pep_sex_2024 changes made #1110
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@kurus21 Can you remove input & output folder and confirm? |
| skiprows=7, | ||
| skipfooter=102, | ||
| header=None) | ||
| df.columns = [ |
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pls use df.rename() instead of assuming column order.
| skipfooter=102, | ||
| header=None) | ||
| df.columns = [ |
| 'White Total', 'White Male', 'White Female', 'NonWhite Total', | ||
| 'NonWhite Male', 'NonWhite Female' | ||
| ] | ||
| df = df.drop(columns=[ |
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more readable to list columns of interest to be retained:
df.drop(columns=df.columns.difference(['Count_Person_Male', 'Count_Person_Female']), inplace=True)
Then it can be moved outside the if/else block
| # adding geoid, year and measurement method | ||
| df['Year'] = year | ||
| df.insert(0, 'geo_ID', 'country/USA', True) | ||
| df['Measurement_Method'] = 'dcAggregate/CensusPEPSurvey_PartialAggregate' |
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This seems common to both if and else and can be moved out.
| for col in float_col.columns.values: | ||
| df[col] = df[col].astype('int64') | ||
| df[col] = df[col].astype("str").str.replace("-1", "") | ||
| df.rename(columns={'SEX': 'Year'}, inplace=True) |
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Why is the column 'SEX' being renamed to 'Year' here and in functions below.
| 'POPEST_FEM': 'Count_Person_Female', | ||
| 'YEAR': 'Year' | ||
| }) | ||
| df = df.drop(columns=[ |
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may be easier to to do df.drop(columns=df.columns.difference([])..)
| 'Count_Person_Male', 'Count_Person_Female' | ||
| ] | ||
| df = pd.read_excel(file_path, skiprows=5, skipfooter=7, header=None) | ||
| df.columns = column_name |
| 'July2022Female', | ||
| 'July2023Male', | ||
| 'July2023Female', | ||
| '2023Total', |
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Can we generalize this to 2024 and future years?
| "sc-est2023-syasex-": _state_2023, | ||
| "sc-est2023-agesex-": _state_2023, | ||
| "cc-est2023-agesex-": _county_2023, | ||
| "cc-est2023-agesex-a": _county_2023 |
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can we also extend to handle future years assuming the same format?
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