How to map values for entries which are not present and then fill those values with zero?-CodePudding

I have data from

The possible entries for age_group are and for sex are Male or Female.

The data will be resampled monthly, how can I ensure (map values) to ensure that for each month for each district, there are all age_groups present (sometimes "Unidentified" is not present so it would be to fill the "Unidentified" for both Female and Male and then fill the total_number_individuals_vaccinated value with zero)

CodePudding user response：

have you tried

df[„age_groups“].fillna(„Unidentified“)

and

df[„total_number_individuals_vaccinated“].fillna(0)

CodePudding user response：

Each time you'll recieve new data. You can use:

df_new.loc[:, 'age_group'].nunique()

df_new.loc[:, 'age_group'].value_counts()

to verify the categories. You can check the official pandas api documentation for both functions .value_counts and .unique

When you'll receive a new set of data. Let's call it df_new after reading it. You can fill NaN values using:

df_new.loc[:, 'age_group'] = df_new.loc[:, 'age_group'].fillna("Unidentified")

You can also do the same for the total_number_individuals_vaccinated column

df_new.loc[:, 'total_number_individuals_vaccinated'] = df_new.loc[:, 'total_number_individuals_vaccinated'].fillna("Unidentified")

Then, you can concat your new data with past ones.

df = pd.concat([df_past, df_new], axis=1)