I want to recode or bin multiple numeric columns as categorical values and summing the counts. The columns are ages by individual year from 18yrs to 90+ yrs with the cells containing summed totals by age. I want to create categories to reflect age cohorts and maintain the now cumulative count.
The aim is to categorise the ages in to the following cohorts:
'young_adults_18_29', 'younger_working_age_30_49', 'older_working_age_50_64', 'retirement_age_65_79', 'older_adults_80+'
# df <- original dataframe
max_age = df.columns[-1] # I assumed that the last column value is also the number(age).
df['young_adults_18_29'] = sum(df[i] for i in range(18, 30))
df['younger_working_age_30_49'] = sum(df[i] for i in range(30, 50))
df['older_working_age_50_64'] = sum(df[i] for i in range(50, 65))
df['retirement_age_65_79'] = sum(df[i] for i in range(65, 80))
df['older_adults_80+'] = sum(df[i] for i in range(80, max_age + 1))
df_new = df[['la_code', 'young_adults_18_29', 'younger_working_age_30_49', 'older_working_age_50_64', 'retirement_age_65_79', 'older_adults_80+']]