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35 lines
1.4 KiB
Python
35 lines
1.4 KiB
Python
"""
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This scipt prepares the data, required for us to perform funding calculations. The starting data should be stored
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on the machine this is being run on, and this will prepare the information and upload if
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"""
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import pandas as pd
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from utils.s3 import save_csv_to_s3
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STAGE = "dev"
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DATA_BUCKET = "retrofit-data-{stage}"
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PROJECTS_SCORES_MATRIX_LOCATION = "/Users/khalimconn-kowlessar/Downloads/ECO4 Full Project Scores Matrix.csv"
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WHLG_ELIGIBLE_POSTCODES = "/Users/khalimconn-kowlessar/Downloads/WHLG-eligible-postcodes.xlsx"
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def app():
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# Read in the project scores matrix
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project_scores_matrix = pd.read_csv(PROJECTS_SCORES_MATRIX_LOCATION)
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# Store in AWS S3
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save_csv_to_s3(
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dataframe=project_scores_matrix,
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bucket_name=DATA_BUCKET.format(stage=STAGE),
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file_name="funding/ECO4 Full Project Scores Matrix.csv"
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)
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# Read in the Warm Homes Local Grant eligible postcodes data
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whlg_eligible_postcodes = pd.read_excel(WHLG_ELIGIBLE_POSTCODES, sheet_name="Eligible postcodes", header=1)
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# We tidy up the data before we store
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whlg_eligible_postcodes = whlg_eligible_postcodes[["Postcode"]]
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whlg_eligible_postcodes["Postcode"] = whlg_eligible_postcodes["Postcode"].str.lower()
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save_csv_to_s3(
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dataframe=whlg_eligible_postcodes,
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bucket_name=DATA_BUCKET.format(stage=STAGE),
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file_name="funding/whlg eligible postcodes.csv"
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)
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