Retaining categorical dtype upon dataframe concatenation

Question

I have two dataframes with identical column names and dtypes, similar to the following:

A             object
B             category
C             category
         


        

Answer 1

To complement JohnE's answer, here's a function that does the job by converting to union_categoricals all the category columns present on all input dataframes:

def concatenate(dfs):
    """Concatenate while preserving categorical columns.

    NB: We change the categories in-place for the input dataframes"""
    from pandas.api.types import union_categoricals
    import pandas as pd
    # Iterate on categorical columns common to all dfs
    for col in set.intersection(
        *[
            set(df.select_dtypes(include='category').columns)
            for df in dfs
        ]
    ):
        # Generate the union category across dfs for this column
        uc = union_categoricals([df[col] for df in dfs])
        # Change to union category for all dataframes
        for df in dfs:
            df[col] = pd.Categorical(df[col].values, categories=uc.categories)
    return pd.concat(dfs)

Note the categories are changed in place in the input list:

df1=pd.DataFrame({'a': [1, 2],
                  'x':pd.Categorical(['dog','cat']),
                  'y': pd.Categorical(['banana', 'bread'])})
df2=pd.DataFrame({'x':pd.Categorical(['rat']),
                  'y': pd.Categorical(['apple'])})

concatenate([df1, df2]).dtypes