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Merge pull request #113 from ONSdigital/RDRP-468_intram_region
Rdrp 468 intram region
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,14 @@ | ||
| [ranking] | ||
| Deduced_Data_Type = "Int64" | ||
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| ["Area Code (ITL1)"] | ||
| Deduced_Data_Type = "str" | ||
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| ["Country or region (ITL1)"] | ||
| Deduced_Data_Type = "str" | ||
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| [Notes] | ||
| Deduced_Data_Type = "str" | ||
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| [ITL121CD] | ||
| Deduced_Data_Type = "str" |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,90 @@ | ||
| """The main file for the Intram by PG output.""" | ||
| import logging | ||
| import pandas as pd | ||
| from datetime import datetime | ||
| from typing import Callable, Dict, Any | ||
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| from src.outputs.outputs_helpers import aggregate_output | ||
| import src.outputs.map_output_cols as map_o | ||
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| OutputMainLogger = logging.getLogger(__name__) | ||
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| def output_intram_by_itl1( | ||
| df: pd.DataFrame, | ||
| config: Dict[str, Any], | ||
| write_csv: Callable, | ||
| run_id: int, | ||
| postcode_mapper: pd.DataFrame, | ||
| itl_mapper: pd.DataFrame, | ||
| itl1_detailed: pd.DataFrame, | ||
| ): | ||
| """Run the outputs module. | ||
| Args: | ||
| df (pd.DataFrame): The dataset main with weights not applied | ||
| config (dict): The configuration settings. | ||
| write_csv (Callable): Function to write to a csv file. | ||
| This will be the hdfs or network version depending on settings. | ||
| run_id (int): The current run id | ||
| postcode_mapper (pd.DataFrame): Maps postcodes to regional codes | ||
| itl_mapper (pd.DataFrame): Maps regional codes to ITL Level 1 | ||
| itl1_detailed (pd.DataFrame): Details of ITL Level 1 | ||
| """ | ||
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| NETWORK_OR_HDFS = config["global"]["network_or_hdfs"] | ||
| paths = config[f"{NETWORK_OR_HDFS}_paths"] | ||
| output_path = paths["output_path"] | ||
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| # Join region code | ||
| df = map_o.join_itl_regions(df, postcode_mapper) | ||
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| # Join itl level 1 | ||
| reg_code = "LAU121CD" | ||
| itl_code = "ITL121CD" | ||
| itl1_mapper = itl_mapper[[reg_code, itl_code]] | ||
| df = df.merge(itl1_mapper, how="left", left_on="itl", right_on=reg_code) | ||
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| # Group by ITL level 1 and aggregate intram | ||
| value_col = "211" | ||
| agg_method = "sum" | ||
| df_agg = aggregate_output(df, [itl_code], [value_col], agg_method) | ||
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| # Create UK total | ||
| value_uk = df_agg[value_col].sum() | ||
| df_uk = pd.DataFrame({itl_code: ["TLA"], value_col: value_uk}) | ||
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| # Create England total | ||
| itls_eng = ["TLC", "TLD", "TLE", "TLF", "TLG", "TLH", "TLI", "TLJ", "TLK"] | ||
| df_eng = df_agg[df_agg[itl_code].isin(itls_eng)] | ||
| value_eng = df_eng[value_col].sum() | ||
| df_eng = pd.DataFrame({itl_code: ["TLB"], value_col: value_eng}) | ||
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| # Concatinate totals | ||
| df_agg = pd.concat([df_agg, df_uk]) | ||
| df_agg = pd.concat([df_agg, df_eng]) | ||
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| # Merge with labels and ranks | ||
| df_merge = itl1_detailed.merge( | ||
| df_agg, | ||
| how="left", | ||
| left_on=itl_code, | ||
| right_on=itl_code) | ||
| df_merge[value_col] = df_merge[value_col].fillna(0) | ||
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| # Sort by rank | ||
| df_merge = df_merge.sort_values("ranking", axis=0, ascending=True) | ||
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| # Select and rename the correct columns | ||
| code = "Area Code (ITL1)" | ||
| detail = "Country or region (ITL1)" | ||
| notes = "Notes" | ||
| value_title = "2023" | ||
| df_merge = df_merge[[code, detail, value_col, notes]].rename( | ||
| columns={value_col: value_title}) | ||
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| # Outputting the CSV file with timestamp and run_id | ||
| tdate = datetime.now().strftime("%Y-%m-%d") | ||
| filename = f"output_intram_by_itl1_{tdate}_v{run_id}.csv" | ||
| write_csv(f"{output_path}/output_intram_by_itl1/{filename}", df_merge) |
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