From 0b29c86dca80c993e187fd8af5194018b1a7736b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Micha=C5=82=20Sar?= <101715585+michal-sar@users.noreply.github.com> Date: Thu, 9 May 2024 08:21:38 +0200 Subject: [PATCH] add more data --- main.ipynb | 322 ++++++++++++++++++++++++++++++----------------------- 1 file changed, 184 insertions(+), 138 deletions(-) diff --git a/main.ipynb b/main.ipynb index 8490866a..ccff3640 100644 --- a/main.ipynb +++ b/main.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 99, "metadata": {}, "outputs": [], "source": [ @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 100, "metadata": {}, "outputs": [], "source": [ @@ -34,14 +34,14 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 101, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_26160\\3760256257.py:1: DtypeWarning: Columns (25) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_28456\\3760256257.py:1: DtypeWarning: Columns (25) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df_dofinansowanie = pd.read_csv(\n" ] } @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 102, "metadata": {}, "outputs": [], "source": [ @@ -88,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 103, "metadata": {}, "outputs": [], "source": [ @@ -105,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 104, "metadata": {}, "outputs": [], "source": [ @@ -124,14 +124,14 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 105, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_26160\\1671418303.py:1: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_28456\\1671418303.py:1: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df_fina_1 = pd.read_csv(\n" ] }, @@ -424,7 +424,7 @@ "[47083 rows x 11 columns]" ] }, - "execution_count": 130, + "execution_count": 105, "metadata": {}, "output_type": "execute_result" } @@ -454,14 +454,14 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 106, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_26160\\2161929356.py:1: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_28456\\2161929356.py:1: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df_fina_2 = pd.read_csv(\n" ] }, @@ -829,7 +829,7 @@ "[47083 rows x 14 columns]" ] }, - "execution_count": 131, + "execution_count": 106, "metadata": {}, "output_type": "execute_result" } @@ -862,7 +862,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 107, "metadata": {}, "outputs": [ { @@ -1079,7 +1079,7 @@ "[48611 rows x 8 columns]" ] }, - "execution_count": 132, + "execution_count": 107, "metadata": {}, "output_type": "execute_result" } @@ -1106,7 +1106,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 108, "metadata": {}, "outputs": [ { @@ -1349,7 +1349,7 @@ "[48611 rows x 8 columns]" ] }, - "execution_count": 133, + "execution_count": 108, "metadata": {}, "output_type": "execute_result" } @@ -1376,7 +1376,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 109, "metadata": {}, "outputs": [ { @@ -1606,7 +1606,7 @@ "[48611 rows x 8 columns]" ] }, - "execution_count": 134, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" } @@ -1640,7 +1640,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 110, "metadata": {}, "outputs": [], "source": [ @@ -1649,7 +1649,34 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 111, + "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": 112, "metadata": {}, "outputs": [], "source": [ @@ -1659,7 +1686,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 113, "metadata": {}, "outputs": [], "source": [ @@ -1669,7 +1696,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 114, "metadata": {}, "outputs": [], "source": [ @@ -1682,7 +1709,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 115, "metadata": {}, "outputs": [], "source": [ @@ -1705,13 +1732,21 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 116, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Index(['Kod', 'Rok', 'Suma', 'Powierzchnia', 'Wynagrodzenie_ogolem',\n", + "Index(['Kod', 'Rok', 'Suma', 'Wojewodztwo_Dolnoslaskie',\n", + " 'Wojewodztwo_Kujawsko_Pomorskie', 'Wojewodztwo_Lodzkie',\n", + " 'Wojewodztwo_Lubelskie', 'Wojewodztwo_Lubuskie',\n", + " 'Wojewodztwo_Malopolskie', 'Wojewodztwo_Mazowieckie',\n", + " 'Wojewodztwo_Opolskie', 'Wojewodztwo_Podkarpackie',\n", + " 'Wojewodztwo_Podlaskie', 'Wojewodztwo_Pomorskie', 'Wojewodztwo_Slaskie',\n", + " 'Wojewodztwo_Swietokrzyskie', 'Wojewodztwo_Warminsko_Mazurskie',\n", + " 'Wojewodztwo_Wielkopolskie', 'Wojewodztwo_Zachodniopomorskie',\n", + " 'Powierzchnia', 'Wynagrodzenie_ogolem',\n", " 'Wynagrodzenie_w_relacji_do_sredniej', 'Dochody_podatek_lesny',\n", " 'Dochody_podatek_PCC', 'Dochody_podatek_od_dzialalnosci_gospodarczej',\n", " 'Dochody_podatek_od_nieruchomosci', 'Dochody_podatek_od_spadkow',\n", @@ -1741,7 +1776,7 @@ " dtype='object')" ] }, - "execution_count": 140, + "execution_count": 116, "metadata": {}, "output_type": "execute_result" } @@ -1752,7 +1787,7 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 117, "metadata": {}, "outputs": [], "source": [ @@ -1768,21 +1803,19 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 118, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Mean Squared Error: 314617008704682.4\n" + "Mean Squared Error: 294627671837056.8\n" ] } ], "source": [ "from sklearn.model_selection import train_test_split\n", - "# from sklearn.preprocessing import StandardScaler\n", - "# from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.tree import DecisionTreeRegressor, plot_tree\n", "from sklearn.metrics import mean_squared_error\n", "import matplotlib.pyplot as plt\n", @@ -1790,56 +1823,68 @@ "df_data.dropna(inplace=True)\n", "\n", "feature_names = [\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", + " '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", "\n", "X = df_data[feature_names]\n", "y = df_data['Suma']\n", "\n", - "# scaler = StandardScaler()\n", - "# scaler = MinMaxScaler()\n", - "# X = scaler.fit_transform(X)\n", - "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n", "\n", "model = DecisionTreeRegressor(random_state=1)\n", @@ -1847,32 +1892,17 @@ "\n", "y_pred = model.predict(X_test)\n", "mse = mean_squared_error(y_test, y_pred)\n", - "print(\"Mean Squared Error:\", mse)\n", - "\n", - "# Dodatkowe dane:\n", - "# Wojewodztwo\n", - "# ..." + "print(\"Mean Squared Error:\", mse)" ] }, { "cell_type": "code", - "execution_count": 143, - "metadata": {}, - "outputs": [], - "source": [ - "# plt.figure(figsize=(15, 10))\n", - "# plot_tree(model, feature_names=feature_names, filled=True, rounded=True)\n", - "# plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 144, + "execution_count": 119, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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", 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" ] @@ -1891,55 +1921,71 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 120, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.39331 — Dochody_z_majatku\n", - "0.09103 — Ludnosc_mezczyzni_w_wieku_produkcyjnym_mobilnym\n", - "0.06877 — Udzialy_w_podatkach_dochodowych_od_osob_prywatnych\n", - "0.06441 — Wplywy_z_oplaty_skarbowej\n", - "0.05557 — Wynagrodzenie_w_relacji_do_sredniej\n", - "0.04363 — Wynagrodzenie_ogolem\n", - "0.03375 — Dochody_podatek_od_nieruchomosci\n", - "0.02552 — Dochody_z_uslug\n", - "0.02151 — Wplywy_z_oplaty_targowej\n", - "0.02078 — Udzialy_w_podatkach_dochodowych_od_osob_fizycznych\n", - "0.01974 — Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym\n", - "0.01962 — Wplywy_z_oplaty_eksploatacyjnej\n", - "0.01867 — Dochody_z_najmu_i_dzierzawy\n", - "0.01638 — Dochody_dofinansowanie_inwestycyjne\n", - "0.01573 — Dochody_podatek_rolny\n", - "0.01439 — Ludnosc_mezczyzni_w_wieku_przedprodukcyjnym\n", - "0.01180 — Dochody_podatek_od_srodkow_transportowych\n", - "0.00875 — Dochody_podatek_od_dzialalnosci_gospodarczej\n", - "0.00809 — Dochody_podatek_PCC\n", - "0.00663 — Dochody_podatek_odrebne_ustawy\n", - "0.00634 — Dochody_podatek_lesny\n", - "0.00547 — Dochody_dofinansowanie_razem\n", - "0.00444 — Wplywy_z_innych_lokalnych_oplat\n", - "0.00427 — Dochody_razem\n", - "0.00398 — Ludnosc_mezczyzni_w_wieku_poprodukcyjnym\n", - "0.00367 — Powierzchnia\n", - "0.00329 — Dochody_podatek_od_spadkow\n", - "0.00248 — Udzialy_w_podatkach_dochodowych_razem\n", - "0.00160 — Ludnosc_kobiety_w_wieku_przedprodukcyjnym\n", - "0.00137 — Ludnosc_kobiety\n", - "0.00128 — Ludnosc_mezczyzni_w_wieku_produkcyjnym\n", + "0.39277 — Dochody_z_majatku\n", + "0.09099 — Ludnosc_mezczyzni_w_wieku_produkcyjnym_mobilnym\n", + "0.06227 — Wynagrodzenie_w_relacji_do_sredniej\n", + "0.05967 — Ludnosc_w_wieku_produkcyjnym_niemobilnym\n", + "0.04757 — Wynagrodzenie_ogolem\n", + "0.02963 — Dochody_podatek_od_nieruchomosci\n", + "0.02897 — Dochody_podatek_rolny\n", + "0.02743 — Udzialy_w_podatkach_dochodowych_razem\n", + "0.02328 — Dochody_z_uslug\n", + "0.02201 — Wplywy_z_oplaty_skarbowej\n", + "0.02172 — Wplywy_z_oplaty_targowej\n", + "0.02024 — Dochody_z_najmu_i_dzierzawy\n", + "0.02010 — Udzialy_w_podatkach_dochodowych_od_osob_fizycznych\n", + "0.01977 — Wplywy_z_oplaty_eksploatacyjnej\n", + "0.01961 — Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym\n", + "0.01413 — Ludnosc_mezczyzni_w_wieku_przedprodukcyjnym\n", + "0.01349 — Dochody_podatek_od_dzialalnosci_gospodarczej\n", + "0.01158 — Wojewodztwo_Lubelskie\n", + "0.00762 — Dochody_podatek_od_srodkow_transportowych\n", + "0.00673 — Dochody_dofinansowanie_inwestycyjne\n", + "0.00610 — Wplywy_z_innych_lokalnych_oplat\n", + "0.00588 — Dochody_podatek_lesny\n", + "0.00567 — Dochody_podatek_PCC\n", + "0.00555 — Udzialy_w_podatkach_dochodowych_od_osob_prywatnych\n", + "0.00449 — Dochody_podatek_od_spadkow\n", + "0.00406 — Dochody_razem\n", + "0.00401 — Dochody_dofinansowanie_razem\n", + "0.00381 — Ludnosc_mezczyzni_w_wieku_poprodukcyjnym\n", + "0.00297 — Powierzchnia\n", + "0.00270 — Wojewodztwo_Dolnoslaskie\n", + "0.00267 — Dochody_podatek_odrebne_ustawy\n", + "0.00177 — Wojewodztwo_Swietokrzyskie\n", + "0.00123 — Ludnosc_kobiety_w_wieku_przedprodukcyjnym\n", "0.00115 — Ludnosc_mezczyzni_w_wieku_produkcyjnym_niemobilnym\n", - "0.00095 — Ludnosc_w_wieku_przedprodukcyjnym\n", - "0.00072 — Ludnosc_mezczyzni\n", - "0.00030 — Ludnosc_w_wieku_poprodukcyjnym\n", - "0.00013 — Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym\n", - "0.00012 — Ludnosc_kobiety_w_wieku_produkcyjnym\n", - "0.00012 — Ludnosc_kobiety_w_wieku_poprodukcyjnym\n", - "0.00009 — Ludnosc_ogolem\n", - "0.00008 — Ludnosc_w_wieku_produkcyjnym\n", - "0.00005 — Ludnosc_w_wieku_produkcyjnym_niemobilnym\n", - "0.00001 — Ludnosc_w_wieku_produkcyjnym_mobilnym\n" + "0.00101 — Ludnosc_mezczyzni_w_wieku_produkcyjnym\n", + "0.00096 — Wojewodztwo_Podkarpackie\n", + "0.00092 — Wojewodztwo_Wielkopolskie\n", + "0.00091 — Ludnosc_w_wieku_przedprodukcyjnym\n", + "0.00089 — Wojewodztwo_Slaskie\n", + "0.00075 — Wojewodztwo_Opolskie\n", + "0.00072 — Wojewodztwo_Warminsko_Mazurskie\n", + "0.00061 — Wojewodztwo_Podlaskie\n", + "0.00051 — Wojewodztwo_Malopolskie\n", + "0.00030 — Ludnosc_w_wieku_produkcyjnym_mobilnym\n", + "0.00017 — Ludnosc_kobiety_w_wieku_produkcyjnym\n", + "0.00017 — Ludnosc_kobiety_w_wieku_poprodukcyjnym\n", + "0.00009 — Ludnosc_kobiety\n", + "0.00005 — Ludnosc_ogolem\n", + "0.00005 — Wojewodztwo_Lubuskie\n", + "0.00004 — Ludnosc_mezczyzni\n", + "0.00004 — Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym\n", + "0.00004 — Wojewodztwo_Kujawsko_Pomorskie\n", + "0.00004 — Wojewodztwo_Lodzkie\n", + "0.00003 — Ludnosc_w_wieku_poprodukcyjnym\n", + "0.00002 — Ludnosc_w_wieku_produkcyjnym\n", + "0.00002 — Wojewodztwo_Zachodniopomorskie\n", + "0.00001 — Wojewodztwo_Pomorskie\n", + "0.00001 — Wojewodztwo_Mazowieckie\n" ] } ],