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Poland cenus data #1751
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1d79cb1
feat(poland): add auto-refresh logic and schema for demographics
abhishekjaisw 78161f8
finalize census demographics import and auto-refresh
abhishekjaisw 7bfe83a
Merge branch 'master' into Poland_census
abhishekjaisw 329e86b
poland auto refresh updates
abhishekjaisw bdab9e0
poland auto refresh updates v1
abhishekjaisw b6ad051
poland auto refresh updates v2
abhishekjaisw 792e796
poland auto refresh updates v3
abhishekjaisw 4472c6d
poland auto refresh updates v4
abhishekjaisw 3cdae22
Merge branch 'master' into Poland_census
abhishekjaisw 8ea07d4
Merge branch 'master' into Poland_census
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| # Poland Demographics Dataset | ||
| ## Overview | ||
| This dataset provides foundational demographic and socio-economic statistics for Poland, sourced directly from official national datasets. | ||
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| ## Data Source | ||
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| **Source URL:** | ||
| https://stat.gov.pl/en/databases/ | ||
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| The data comes from Poland's official statistical authority and includes comprehensive demographic variables such as population counts, age distributions, and other census-related metrics. | ||
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| ## How To Download Input Data | ||
| To download the data, you'll need to use the provided download script download_input_data.py. This script processes the statvar_imports/statistics_poland/poland_data_sample/poland_raw.xlsx file to generate StatisticsPoland_input.csv inside a new "poland_input" folder. | ||
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| type of place: State. | ||
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| statvars: Demographics | ||
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| years: 2003 to 2024. | ||
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| ## Processing Instructions | ||
| To process the Poland Census data and generate statistical variables, use the following command from the "data" directory: | ||
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| **Download input file** | ||
| ```bash | ||
| python3 statvar_imports/statistics_poland/download_input_data.py | ||
| ``` | ||
| **For Test Data Run** | ||
| ```bash | ||
| python3 tools/statvar_importer/stat_var_processor.py \ | ||
| --input_data='statvar_imports/statistics_poland/test/StatisticsPoland_input.csv' \ | ||
| --pv_map='statvar_imports/statistics_poland/StatisticsPoland_pvmap.csv' \ | ||
| --output_path='statvar_imports/statistics_poland/test/StatisticsPoland_output' \ | ||
| --config_file='statvar_imports/statistics_poland/Statistics_Poland_metadata.csv' \ | ||
| --existing_statvar_mcf=gs://unresolved_mcf/scripts/statvar/stat_vars.mcf | ||
| ``` | ||
| **For Main data run** | ||
| ```bash | ||
| python3 tools/statvar_importer/stat_var_processor.py \ | ||
| --input_data='statvar_imports/statistics_poland/poland_input/StatisticsPoland_input.csv' \ | ||
| --pv_map='statvar_imports/statistics_poland/StatisticsPoland_pvmap.csv' \ | ||
| --output_path='statvar_imports/statistics_poland/poland_output/StatisticsPoland_output' \ | ||
| --config_file='statvar_imports/statistics_poland/Statistics_Poland_metadata.csv' \ | ||
| --existing_statvar_mcf=gs://unresolved_mcf/scripts/statvar/stat_vars.mcf | ||
| ``` | ||
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statvar_imports/statistics_poland/StatisticsPoland_pvmap.csv
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| key,p1,v1,p2,v2,p3,v3,p4,v4,p5,v5 | ||
| ,,,,,,,,,, | ||
| Code,measuredProperty,count,populationType,Person,,,,,, | ||
| males,gender,Male,,,,,,,, | ||
| females,gender,Female,,,,,,,, | ||
| total,,,,,,,,,, | ||
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| in urban areas,placeOfResidenceClassification,Urban,,,,,,,, | ||
| in rural areas,placeOfResidenceClassification,Rural,,,,,,,, | ||
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| 0-2,age,Years0To2,,,,,,,, | ||
| 3-6,age,Years3To6,,,,,,,, | ||
| 7-12,age,Years7To12,,,,,,,, | ||
| 13-15,age,Years13To15,,,,,,,, | ||
| 16-19,age,Years16To19,,,,,,,, | ||
| 20-24,age,Years20To24,,,,,,,, | ||
| 25-34,age,Years25To34,,,,,,,, | ||
| 35-44,age,Years35To44,,,,,,,, | ||
| 45-54,age,Years45To54,,,,,,,, | ||
| 55-64,age,Years55To64,,,,,,,, | ||
| 65 and more,age,Years65Onwards,,,,,,,, | ||
| ,,,,,,,,,, | ||
| ,,,,,,,,,, | ||
| POLAND,observationAbout,country/POL,#Header,observationAbout,,,,,, | ||
| DOLNOŚLĄSKIE,observationAbout,nuts/PL51,#Header,observationAbout,,,,,, | ||
| KUJAWSKO-POMORSKIE,observationAbout,nuts/PL61,#Header,observationAbout,,,,,, | ||
| LUBELSKIE,observationAbout,nuts/PL81,#Header,observationAbout,,,,,, | ||
| LUBUSKIE,observationAbout,nuts/PL43,#Header,observationAbout,,,,,, | ||
| ŁÓDZKIE,observationAbout,nuts/PL71,#Header,observationAbout,,,,,, | ||
| MAŁOPOLSKIE,observationAbout,nuts/PL21,#Header,observationAbout,,,,,, | ||
| MAZOWIECKIE,observationAbout,nuts/PL9,#Header,observationAbout,,,,,, | ||
| OPOLSKIE,observationAbout,nuts/PL52,#Header,observationAbout,,,,,, | ||
| PODKARPACKIE,observationAbout,nuts/PL82,#Header,observationAbout,,,,,, | ||
| PODLASKIE,observationAbout,nuts/PL84,#Header,observationAbout,,,,,, | ||
| POMORSKIE,observationAbout,nuts/PL63,#Header,observationAbout,,,,,, | ||
| ŚLĄSKIE,observationAbout,nuts/PL22,#Header,observationAbout,,,,,, | ||
| ŚWIĘTOKRZYSKIE,observationAbout,nuts/PL72,#Header,observationAbout,,,,,, | ||
| WARMIŃSKO-MAZURSKIE,observationAbout,nuts/PL62,#Header,observationAbout,,,,,, | ||
| WIELKOPOLSKIE,observationAbout,nuts/PL41,#Header,observationAbout,,,,,, | ||
| ZACHODNIOPOMORSKIE,observationAbout,nuts/PL42,#Header,observationAbout,,,,,, | ||
| ,,,,,,,,,, | ||
| 2003,observationDate,2003,value,{Number},,,,,, | ||
| 2004,observationDate,2004,value,{Number},,,,,, | ||
| 2005,observationDate,2005,value,{Number},,,,,, | ||
| 2006,observationDate,2006,value,{Number},,,,,, | ||
| 2007,observationDate,2007,value,{Number},,,,,, | ||
| 2008,observationDate,2008,value,{Number},,,,,, | ||
| 2009,observationDate,2009,value,{Number},,,,,, | ||
| 2010,observationDate,2010,value,{Number},,,,,, | ||
| 2011,observationDate,2011,value,{Number},,,,,, | ||
| 2012,observationDate,2012,value,{Number},,,,,, | ||
| 2013,observationDate,2013,value,{Number},,,,,, | ||
| 2014,observationDate,2014,value,{Number},,,,,, | ||
| 2015,observationDate,2015,value,{Number},,,,,, | ||
| 2016,observationDate,2016,value,{Number},,,,,, | ||
| 2017,observationDate,2017,value,{Number},,,,,, | ||
| 2018,observationDate,2018,value,{Number},,,,,, | ||
| 2019,observationDate,2019,value,{Number},,,,,, | ||
| 2020,observationDate,2020,value,{Number},,,,,, | ||
| 2021,observationDate,2021,value,{Number},,,,,, | ||
| 2022,observationDate,2022,value,{Number},,,,,, | ||
| 2023,observationDate,2023,value,{Number},,,,,, | ||
| 2024,observationDate,2024,value,{Number},,,,,, | ||
| 2025,observationDate,2025,value,{Number},,,,,, | ||
| 2026,observationDate,2026,value,{Number},,,,,, | ||
| 2027,observationDate,2027,value,{Number},,,,,, | ||
| 2028,observationDate,2028,value,{Number},,,,,, | ||
| 2029,observationDate,2029,value,{Number},,,,,, | ||
| 2030,observationDate,2030,value,{Number},,,,,, | ||
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statvar_imports/statistics_poland/Statistics_Poland_metadata.csv
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| config,value | ||
| provenance_url,https://bdl.stat.gov.pl/bdl/dane/podgrup/tablica | ||
| output_columns,"observationDate,observationAbout,value,variableMeasured" | ||
| places_within,country/POL | ||
| #place_types,"AdministrativeArea,AdministrativeArea1,AdministrativeArea2,State" | ||
| #debug,1 | ||
| #input_rows,100 | ||
| #word_delimiter,'' | ||
| #skip_rows,1 | ||
| header_rows,5 | ||
| mapped_columns,2 | ||
| dc_api_root,https://api.datacommons.org | ||
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| import pandas as pd | ||
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| import os | ||
| import logging | ||
| from datetime import datetime | ||
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| # Configure logging | ||
| logging.basicConfig( | ||
| level=logging.INFO, | ||
| format='%(levelname)s: %(message)s' | ||
| ) | ||
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| # Configuration | ||
| INPUT_FILE = "statvar_imports/statistics_poland/poland_data_sample/poland_raw.xlsx" | ||
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| OUTPUT_DIR = "statvar_imports/statistics_poland/poland_input" | ||
| OUTPUT_FILE = os.path.join(OUTPUT_DIR, "StatisticsPoland_input.csv") | ||
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| # Target functional age groups | ||
| TARGET_AGES = [ | ||
| "0-2", "3-6", "7-12", "13-15", "16-19", "20-24", | ||
| "25-34", "35-44", "45-54", "55-64", "65 i więcej" | ||
| ] | ||
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| def process_poland_pivot(): | ||
| if not os.path.exists(INPUT_FILE): | ||
| logging.error(f"{INPUT_FILE} not found.") | ||
| return | ||
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| logging.info(f"Starting generic processing. Saving to: {OUTPUT_FILE}") | ||
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| try: | ||
| # 1. Load the 'DANE' sheet | ||
| df = pd.read_excel(INPUT_FILE, sheet_name='DANE') | ||
| df.columns = ['Code', 'Name', 'Age', 'Sex', 'Location', 'Year', 'Value', 'Unit', 'Attr'] | ||
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| # 2. Generic Filtering | ||
| df = df[df['Age'].isin(TARGET_AGES)] | ||
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| # DYNAMIC YEAR LOGIC | ||
| current_year = datetime.now().year | ||
| available_years = sorted([y for y in df['Year'].unique() if y <= current_year]) | ||
| df = df[df['Year'].isin(available_years)] | ||
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| # 3. Translation Logic | ||
| translations = { | ||
| 'mężczyźni': 'males', | ||
| 'kobiety': 'females', | ||
| 'ogółem': 'total', | ||
| 'w miastach': 'in urban areas', | ||
| 'na wsi': 'in rural areas', | ||
| 'POLSKA': 'POLAND', | ||
| '65 i więcej': '65 and more' | ||
| } | ||
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| # Refactored repetitive replace calls into a loop | ||
| for col in ['Sex', 'Location', 'Name', 'Age']: | ||
| df[col] = df[col].replace(translations) | ||
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| # 4. Create the Pivot Table | ||
| pivot_df = df.pivot_table( | ||
| index=['Code', 'Name'], | ||
| columns=['Age', 'Sex', 'Location', 'Year'], | ||
| values='Value' | ||
| ) | ||
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| # 5. Format Geographic Codes (ensuring 7-digit padding) | ||
| pivot_df.index = pivot_df.index.set_levels( | ||
| pivot_df.index.levels[0].astype(str).str.zfill(7), level=0 | ||
| ) | ||
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| # 6. Save result | ||
| os.makedirs(OUTPUT_DIR, exist_ok=True) | ||
| pivot_df.to_csv(OUTPUT_FILE, encoding='utf-8') | ||
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| logging.info(f"SUCCESS: {OUTPUT_FILE} has been updated.") | ||
| logging.info(f"Years Included: {available_years}") | ||
| logging.info(f"Total Geographies Processed: {pivot_df.shape[0]}") | ||
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| except Exception as e: | ||
| logging.error(f"Processing Error: {e}") | ||
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| if __name__ == "__main__": | ||
| process_poland_pivot() | ||
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| { | ||
| "import_specifications": [ | ||
| { | ||
| "import_name": "statistics_poland", | ||
| "curator_emails": [ | ||
| "support@datacommons.org" | ||
| ], | ||
| "provenance_url": "https://bdl.stat.gov.pl/bdl/dane/podgrup/tablica", | ||
| "provenance_description": "Population data for demographic variables such as population counts, age distributions, and other census-related metrics in Poland", | ||
| "scripts": [ | ||
| "download_input_data.py", | ||
| "../../tools/statvar_importer/stat_var_processor.py --input_data=poland_input/StatisticsPoland_input.csv --pv_map=StatisticsPoland_pvmap.csv --config_file=Statistics_Poland_metadata.csv --output_path=poland_output/StatisticsPoland_output" | ||
| ], | ||
| "source_files": [ | ||
| "poland_input/StatisticsPoland_input.csv" | ||
| ], | ||
| "import_inputs": [ | ||
| { | ||
| "template_mcf": "poland_output/StatisticsPoland_output.tmcf", | ||
| "cleaned_csv": "poland_output/StatisticsPoland_output.csv" | ||
| } | ||
| ], | ||
| "cron_schedule": "0 0 1 1,4,7,10 *" | ||
| } | ||
| ] | ||
| } |
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