Group pandas dataframe and calculate mean for multiple columns

Question

I'm trying to group a pandas dataframe by a column and then also calculate the mean for multiple columns. In the sample below I would like to group by the 'category' column and then calculate the mean for the 'score' and 'priority' columns. All three columns should be in the resulting dataframe.

I am able to group and calculate the mean for the first column but I don't know how to add the second column. Below my attempt.

Any guidance greatly appreciated.

import pandas as pd

data = [['A', 2, 1], ['A', 4, 2], ['B', 5, 3], ['B', 2, 3]]
df = pd.DataFrame(data, columns=['category', 'score', 'priority'])
print(df)

#  This fails:
results_df = df.groupby('category')['score'].agg(['mean',])['priority'].agg(['mean',])
print(results_df)

Code Different · Accepted Answer · 2022-05-24 19:27:18Z

4

df.groupby("category", as_index=False).mean()

answered May 24, 2022 at 19:27

Code Different

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Comments

Tim Erickson · Accepted Answer · 2022-05-26 16:58:05Z

1

Your first three lines correctly print out the result

  category  score  priority
0        A      2         1
1        A      4         2
2        B      5         3
3        B      2         3

Now add this line:

df.groupby("category").mean(numeric_only=True)

and you will see:

          score  priority
category                 
A           3.0       1.5
B           3.5       3.0

which is probably what you're looking for. Running mean(numeric_only=True) on a DataFrame calculates means for all numeric columns. (You can leave it out right now, but you'll get a deprecated-feature message.)

edited May 26, 2022 at 16:58

Tim Erickson

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answered May 25, 2022 at 4:45

AKS

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