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WUT_Computer_Science/Programming/PBAD/data.ipynb
Krzysztof kuhy Rudnicki 7cf25fc048 Add 'Programming/PBAD/' from commit '71efbceab4e90b2b29e9e8882fd06d369c07a030' 
git-subtree-dir: Programming/PBAD
git-subtree-mainline: 838184c85b
git-subtree-split: 71efbceab4
2026-02-06 22:14:48 +01:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"..."
]
},
{
"cell_type": "code",
"execution_count": 762,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 763,
"metadata": {},
"outputs": [],
"source": [
"pd.options.display.float_format = '{:.2f}'.format"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"..."
]
},
{
"cell_type": "code",
"execution_count": 764,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_24540\\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": 765,
"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": 766,
"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": 767,
"metadata": {},
"outputs": [],
"source": [
"df_dofinansowanie_agg = df_dofinansowanie \\\n",
" .groupby(['TERYT pe?ny', 'Program operacyjny', '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', 'Program operacyjny': 'Program_operacyjny', '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": 768,
"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": 769,
"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": 770,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_24540\\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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"</style>\n",
"<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",
" <th>Dochody_podatek_od_dzialalnosci_gospodarczej</th>\n",
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" <td>1344365.01</td>\n",
" <td>NaN</td>\n",
" <td>14944781.74</td>\n",
" <td>NaN</td>\n",
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" <td>3263011</td>\n",
" <td>2018</td>\n",
" <td>154462.39</td>\n",
" <td>5361951.37</td>\n",
" <td>572868.36</td>\n",
" <td>108107448.79</td>\n",
" <td>437144.83</td>\n",
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" <td>51297.75</td>\n",
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" <td>468411.51</td>\n",
" <td>38527846.59</td>\n",
" <td>228886.23</td>\n",
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" <td>329522.12</td>\n",
" <td>78767466.83</td>\n",
" <td>552009.16</td>\n",
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" <td>163778.36</td>\n",
" <td>9082482.28</td>\n",
" <td>492045.28</td>\n",
" <td>78491368.35</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",
"2 11683.60 16647124.98 \n",
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"... ... ... \n",
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"47080 48689.09 85538260.14 \n",
"47081 59824.46 89840077.70 \n",
"47082 50987.00 94936379.09 \n",
"\n",
"Rodzaje dochodów Dochody_razem \n",
"0 41378568.00 \n",
"1 43417443.00 \n",
"2 50319253.08 \n",
"3 62025513.24 \n",
"4 80755930.93 \n",
"... ... \n",
"47078 261780766.79 \n",
"47079 167638796.15 \n",
"47080 263006955.07 \n",
"47081 252345800.93 \n",
"47082 259310641.60 \n",
"\n",
"[47083 rows x 11 columns]"
]
},
"execution_count": 770,
"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": 771,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_24540\\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",
" <th>Rok</th>\n",
" <th>Dochody_z_majatku</th>\n",
" <th>Dochody_z_najmu_i_dzierzawy</th>\n",
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" <td>17156194.00</td>\n",
" <td>42840.00</td>\n",
" <td>NaN</td>\n",
" <td>1282943.00</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0201011</td>\n",
" <td>2006</td>\n",
" <td>8528727.69</td>\n",
" <td>NaN</td>\n",
" <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": 771,
"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": 772,
"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>32210</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>32211</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>32212</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>32213</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>32214</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>32215 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",
"32210 3263011 2018 40910.00 10472.00 \n",
"32211 3263011 2019 40888.00 10788.00 \n",
"32212 3263011 2020 40326.00 10962.00 \n",
"32213 3263011 2021 39834.00 11050.00 \n",
"32214 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",
"32210 24549.00 14683.00 \n",
"32211 24209.00 14429.00 \n",
"32212 23544.00 13798.00 \n",
"32213 22976.00 13277.00 \n",
"32214 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",
"32210 9866.00 \n",
"32211 9780.00 \n",
"32212 9746.00 \n",
"32213 9699.00 \n",
"32214 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",
"32210 5889.00 \n",
"32211 5891.00 \n",
"32212 5820.00 \n",
"32213 5808.00 \n",
"32214 5725.00 \n",
"\n",
"[32215 rows x 8 columns]"
]
},
"execution_count": 772,
"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.loc[df_ludn_1['Kod'].str.endswith(('1', '2', '3'))]\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": 773,
"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>32210</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>32211</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>32212</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>32213</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>32214</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>32215 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",
"32210 3263011 2018 19690.00 \n",
"32211 3263011 2019 19683.00 \n",
"32212 3263011 2020 19356.00 \n",
"32213 3263011 2021 19096.00 \n",
"32214 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",
"32210 3501.00 \n",
"32211 3644.00 \n",
"32212 3749.00 \n",
"32213 3852.00 \n",
"32214 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",
"32210 13202.00 \n",
"32211 13044.00 \n",
"32212 12617.00 \n",
"32213 12267.00 \n",
"32214 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",
"32210 7547.00 \n",
"32211 7417.00 \n",
"32212 6986.00 \n",
"32213 6747.00 \n",
"32214 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",
"32210 5655.00 \n",
"32211 5627.00 \n",
"32212 5631.00 \n",
"32213 5520.00 \n",
"32214 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",
"32210 2987.00 \n",
"32211 2995.00 \n",
"32212 2990.00 \n",
"32213 2977.00 \n",
"32214 2959.00 \n",
"\n",
"[32215 rows x 8 columns]"
]
},
"execution_count": 773,
"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.loc[df_ludn_2['Kod'].str.endswith(('1', '2', '3'))]\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": 774,
"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>32210</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>32211</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>32212</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>32213</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>32214</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>32215 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",
"32210 3263011 2018 21220.00 6971.00 \n",
"32211 3263011 2019 21205.00 7144.00 \n",
"32212 3263011 2020 20970.00 7213.00 \n",
"32213 3263011 2021 20738.00 7198.00 \n",
"32214 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",
"32210 11347.00 \n",
"32211 11165.00 \n",
"32212 10927.00 \n",
"32213 10709.00 \n",
"32214 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",
"32210 7136.00 \n",
"32211 7012.00 \n",
"32212 6812.00 \n",
"32213 6530.00 \n",
"32214 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",
"32210 4211.00 \n",
"32211 4153.00 \n",
"32212 4115.00 \n",
"32213 4179.00 \n",
"32214 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",
"32210 2902.00 \n",
"32211 2896.00 \n",
"32212 2830.00 \n",
"32213 2831.00 \n",
"32214 2766.00 \n",
"\n",
"[32215 rows x 8 columns]"
]
},
"execution_count": 774,
"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.loc[df_ludn_3['Kod'].str.endswith(('1', '2', '3'))]\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": 775,
"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.loc[df_ludn_4['Kod'].str.endswith(('1', '2', '3'))]\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": 776,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Kierunki migracji</th>\n",
" <th>Kod</th>\n",
" <th>Rok</th>\n",
" <th>Saldo_migracji_na_1000_ludnosci</th>\n",
" <th>Saldo_migracji</th>\n",
" <th>Wymeldowania_do_miast_kobiety</th>\n",
" <th>Wymeldowania_do_miast_mezczyzni</th>\n",
" <th>Wymeldowania_do_miast_ogolem</th>\n",
" <th>Wymeldowania_na_wies_kobiety</th>\n",
" <th>Wymeldowania_na_wies_mezczyzni</th>\n",
" <th>Wymeldowania_na_wies_ogolem</th>\n",
" <th>...</th>\n",
" <th>Wymeldowania_za_granice_ogolem</th>\n",
" <th>Zameldowania_kobiety</th>\n",
" <th>Zameldowania_mezczyzni</th>\n",
" <th>Zameldowania_ogolem</th>\n",
" <th>Zameldowania_z_miast_kobiety</th>\n",
" <th>Zameldowania_z_miast_mezczyzni</th>\n",
" <th>Zameldowania_z_miast_ogolem</th>\n",
" <th>Zameldowania_ze_wsi_kobiety</th>\n",
" <th>Zameldowania_ze_wsi_mezczyzni</th>\n",
" <th>Zameldowania_ze_wsi_ogolem</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0201011</td>\n",
" <td>2010</td>\n",
" <td>-3.70</td>\n",
" <td>-151.00</td>\n",
" <td>108.00</td>\n",
" <td>96.00</td>\n",
" <td>204.00</td>\n",
" <td>170.00</td>\n",
" <td>177.00</td>\n",
" <td>347.00</td>\n",
" <td>...</td>\n",
" <td>0.00</td>\n",
" <td>223.00</td>\n",
" <td>177.00</td>\n",
" <td>400.00</td>\n",
" <td>70.00</td>\n",
" <td>52.00</td>\n",
" <td>122.00</td>\n",
" <td>147.00</td>\n",
" <td>118.00</td>\n",
" <td>265.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0201011</td>\n",
" <td>2011</td>\n",
" <td>-4.60</td>\n",
" <td>-186.00</td>\n",
" <td>111.00</td>\n",
" <td>99.00</td>\n",
" <td>210.00</td>\n",
" <td>170.00</td>\n",
" <td>157.00</td>\n",
" <td>327.00</td>\n",
" <td>...</td>\n",
" <td>1.00</td>\n",
" <td>196.00</td>\n",
" <td>156.00</td>\n",
" <td>352.00</td>\n",
" <td>67.00</td>\n",
" <td>59.00</td>\n",
" <td>126.00</td>\n",
" <td>125.00</td>\n",
" <td>94.00</td>\n",
" <td>219.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0201011</td>\n",
" <td>2012</td>\n",
" <td>-3.70</td>\n",
" <td>-149.00</td>\n",
" <td>100.00</td>\n",
" <td>92.00</td>\n",
" <td>192.00</td>\n",
" <td>147.00</td>\n",
" <td>153.00</td>\n",
" <td>300.00</td>\n",
" <td>...</td>\n",
" <td>9.00</td>\n",
" <td>197.00</td>\n",
" <td>155.00</td>\n",
" <td>352.00</td>\n",
" <td>78.00</td>\n",
" <td>61.00</td>\n",
" <td>139.00</td>\n",
" <td>116.00</td>\n",
" <td>92.00</td>\n",
" <td>208.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0201011</td>\n",
" <td>2013</td>\n",
" <td>-4.80</td>\n",
" <td>-191.00</td>\n",
" <td>115.00</td>\n",
" <td>88.00</td>\n",
" <td>203.00</td>\n",
" <td>182.00</td>\n",
" <td>158.00</td>\n",
" <td>340.00</td>\n",
" <td>...</td>\n",
" <td>24.00</td>\n",
" <td>211.00</td>\n",
" <td>165.00</td>\n",
" <td>376.00</td>\n",
" <td>83.00</td>\n",
" <td>58.00</td>\n",
" <td>141.00</td>\n",
" <td>128.00</td>\n",
" <td>101.00</td>\n",
" <td>229.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0201011</td>\n",
" <td>2014</td>\n",
" <td>-4.20</td>\n",
" <td>-167.00</td>\n",
" <td>100.00</td>\n",
" <td>86.00</td>\n",
" <td>186.00</td>\n",
" <td>168.00</td>\n",
" <td>161.00</td>\n",
" <td>329.00</td>\n",
" <td>...</td>\n",
" <td>41.00</td>\n",
" <td>196.00</td>\n",
" <td>193.00</td>\n",
" <td>389.00</td>\n",
" <td>71.00</td>\n",
" <td>71.00</td>\n",
" <td>142.00</td>\n",
" <td>125.00</td>\n",
" <td>121.00</td>\n",
" <td>246.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32210</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>32211</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>32212</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>32213</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>32214</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>32215 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",
"32210 3263011 2018 1.70 \n",
"32211 3263011 2019 3.40 \n",
"32212 3263011 2020 3.20 \n",
"32213 3263011 2021 -1.40 \n",
"32214 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",
"32210 71.00 125.00 \n",
"32211 141.00 151.00 \n",
"32212 129.00 98.00 \n",
"32213 -55.00 122.00 \n",
"32214 -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",
"32210 152.00 \n",
"32211 116.00 \n",
"32212 99.00 \n",
"32213 126.00 \n",
"32214 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",
"32210 277.00 40.00 \n",
"32211 267.00 48.00 \n",
"32212 197.00 40.00 \n",
"32213 248.00 63.00 \n",
"32214 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",
"32210 66.00 \n",
"32211 53.00 \n",
"32212 44.00 \n",
"32213 50.00 \n",
"32214 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",
"32210 106.00 ... \n",
"32211 101.00 ... \n",
"32212 84.00 ... \n",
"32213 113.00 ... \n",
"32214 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",
"32210 NaN 245.00 \n",
"32211 NaN 273.00 \n",
"32212 NaN 226.00 \n",
"32213 NaN 171.00 \n",
"32214 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",
"32210 240.00 485.00 \n",
"32211 259.00 532.00 \n",
"32212 203.00 429.00 \n",
"32213 168.00 339.00 \n",
"32214 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",
"32210 156.00 \n",
"32211 179.00 \n",
"32212 159.00 \n",
"32213 109.00 \n",
"32214 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",
"32210 138.00 \n",
"32211 149.00 \n",
"32212 131.00 \n",
"32213 95.00 \n",
"32214 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",
"32210 294.00 73.00 \n",
"32211 328.00 71.00 \n",
"32212 290.00 52.00 \n",
"32213 204.00 49.00 \n",
"32214 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",
"32210 79.00 152.00 \n",
"32211 90.00 161.00 \n",
"32212 53.00 105.00 \n",
"32213 46.00 95.00 \n",
"32214 39.00 77.00 \n",
"\n",
"[32215 rows x 25 columns]"
]
},
"execution_count": 776,
"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.loc[df_ludn_5['Kod'].str.endswith(('1', '2', '3'))]\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": 777,
"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": 777,
"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": 778,
"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": 778,
"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": 779,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"13451\n"
]
}
],
"source": [
"df_data = df_dofinansowanie_agg.copy()\n",
"print(len(df_data))"
]
},
{
"cell_type": "code",
"execution_count": 780,
"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": 781,
"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": 782,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"13451\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": 783,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"13451\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": 784,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"13451\n",
"13451\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": 785,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"13451\n",
"13451\n",
"13451\n",
"13451\n",
"13451\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.merge(df_ludn_1, left_on=[df_data['Kod'].str.slice(stop=-1), 'Rok'], right_on=[df_ludn_1['Kod'].str.slice(stop=-1), 'Rok'], how='left', suffixes=(None, '_ludn_1'))\n",
"df_data = df_data.drop(['key_0', 'Kod_ludn_1'], axis=1)\n",
"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.merge(df_ludn_2, left_on=[df_data['Kod'].str.slice(stop=-1), 'Rok'], right_on=[df_ludn_2['Kod'].str.slice(stop=-1), 'Rok'], how='left', suffixes=(None, '_ludn_2'))\n",
"df_data = df_data.drop(['key_0', 'Kod_ludn_2'], axis=1)\n",
"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.merge(df_ludn_3, left_on=[df_data['Kod'].str.slice(stop=-1), 'Rok'], right_on=[df_ludn_3['Kod'].str.slice(stop=-1), 'Rok'], how='left', suffixes=(None, '_ludn_3'))\n",
"df_data = df_data.drop(['key_0', 'Kod_ludn_3'], axis=1)\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.merge(df_ludn_4, left_on=[df_data['Kod'].str.slice(stop=-1), 'Rok'], right_on=[df_ludn_4['Kod'].str.slice(stop=-1), '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.merge(df_ludn_5, left_on=[df_data['Kod'].str.slice(stop=-1), 'Rok'], right_on=[df_ludn_5['Kod'].str.slice(stop=-1), '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))\n",
"\n",
"# df_data['Kod'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 786,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"13451\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": 787,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"13451\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": 788,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.44252452388990926"
]
},
"execution_count": 788,
"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": 789,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Kod 0\n",
"Program_operacyjny 0\n",
"Rok 0\n",
"Suma 0\n",
"Wojewodztwo_Dolnoslaskie 0\n",
" ... \n",
"Bezrobotne_kobiety 280\n",
"Bezrobotni_mezczyzni 280\n",
"Bezrobotni_ogolem 280\n",
"Bezrobotni_powyzej_50_roku_zycia 280\n",
"Gestosc_zaludnienia 97\n",
"Length: 108, dtype: int64"
]
},
"execution_count": 789,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_data.isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": 790,
"metadata": {},
"outputs": [],
"source": [
"# # 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",
"# df_data.drop(columns=[\n",
"# 'Program_operacyjny',\n",
"# 'Wymeldowania_za_granice_kobiety',\n",
"# 'Wymeldowania_za_granice_mezczyzni',\n",
"# 'Wymeldowania_za_granice_ogolem',\n",
"# 'Bezrobotni_do_30_roku_zycia',\n",
" \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",
"# 'Gmina_miejska', # 98\n",
"# 'Gmina_miejsko_wiejska', # 99\n",
"# 'Gmina_wiejska',\n",
"# 'Wynagrodzenie_ogolem','Wynagrodzenie_w_relacji_do_sredniej','Dochody_podatek_lesny','Dochody_podatek_PCC','Dochody_podatek_od_dzialalnosci_gospodarczej','Dochody_podatek_od_nieruchomosci',\n",
"# 'Dochody_podatek_od_spadkow','Dochody_podatek_od_srodkow_transportowych','Dochody_podatek_rolny','Dochody_podatek_odrebne_ustawy','Dochody_razem','Dochody_z_majatku',\n",
"# 'Dochody_z_najmu_i_dzierzawy','Dochody_z_uslug','Dochody_dofinansowanie_inwestycyjne','Dochody_dofinansowanie_razem','Udzialy_w_podatkach_dochodowych_od_osob_fizycznych','Udzialy_w_podatkach_dochodowych_od_osob_prywatnych',\n",
"# 'Miejsca_noclegowe_caloroczne',\n",
"# 'Miejsca_noclegowe_ogolem',\n",
"# 'Obiekty_caloroczne',\n",
"# 'Obiekty_ogolem',\n",
"# 'Turysci_ogolem',\n",
"# 'Turysci_zagraniczni',\n",
"# 'Udzialy_w_podatkach_dochodowych_razem','Wplywy_z_innych_lokalnych_oplat','Wplywy_z_oplaty_eksploatacyjnej','Wplywy_z_oplaty_skarbowej','Wplywy_z_oplaty_targowej'], inplace=True, errors='ignore')\n",
"\n",
"# df_data[(df_data.isna().any(axis=1)) & (df_data['Rok'] != 2023)].columns # ['Rok'].drop_duplicates().reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 791,
"metadata": {},
"outputs": [],
"source": [
"# df_data['Suma'] = df_data['Suma'] / df_data['Ludnosc']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"..."
]
},
{
"cell_type": "code",
"execution_count": 792,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Kod\n",
"Program_operacyjny\n",
"Rok\n",
"Suma\n",
"Wojewodztwo_Dolnoslaskie\n",
"Wojewodztwo_Kujawsko_Pomorskie\n",
"Wojewodztwo_Lodzkie\n",
"Wojewodztwo_Lubelskie\n",
"Wojewodztwo_Lubuskie\n",
"Wojewodztwo_Malopolskie\n",
"Wojewodztwo_Mazowieckie\n",
"Wojewodztwo_Opolskie\n",
"Wojewodztwo_Podkarpackie\n",
"Wojewodztwo_Podlaskie\n",
"Wojewodztwo_Pomorskie\n",
"Wojewodztwo_Slaskie\n",
"Wojewodztwo_Swietokrzyskie\n",
"Wojewodztwo_Warminsko_Mazurskie\n",
"Wojewodztwo_Wielkopolskie\n",
"Wojewodztwo_Zachodniopomorskie\n",
"Gmina_miejska\n",
"Gmina_miejsko_wiejska\n",
"Gmina_wiejska\n",
"Powierzchnia\n",
"Wynagrodzenie_ogolem\n",
"Wynagrodzenie_w_relacji_do_sredniej\n",
"Dochody_podatek_lesny\n",
"Dochody_podatek_PCC\n",
"Dochody_podatek_od_dzialalnosci_gospodarczej\n",
"Dochody_podatek_od_nieruchomosci\n",
"Dochody_podatek_od_spadkow\n",
"Dochody_podatek_od_srodkow_transportowych\n",
"Dochody_podatek_rolny\n",
"Dochody_podatek_odrebne_ustawy\n",
"Dochody_razem\n",
"Dochody_z_majatku\n",
"Dochody_z_najmu_i_dzierzawy\n",
"Dochody_z_uslug\n",
"Dochody_dofinansowanie_inwestycyjne\n",
"Dochody_dofinansowanie_razem\n",
"Udzialy_w_podatkach_dochodowych_od_osob_fizycznych\n",
"Udzialy_w_podatkach_dochodowych_od_osob_prywatnych\n",
"Udzialy_w_podatkach_dochodowych_razem\n",
"Wplywy_z_innych_lokalnych_oplat\n",
"Wplywy_z_oplaty_eksploatacyjnej\n",
"Wplywy_z_oplaty_skarbowej\n",
"Wplywy_z_oplaty_targowej\n",
"Ludnosc_ogolem\n",
"Ludnosc_w_wieku_poprodukcyjnym\n",
"Ludnosc_w_wieku_produkcyjnym\n",
"Ludnosc_w_wieku_produkcyjnym_mobilnym\n",
"Ludnosc_w_wieku_produkcyjnym_niemobilnym\n",
"Ludnosc_w_wieku_przedprodukcyjnym\n",
"Ludnosc_mezczyzni\n",
"Ludnosc_mezczyzni_w_wieku_poprodukcyjnym\n",
"Ludnosc_mezczyzni_w_wieku_produkcyjnym\n",
"Ludnosc_mezczyzni_w_wieku_produkcyjnym_mobilnym\n",
"Ludnosc_mezczyzni_w_wieku_produkcyjnym_niemobilnym\n",
"Ludnosc_mezczyzni_w_wieku_przedprodukcyjnym\n",
"Ludnosc_kobiety\n",
"Ludnosc_kobiety_w_wieku_poprodukcyjnym\n",
"Ludnosc_kobiety_w_wieku_produkcyjnym\n",
"Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym\n",
"Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym\n",
"Ludnosc_kobiety_w_wieku_przedprodukcyjnym\n",
"Ludnosc_na_1_km2\n",
"Ludnosc\n",
"Ludnosc_kobiety_ludn_4\n",
"Ludnosc_mezczyzni_ludn_4\n",
"Wskaznik_urbanizacji\n",
"Zmiana_liczby_ludnosci\n",
"Saldo_migracji_na_1000_ludnosci\n",
"Saldo_migracji\n",
"Wymeldowania_do_miast_kobiety\n",
"Wymeldowania_do_miast_mezczyzni\n",
"Wymeldowania_do_miast_ogolem\n",
"Wymeldowania_na_wies_kobiety\n",
"Wymeldowania_na_wies_mezczyzni\n",
"Wymeldowania_na_wies_ogolem\n",
"Wymeldowania_kobiety\n",
"Wymeldowania_mezczyzni\n",
"Wymeldowania_ogolem\n",
"Zameldowania_kobiety\n",
"Zameldowania_mezczyzni\n",
"Zameldowania_ogolem\n",
"Zameldowania_z_miast_kobiety\n",
"Zameldowania_z_miast_mezczyzni\n",
"Zameldowania_z_miast_ogolem\n",
"Zameldowania_ze_wsi_kobiety\n",
"Zameldowania_ze_wsi_mezczyzni\n",
"Zameldowania_ze_wsi_ogolem\n",
"Miejsca_noclegowe_caloroczne\n",
"Miejsca_noclegowe_ogolem\n",
"Obiekty_caloroczne\n",
"Obiekty_ogolem\n",
"Turysci_ogolem\n",
"Turysci_zagraniczni\n",
"Bezrobotni_do_25_roku_zycia\n",
"Dlugotrwale_bezrobotni\n",
"Bezrobotne_kobiety\n",
"Bezrobotni_mezczyzni\n",
"Bezrobotni_ogolem\n",
"Bezrobotni_powyzej_50_roku_zycia\n",
"Gestosc_zaludnienia\n"
]
}
],
"source": [
"# Usunięcie niepotrzebnych kolumn...\n",
"df_data.drop(columns=[\n",
" # 'Program_operacyjny',\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",
"df_data.to_csv('dane1.csv', index=False)\n",
"\n",
"for c in df_data.columns:\n",
" print(c)"
]
},
{
"cell_type": "code",
"execution_count": 793,
"metadata": {},
"outputs": [],
"source": [
"s = df_data.isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": 794,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Wynagrodzenie_ogolem 97\n",
"Wynagrodzenie_w_relacji_do_sredniej 97\n",
"Dochody_podatek_lesny 97\n",
"Dochody_podatek_PCC 97\n",
"Dochody_podatek_od_dzialalnosci_gospodarczej 97\n",
" ... \n",
"Bezrobotne_kobiety 280\n",
"Bezrobotni_mezczyzni 280\n",
"Bezrobotni_ogolem 280\n",
"Bezrobotni_powyzej_50_roku_zycia 280\n",
"Gestosc_zaludnienia 97\n",
"Length: 80, dtype: int64"
]
},
"execution_count": 794,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[s > 0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"..."
]
},
{
"cell_type": "code",
"execution_count": 795,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"13451\n",
"13067\n",
"Mean Squared Error: 2235175313583462.0\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",
" 'Gmina_miejska', # 98\n",
" 'Gmina_miejsko_wiejska', # 99\n",
" 'Gmina_wiejska'] # 100\n",
"\n",
"df_data.drop(columns=[\n",
" 'Program_operacyjny',\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": 796,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Turysci_ogolem 0.65\n",
"Turysci_zagraniczni 0.60\n",
"Bezrobotni_powyzej_50_roku_zycia 0.47\n",
"Bezrobotni_mezczyzni 0.47\n",
"Bezrobotni_ogolem 0.46\n",
"Dlugotrwale_bezrobotni 0.45\n",
"Bezrobotne_kobiety 0.45\n",
"Bezrobotni_do_25_roku_zycia 0.44\n",
"Dochody_z_majatku 0.42\n",
"Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym 0.39\n",
"Ludnosc_w_wieku_produkcyjnym_niemobilnym 0.39\n",
"Ludnosc_mezczyzni_w_wieku_produkcyjnym_niemobilnym 0.39\n",
"Ludnosc_kobiety_w_wieku_produkcyjnym 0.39\n",
"Ludnosc_w_wieku_produkcyjnym 0.39\n",
"Ludnosc_mezczyzni_w_wieku_produkcyjnym 0.39\n",
"Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym 0.38\n",
"Ludnosc_w_wieku_produkcyjnym_mobilnym 0.38\n",
"Ludnosc_mezczyzni_w_wieku_produkcyjnym_mobilnym 0.38\n",
"Ludnosc_kobiety_ludn_4 0.38\n",
"Ludnosc_kobiety 0.38\n",
"Dochody_z_uslug 0.38\n",
"Ludnosc_ogolem 0.38\n",
"Ludnosc 0.38\n",
"Zameldowania_z_miast_kobiety 0.38\n",
"Ludnosc_mezczyzni 0.38\n",
"Ludnosc_mezczyzni_ludn_4 0.38\n",
"Ludnosc_kobiety_w_wieku_poprodukcyjnym 0.38\n",
"Zameldowania_z_miast_ogolem 0.38\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": 797,
"metadata": {},
"outputs": [
{
"data": {
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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": 798,
"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": 799,
"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": 800,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|--- Dlugotrwale_bezrobotni <= 25668.00\n",
"| |--- Bezrobotni_mezczyzni <= 19299.50\n",
"| | |--- Ludnosc_w_wieku_produkcyjnym <= 185983.00\n",
"| | | |--- Turysci_zagraniczni <= 38.50\n",
"| | | | |--- Ludnosc_kobiety <= 89935.50\n",
"| | | | | |--- Bezrobotni_powyzej_50_roku_zycia <= 340.75\n",
"| | | | | | |--- Turysci_ogolem <= 3.00\n",
"| | | | | | | |--- Ludnosc_w_wieku_produkcyjnym_niemobilnym <= 3444.50\n",
"| | | | | | | | |--- Dochody_podatek_od_srodkow_transportowych <= 1364063.19\n",
"| | | | | | | | | |--- Wynagrodzenie_w_relacji_do_sredniej <= 167.35\n",
"| | | | | | | | | | |--- Obiekty_ogolem <= 229.00\n",
"| | | | | | | | | | | |--- truncated branch of depth 60\n",
"| | | | | | | | | | |--- Obiekty_ogolem > 229.00\n",
"| | | | | | | | | | | |--- value: [83037656.42]\n",
"| | | | | | | | | |--- Wynagrodzenie_w_relacji_do_sredniej > 167.35\n",
"| | | | | | | | | | |--- value: [89747833.80]\n",
"| | | | | | | | |--- Dochody_podatek_od_srodkow_transportowych > 1364063.19\n",
"| | | | | | | | | |--- Dochody_podatek_lesny <= 268.50\n",
"| | | | | | | | | | |--- value: [481929545.58]\n",
"| | | | | | | | | |--- Dochody_podatek_lesny > 268.50\n",
"| | | | | | | | | | |--- Wymeldowania_na_wies_ogolem <= 107.50\n",
"| | | | | | | | | | | |--- truncated branch of depth 9\n",
"| | | | | | | | | | |--- Wymeldowania_na_wies_ogolem > 107.50\n",
"| | | | | | | | | | | |--- truncated branch of depth 2\n",
"| | | | | | | |--- Ludnosc_w_wieku_produkcyjnym_niemobilnym > 3444.50\n",
"| | | | | | | | |--- Ludnosc_w_wieku_produkcyjnym_niemobilnym <= 3446.00\n",
"| | | | | | | | | |--- value: [67905000.00]\n",
"| | | | | | | | |--- Ludnosc_w_wieku_produkcyjnym_niemobilnym > 3446.00\n",
"| | | | | | | | | |--- Ludnosc_na_1_km2 <= 228.35\n",
"| | | | | | | | | | |--- Saldo_migracji <= -261.50\n",
"| | | | | | | | | | | |--- value: [59513803.02]\n",
"| | | | | | | | | | |--- Saldo_migracji > -261.50\n",
"| | | | | | | | | | | |--- truncated branch of depth 43\n",
"| | | | | | | | | |--- Ludnosc_na_1_km2 > 228.35\n",
"| | | | | | | | | | |--- Wojewodztwo_Opolskie <= 0.50\n",
"| | | | | | | | | | | |--- truncated branch of depth 46\n",
"| | | | | | | | | | |--- Wojewodztwo_Opolskie > 0.50\n",
"| | | | | | | | | | | |--- truncated branch of depth 11\n",
"| | | | | | |--- Turysci_ogolem > 3.00\n",
"| | | | | | | |--- Wymeldowania_na_wies_ogolem <= 15.50\n",
"| | | | | | | | |--- Bezrobotni_powyzej_50_roku_zycia <= 96.50\n",
"| | | | | | | | | |--- Miejsca_noclegowe_ogolem <= 12.00\n",
"| | | | | | | | | | |--- value: [160629826.38]\n",
"| | | | | | | | | |--- Miejsca_noclegowe_ogolem > 12.00\n",
"| | | | | | | | | | |--- value: [171383125.19]\n",
"| | | | | | | | |--- Bezrobotni_powyzej_50_roku_zycia > 96.50\n",
"| | | | | | | | | |--- value: [342766250.37]\n",
"| | | | | | | |--- Wymeldowania_na_wies_ogolem > 15.50\n",
"| | | | | | | | |--- Wymeldowania_na_wies_ogolem <= 19.50\n",
"| | | | | | | | | |--- Wplywy_z_oplaty_targowej <= 3406.00\n",
"| | | | | | | | | | |--- Wymeldowania_mezczyzni <= 28.50\n",
"| | | | | | | | | | | |--- value: [68553250.07]\n",
"| | | | | | | | | | |--- Wymeldowania_mezczyzni > 28.50\n",
"| | | | | | | | | | | |--- value: [117085504.63]\n",
"| | | | | | | | | |--- Wplywy_z_oplaty_targowej > 3406.00\n",
"| | | | | | | | | | |--- Wojewodztwo_Lubelskie <= 0.50\n",
"| | | | | | | | | | | |--- value: [447130.67]\n",
"| | | | | | | | | | |--- Wojewodztwo_Lubelskie > 0.50\n",
"| | | | | | | | | | | |--- value: [945154.23]\n",
"| | | | | | | | |--- Wymeldowania_na_wies_ogolem > 19.50\n",
"| | | | | | | | | |--- Ludnosc_mezczyzni_w_wieku_produkcyjnym_niemobilnym <= 1803.00\n",
"| | | | | | | | | | |--- Udzialy_w_podatkach_dochodowych_od_osob_prywatnych <= 3761.75\n",
"| | | | | | | | | | | |--- value: [31956702.57]\n",
"| | | | | | | | | | |--- Udzialy_w_podatkach_dochodowych_od_osob_prywatnych > 3761.75\n",
"| | | | | | | | | | | |--- truncated branch of depth 18\n",
"| | | | | | | | | |--- Ludnosc_mezczyzni_w_wieku_produkcyjnym_niemobilnym > 1803.00\n",
"| | | | | | | | | | |--- Bezrobotne_kobiety <= 293.25\n",
"| | | | | | | | | | | |--- truncated branch of depth 4\n",
"| | | | | | | | | | |--- Bezrobotne_kobiety > 293.25\n",
"| | | | | | | | | | | |--- truncated branch of depth 6\n",
"| | | | | |--- Bezrobotni_powyzej_50_roku_zycia > 340.75\n",
"| | | | | | |--- Ludnosc_kobiety_w_wieku_poprodukcyjnym <= 2095.50\n",
"| | | | | | | |--- value: [164709707.63]\n",
"| | | | | | |--- Ludnosc_kobiety_w_wieku_poprodukcyjnym > 2095.50\n",
"| | | | | | | |--- Gestosc_zaludnienia <= 3.93\n",
"| | | | | | | | |--- Obiekty_caloroczne <= 0.50\n",
"| | | | | | | | | |--- Wynagrodzenie_ogolem <= 3259.15\n",
"| | | | | | | | | | |--- value: [150089223.44]\n",
"| | | | | | | | | |--- Wynagrodzenie_ogolem > 3259.15\n",
"| | | | | | | | | | |--- Zameldowania_z_miast_kobiety <= 417.50\n",
"| | | | | | | | | | | |--- truncated branch of depth 6\n",
"| | | | | | | | | | |--- Zameldowania_z_miast_kobiety > 417.50\n",
"| | | | | | | | | | | |--- value: [93414400.87]\n",
"| | | | | | | | |--- Obiekty_caloroczne > 0.50\n",
"| | | | | | | | | |--- Zmiana_liczby_ludnosci <= -20.20\n",
"| | | | | | | | | | |--- value: [63239190.39]\n",
"| | | | | | | | | |--- Zmiana_liczby_ludnosci > -20.20\n",
"| | | | | | | | | | |--- Bezrobotni_mezczyzni <= 1110.25\n",
"| | | | | | | | | | | |--- truncated branch of depth 23\n",
"| | | | | | | | | | |--- Bezrobotni_mezczyzni > 1110.25\n",
"| | | | | | | | | | | |--- truncated branch of depth 20\n",
"| | | | | | | |--- Gestosc_zaludnienia > 3.93\n",
"| | | | | | | | |--- value: [135056683.01]\n",
"| | | | |--- Ludnosc_kobiety > 89935.50\n",
"| | | | | |--- Wplywy_z_oplaty_skarbowej <= 1133715.12\n",
"| | | | | | |--- value: [180173724.25]\n",
"| | | | | |--- Wplywy_z_oplaty_skarbowej > 1133715.12\n",
"| | | | | | |--- Ludnosc_mezczyzni_w_wieku_poprodukcyjnym <= 11972.00\n",
"| | | | | | | |--- Wymeldowania_do_miast_mezczyzni <= 326.00\n",
"| | | | | | | | |--- Ludnosc_mezczyzni_w_wieku_produkcyjnym_niemobilnym <= 21084.50\n",
"| | | | | | | | | |--- value: [30977320.85]\n",
"| | | | | | | | |--- Ludnosc_mezczyzni_w_wieku_produkcyjnym_niemobilnym > 21084.50\n",
"| | | | | | | | | |--- Dochody_z_najmu_i_dzierzawy <= 9119502.75\n",
"| | | | | | | | | | |--- value: [59862743.58]\n",
"| | | | | | | | | |--- Dochody_z_najmu_i_dzierzawy > 9119502.75\n",
"| | | | | | | | | | |--- value: [70407306.85]\n",
"| | | | | | | |--- Wymeldowania_do_miast_mezczyzni > 326.00\n",
"| | | | | | | | |--- Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym <= 17482.50\n",
"| | | | | | | | | |--- value: [102472326.23]\n",
"| | | | | | | | |--- Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym > 17482.50\n",
"| | | | | | | | | |--- value: [127981984.10]\n",
"| | | | | | |--- Ludnosc_mezczyzni_w_wieku_poprodukcyjnym > 11972.00\n",
"| | | | | | | |--- Zameldowania_kobiety <= 1158.50\n",
"| | | | | | | | |--- Dochody_podatek_od_nieruchomosci <= 183512512.00\n",
"| | | | | | | | | |--- Udzialy_w_podatkach_dochodowych_od_osob_prywatnych <= 12303453.00\n",
"| | | | | | | | | | |--- Wymeldowania_na_wies_mezczyzni <= 439.00\n",
"| | | | | | | | | | | |--- truncated branch of depth 5\n",
"| | | | | | | | | | |--- Wymeldowania_na_wies_mezczyzni > 439.00\n",
"| | | | | | | | | | | |--- truncated branch of depth 3\n",
"| | | | | | | | | |--- Udzialy_w_podatkach_dochodowych_od_osob_prywatnych > 12303453.00\n",
"| | | | | | | | | | |--- Dochody_podatek_lesny <= 48150.42\n",
"| | | | | | | | | | | |--- truncated branch of depth 8\n",
"| | | | | | | | | | |--- Dochody_podatek_lesny > 48150.42\n",
"| | | | | | | | | | | |--- truncated branch of depth 9\n",
"| | | | | | | | |--- Dochody_podatek_od_nieruchomosci > 183512512.00\n",
"| | | | | | | | | |--- Ludnosc_mezczyzni_w_wieku_produkcyjnym_niemobilnym <= 31586.00\n",
"| | | | | | | | | | |--- Wplywy_z_oplaty_targowej <= 595514.12\n",
"| | | | | | | | | | | |--- value: [66090255.07]\n",
"| | | | | | | | | | |--- Wplywy_z_oplaty_targowej > 595514.12\n",
"| | | | | | | | | | | |--- value: [91810436.67]\n",
"| | | | | | | | | |--- Ludnosc_mezczyzni_w_wieku_produkcyjnym_niemobilnym > 31586.00\n",
"| | | | | | | | | | |--- value: [3978361.07]\n",
"| | | | | | | |--- Zameldowania_kobiety > 1158.50\n",
"| | | | | | | | |--- Dochody_dofinansowanie_inwestycyjne <= 53727986.00\n",
"| | | | | | | | | |--- Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym <= 17278.50\n",
"| | | | | | | | | | |--- Wymeldowania_na_wies_ogolem <= 1251.00\n",
"| | | | | | | | | | | |--- value: [29960100.33]\n",
"| | | | | | | | | | |--- Wymeldowania_na_wies_ogolem > 1251.00\n",
"| | | | | | | | | | | |--- truncated branch of depth 4\n",
"| | | | | | | | | |--- Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym > 17278.50\n",
"| | | | | | | | | | |--- Saldo_migracji_na_1000_ludnosci <= -2.45\n",
"| | | | | | | | | | | |--- truncated branch of depth 2\n",
"| | | | | | | | | | |--- Saldo_migracji_na_1000_ludnosci > -2.45\n",
"| | | | | | | | | | | |--- truncated branch of depth 7\n",
"| | | | | | | | |--- Dochody_dofinansowanie_inwestycyjne > 53727986.00\n",
"| | | | | | | | | |--- value: [144736499.65]\n",
"| | | |--- Turysci_zagraniczni > 38.50\n",
"| | | | |--- Wplywy_z_oplaty_eksploatacyjnej <= -4393.63\n",
"| | | | | |--- value: [964190154.28]\n",
"| | | | |--- Wplywy_z_oplaty_eksploatacyjnej > -4393.63\n",
"| | | | | |--- Ludnosc_kobiety_w_wieku_poprodukcyjnym <= 17303.50\n",
"| | | | | | |--- Saldo_migracji_na_1000_ludnosci <= 19.15\n",
"| | | | | | | |--- Dochody_podatek_od_nieruchomosci <= 1291011.81\n",
"| | | | | | | | |--- Ludnosc_w_wieku_produkcyjnym <= 3086.00\n",
"| | | | | | | | | |--- value: [171383125.19]\n",
"| | | | | | | | |--- Ludnosc_w_wieku_produkcyjnym > 3086.00\n",
"| | | | | | | | | |--- Ludnosc_mezczyzni_w_wieku_przedprodukcyjnym <= 641.00\n",
"| | | | | | | | | | |--- value: [122764445.52]\n",
"| | | | | | | | | |--- Ludnosc_mezczyzni_w_wieku_przedprodukcyjnym > 641.00\n",
"| | | | | | | | | | |--- value: [146824455.62]\n",
"| | | | | | | |--- Dochody_podatek_od_nieruchomosci > 1291011.81\n",
"| | | | | | | | |--- Ludnosc_w_wieku_produkcyjnym_niemobilnym <= 14363.50\n",
"| | | | | | | | | |--- Turysci_ogolem <= 289.00\n",
"| | | | | | | | | | |--- value: [162582295.42]\n",
"| | | | | | | | | |--- Turysci_ogolem > 289.00\n",
"| | | | | | | | | | |--- Wynagrodzenie_w_relacji_do_sredniej <= 70.60\n",
"| | | | | | | | | | | |--- value: [158370335.96]\n",
"| | | | | | | | | | |--- Wynagrodzenie_w_relacji_do_sredniej > 70.60\n",
"| | | | | | | | | | | |--- truncated branch of depth 26\n",
"| | | | | | | | |--- Ludnosc_w_wieku_produkcyjnym_niemobilnym > 14363.50\n",
"| | | | | | | | | |--- Ludnosc_kobiety <= 29627.00\n",
"| | | | | | | | | | |--- value: [675624217.52]\n",
"| | | | | | | | | |--- Ludnosc_kobiety > 29627.00\n",
"| | | | | | | | | | |--- Dochody_razem <= 112764740.00\n",
"| | | | | | | | | | | |--- truncated branch of depth 3\n",
"| | | | | | | | | | |--- Dochody_razem > 112764740.00\n",
"| | | | | | | | | | | |--- truncated branch of depth 16\n",
"| | | | | | |--- Saldo_migracji_na_1000_ludnosci > 19.15\n",
"| | | | | | | |--- Wymeldowania_na_wies_mezczyzni <= 12.50\n",
"| | | | | | | | |--- value: [819103731.56]\n",
"| | | | | | | |--- Wymeldowania_na_wies_mezczyzni > 12.50\n",
"| | | | | | | | |--- Ludnosc_kobiety_w_wieku_poprodukcyjnym <= 2525.50\n",
"| | | | | | | | | |--- Dlugotrwale_bezrobotni <= 341.50\n",
"| | | | | | | | | | |--- Wymeldowania_na_wies_kobiety <= 19.50\n",
"| | | | | | | | | | | |--- truncated branch of depth 2\n",
"| | | | | | | | | | |--- Wymeldowania_na_wies_kobiety > 19.50\n",
"| | | | | | | | | | | |--- truncated branch of depth 4\n",
"| | | | | | | | | |--- Dlugotrwale_bezrobotni > 341.50\n",
"| | | | | | | | | | |--- value: [45343367.61]\n",
"| | | | | | | | |--- Ludnosc_kobiety_w_wieku_poprodukcyjnym > 2525.50\n",
"| | | | | | | | | |--- Wplywy_z_oplaty_eksploatacyjnej <= 50673.37\n",
"| | | | | | | | | | |--- value: [61979737.09]\n",
"| | | | | | | | | |--- Wplywy_z_oplaty_eksploatacyjnej > 50673.37\n",
"| | | | | | | | | | |--- value: [121810368.38]\n",
"| | | | | |--- Ludnosc_kobiety_w_wieku_poprodukcyjnym > 17303.50\n",
"| | | | | | |--- Dochody_podatek_od_spadkow <= 1733235.94\n",
"| | | | | | | |--- Wynagrodzenie_w_relacji_do_sredniej <= 88.95\n",
"| | | | | | | | |--- Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym <= 17244.00\n",
"| | | | | | | | | |--- Gestosc_zaludnienia <= 1.62\n",
"| | | | | | | | | | |--- value: [114787825.97]\n",
"| | | | | | | | | |--- Gestosc_zaludnienia > 1.62\n",
"| | | | | | | | | | |--- value: [118788666.24]\n",
"| | | | | | | | |--- Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym > 17244.00\n",
"| | | | | | | | | |--- Ludnosc_w_wieku_produkcyjnym <= 125722.00\n",
"| | | | | | | | | | |--- value: [85637902.19]\n",
"| | | | | | | | | |--- Ludnosc_w_wieku_produkcyjnym > 125722.00\n",
"| | | | | | | | | | |--- value: [70210346.90]\n",
"| | | | | | | |--- Wynagrodzenie_w_relacji_do_sredniej > 88.95\n",
"| | | | | | | | |--- Dochody_z_najmu_i_dzierzawy <= 6061333.00\n",
"| | | | | | | | | |--- Wymeldowania_ogolem <= 2041.50\n",
"| | | | | | | | | | |--- Ludnosc_w_wieku_produkcyjnym <= 109787.00\n",
"| | | | | | | | | | | |--- truncated branch of depth 2\n",
"| | | | | | | | | | |--- Ludnosc_w_wieku_produkcyjnym > 109787.00\n",
"| | | | | | | | | | | |--- truncated branch of depth 2\n",
"| | | | | | | | | |--- Wymeldowania_ogolem > 2041.50\n",
"| | | | | | | | | | |--- value: [110484937.69]\n",
"| | | | | | | | |--- Dochody_z_najmu_i_dzierzawy > 6061333.00\n",
"| | | | | | | | | |--- Ludnosc_mezczyzni <= 71811.00\n",
"| | | | | | | | | | |--- value: [255623586.25]\n",
"| | | | | | | | | |--- Ludnosc_mezczyzni > 71811.00\n",
"| | | | | | | | | | |--- value: [199270168.64]\n",
"| | | | | | |--- Dochody_podatek_od_spadkow > 1733235.94\n",
"| | | | | | | |--- Dochody_dofinansowanie_razem <= 7532695.62\n",
"| | | | | | | | |--- Dochody_razem <= 517752880.00\n",
"| | | | | | | | | |--- value: [292699374.69]\n",
"| | | | | | | | |--- Dochody_razem > 517752880.00\n",
"| | | | | | | | | |--- value: [257797653.62]\n",
"| | | | | | | |--- Dochody_dofinansowanie_razem > 7532695.62\n",
"| | | | | | | | |--- value: [381077102.66]\n",
"| | |--- Ludnosc_w_wieku_produkcyjnym > 185983.00\n",
"| | | |--- Wynagrodzenie_w_relacji_do_sredniej <= 133.45\n",
"| | | | |--- Turysci_ogolem <= 280537.00\n",
"| | | | | |--- Ludnosc_w_wieku_produkcyjnym <= 187736.50\n",
"| | | | | | |--- value: [368605477.02]\n",
"| | | | | |--- Ludnosc_w_wieku_produkcyjnym > 187736.50\n",
"| | | | | | |--- Wplywy_z_oplaty_targowej <= 2805425.38\n",
"| | | | | | | |--- Dochody_podatek_rolny <= 2382003.62\n",
"| | | | | | | | |--- Bezrobotni_mezczyzni <= 3058.50\n",
"| | | | | | | | | |--- Dochody_z_uslug <= 82099316.00\n",
"| | | | | | | | | | |--- Ludnosc_kobiety_w_wieku_poprodukcyjnym <= 59429.50\n",
"| | | | | | | | | | | |--- value: [178152459.17]\n",
"| | | | | | | | | | |--- Ludnosc_kobiety_w_wieku_poprodukcyjnym > 59429.50\n",
"| | | | | | | | | | | |--- truncated branch of depth 2\n",
"| | | | | | | | | |--- Dochody_z_uslug > 82099316.00\n",
"| | | | | | | | | | |--- Wynagrodzenie_w_relacji_do_sredniej <= 110.55\n",
"| | | | | | | | | | | |--- truncated branch of depth 5\n",
"| | | | | | | | | | |--- Wynagrodzenie_w_relacji_do_sredniej > 110.55\n",
"| | | | | | | | | | | |--- truncated branch of depth 5\n",
"| | | | | | | | |--- Bezrobotni_mezczyzni > 3058.50\n",
"| | | | | | | | | |--- Wymeldowania_mezczyzni <= 659.50\n",
"| | | | | | | | | | |--- Dochody_podatek_PCC <= 55368212.00\n",
"| | | | | | | | | | | |--- truncated branch of depth 5\n",
"| | | | | | | | | | |--- Dochody_podatek_PCC > 55368212.00\n",
"| | | | | | | | | | | |--- value: [123333674.24]\n",
"| | | | | | | | | |--- Wymeldowania_mezczyzni > 659.50\n",
"| | | | | | | | | | |--- Wplywy_z_innych_lokalnych_oplat <= 71795516.00\n",
"| | | | | | | | | | | |--- truncated branch of depth 7\n",
"| | | | | | | | | | |--- Wplywy_z_innych_lokalnych_oplat > 71795516.00\n",
"| | | | | | | | | | | |--- truncated branch of depth 9\n",
"| | | | | | | |--- Dochody_podatek_rolny > 2382003.62\n",
"| | | | | | | | |--- value: [315084220.12]\n",
"| | | | | | |--- Wplywy_z_oplaty_targowej > 2805425.38\n",
"| | | | | | | |--- Zameldowania_z_miast_ogolem <= 4236.50\n",
"| | | | | | | | |--- value: [258878709.16]\n",
"| | | | | | | |--- Zameldowania_z_miast_ogolem > 4236.50\n",
"| | | | | | | | |--- value: [344302462.59]\n",
"| | | | |--- Turysci_ogolem > 280537.00\n",
"| | | | | |--- Dochody_z_najmu_i_dzierzawy <= 70666956.00\n",
"| | | | | | |--- Wojewodztwo_Slaskie <= 0.50\n",
"| | | | | | | |--- Wymeldowania_do_miast_mezczyzni <= 976.00\n",
"| | | | | | | | |--- value: [570198401.95]\n",
"| | | | | | | |--- Wymeldowania_do_miast_mezczyzni > 976.00\n",
"| | | | | | | | |--- value: [561559712.26]\n",
"| | | | | | |--- Wojewodztwo_Slaskie > 0.50\n",
"| | | | | | | |--- value: [773787942.19]\n",
"| | | | | |--- Dochody_z_najmu_i_dzierzawy > 70666956.00\n",
"| | | | | | |--- Wplywy_z_oplaty_targowej <= 2307080.88\n",
"| | | | | | | |--- Bezrobotni_mezczyzni <= 8035.25\n",
"| | | | | | | | |--- value: [251006633.98]\n",
"| | | | | | | |--- Bezrobotni_mezczyzni > 8035.25\n",
"| | | | | | | | |--- value: [336825560.70]\n",
"| | | | | | |--- Wplywy_z_oplaty_targowej > 2307080.88\n",
"| | | | | | | |--- value: [546788438.22]\n",
"| | | |--- Wynagrodzenie_w_relacji_do_sredniej > 133.45\n",
"| | | | |--- Dochody_podatek_rolny <= 1140017.00\n",
"| | | | | |--- value: [845270363.07]\n",
"| | | | |--- Dochody_podatek_rolny > 1140017.00\n",
"| | | | | |--- value: [351302277.11]\n",
"| |--- Bezrobotni_mezczyzni > 19299.50\n",
"| | |--- Saldo_migracji <= -715.50\n",
"| | | |--- value: [1636234649.62]\n",
"| | |--- Saldo_migracji > -715.50\n",
"| | | |--- value: [1156609061.12]\n",
"|--- Dlugotrwale_bezrobotni > 25668.00\n",
"| |--- value: [4888827044.14]\n",
"\n"
]
}
],
"source": [
"print(export_text(model, feature_names=feature_names))"
]
},
{
"cell_type": "code",
"execution_count": 801,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.60288 — Dlugotrwale_bezrobotni\n",
"0.13303 — Bezrobotni_mezczyzni\n",
"0.06044 — Ludnosc_w_wieku_produkcyjnym\n",
"0.02626 — Wynagrodzenie_w_relacji_do_sredniej\n",
"0.02598 — Turysci_ogolem\n",
"0.02175 — Wplywy_z_oplaty_eksploatacyjnej\n",
"0.01448 — Wymeldowania_na_wies_mezczyzni\n",
"0.01030 — Turysci_zagraniczni\n",
"0.01009 — Ludnosc_kobiety\n",
"0.00950 — Ludnosc_kobiety_w_wieku_poprodukcyjnym\n",
"0.00926 — Dochody_podatek_rolny\n",
"0.00725 — Dochody_podatek_lesny\n",
"0.00470 — Wplywy_z_oplaty_targowej\n",
"0.00468 — Dochody_z_najmu_i_dzierzawy\n",
"0.00453 — Saldo_migracji\n",
"0.00381 — Wymeldowania_na_wies_ogolem\n",
"0.00296 — Saldo_migracji_na_1000_ludnosci\n",
"0.00290 — Wynagrodzenie_ogolem\n",
"0.00288 — Ludnosc_kobiety_w_wieku_przedprodukcyjnym\n",
"0.00223 — Dochody_razem\n",
"0.00206 — Wplywy_z_innych_lokalnych_oplat\n",
"0.00193 — Zameldowania_kobiety\n",
"0.00190 — Dochody_podatek_od_spadkow\n",
"0.00180 — Wymeldowania_mezczyzni\n",
"0.00156 — Dochody_podatek_od_nieruchomosci\n",
"0.00156 — Wplywy_z_oplaty_skarbowej\n",
"0.00147 — Ludnosc_w_wieku_produkcyjnym_niemobilnym\n",
"0.00141 — Dochody_z_uslug\n",
"0.00139 — Dochody_podatek_PCC\n",
"0.00136 — Zameldowania_z_miast_kobiety\n",
"0.00134 — Zmiana_liczby_ludnosci\n",
"0.00126 — Bezrobotni_powyzej_50_roku_zycia\n",
"0.00122 — Ludnosc_w_wieku_produkcyjnym_mobilnym\n",
"0.00119 — Miejsca_noclegowe_ogolem\n",
"0.00105 — Ludnosc_mezczyzni_w_wieku_poprodukcyjnym\n",
"0.00097 — Wojewodztwo_Slaskie\n",
"0.00076 — Dochody_z_majatku\n",
"0.00071 — Udzialy_w_podatkach_dochodowych_od_osob_prywatnych\n",
"0.00064 — Zameldowania_ze_wsi_ogolem\n",
"0.00060 — Zameldowania_ze_wsi_kobiety\n",
"0.00054 — Dochody_podatek_od_srodkow_transportowych\n",
"0.00051 — Gestosc_zaludnienia\n",
"0.00049 — Dochody_dofinansowanie_razem\n",
"0.00044 — Wymeldowania_do_miast_mezczyzni\n",
"0.00044 — Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym\n",
"0.00040 — Dochody_dofinansowanie_inwestycyjne\n",
"0.00039 — Zameldowania_z_miast_ogolem\n",
"0.00037 — Bezrobotni_ogolem\n",
"0.00035 — Udzialy_w_podatkach_dochodowych_razem\n",
"0.00033 — Zameldowania_ze_wsi_mezczyzni\n",
"0.00032 — Obiekty_caloroczne\n",
"0.00031 — Ludnosc_na_1_km2\n",
"0.00028 — Wymeldowania_kobiety\n",
"0.00026 — Wojewodztwo_Opolskie\n",
"0.00026 — Ludnosc_mezczyzni_w_wieku_produkcyjnym_niemobilnym\n",
"0.00024 — Zameldowania_z_miast_mezczyzni\n",
"0.00024 — Obiekty_ogolem\n",
"0.00024 — Wojewodztwo_Dolnoslaskie\n",
"0.00021 — Miejsca_noclegowe_caloroczne\n",
"0.00021 — Bezrobotne_kobiety\n",
"0.00020 — Wskaznik_urbanizacji\n",
"0.00020 — Ludnosc_w_wieku_przedprodukcyjnym\n",
"0.00019 — Wojewodztwo_Lubelskie\n",
"0.00019 — Wymeldowania_ogolem\n",
"0.00019 — Bezrobotni_do_25_roku_zycia\n",
"0.00017 — Dochody_podatek_od_dzialalnosci_gospodarczej\n",
"0.00013 — Wojewodztwo_Podlaskie\n",
"0.00012 — Wymeldowania_na_wies_kobiety\n",
"0.00012 — Dochody_podatek_odrebne_ustawy\n",
"0.00011 — Ludnosc_mezczyzni\n",
"0.00009 — Gmina_miejsko_wiejska\n",
"0.00008 — Ludnosc_mezczyzni_w_wieku_produkcyjnym\n",
"0.00007 — Zameldowania_mezczyzni\n",
"0.00007 — Powierzchnia\n",
"0.00006 — Ludnosc\n",
"0.00006 — Wymeldowania_do_miast_ogolem\n",
"0.00005 — Ludnosc_w_wieku_poprodukcyjnym\n",
"0.00005 — Ludnosc_ogolem\n",
"0.00004 — Wojewodztwo_Malopolskie\n",
"0.00004 — Wymeldowania_do_miast_kobiety\n",
"0.00003 — Ludnosc_mezczyzni_w_wieku_przedprodukcyjnym\n",
"0.00003 — Wojewodztwo_Lodzkie\n",
"0.00003 — Udzialy_w_podatkach_dochodowych_od_osob_fizycznych\n",
"0.00002 — Wojewodztwo_Wielkopolskie\n",
"0.00002 — Ludnosc_mezczyzni_w_wieku_produkcyjnym_mobilnym\n",
"0.00002 — Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym\n",
"0.00002 — Wojewodztwo_Kujawsko_Pomorskie\n",
"0.00002 — Wojewodztwo_Lubuskie\n",
"0.00002 — Zameldowania_ogolem\n",
"0.00001 — Wojewodztwo_Podkarpackie\n",
"0.00001 — Ludnosc_kobiety_w_wieku_produkcyjnym\n",
"0.00001 — Wojewodztwo_Pomorskie\n",
"0.00001 — Gmina_wiejska\n",
"0.00000 — Wojewodztwo_Zachodniopomorskie\n",
"0.00000 — Wojewodztwo_Mazowieckie\n",
"0.00000 — Gmina_miejska\n",
"0.00000 — Wojewodztwo_Warminsko_Mazurskie\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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