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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "bc9fba0f",
"metadata": {},
"source": [
"# DataFrame for EU regional data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0e470150",
"metadata": {},
"outputs": [],
"source": [
"import os, sys\n",
"sys.path.insert(1, os.path.abspath('..'))\n",
"\n",
"from stat_mod import *\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b380d28d-7e6e-4f5b-82e3-e1d9d2ec214a",
"metadata": {},
"outputs": [],
"source": [
"# Create dicts with EU NUTS 2 regions\n",
"eu_regions = { 'AT': 'Western Europe',\n",
"'BE': 'Western Europe', 'FR': 'Western Europe', 'DE': 'Western Europe',\n",
"'IE': 'Western Europe', 'LU': 'Western Europe', 'NL': 'Western Europe',\n",
"'CY': 'Southern Europe', 'EL': 'Southern Europe', 'IT': 'Southern Europe',\n",
"'MT': 'Southern Europe', 'PT': 'Southern Europe', 'ES': 'Southern Europe',\n",
"'DK': 'Northern Europe', 'EE': 'Northern Europe', 'FI': 'Northern Europe',\n",
"'LV': 'Northern Europe', 'LT': 'Northern Europe', 'SE': 'Northern Europe',\n",
"'BG': 'Central and Eastern Europe', 'HR': 'Central and Eastern Europe',\n",
"'CZ': 'Central and Eastern Europe', 'RO': 'Central and Eastern Europe',\n",
"'SK': 'Central and Eastern Europe', 'SI': 'Central and Eastern Europe',\n",
"'PL': 'Central and Eastern Europe', 'HU': 'Central and Eastern Europe' }\n",
"\n",
"regions = {}\n",
"for item in countries.values():\n",
" regions.update(nuts_codes[item])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "dd5d63ab-9317-4aa0-a366-7f412cfd4cf4",
"metadata": {},
"outputs": [],
"source": [
"# Regional \"GDP\"\n",
"def get_gdp_region():\n",
" params = {'unit': 'MIO_EUR', 'geo': list(regions.keys()), 'time': 2020}\n",
" df = client.get_dataset('nama_10r_2gdp', params).to_dataframe()\n",
" \n",
" # Remove NAs\n",
" df.dropna(inplace = True)\n",
" df['region_name'] = df['geo'].apply(lambda x: regions[x])\n",
" df['Country'] = df['geo'].str[:2]\n",
" df['EU Region'] = df['Country'].apply(lambda x: eu_regions[x])\n",
" df.set_index('region_name', inplace=True) \n",
" df.rename(columns = {'values': 'GDP'}, inplace = True)\n",
" df['GDP'] = df['GDP'] / 1000\n",
" cols = ['Country', 'EU Region', 'GDP',]\n",
" \n",
" return df[cols]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ff71efcc-e4a6-4dce-9881-f431ddda628f",
"metadata": {},
"outputs": [],
"source": [
"# Regional \"GDP\" per capita\n",
"def get_gdp_capita_region():\n",
" params = {'unit': 'EUR_HAB', 'time': 2020,\n",
" 'geo': list(regions.keys())}\n",
" df = client.get_dataset('nama_10r_2gdp', params).to_dataframe()\n",
"\n",
" # Remove NAs\n",
" df.dropna(inplace = True)\n",
" df['region_name'] = df['geo'].apply(lambda x: regions[x])\n",
" df.set_index('region_name', inplace=True) \n",
" df.rename(columns = {'values': 'GDP per Capita'}, inplace = True)\n",
" df = df[['GDP per Capita']]\n",
" \n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "7c9a96ad-052a-49b0-b364-cea1adb39312",
"metadata": {},
"outputs": [],
"source": [
"# Regional unemployment\n",
"def get_unemployment_region():\n",
" params = {'sex': 'T', 'geo': list(regions.keys()), 'time': 2021,\n",
" 'age': 'Y15-74', 'isced11': 'TOTAL'} \n",
" df = client.get_dataset('lfst_r_lfu3rt', params).to_dataframe()\n",
"\n",
" # Remove NAs\n",
" df.dropna(inplace = True)\n",
" df['region_name'] = df['geo'].apply(lambda x: regions[x])\n",
" df.rename(columns = {'values': 'Unemployment %'}, inplace = True)\n",
" df.set_index('region_name', inplace=True) \n",
" df = df[['Unemployment %']]\n",
" \n",
" return df\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d88a8821-1ff9-4081-90a5-710b11b03e1a",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Life expectancy\n",
"def get_life_expectancy():\n",
" params = {'sex': 'T', 'geo': list(regions.keys()), 'time': 2020}\n",
" df = client.get_dataset('tgs00101', params).to_dataframe()\n",
"\n",
" # Remove NAs\n",
" df.dropna(inplace = True)\n",
" df['region_name'] = df['geo'].apply(lambda x: regions[x])\n",
" df.rename(columns = {'values': 'Life Expectancy'}, inplace = True)\n",
" df.set_index('region_name', inplace=True) \n",
" df = df[['Life Expectancy']]\n",
" \n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "8d9569f3-7ce7-429e-b22a-4abf7a5032bb",
"metadata": {},
"outputs": [],
"source": [
"# Get tertiary Educational attainment\n",
"def get_tertiary_education():\n",
" params = {'sex': 'T', 'geo': list(regions.keys()), 'time': 2020}\n",
" df = client.get_dataset('tgs00109', params).to_dataframe()\n",
" \n",
" # Remove NAs\n",
" df.dropna(inplace = True)\n",
" df['region_name'] = df['geo'].apply(lambda x: regions[x])\n",
" df.rename(columns = {'values': 'Tertiary Educational Attainment %'}, inplace = True)\n",
" df.set_index('region_name', inplace=True) \n",
" df = df[['Tertiary Educational Attainment %']]\n",
" \n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2206a2e3-4e77-4725-8bc5-b84978e52dcc",
"metadata": {},
"outputs": [],
"source": [
"# Population density\n",
"def get_population_density():\n",
" params = {'geo': list(regions.keys()), 'time': 2019}\n",
" df = client.get_dataset('tgs00024', params).to_dataframe()\n",
"\n",
" # Remove NAs\n",
" df.dropna(inplace = True)\n",
" df['region_name'] = df['geo'].apply(lambda x: regions[x])\n",
" df.rename(columns = {'values': 'Population Density'}, inplace = True)\n",
" df.set_index('region_name', inplace=True) \n",
" df = df[['Population Density']]\n",
" \n",
" return df\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "2eb66c5f-229b-41c4-adf3-db0ed0cb9589",
"metadata": {},
"outputs": [],
"source": [
"# Poverty Risk\n",
"def get_poverty_risk():\n",
" params = {'geo': list(regions.keys()), 'time': 2019}\n",
" df = client.get_dataset('ilc_peps11', params).to_dataframe()\n",
" \n",
" # Remove NAs\n",
" df.dropna(inplace = True)\n",
" df['region_name'] = df['geo'].apply(lambda x: regions[x])\n",
" df.rename(columns = {'values': 'People at Risk of Poverty %'}, inplace = True)\n",
" df.set_index('region_name', inplace=True) \n",
" df = df[['People at Risk of Poverty %']]\n",
" \n",
" return df\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "757f2be5-2e42-4075-bda1-294d238f5aed",
"metadata": {},
"outputs": [],
"source": [
"# Regional availability of doctors\n",
"def get_doctors():\n",
" params = {'geo': list(regions.keys()), 'time': 2019,'unit': 'P_HTHAB',\n",
" 'isco08': 'OC221' }\n",
" df = client.get_dataset('hlth_rs_prsrg', params).to_dataframe()\n",
"\n",
" # Remove NAs\n",
" df.dropna(inplace = True)\n",
" df['region_name'] = df['geo'].apply(lambda x: regions[x])\n",
" df.rename(columns = {'values': 'Doctors per 100000'}, inplace = True)\n",
" df.set_index('region_name', inplace=True) \n",
" df = df[['Doctors per 100000']]\n",
" \n",
" return df\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "f8c52f1a-a7ca-4a0c-8b29-fa22ecc32f47",
"metadata": {},
"outputs": [],
"source": [
"# Get deaths in road accidents\n",
"def get_fatal_road_accidents():\n",
" params = {'victim': 'KIL', 'geo': list(regions.keys()), 'time': 2020,\n",
" 'unit': 'P_MHAB'}\n",
" df = client.get_dataset('tran_r_acci', params).to_dataframe()\n",
"\n",
" # Remove NAs\n",
" df.dropna(inplace = True)\n",
" df['region_name'] = df['geo'].apply(lambda x: regions[x])\n",
" df.rename(columns = {'values': 'Fatal Road Accidents per Million'}, inplace = True)\n",
" df.set_index('region_name', inplace=True) \n",
" df = df[['Fatal Road Accidents per Million']]\n",
" \n",
" return df\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "efc48ba6-062d-4e0a-86c1-0df22c6238ff",
"metadata": {},
"outputs": [],
"source": [
"# Regular Internet Users\n",
"\n",
"def get_regular_internet_users():\n",
" params = {'indic_is': 'I_IDAY', 'geo': list(regions.keys()), 'time': 2021,\n",
" 'unit': 'PC_IND'}\n",
" df = client.get_dataset('isoc_r_iuse_i', params).to_dataframe()\n",
"\n",
" # Remove NAs\n",
" df.dropna(inplace = True)\n",
" df['region_name'] = df['geo'].apply(lambda x: regions[x])\n",
" df.rename(columns = {'values': 'Regular Internet Users %'}, inplace = True)\n",
" df.set_index('region_name', inplace=True) \n",
" df = df[['Regular Internet Users %']]\n",
" \n",
" return df\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "d7b173c5-aa6c-4b21-b1a1-c642b18184ee",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Get all data\n",
"df = get_gdp_region()\n",
"df = df.join(get_gdp_capita_region())\n",
"df = df.join(get_unemployment_region())\n",
"df = df.join(get_life_expectancy())\n",
"df = df.join(get_doctors())\n",
"df = df.join(get_fatal_road_accidents())\n",
"df = df.join(get_tertiary_education())\n",
"df = df.join(get_population_density())\n",
"df = df.join(get_poverty_risk())\n",
"df = df.join(get_regular_internet_users())\n",
"\n",
"# Remove NAs\n",
"df.dropna(thresh = 4, inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "478f421c-49e8-4334-ad2d-65cafd5b380f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 242 entries, Abruzzo to Южен централен (Yuzhen tsentralen)\n",
"Data columns (total 12 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Country 242 non-null object \n",
" 1 EU Region 242 non-null object \n",
" 2 GDP 242 non-null float64\n",
" 3 GDP per Capita 242 non-null float64\n",
" 4 Unemployment % 238 non-null float64\n",
" 5 Life Expectancy 238 non-null float64\n",
" 6 Doctors per 100000 173 non-null float64\n",
" 7 Fatal Road Accidents per Million 239 non-null float64\n",
" 8 Tertiary Educational Attainment % 238 non-null float64\n",
" 9 Population Density 239 non-null float64\n",
" 10 People at Risk of Poverty % 183 non-null float64\n",
" 11 Regular Internet Users % 169 non-null float64\n",
"dtypes: float64(10), object(2)\n",
"memory usage: 24.6+ KB\n"
]
}
],
"source": [
"# Check cols\n",
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "9a335ba4-330e-49a4-bec4-bf37dc4c2e50",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Save to CSV\n",
"df.to_csv('../data/eu_regional_data.csv',\n",
" float_format = '%.2f', encoding = 'utf-8')"
]
}
],
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"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
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