\n",
" <td>40910.00</td>\n",
" <td>10472.00</td>\n",
" <td>24549.00</td>\n",
" <td>14683.00</td>\n",
" <td>9866.00</td>\n",
" <td>5889.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48607</th>\n",
" <td>3263011</td>\n",
" <td>2019</td>\n",
" <td>40888.00</td>\n",
" <td>10788.00</td>\n",
" <td>24209.00</td>\n",
" <td>14429.00</td>\n",
" <td>9780.00</td>\n",
" <td>5891.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48608</th>\n",
" <td>3263011</td>\n",
" <td>2020</td>\n",
" <td>40326.00</td>\n",
" <td>10962.00</td>\n",
" <td>23544.00</td>\n",
" <td>13798.00</td>\n",
" <td>9746.00</td>\n",
" <td>5820.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48609</th>\n",
" <td>3263011</td>\n",
" <td>2021</td>\n",
" <td>39834.00</td>\n",
" <td>11050.00</td>\n",
" <td>22976.00</td>\n",
" <td>13277.00</td>\n",
" <td>9699.00</td>\n",
" <td>5808.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48610</th>\n",
" <td>3263011</td>\n",
" <td>2022</td>\n",
" <td>39368.00</td>\n",
" <td>11157.00</td>\n",
" <td>22486.00</td>\n",
" <td>12802.00</td>\n",
" <td>9684.00</td>\n",
" <td>5725.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>48611 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
"Wiek Kod Rok Ludnosc_ogolem Ludnosc_w_wieku_poprodukcyjnym \n",
"0 0201011 2010 40309.00 7683.00 \\\n",
"1 0201011 2011 40119.00 8020.00 \n",
"2 0201011 2012 39851.00 8392.00 \n",
"3 0201011 2013 39603.00 8678.00 \n",
"4 0201011 2014 39464.00 8971.00 \n",
"... ... ... ... ... \n",
"48606 3263011 2018 40910.00 10472.00 \n",
"48607 3263011 2019 40888.00 10788.00 \n",
"48608 3263011 2020 40326.00 10962.00 \n",
"48609 3263011 2021 39834.00 11050.00 \n",
"48610 3263011 2022 39368.00 11157.00 \n",
"\n",
"Wiek Ludnosc_w_wieku_produkcyjnym Ludnosc_w_wieku_produkcyjnym_mobilnym \n",
"0 26085.00 15183.00 \\\n",
"1 25647.00 15047.00 \n",
"2 25160.00 14932.00 \n",
"3 24720.00 14784.00 \n",
"4 24307.00 14645.00 \n",
"... ... ... \n",
"48606 24549.00 14683.00 \n",
"48607 24209.00 14429.00 \n",
"48608 23544.00 13798.00 \n",
"48609 22976.00 13277.00 \n",
"48610 22486.00 12802.00 \n",
"\n",
"Wiek Ludnosc_w_wieku_produkcyjnym_niemobilnym \n",
"0 10902.00 \\\n",
"1 10600.00 \n",
"2 10228.00 \n",
"3 9936.00 \n",
"4 9662.00 \n",
"... ... \n",
"48606 9866.00 \n",
"48607 9780.00 \n",
"48608 9746.00 \n",
"48609 9699.00 \n",
"48610 9684.00 \n",
"\n",
"Wiek Ludnosc_w_wieku_przedprodukcyjnym \n",
"0 6541.00 \n",
"1 6452.00 \n",
"2 6299.00 \n",
"3 6205.00 \n",
"4 6186.00 \n",
"... ... \n",
"48606 5889.00 \n",
"48607 5891.00 \n",
"48608 5820.00 \n",
"48609 5808.00 \n",
"48610 5725.00 \n",
"\n",
"[48611 rows x 8 columns]"
]
},
"execution_count": 421,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_ludn_1 = pd.read_csv( # ogolem\n",
" 'LUDN_1342_CREL_1.csv',\n",
" sep=';',\n",
" converters={'Kod': str},\n",
" decimal=',')\n",
"df_ludn_1 = df_ludn_1[['Kod', 'Wiek', 'Rok', 'Wartosc']]\n",
"df_ludn_1 = df_ludn_1.dropna()\n",
"df_ludn_1 = df_ludn_1.pivot_table(index=['Kod', 'Rok'], columns='Wiek', values='Wartosc').reset_index()\n",
"df_ludn_1 = df_ludn_1.rename(columns={\n",
" 'ogółem': 'Ludnosc_ogolem',\n",
" 'w wieku poprodukcyjnym': 'Ludnosc_w_wieku_poprodukcyjnym',\n",
" 'w wieku produkcyjnym': 'Ludnosc_w_wieku_produkcyjnym',\n",
" 'w wieku produkcyjnym mobilnym': 'Ludnosc_w_wieku_produkcyjnym_mobilnym',\n",
" 'w wieku produkcyjnym niemobilnym': 'Ludnosc_w_wieku_produkcyjnym_niemobilnym',\n",
" 'w wieku przedprodukcyjnym': 'Ludnosc_w_wieku_przedprodukcyjnym'})\n",
"\n",
"df_ludn_1"
]
},
{
"cell_type": "code",
"execution_count": 422,
"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>Wiek</th>\n",
" <th>Kod</th>\n",
" <th>Rok</th>\n",
" <th>Ludnosc_mezczyzni</th>\n",
" <th>Ludnosc_mezczyzni_w_wieku_poprodukcyjnym</th>\n",
" <th>Ludnosc_mezczyzni_w_wieku_produkcyjnym</th>\n",
" <th>Ludnosc_mezczyzni_w_wieku_produkcyjnym_mobilnym</th>\n",
" <th>Ludnosc_mezczyzni_w_wieku_produkcyjnym_niemobilnym</th>\n",
" <th>Ludnosc_mezczyzni_w_wieku_przedprodukcyjnym</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0201011</td>\n",
" <td>2010</td>\n",
" <td>19085.00</td>\n",
" <td>2153.00</td>\n",
" <td>13535.00</td>\n",
" <td>7720.00</td>\n",
" <td>5815.00</td>\n",
" <td>3397.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0201011</td>\n",
" <td>2011</td>\n",
" <td>18985.00</td>\n",
" <td>2222.00</td>\n",
" <td>13398.00</td>\n",
" <td>7647.00</td>\n",
" <td>5751.00</td>\n",
" <td>3365.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0201011</td>\n",
" <td>2012</td>\n",
" <td>18859.00</td>\n",
" <td>2370.00</td>\n",
" <td>13238.00</td>\n",
" <td>7611.00</td>\n",
" <td>5627.00</td>\n",
" <td>3251.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0201011</td>\n",
" <td>2013</td>\n",
" <td>18737.00</td>\n",
" <td>2477.00</td>\n",
" <td>13028.00</td>\n",
" <td>7501.00</td>\n",
" <td>5527.00</td>\n",
" <td>3232.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0201011</td>\n",
" <td>2014</td>\n",
" <td>18640.00</td>\n",
" <td>2620.00</td>\n",
" <td>12832.00</td>\n",
" <td>7442.00</td>\n",
" <td>5390.00</td>\n",
" <td>3188.00</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>48606</th>\n",
" <td>3263011</td>\n",
" <td>2018</td>\n",
" <td>19690.00</td>\n",
" <td>3501.00</td>\n",
" <td>13202.00</td>\n",
" <td>7547.00</td>\n",
" <td>5655.00</td>\n",
" <td>2987.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48607</th>\n",
" <td>3263011</td>\n",
" <td>2019</td>\n",
" <td>19683.00</td>\n",
" <td>3644.00</td>\n",
" <td>13044.00</td>\n",
" <td>7417.00</td>\n",
" <td>5627.00</td>\n",
" <td>2995.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48608</th>\n",
" <td>3263011</td>\n",
" <td>2020</td>\n",
" <td>19356.00</td>\n",
" <td>3749.00</td>\n",
" <td>12617.00</td>\n",
" <td>6986.00</td>\n",
" <td>5631.00</td>\n",
" <td>2990.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48609</th>\n",
" <td>3263011</td>\n",
" <td>2021</td>\n",
" <td>19096.00</td>\n",
" <td>3852.00</td>\n",
" <td>12267.00</td>\n",
" <td>6747.00</td>\n",
" <td>5520.00</td>\n",
" <td>2977.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48610</th>\n",
" <td>3263011</td>\n",
" <td>2022</td>\n",
" <td>18869.00</td>\n",
" <td>3901.00</td>\n",
" <td>12009.00</td>\n",
" <td>6485.00</td>\n",
" <td>5524.00</td>\n",
" <td>2959.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>48611 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
"Wiek Kod Rok Ludnosc_mezczyzni \n",
"0 0201011 2010 19085.00 \\\n",
"1 0201011 2011 18985.00 \n",
"2 0201011 2012 18859.00 \n",
"3 0201011 2013 18737.00 \n",
"4 0201011 2014 18640.00 \n",
"... ... ... ... \n",
"48606 3263011 2018 19690.00 \n",
"48607 3263011 2019 19683.00 \n",
"48608 3263011 2020 19356.00 \n",
"48609 3263011 2021 19096.00 \n",
"48610 3263011 2022 18869.00 \n",
"\n",
"Wiek Ludnosc_mezczyzni_w_wieku_poprodukcyjnym \n",
"0 2153.00 \\\n",
"1 2222.00 \n",
"2 2370.00 \n",
"3 2477.00 \n",
"4 2620.00 \n",
"... ... \n",
"48606 3501.00 \n",
"48607 3644.00 \n",
"48608 3749.00 \n",
"48609 3852.00 \n",
"48610 3901.00 \n",
"\n",
"Wiek Ludnosc_mezczyzni_w_wieku_produkcyjnym \n",
"0 13535.00 \\\n",
"1 13398.00 \n",
"2 13238.00 \n",
"3 13028.00 \n",
"4 12832.00 \n",
"... ... \n",
"48606 13202.00 \n",
"48607 13044.00 \n",
"48608 12617.00 \n",
"48609 12267.00 \n",
"48610 12009.00 \n",
"\n",
"Wiek Ludnosc_mezczyzni_w_wieku_produkcyjnym_mobilnym \n",
"0 7720.00 \\\n",
"1 7647.00 \n",
"2 7611.00 \n",
"3 7501.00 \n",
"4 7442.00 \n",
"... ... \n",
"48606 7547.00 \n",
"48607 7417.00 \n",
"48608 6986.00 \n",
"48609 6747.00 \n",
"48610 6485.00 \n",
"\n",
"Wiek Ludnosc_mezczyzni_w_wieku_produkcyjnym_niemobilnym \n",
"0 5815.00 \\\n",
"1 5751.00 \n",
"2 5627.00 \n",
"3 5527.00 \n",
"4 5390.00 \n",
"... ... \n",
"48606 5655.00 \n",
"48607 5627.00 \n",
"48608 5631.00 \n",
"48609 5520.00 \n",
"48610 5524.00 \n",
"\n",
"Wiek Ludnosc_mezczyzni_w_wieku_przedprodukcyjnym \n",
"0 3397.00 \n",
"1 3365.00 \n",
"2 3251.00 \n",
"3 3232.00 \n",
"4 3188.00 \n",
"... ... \n",
"48606 2987.00 \n",
"48607 2995.00 \n",
"48608 2990.00 \n",
"48609 2977.00 \n",
"48610 2959.00 \n",
"\n",
"[48611 rows x 8 columns]"
]
},
"execution_count": 422,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_ludn_2 = pd.read_csv( # mezczyzni\n",
" 'LUDN_1342_CREL_2.csv',\n",
" sep=';',\n",
" converters={'Kod': str},\n",
" decimal=',')\n",
"df_ludn_2 = df_ludn_2[['Kod', 'Wiek', 'Rok', 'Wartosc']]\n",
"df_ludn_2 = df_ludn_2.dropna()\n",
"df_ludn_2 = df_ludn_2.pivot_table(index=['Kod', 'Rok'], columns='Wiek', values='Wartosc').reset_index()\n",
"df_ludn_2 = df_ludn_2.rename(columns={\n",
" 'ogółem': 'Ludnosc_mezczyzni',\n",
" 'w wieku poprodukcyjnym': 'Ludnosc_mezczyzni_w_wieku_poprodukcyjnym',\n",
" 'w wieku produkcyjnym': 'Ludnosc_mezczyzni_w_wieku_produkcyjnym',\n",
" 'w wieku produkcyjnym mobilnym': 'Ludnosc_mezczyzni_w_wieku_produkcyjnym_mobilnym',\n",
" 'w wieku produkcyjnym niemobilnym': 'Ludnosc_mezczyzni_w_wieku_produkcyjnym_niemobilnym',\n",
" 'w wieku przedprodukcyjnym': 'Ludnosc_mezczyzni_w_wieku_przedprodukcyjnym'})\n",
"\n",
"df_ludn_2"
]
},
{
"cell_type": "code",
"execution_count": 423,
"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>Wiek</th>\n",
" <th>Kod</th>\n",
" <th>Rok</th>\n",
" <th>Ludnosc_kobiety</th>\n",
" <th>Ludnosc_kobiety_w_wieku_poprodukcyjnym</th>\n",
" <th>Ludnosc_kobiety_w_wieku_produkcyjnym</th>\n",
" <th>Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym</th>\n",
" <th>Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym</th>\n",
" <th>Ludnosc_kobiety_w_wieku_przedprodukcyjnym</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0201011</td>\n",
" <td>2010</td>\n",
" <td>21224.00</td>\n",
" <td>5530.00</td>\n",
" <td>12550.00</td>\n",
" <td>7463.00</td>\n",
" <td>5087.00</td>\n",
" <td>3144.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0201011</td>\n",
" <td>2011</td>\n",
" <td>21134.00</td>\n",
" <td>5798.00</td>\n",
" <td>12249.00</td>\n",
" <td>7400.00</td>\n",
" <td>4849.00</td>\n",
" <td>3087.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0201011</td>\n",
" <td>2012</td>\n",
" <td>20992.00</td>\n",
" <td>6022.00</td>\n",
" <td>11922.00</td>\n",
" <td>7321.00</td>\n",
" <td>4601.00</td>\n",
" <td>3048.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0201011</td>\n",
" <td>2013</td>\n",
" <td>20866.00</td>\n",
" <td>6201.00</td>\n",
" <td>11692.00</td>\n",
" <td>7283.00</td>\n",
" <td>4409.00</td>\n",
" <td>2973.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0201011</td>\n",
" <td>2014</td>\n",
" <td>20824.00</td>\n",
" <td>6351.00</td>\n",
" <td>11475.00</td>\n",
" <td>7203.00</td>\n",
" <td>4272.00</td>\n",
" <td>2998.00</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>48606</th>\n",
" <td>3263011</td>\n",
" <td>2018</td>\n",
" <td>21220.00</td>\n",
" <td>6971.00</td>\n",
" <td>11347.00</td>\n",
" <td>7136.00</td>\n",
" <td>4211.00</td>\n",
" <td>2902.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48607</th>\n",
" <td>3263011</td>\n",
" <td>2019</td>\n",
" <td>21205.00</td>\n",
" <td>7144.00</td>\n",
" <td>11165.00</td>\n",
" <td>7012.00</td>\n",
" <td>4153.00</td>\n",
" <td>2896.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48608</th>\n",
" <td>3263011</td>\n",
" <td>2020</td>\n",
" <td>20970.00</td>\n",
" <td>7213.00</td>\n",
" <td>10927.00</td>\n",
" <td>6812.00</td>\n",
" <td>4115.00</td>\n",
" <td>2830.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48609</th>\n",
" <td>3263011</td>\n",
" <td>2021</td>\n",
" <td>20738.00</td>\n",
" <td>7198.00</td>\n",
" <td>10709.00</td>\n",
" <td>6530.00</td>\n",
" <td>4179.00</td>\n",
" <td>2831.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48610</th>\n",
" <td>3263011</td>\n",
" <td>2022</td>\n",
" <td>20499.00</td>\n",
" <td>7256.00</td>\n",
" <td>10477.00</td>\n",
" <td>6317.00</td>\n",
" <td>4160.00</td>\n",
" <td>2766.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>48611 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
"Wiek Kod Rok Ludnosc_kobiety Ludnosc_kobiety_w_wieku_poprodukcyjnym \n",
"0 0201011 2010 21224.00 5530.00 \\\n",
"1 0201011 2011 21134.00 5798.00 \n",
"2 0201011 2012 20992.00 6022.00 \n",
"3 0201011 2013 20866.00 6201.00 \n",
"4 0201011 2014 20824.00 6351.00 \n",
"... ... ... ... ... \n",
"48606 3263011 2018 21220.00 6971.00 \n",
"48607 3263011 2019 21205.00 7144.00 \n",
"48608 3263011 2020 20970.00 7213.00 \n",
"48609 3263011 2021 20738.00 7198.00 \n",
"48610 3263011 2022 20499.00 7256.00 \n",
"\n",
"Wiek Ludnosc_kobiety_w_wieku_produkcyjnym \n",
"0 12550.00 \\\n",
"1 12249.00 \n",
"2 11922.00 \n",
"3 11692.00 \n",
"4 11475.00 \n",
"... ... \n",
"48606 11347.00 \n",
"48607 11165.00 \n",
"48608 10927.00 \n",
"48609 10709.00 \n",
"48610 10477.00 \n",
"\n",
"Wiek Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym \n",
"0 7463.00 \\\n",
"1 7400.00 \n",
"2 7321.00 \n",
"3 7283.00 \n",
"4 7203.00 \n",
"... ... \n",
"48606 7136.00 \n",
"48607 7012.00 \n",
"48608 6812.00 \n",
"48609 6530.00 \n",
"48610 6317.00 \n",
"\n",
"Wiek Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym \n",
"0 5087.00 \\\n",
"1 4849.00 \n",
"2 4601.00 \n",
"3 4409.00 \n",
"4 4272.00 \n",
"... ... \n",
"48606 4211.00 \n",
"48607 4153.00 \n",
"48608 4115.00 \n",
"48609 4179.00 \n",
"48610 4160.00 \n",
"\n",
"Wiek Ludnosc_kobiety_w_wieku_przedprodukcyjnym \n",
"0 3144.00 \n",
"1 3087.00 \n",
"2 3048.00 \n",
"3 2973.00 \n",
"4 2998.00 \n",
"... ... \n",
"48606 2902.00 \n",
"48607 2896.00 \n",
"48608 2830.00 \n",
"48609 2831.00 \n",
"48610 2766.00 \n",
"\n",
"[48611 rows x 8 columns]"
]
},
"execution_count": 423,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_ludn_3 = pd.read_csv( # kobiety\n",
" 'LUDN_1342_CREL_3.csv',\n",
" sep=';',\n",
" converters={'Kod': str},\n",
" decimal=',')\n",
"df_ludn_3 = df_ludn_3[['Kod', 'Wiek', 'Rok', 'Wartosc']]\n",
"df_ludn_3 = df_ludn_3.dropna()\n",
"df_ludn_3 = df_ludn_3.pivot_table(index=['Kod', 'Rok'], columns='Wiek', values='Wartosc').reset_index()\n",
"df_ludn_3 = df_ludn_3.rename(columns={\n",
" 'ogółem': 'Ludnosc_kobiety',\n",
" 'w wieku poprodukcyjnym': 'Ludnosc_kobiety_w_wieku_poprodukcyjnym',\n",
" 'w wieku produkcyjnym': 'Ludnosc_kobiety_w_wieku_produkcyjnym',\n",
" 'w wieku produkcyjnym mobilnym': 'Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym',\n",
" 'w wieku produkcyjnym niemobilnym': 'Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym',\n",
" 'w wieku przedprodukcyjnym': 'Ludnosc_kobiety_w_wieku_przedprodukcyjnym'})\n",
"\n",
"df_ludn_3"
]
},
{
"cell_type": "code",
"execution_count": 424,
"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": 425,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>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": 425,
"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": 426,
"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": 426,
"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": 427,
"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": 427,
"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": {},
"source": [
"..."
]
},
{
"cell_type": "code",
"execution_count": 428,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2273\n"
]
}
],
"source": [
"df_data = df_dofinansowanie_agg.copy()\n",
"print(len(df_data))"
]
},
{
"cell_type": "code",
"execution_count": 429,
"metadata": {},
"outputs": [],
"source": [
"wojewodztwo_dictionary = {\n",
"'02': 'Dolnoslaskie',\n",
"'04': 'Kujawsko_Pomorskie',\n",
"'06': 'Lubelskie',\n",
"'08': 'Lubuskie',\n",
"'10': 'Lodzkie',\n",
"'12': 'Malopolskie',\n",
"'14': 'Mazowieckie',\n",
"'16': 'Opolskie',\n",
"'18': 'Podkarpackie',\n",
"'20': 'Podlaskie',\n",
"'22': 'Pomorskie',\n",
"'24': 'Slaskie',\n",
"'26': 'Swietokrzyskie',\n",
"'28': 'Warminsko_Mazurskie',\n",
"'30': 'Wielkopolskie',\n",
"'32': 'Zachodniopomorskie'}\n",
"\n",
"df_data = pd.concat([df_data, pd.get_dummies(df_data['Kod'].apply(lambda x: wojewodztwo_dictionary.get(x[:2], None)), prefix='Wojewodztwo').astype(int)], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 430,
"metadata": {},
"outputs": [],
"source": [
"rodzaj_gminy_dictionary = {\n",
"'1': 'Gmina miejska',\n",
"'2': 'Gmina wiejska',\n",
"'3': 'Gmina miejsko-wiejska'}\n",
"\n",
"df_data = pd.concat([df_data, pd.get_dummies(df_data['Kod'].apply(lambda x: rodzaj_gminy_dictionary.get(x[-1], None))).astype(int)], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 431,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2273\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)\n",
"print(len(df_data))"
]
},
{
"cell_type": "code",
"execution_count": 432,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2273\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)\n",
"print(len(df_data))"
]
},
{
"cell_type": "code",
"execution_count": 433,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2273\n",
"2273\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",
"print(len(df_data))"
]
},
{
"cell_type": "code",
"execution_count": 434,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2273\n",
"2273\n",
"2273\n",
"2273\n",
"2273\n"
]
}
],
"source": [
"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=['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=['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": 435,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2273\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": 436,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2273\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": 437,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.6733698870134954"
]
},
"execution_count": 437,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_data['Gestosc_zaludnienia'] = df_data['Ludnosc'] / df_data['Powierzchnia']\n",
"\n",
"df_data['Gestosc_zaludnienia'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 438,
"metadata": {},
"outputs": [],
"source": [
"df_data['Suma'] = df_data['Suma'] / df_data['Ludnosc']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"..."
]
},
{
"cell_type": "code",
"execution_count": 439,
"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>Suma</th>\n",
" <th>Ludnosc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>62917.04</td>\n",
" <td>39.46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3520.19</td>\n",
" <td>39.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>224163.57</td>\n",
" <td>37.66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>32473.38</td>\n",
" <td>14.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>25713.81</td>\n",
" <td>5.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2268</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2269</th>\n",
" <td>196539.43</td>\n",
" <td>41.28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2270</th>\n",
" <td>7900.24</td>\n",
" <td>41.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2271</th>\n",
" <td>11386418.96</td>\n",
" <td>41.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2272</th>\n",
" <td>188601.28</td>\n",
" <td>40.33</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2273 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" Suma Ludnosc\n",
"0 62917.04 39.46\n",
"1 3520.19 39.08\n",
"2 224163.57 37.66\n",
"3 32473.38 14.09\n",
"4 25713.81 5.35\n",
"... ... ...\n",
"2268 NaN NaN\n",
"2269 196539.43 41.28\n",
"2270 7900.24 41.15\n",
"2271 11386418.96 41.03\n",
"2272 188601.28 40.33\n",
"\n",
"[2273 rows x 2 columns]"
]
},
"execution_count": 439,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_data[['Suma', 'Ludnosc']]"
]
},
{
"cell_type": "code",
"execution_count": 440,
"metadata": {},
"outputs": [],
"source": [
"# df_data[df_data.isna().any(axis=1)] # ['Rok'].drop_duplicates().reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 441,
"metadata": {},
"outputs": [],
"source": [
"s = df_data.isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": 442,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Wymeldowania_za_granice_kobiety 775\n",
"Wymeldowania_za_granice_mezczyzni 775\n",
"Wymeldowania_za_granice_ogolem 775\n",
"Turysci_ogolem 1724\n",
"Turysci_zagraniczni 1724\n",
"Bezrobotni_do_30_roku_zycia 657\n",
"dtype: int64"
]
},
"execution_count": 442,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[s > 330]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"..."
]
},
{
"cell_type": "code",
"execution_count": 443,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2273\n",
"2191\n",
"Mean Squared Error: 20582414771111.633\n"
]
}
],
"source": [
"from sklearn.model_selection import train_test_split\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[[\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",
" 'Wynagrodzenie_ogolem', # 2\n",
" 'Wynagrodzenie_w_relacji_do_sredniej', # 3\n",
" 'Dochody_podatek_lesny', # 4\n",
" 'Dochody_podatek_PCC', # 5\n",
" 'Dochody_podatek_od_dzialalnosci_gospodarczej', # 6\n",
" 'Dochody_podatek_od_nieruchomosci', # 7\n",
" 'Dochody_podatek_od_spadkow', # 8\n",
" 'Dochody_podatek_od_srodkow_transportowych', # 9\n",
" 'Dochody_podatek_rolny', # 10\n",
" 'Dochody_podatek_odrebne_ustawy', # 11\n",
" 'Dochody_razem', # 12\n",
" 'Dochody_z_majatku', # 13\n",
" 'Dochody_z_najmu_i_dzierzawy', # 14\n",
" 'Dochody_z_uslug', # 15\n",
" 'Dochody_dofinansowanie_inwestycyjne', # 16\n",
" 'Dochody_dofinansowanie_razem', # 17\n",
" 'Udzialy_w_podatkach_dochodowych_od_osob_fizycznych', # 18\n",
" 'Udzialy_w_podatkach_dochodowych_od_osob_prywatnych', # 19\n",
" 'Udzialy_w_podatkach_dochodowych_razem', # 20\n",
" 'Wplywy_z_innych_lokalnych_oplat', # 21\n",
" 'Wplywy_z_oplaty_eksploatacyjnej', # 22\n",
" 'Wplywy_z_oplaty_skarbowej', # 23\n",
" 'Wplywy_z_oplaty_targowej', # 24\n",
" 'Ludnosc_ogolem', # 25\n",
" 'Ludnosc_w_wieku_poprodukcyjnym', # 26\n",
" 'Ludnosc_w_wieku_produkcyjnym', # 27\n",
" 'Ludnosc_w_wieku_produkcyjnym_mobilnym', # 28\n",
" 'Ludnosc_w_wieku_produkcyjnym_niemobilnym', # 29\n",
" 'Ludnosc_w_wieku_przedprodukcyjnym', # 30\n",
" 'Ludnosc_mezczyzni', # 31\n",
" 'Ludnosc_mezczyzni_w_wieku_poprodukcyjnym', # 32\n",
" 'Ludnosc_mezczyzni_w_wieku_produkcyjnym', # 33\n",
" 'Ludnosc_mezczyzni_w_wieku_produkcyjnym_mobilnym', # 34\n",
" 'Ludnosc_mezczyzni_w_wieku_produkcyjnym_niemobilnym', # 35\n",
" 'Ludnosc_mezczyzni_w_wieku_przedprodukcyjnym', # 36\n",
" 'Ludnosc_kobiety', # 37\n",
" 'Ludnosc_kobiety_w_wieku_poprodukcyjnym', # 38\n",
" 'Ludnosc_kobiety_w_wieku_produkcyjnym', # 39\n",
" 'Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym', # 40\n",
" 'Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym', # 41\n",
" 'Ludnosc_kobiety_w_wieku_przedprodukcyjnym', # 42\n",
" 'Wojewodztwo_Dolnoslaskie', # 43\n",
" 'Wojewodztwo_Kujawsko_Pomorskie', # 44\n",
" 'Wojewodztwo_Lubelskie', # 45\n",
" 'Wojewodztwo_Lubuskie', # 46\n",
" 'Wojewodztwo_Lodzkie', # 47\n",
" 'Wojewodztwo_Malopolskie', # 48\n",
" 'Wojewodztwo_Mazowieckie', # 49\n",
" 'Wojewodztwo_Opolskie', # 50\n",
" 'Wojewodztwo_Podkarpackie', # 51\n",
" 'Wojewodztwo_Podlaskie', # 52\n",
" 'Wojewodztwo_Pomorskie', # 53\n",
" 'Wojewodztwo_Slaskie', # 54\n",
" 'Wojewodztwo_Swietokrzyskie', # 55\n",
" 'Wojewodztwo_Warminsko_Mazurskie', # 56\n",
" 'Wojewodztwo_Wielkopolskie', # 57\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",
"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)"
]
},
{
"cell_type": "code",
"execution_count": 444,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gmina wiejska 0.14\n",
"Wojewodztwo_Dolnoslaskie 0.09\n",
"Dochody_podatek_rolny 0.07\n",
"Saldo_migracji_na_1000_ludnosci 0.06\n",
"Zmiana_liczby_ludnosci 0.05\n",
"Wojewodztwo_Warminsko_Mazurskie 0.05\n",
"Wojewodztwo_Pomorskie 0.04\n",
"Wojewodztwo_Opolskie 0.02\n",
"Saldo_migracji 0.01\n",
"Wojewodztwo_Mazowieckie 0.01\n",
"Turysci_ogolem 0.01\n",
"Turysci_zagraniczni 0.01\n",
"Wojewodztwo_Podkarpackie -0.00\n",
"Wplywy_z_oplaty_eksploatacyjnej -0.01\n",
"Wojewodztwo_Swietokrzyskie -0.01\n",
"Wojewodztwo_Zachodniopomorskie -0.01\n",
"Powierzchnia -0.01\n",
"Wojewodztwo_Slaskie -0.01\n",
"Wojewodztwo_Lubelskie -0.01\n",
"Obiekty_ogolem -0.02\n",
"Wojewodztwo_Lubuskie -0.02\n",
"Miejsca_noclegowe_ogolem -0.02\n",
"Wojewodztwo_Podlaskie -0.02\n",
"Dochody_podatek_PCC -0.02\n",
"Dochody_podatek_od_spadkow -0.02\n",
"Udzialy_w_podatkach_dochodowych_od_osob_prywatnych -0.02\n",
"Wynagrodzenie_w_relacji_do_sredniej -0.02\n",
"Dochody_z_uslug -0.02\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": 445,
"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, 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": 446,
"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": 447,
"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**6)\n",
"plt.ylim(0, 3*10**6)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 448,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|--- Wplywy_z_oplaty_eksploatacyjnej <= -7751.83\n",
"| |--- value: [132625880.92]\n",
"|--- Wplywy_z_oplaty_eksploatacyjnej > -7751.83\n",
"| |--- Wplywy_z_oplaty_skarbowej <= 4494.45\n",
"| | |--- Wymeldowania_ogolem <= 56.50\n",
"| | | |--- Ludnosc_ogolem <= 3786.50\n",
"| | | | |--- Wplywy_z_oplaty_skarbowej <= 3336.50\n",
"| | | | | |--- Dochody_podatek_rolny <= 52241.90\n",
"| | | | | | |--- value: [223720.88]\n",
"| | | | | |--- Dochody_podatek_rolny > 52241.90\n",
"| | | | | | |--- Zameldowania_z_miast_ogolem <= 5.50\n",
"| | | | | | | |--- value: [146790.02]\n",
"| | | | | | |--- Zameldowania_z_miast_ogolem > 5.50\n",
"| | | | | | | |--- value: [147649.40]\n",
"| | | | |--- Wplywy_z_oplaty_skarbowej > 3336.50\n",
"| | | | | |--- Wymeldowania_do_miast_mezczyzni <= 4.00\n",
"| | | | | | |--- value: [4988446.69]\n",
"| | | | | |--- Wymeldowania_do_miast_mezczyzni > 4.00\n",
"| | | | | | |--- Dochody_z_najmu_i_dzierzawy <= 47510.35\n",
"| | | | | | | |--- value: [8029322.26]\n",
"| | | | | | |--- Dochody_z_najmu_i_dzierzawy > 47510.35\n",
"| | | | | | | |--- value: [7792324.25]\n",
"| | | |--- Ludnosc_ogolem > 3786.50\n",
"| | | | |--- value: [35264017.53]\n",
"| | |--- Wymeldowania_ogolem > 56.50\n",
"| | | |--- value: [106444841.13]\n",
"| |--- Wplywy_z_oplaty_skarbowej > 4494.45\n",
"| | |--- Wplywy_z_oplaty_skarbowej <= 12330.00\n",
"| | | |--- Dochody_podatek_od_dzialalnosci_gospodarczej <= -156.50\n",
"| | | | |--- value: [64551082.93]\n",
"| | | |--- Dochody_podatek_od_dzialalnosci_gospodarczej > -156.50\n",
"| | | | |--- Dochody_podatek_lesny <= 1523.00\n",
"| | | | | |--- Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym <= 393.00\n",
"| | | | | | |--- value: [50040444.36]\n",
"| | | | | |--- Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym > 393.00\n",
"| | | | | | |--- value: [593983.01]\n",
"| | | | |--- Dochody_podatek_lesny > 1523.00\n",
"| | | | | |--- Dochody_podatek_PCC <= 206681.93\n",
"| | | | | | |--- Bezrobotni_mezczyzni <= 112.00\n",
"| | | | | | | |--- Wplywy_z_oplaty_targowej <= 1527.50\n",
"| | | | | | | | |--- Zameldowania_z_miast_ogolem <= 26.50\n",
"| | | | | | | | | |--- Zameldowania_mezczyzni <= 6.00\n",
"| | | | | | | | | | |--- Zameldowania_kobiety <= 6.00\n",
"| | | | | | | | | | | |--- value: [1029930.53]\n",
"| | | | | | | | | | |--- Zameldowania_kobiety > 6.00\n",
"| | | | | | | | | | | |--- value: [1142691.81]\n",
"| | | | | | | | | |--- Zameldowania_mezczyzni > 6.00\n",
"| | | | | | | | | | |--- Dochody_podatek_lesny <= 8673.77\n",
"| | | | | | | | | | | |--- value: [1080165.70]\n",
"| | | | | | | | | | |--- Dochody_podatek_lesny > 8673.77\n",
"| | | | | | | | | | | |--- truncated branch of depth 11\n",
"| | | | | | | | |--- Zameldowania_z_miast_ogolem > 26.50\n",
"| | | | | | | | | |--- Wymeldowania_mezczyzni <= 27.50\n",
"| | | | | | | | | | |--- Dochody_podatek_od_srodkow_transportowych <= 144168.41\n",
"| | | | | | | | | | | |--- truncated branch of depth 3\n",
"| | | | | | | | | | |--- Dochody_podatek_od_srodkow_transportowych > 144168.41\n",
"| | | | | | | | | | | |--- value: [3266211.60]\n",
"| | | | | | | | | |--- Wymeldowania_mezczyzni > 27.50\n",
"| | | | | | | | | | |--- Wymeldowania_na_wies_ogolem <= 34.50\n",
"| | | | | | | | | | | |--- truncated branch of depth 4\n",
"| | | | | | | | | | |--- Wymeldowania_na_wies_ogolem > 34.50\n",
"| | | | | | | | | | | |--- value: [886572.54]\n",
"| | | | | | | |--- Wplywy_z_oplaty_targowej > 1527.50\n",
"| | | | | | | | |--- Dochody_dofinansowanie_inwestycyjne <= 500.00\n",
"| | | | | | | | | |--- Ludnosc_ogolem <= 3521.00\n",
"| | | | | | | | | | |--- Wplywy_z_oplaty_targowej <= 9416.50\n",
"| | | | | | | | | | | |--- value: [2852332.52]\n",
"| | | | | | | | | | |--- Wplywy_z_oplaty_targowej > 9416.50\n",
"| | | | | | | | | | | |--- value: [2869988.58]\n",
"| | | | | | | | | |--- Ludnosc_ogolem > 3521.00\n",
"| | | | | | | | | | |--- Dochody_podatek_od_spadkow <= 6173.00\n",
"| | | | | | | | | | | |--- value: [4041277.35]\n",
"| | | | | | | | | | |--- Dochody_podatek_od_spadkow > 6173.00\n",
"| | | | | | | | | | | |--- truncated branch of depth 2\n",
"| | | | | | | | |--- Dochody_dofinansowanie_inwestycyjne > 500.00\n",
"| | | | | | | | | |--- Zameldowania_ze_wsi_mezczyzni <= 11.00\n",
"| | | | | | | | | | |--- Dochody_z_uslug <= 254898.80\n",
"| | | | | | | | | | | |--- truncated branch of depth 2\n",
"| | | | | | | | | | |--- Dochody_z_uslug > 254898.80\n",
"| | | | | | | | | | | |--- truncated branch of depth 4\n",
"| | | | | | | | | |--- Zameldowania_ze_wsi_mezczyzni > 11.00\n",
"| | | | | | | | | | |--- Wymeldowania_na_wies_ogolem <= 21.00\n",
"| | | | | | | | | | | |--- value: [3043529.06]\n",
"| | | | | | | | | | |--- Wymeldowania_na_wies_ogolem > 21.00\n",
"| | | | | | | | | | | |--- value: [1660371.13]\n",
"| | | | | | |--- Bezrobotni_mezczyzni > 112.00\n",
"| | | | | | | |--- Dochody_podatek_od_srodkow_transportowych <= 112158.71\n",
"| | | | | | | | |--- Zameldowania_z_miast_kobiety <= 29.50\n",
"| | | | | | | | | |--- Wymeldowania_na_wies_mezczyzni <= 11.50\n",
"| | | | | | | | | | |--- Udzialy_w_podatkach_dochodowych_razem <= 2784626.12\n",
"| | | | | | | | | | | |--- truncated branch of depth 5\n",
"| | | | | | | | | | |--- Udzialy_w_podatkach_dochodowych_razem > 2784626.12\n",
"| | | | | | | | | | | |--- value: [1011703.86]\n",
"| | | | | | | | | |--- Wymeldowania_na_wies_mezczyzni > 11.50\n",
"| | | | | | | | | | |--- Zameldowania_z_miast_ogolem <= 19.00\n",
"| | | | | | | | | | | |--- truncated branch of depth 3\n",
"| | | | | | | | | | |--- Zameldowania_z_miast_ogolem > 19.00\n",
"| | | | | | | | | | | |--- value: [5361057.90]\n",
"| | | | | | | | |--- Zameldowania_z_miast_kobiety > 29.50\n",
"| | | | | | | | | |--- value: [17138312.52]\n",
"| | | | | | | |--- Dochody_podatek_od_srodkow_transportowych > 112158.71\n",
"| | | | | | | | |--- Udzialy_w_podatkach_dochodowych_od_osob_fizycznych <= 2183203.00\n",
"| | | | | | | | | |--- Ludnosc_kobiety_w_wieku_produkcyjnym <= 1325.50\n",
"| | | | | | | | | | |--- value: [16479146.65]\n",
"| | | | | | | | | |--- Ludnosc_kobiety_w_wieku_produkcyjnym > 1325.50\n",
"| | | | | | | | | | |--- Udzialy_w_podatkach_dochodowych_razem <= 1710148.12\n",
"| | | | | | | | | | | |--- truncated branch of depth 2\n",
"| | | | | | | | | | |--- Udzialy_w_podatkach_dochodowych_razem > 1710148.12\n",
"| | | | | | | | | | | |--- value: [19699871.32]\n",
"| | | | | | | | |--- Udzialy_w_podatkach_dochodowych_od_osob_fizycznych > 2183203.00\n",
"| | | | | | | | | |--- Ludnosc_kobiety <= 3141.00\n",
"| | | | | | | | | | |--- value: [5923600.26]\n",
"| | | | | | | | | |--- Ludnosc_kobiety > 3141.00\n",
"| | | | | | | | | | |--- value: [6925131.43]\n",
"| | | | | |--- Dochody_podatek_PCC > 206681.93\n",
"| | | | | | |--- Bezrobotne_kobiety <= 57.00\n",
"| | | | | | | |--- value: [22537878.79]\n",
"| | | | | | |--- Bezrobotne_kobiety > 57.00\n",
"| | | | | | | |--- value: [24414621.52]\n",
"| | |--- Wplywy_z_oplaty_skarbowej > 12330.00\n",
"| | | |--- Wymeldowania_na_wies_mezczyzni <= 20.50\n",
"| | | | |--- Dochody_podatek_lesny <= 15.00\n",
"| | | | | |--- Dochody_dofinansowanie_razem <= 257331.97\n",
"| | | | | | |--- value: [3008745.87]\n",
"| | | | | |--- Dochody_dofinansowanie_razem > 257331.97\n",
"| | | | | | |--- value: [70149861.07]\n",
"| | | | |--- Dochody_podatek_lesny > 15.00\n",
"| | | | | |--- Turysci_zagraniczni <= 3835.00\n",
"| | | | | | |--- Wplywy_z_oplaty_targowej <= 1021444.50\n",
"| | | | | | | |--- Wojewodztwo_Opolskie <= 0.50\n",
"| | | | | | | | |--- Turysci_zagraniczni <= 620.50\n",
"| | | | | | | | | |--- Dochody_z_najmu_i_dzierzawy <= 60187.61\n",
"| | | | | | | | | | |--- Dochody_z_najmu_i_dzierzawy <= 59932.48\n",
"| | | | | | | | | | | |--- truncated branch of depth 16\n",
"| | | | | | | | | | |--- Dochody_z_najmu_i_dzierzawy > 59932.48\n",
"| | | | | | | | | | | |--- value: [23878760.16]\n",
"| | | | | | | | | |--- Dochody_z_najmu_i_dzierzawy > 60187.61\n",
"| | | | | | | | | | |--- Wynagrodzenie_ogolem <= 6881.45\n",
"| | | | | | | | | | | |--- truncated branch of depth 31\n",
"| | | | | | | | | | |--- Wynagrodzenie_ogolem > 6881.45\n",
"| | | | | | | | | | | |--- truncated branch of depth 2\n",
"| | | | | | | | |--- Turysci_zagraniczni > 620.50\n",
"| | | | | | | | | |--- Dochody_podatek_rolny <= 1169820.38\n",
"| | | | | | | | | | |--- Wojewodztwo_Lubelskie <= 0.50\n",
"| | | | | | | | | | | |--- truncated branch of depth 7\n",
"| | | | | | | | | | |--- Wojewodztwo_Lubelskie > 0.50\n",
"| | | | | | | | | | | |--- value: [10510654.58]\n",
"| | | | | | | | | |--- Dochody_podatek_rolny > 1169820.38\n",
"| | | | | | | | | | |--- Ludnosc_w_wieku_produkcyjnym <= 7303.50\n",
"| | | | | | | | | | | |--- value: [16143701.14]\n",
"| | | | | | | | | | |--- Ludnosc_w_wieku_produkcyjnym > 7303.50\n",
"| | | | | | | | | | | |--- value: [9428912.87]\n",
"| | | | | | | |--- Wojewodztwo_Opolskie > 0.50\n",
"| | | | | | | | |--- Gestosc_zaludnienia <= 0.04\n",
"| | | | | | | | | |--- value: [29647883.87]\n",
"| | | | | | | | |--- Gestosc_zaludnienia > 0.04\n",
"| | | | | | | | | |--- Dochody_podatek_od_spadkow <= 66477.52\n",
"| | | | | | | | | | |--- Dochody_podatek_od_dzialalnosci_gospodarczej <= 19548.15\n",
"| | | | | | | | | | | |--- truncated branch of depth 5\n",
"| | | | | | | | | | |--- Dochody_podatek_od_dzialalnosci_gospodarczej > 19548.15\n",
"| | | | | | | | | | | |--- truncated branch of depth 3\n",
"| | | | | | | | | |--- Dochody_podatek_od_spadkow > 66477.52\n",
"| | | | | | | | | | |--- value: [11839160.68]\n",
"| | | | | | |--- Wplywy_z_oplaty_targowej > 1021444.50\n",
"| | | | | | | |--- value: [16775284.13]\n",
"| | | | | |--- Turysci_zagraniczni > 3835.00\n",
"| | | | | | |--- Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym <= 2067.50\n",
"| | | | | | | |--- Ludnosc_w_wieku_poprodukcyjnym <= 1294.00\n",
"| | | | | | | | |--- Dochody_podatek_od_srodkow_transportowych <= 350567.75\n",
"| | | | | | | | | |--- Dochody_podatek_od_nieruchomosci <= 10820965.50\n",
"| | | | | | | | | | |--- Wymeldowania_na_wies_ogolem <= 22.50\n",
"| | | | | | | | | | | |--- truncated branch of depth 3\n",
"| | | | | | | | | | |--- Wymeldowania_na_wies_ogolem > 22.50\n",
"| | | | | | | | | | | |--- value: [5838.01]\n",
"| | | | | | | | | |--- Dochody_podatek_od_nieruchomosci > 10820965.50\n",
"| | | | | | | | | | |--- value: [523333.48]\n",
"| | | | | | | | |--- Dochody_podatek_od_srodkow_transportowych > 350567.75\n",
"| | | | | | | | | |--- value: [3219570.01]\n",
"| | | | | | | |--- Ludnosc_w_wieku_poprodukcyjnym > 1294.00\n",
"| | | | | | | | |--- Bezrobotni_ogolem <= 326.25\n",
"| | | | | | | | | |--- value: [9680488.61]\n",
"| | | | | | | | |--- Bezrobotni_ogolem > 326.25\n",
"| | | | | | | | | |--- value: [6533125.31]\n",
"| | | | | | |--- Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym > 2067.50\n",
"| | | | | | | |--- value: [68031871.39]\n",
"| | | |--- Wymeldowania_na_wies_mezczyzni > 20.50\n",
"| | | | |--- Dochody_podatek_rolny <= 3939909.12\n",
"| | | | | |--- Turysci_zagraniczni <= 14.50\n",
"| | | | | | |--- Dochody_podatek_od_srodkow_transportowych <= 510970.09\n",
"| | | | | | | |--- Dochody_podatek_od_dzialalnosci_gospodarczej <= 574395.34\n",
"| | | | | | | | |--- Bezrobotni_do_25_roku_zycia <= 287.25\n",
"| | | | | | | | | |--- Wynagrodzenie_ogolem <= 4332.75\n",
"| | | | | | | | | | |--- Wynagrodzenie_w_relacji_do_sredniej <= 97.90\n",
"| | | | | | | | | | | |--- truncated branch of depth 27\n",
"| | | | | | | | | | |--- Wynagrodzenie_w_relacji_do_sredniej > 97.90\n",
"| | | | | | | | | | | |--- truncated branch of depth 3\n",
"| | | | | | | | | |--- Wynagrodzenie_ogolem > 4332.75\n",
"| | | | | | | | | | |--- Udzialy_w_podatkach_dochodowych_od_osob_prywatnych <= 1913257.88\n",
"| | | | | | | | | | | |--- truncated branch of depth 23\n",
"| | | | | | | | | | |--- Udzialy_w_podatkach_dochodowych_od_osob_prywatnych > 1913257.88\n",
"| | | | | | | | | | | |--- truncated branch of depth 7\n",
"| | | | | | | | |--- Bezrobotni_do_25_roku_zycia > 287.25\n",
"| | | | | | | | | |--- value: [4226674.84]\n",
"| | | | | | | |--- Dochody_podatek_od_dzialalnosci_gospodarczej > 574395.34\n",
"| | | | | | | | |--- value: [11386418.96]\n",
"| | | | | | |--- Dochody_podatek_od_srodkow_transportowych > 510970.09\n",
"| | | | | | | |--- Wplywy_z_innych_lokalnych_oplat <= 4156267.12\n",
"| | | | | | | | |--- Wplywy_z_innych_lokalnych_oplat <= 4121841.12\n",
"| | | | | | | | | |--- Wynagrodzenie_w_relacji_do_sredniej <= 111.65\n",
"| | | | | | | | | | |--- Ludnosc_na_1_km2 <= 233.25\n",
"| | | | | | | | | | | |--- truncated branch of depth 21\n",
"| | | | | | | | | | |--- Ludnosc_na_1_km2 > 233.25\n",
"| | | | | | | | | | | |--- truncated branch of depth 17\n",
"| | | | | | | | | |--- Wynagrodzenie_w_relacji_do_sredniej > 111.65\n",
"| | | | | | | | | | |--- Bezrobotni_ogolem <= 485.50\n",
"| | | | | | | | | | | |--- value: [3730137.10]\n",
"| | | | | | | | | | |--- Bezrobotni_ogolem > 485.50\n",
"| | | | | | | | | | | |--- value: [307553.43]\n",
"| | | | | | | | |--- Wplywy_z_innych_lokalnych_oplat > 4121841.12\n",
"| | | | | | | | | |--- Wplywy_z_oplaty_eksploatacyjnej <= 14062.80\n",
"| | | | | | | | | | |--- value: [4671836.69]\n",
"| | | | | | | | | |--- Wplywy_z_oplaty_eksploatacyjnej > 14062.80\n",
"| | | | | | | | | | |--- value: [863032.31]\n",
"| | | | | | | |--- Wplywy_z_innych_lokalnych_oplat > 4156267.12\n",
"| | | | | | | | |--- Gestosc_zaludnienia <= 3.88\n",
"| | | | | | | | | |--- Wynagrodzenie_ogolem <= 3962.87\n",
"| | | | | | | | | | |--- Wskaznik_urbanizacji <= 45.15\n",
"| | | | | | | | | | | |--- truncated branch of depth 3\n",
"| | | | | | | | | | |--- Wskaznik_urbanizacji > 45.15\n",
"| | | | | | | | | | | |--- truncated branch of depth 22\n",
"| | | | | | | | | |--- Wynagrodzenie_ogolem > 3962.87\n",
"| | | | | | | | | | |--- Wplywy_z_oplaty_eksploatacyjnej <= 5666409.75\n",
"| | | | | | | | | | | |--- truncated branch of depth 22\n",
"| | | | | | | | | | |--- Wplywy_z_oplaty_eksploatacyjnej > 5666409.75\n",
"| | | | | | | | | | | |--- value: [1478204.01]\n",
"| | | | | | | | |--- Gestosc_zaludnienia > 3.88\n",
"| | | | | | | | | |--- value: [2622969.18]\n",
"| | | | | |--- Turysci_zagraniczni > 14.50\n",
"| | | | | | |--- Udzialy_w_podatkach_dochodowych_od_osob_prywatnych <= 115357.00\n",
"| | | | | | | |--- Bezrobotni_do_25_roku_zycia <= 231.75\n",
"| | | | | | | | |--- Dochody_podatek_rolny <= 751889.41\n",
"| | | | | | | | | |--- Miejsca_noclegowe_caloroczne <= 26.00\n",
"| | | | | | | | | | |--- value: [2137619.22]\n",
"| | | | | | | | | |--- Miejsca_noclegowe_caloroczne > 26.00\n",
"| | | | | | | | | | |--- Dochody_podatek_od_dzialalnosci_gospodarczej <= 21053.86\n",
"| | | | | | | | | | | |--- truncated branch of depth 2\n",
"| | | | | | | | | | |--- Dochody_podatek_od_dzialalnosci_gospodarczej > 21053.86\n",
"| | | | | | | | | | | |--- truncated branch of depth 4\n",
"| | | | | | | | |--- Dochody_podatek_rolny > 751889.41\n",
"| | | | | | | | | |--- Wymeldowania_do_miast_ogolem <= 103.00\n",
"| | | | | | | | | | |--- value: [6335790.21]\n",
"| | | | | | | | | |--- Wymeldowania_do_miast_ogolem > 103.00\n",
"| | | | | | | | | | |--- Obiekty_caloroczne <= 1.50\n",
"| | | | | | | | | | | |--- value: [3478051.49]\n",
"| | | | | | | | | | |--- Obiekty_caloroczne > 1.50\n",
"| | | | | | | | | | | |--- value: [2808492.26]\n",
"| | | | | | | |--- Bezrobotni_do_25_roku_zycia > 231.75\n",
"| | | | | | | | |--- Zameldowania_mezczyzni <= 87.50\n",
"| | | | | | | | | |--- value: [9530029.04]\n",
"| | | | | | | | |--- Zameldowania_mezczyzni > 87.50\n",
"| | | | | | | | | |--- value: [14823699.73]\n",
"| | | | | | |--- Udzialy_w_podatkach_dochodowych_od_osob_prywatnych > 115357.00\n",
"| | | | | | | |--- Wojewodztwo_Opolskie <= 0.50\n",
"| | | | | | | | |--- Dochody_z_uslug <= 4892.30\n",
"| | | | | | | | | |--- Wymeldowania_kobiety <= 110.00\n",
"| | | | | | | | | | |--- value: [726982.20]\n",
"| | | | | | | | | |--- Wymeldowania_kobiety > 110.00\n",
"| | | | | | | | | | |--- value: [7756910.77]\n",
"| | | | | | | | |--- Dochody_z_uslug > 4892.30\n",
"| | | | | | | | | |--- Wynagrodzenie_w_relacji_do_sredniej <= 70.60\n",
"| | | | | | | | | | |--- Wskaznik_urbanizacji <= 58.35\n",
"| | | | | | | | | | | |--- value: [3473795.48]\n",
"| | | | | | | | | | |--- Wskaznik_urbanizacji > 58.35\n",
"| | | | | | | | | | | |--- value: [4769144.63]\n",
"| | | | | | | | | |--- Wynagrodzenie_w_relacji_do_sredniej > 70.60\n",
"| | | | | | | | | | |--- Wplywy_z_innych_lokalnych_oplat <= 566851.53\n",
"| | | | | | | | | | | |--- truncated branch of depth 9\n",
"| | | | | | | | | | |--- Wplywy_z_innych_lokalnych_oplat > 566851.53\n",
"| | | | | | | | | | | |--- truncated branch of depth 23\n",
"| | | | | | | |--- Wojewodztwo_Opolskie > 0.50\n",
"| | | | | | | | |--- Udzialy_w_podatkach_dochodowych_od_osob_fizycznych <= 9089692.00\n",
"| | | | | | | | | |--- value: [12432814.85]\n",
"| | | | | | | | |--- Udzialy_w_podatkach_dochodowych_od_osob_fizycznych > 9089692.00\n",
"| | | | | | | | | |--- Zameldowania_z_miast_kobiety <= 140.50\n",
"| | | | | | | | | | |--- Dochody_podatek_lesny <= 120971.51\n",
"| | | | | | | | | | | |--- truncated branch of depth 3\n",
"| | | | | | | | | | |--- Dochody_podatek_lesny > 120971.51\n",
"| | | | | | | | | | | |--- value: [1383897.89]\n",
"| | | | | | | | | |--- Zameldowania_z_miast_kobiety > 140.50\n",
"| | | | | | | | | | |--- value: [4817436.62]\n",
"| | | | |--- Dochody_podatek_rolny > 3939909.12\n",
"| | | | | |--- value: [9110050.20]\n",
"\n"
]
}
],
"source": [
"print(export_text(model, feature_names=feature_names))"
]
},
{
"cell_type": "code",
"execution_count": 449,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.32120 — Wplywy_z_oplaty_eksploatacyjnej\n",
"0.15728 — Wymeldowania_ogolem\n",
"0.07384 — Wplywy_z_oplaty_skarbowej\n",
"0.07115 — Dochody_podatek_od_dzialalnosci_gospodarczej\n",
"0.07098 — Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym\n",
"0.06455 — Dochody_podatek_lesny\n",
"0.04225 — Dochody_dofinansowanie_razem\n",
"0.02294 — Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym\n",
"0.01696 — Dochody_podatek_PCC\n",
"0.01605 — Ludnosc_ogolem\n",
"0.01528 — Turysci_zagraniczni\n",
"0.01449 — Dochody_podatek_od_srodkow_transportowych\n",
"0.01294 — Gestosc_zaludnienia\n",
"0.01121 — Dochody_z_najmu_i_dzierzawy\n",
"0.00803 — Bezrobotni_mezczyzni\n",
"0.00639 — Udzialy_w_podatkach_dochodowych_od_osob_fizycznych\n",
"0.00608 — Dochody_podatek_rolny\n",
"0.00596 — Wymeldowania_na_wies_mezczyzni\n",
"0.00590 — Wynagrodzenie_ogolem\n",
"0.00527 — Wymeldowania_kobiety\n",
"0.00507 — Wplywy_z_oplaty_targowej\n",
"0.00475 — Zameldowania_z_miast_kobiety\n",
"0.00388 — Saldo_migracji_na_1000_ludnosci\n",
"0.00365 — Bezrobotni_do_25_roku_zycia\n",
"0.00357 — Wojewodztwo_Opolskie\n",
"0.00292 — Zmiana_liczby_ludnosci\n",
"0.00276 — Wynagrodzenie_w_relacji_do_sredniej\n",
"0.00266 — Dochody_podatek_od_spadkow\n",
"0.00202 — Ludnosc_w_wieku_poprodukcyjnym\n",
"0.00195 — Udzialy_w_podatkach_dochodowych_od_osob_prywatnych\n",
"0.00188 — Wplywy_z_innych_lokalnych_oplat\n",
"0.00156 — Wojewodztwo_Lubelskie\n",
"0.00134 — Wymeldowania_na_wies_kobiety\n",
"0.00106 — Udzialy_w_podatkach_dochodowych_razem\n",
"0.00088 — Ludnosc_kobiety_w_wieku_poprodukcyjnym\n",
"0.00088 — Dochody_z_uslug\n",
"0.00079 — Powierzchnia\n",
"0.00066 — Dochody_podatek_od_nieruchomosci\n",
"0.00062 — Zameldowania_z_miast_ogolem\n",
"0.00057 — Zameldowania_z_miast_mezczyzni\n",
"0.00055 — Zameldowania_mezczyzni\n",
"0.00054 — Zameldowania_ze_wsi_mezczyzni\n",
"0.00047 — Ludnosc_w_wieku_produkcyjnym\n",
"0.00042 — Miejsca_noclegowe_ogolem\n",
"0.00036 — Dochody_dofinansowanie_inwestycyjne\n",
"0.00036 — Ludnosc_mezczyzni_w_wieku_produkcyjnym\n",
"0.00035 — Wymeldowania_na_wies_ogolem\n",
"0.00032 — Ludnosc_kobiety_w_wieku_produkcyjnym\n",
"0.00030 — Wojewodztwo_Podkarpackie\n",
"0.00028 — Wymeldowania_do_miast_ogolem\n",
"0.00027 — Wymeldowania_do_miast_kobiety\n",
"0.00027 — Dochody_z_majatku\n",
"0.00023 — Bezrobotne_kobiety\n",
"0.00021 — Wymeldowania_do_miast_mezczyzni\n",
"0.00021 — Ludnosc_w_wieku_produkcyjnym_mobilnym\n",
"0.00021 — Bezrobotni_ogolem\n",
"0.00021 — Ludnosc_w_wieku_przedprodukcyjnym\n",
"0.00018 — Ludnosc_na_1_km2\n",
"0.00017 — Obiekty_ogolem\n",
"0.00017 — Wymeldowania_mezczyzni\n",
"0.00014 — Ludnosc_mezczyzni_w_wieku_poprodukcyjnym\n",
"0.00014 — Zameldowania_ze_wsi_kobiety\n",
"0.00012 — Wskaznik_urbanizacji\n",
"0.00011 — Zameldowania_kobiety\n",
"0.00011 — Miejsca_noclegowe_caloroczne\n",
"0.00010 — Bezrobotni_powyzej_50_roku_zycia\n",
"0.00007 — Zameldowania_ze_wsi_ogolem\n",
"0.00006 — Wojewodztwo_Warminsko_Mazurskie\n",
"0.00006 — Dochody_razem\n",
"0.00005 — Ludnosc_mezczyzni_w_wieku_produkcyjnym_niemobilnym\n",
"0.00004 — Saldo_migracji\n",
"0.00004 — Wojewodztwo_Slaskie\n",
"0.00004 — Wojewodztwo_Pomorskie\n",
"0.00003 — Ludnosc_mezczyzni_w_wieku_przedprodukcyjnym\n",
"0.00003 — Dlugotrwale_bezrobotni\n",
"0.00002 — Ludnosc_mezczyzni_w_wieku_produkcyjnym_mobilnym\n",
"0.00002 — Wojewodztwo_Lodzkie\n",
"0.00001 — Turysci_ogolem\n",
"0.00001 — Ludnosc_kobiety\n",
"0.00001 — Obiekty_caloroczne\n",
"0.00001 — Ludnosc\n",
"0.00001 — Ludnosc_w_wieku_produkcyjnym_niemobilnym\n",
"0.00001 — Wojewodztwo_Mazowieckie\n",
"0.00001 — Dochody_podatek_odrebne_ustawy\n",
"0.00001 — Wojewodztwo_Dolnoslaskie\n",
"0.00000 — Zameldowania_ogolem\n",
"0.00000 — Wojewodztwo_Lubuskie\n",
"0.00000 — Ludnosc_mezczyzni\n",
"0.00000 — Wojewodztwo_Podlaskie\n",
"0.00000 — Ludnosc_kobiety_w_wieku_przedprodukcyjnym\n",
"0.00000 — Wojewodztwo_Wielkopolskie\n",
"0.00000 — Wojewodztwo_Malopolskie\n",
"0.00000 — Wojewodztwo_Zachodniopomorskie\n",
"0.00000 — Wojewodztwo_Kujawsko_Pomorskie\n",
"0.00000 — Wojewodztwo_Swietokrzyskie\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}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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