How to apply value_counts(normalize=True) and value_counts() to pandas series?

user_14460824 0
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I like to show the value_counts(normalize=True) of a series what works well, but I also wanna show the value_counts() not normalized in an additional column.

Code

import pandas as pd

cars = {'Brand': ['Honda Civic','Toyota Corolla','','Audi A4'],
        'Price': [32000,35000,37000,45000]
        }

df = pd.DataFrame(cars, columns = ['Brand', 'Price'])

df.Brand.value_counts(normalize=True)

Expected output

                  perc   count
Toyota Corolla    0.25   1
Audi A4           0.25   1
Honda Civic       0.25   1
                  0.25   1
Name: Brand, dtype: float64

Question

How could I attache both information to the series?

pandas dataframe python
2 Answers

If want use value_counts you need run code without normalize=True:

df = pd.concat([df.Brand.value_counts(normalize=True),
                df.Brand.value_counts()], 
                axis=1,
                keys=('perc','count'))
print (df)
                perc  count
                0.25      1
Honda Civic     0.25      1
Toyota Corolla  0.25      1
Audi A4         0.25      1

Another idea is create perc column in another step, DataFrame.insert is for set position of new column:

df = df.Brand.value_counts().to_frame('count')
df.insert(0, 'perc', df['count'].div(len(df)))
         
print (df)
                perc  count
                0.25      1
Honda Civic     0.25      1
Toyota Corolla  0.25      1
Audi A4         0.25      1

df = df.Brand.value_counts(normalize=True).to_frame('perc')
df['count'] = df['perc'].mul(len(df))
user_29010020

You also can try this way: pd.concat([df.Brand.value_counts(), df.Brand.value_counts(normalize=True)], keys=['counts', 'normalized_counts'], axis=1)

user_200464220
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