I want to create a new frequency column for each column in a pandas dataframe

Let's say I have a dataframe like this:

I want to create a column for each column showing the frequency in which each value happens:

I tried

frequency = list()
for column in df.columns:
     series = (df[column].value_counts(normalize=True, dropna=True)*100)
     overview.append(series)

#overview list
o_colors = overview[0] 
o_animals = overview[1]

df['animals_frequency'] = o_animals

If I try

df.info()

it returns

A simple approach is to calculate the relative frequency for each column values and then join these frequencies back to the original DataFrame.

for col in df.columns:
    # Get relative frequency
    frequency = df[col].value_counts(normalize=True)

    # Format frequency as percent values, rounded to two decimals
    frequency_percent = (frequency*100).round(2).astype(str) + '%'

    # Join frequency values to original DF
    frequency_percent.name = f"{col}_frequency"
    df = df.merge(frequency_percent.to_frame(), left_on=col, right_index=True)