CODEWITHSUNDEEP
pythonpandasgroup-by
Karma
Karma

Reputation: 269

pandas: return average of multiple columns

How do you output average of multiple columns?

Gender   Age     Salary     Yr_exp   cup_coffee_daily
  Male    28    45000.0        6.0                2.0
Female    40    70000.0       15.0               10.0
Female    23    40000.0        1.0                0.0
  Male    35    55000.0       12.0                6.0

I have df.groupby('Gender', as_index=False)['Age', 'Salary', 'Yr_exp'].mean(), but it still only returned the average of the first column Age. How do you return the average of specific columns in different columns? Desired output:

Gender   Age     Salary   Yr_exp
  Male  31.5    50000.0      9.0
Female  31.5    55000.0      8.0

Thanks.

Upvotes: 15

Views: 45693

Answers (2)

VnC
VnC

Reputation: 2026

You can also use pandas.agg():

df.groupby("Gender").agg({'Age' : 'mean', 'Salary' : 'mean', 'Yr_exp': 'mean'})

Would result to:

         Age    Salary  Yr_exp
Gender          
Female  31.5    55000   8
Male    31.5    50000   9

Using .agg() give you the chance to apply different functions to a grouped object - something like:

df.groupby("Gender").agg({'Age' : 'mean', 'Salary' : ['min', 'max'], 'Yr_exp': 'sum'})

Outputs:

          Age         Salary    Yr_exp
         mean    min      max   sum
Gender              
Female  31.5    40000   70000   16
Male    31.5    45000   55000   18

Upvotes: 9

Jonathan Dayton
Jonathan Dayton

Reputation: 592

Given this dataframe:

df = pd.DataFrame({
    "Gender": ["Male", "Female", "Female", "Male"],
    "Age": [28, 40, 23, 35],
    "Salary": [45000, 70000, 40000, 55000],
    "Yr_exp": [6, 15, 1, 12]
})

df
   Age  Gender  Salary  Yr_exp
0   28    Male   45000       6
1   40  Female   70000      15
2   23  Female   40000       1
3   35    Male   55000      12

Group by gender and use the mean() function:

df.groupby("Gender").mean()
         Age   Salary  Yr_exp
Gender                       
Female  31.5  55000.0     8.0
Male    31.5  50000.0     9.0

Edit: you may need to change the way you're indexing after groupby(): df['Age', 'Salary'] gives a KeyError, but df[['Age', 'Salary']] returns the expected:

   Age  Salary
0   28   45000
1   40   70000
2   23   40000
3   35   55000

Try changing 

df.groupby("Gender", as_index=True)['Age', 'Salary', 'Yr_exp'].mean()

to 

df.groupby("Gender", as_index=True)[['Age', 'Salary', 'Yr_exp']].mean()

Upvotes: 22

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