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February 22, 2021 11:10
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test_5
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| # handle 2014 by using np.where to merge 2 incomplete cols (change in ICD coding that year results in 2 diff cols) | |
| ew_dfs[4]['All respiratory diseases (ICD-10 J00-J99)\nICD-10 v 2013 (IRIS)'] = np.where(ew_dfs[4]['All respiratory diseases (ICD-10 J00-J99)\nICD-10 v 2013 (IRIS)'] == ':', ew_dfs[4]['ICD-10 v 2010 (NCHS)'], ew_dfs[4]['All respiratory diseases (ICD-10 J00-J99)\nICD-10 v 2013 (IRIS)']) | |
| ew_dfs[4] = ew_dfs[4].drop(columns=['ICD-10 v 2010 (NCHS)']) | |
| # rename all cols then concat | |
| for df in ew_dfs: | |
| df.columns = ['Week', 'week_end', 'Deaths', 'RespiratoryDeaths', 'Year'] | |
| df_ew_ed = pd.concat(ew_dfs+[df_ew_21]) | |
| # add country | |
| df_ew_ed['Country'] = 'England & Wales' | |
| # format dates | |
| df_ew_ed['week_end'] = pd.to_datetime(df_ew_ed['week_end']) | |
| df_ew_ed['week_start'] = df_ew_ed['week_end'] + dt.timedelta(days=-6) | |
| # format deaths | |
| df_ew_ed['Deaths'] = pd.to_numeric(df_ew_ed['Deaths']) | |
| df_ew_ed['RespiratoryDeaths'] = pd.to_numeric(df_ew_ed['RespiratoryDeaths']) |
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