Get mean and mode of dataframe depending on each column type

You can use describe with 'all' which calculates statistics depending upon the dtype. It determines the top (mode) for object and mean for numeric columns. Then combine.

s = df1.describe(include='all')s = s.loc['top'].combine_first(s.loc['mean'])#Group      Winner#Study        Read#Score    0.883333#Name: top, dtype: object

np.number and select_dtypes

s = df1.select_dtypes(np.number).mean()df1.drop(s.index, axis=1).mode().iloc[0].append(s)Group      WinnerStudy        ReadScore    0.883333dtype: object

Variant

g = df1.dtypes.map(lambda x: np.issubdtype(x, np.number))d = {k: d for k, d in df1.groupby(g, axis=1)}pd.concat([d[False].mode().iloc[0], d[True].mean()])Group      WinnerStudy        ReadScore    0.883333dtype: object

Here is a slight variation on your solution that gets the job done

res = {}for col_name, col_type in zip(df1.columns, df1.dtypes):    if pd.api.types.is_numeric_dtype(col_type):        res[col_name] = df1[col_name].mean()    else:        res[col_name]= df1[col_name].mode()[0]pd.DataFrame(res, index = [0])

returns

    Group   Study   Score0   Winner  Read    0.883333

there could be multiple modes in a Series -- this solution picks the first one