54

I have a this data frame and I would like to calculate a new column as the mean of salary_1, salary_2 and salary_3:

df = pd.DataFrame({
    'salary_1': [230, 345, 222],
    'salary_2': [235, 375, 292],
    'salary_3': [210, 385, 260]
})

      salary_1     salary_2    salary_3
0        230           235        210
1        345           375        385
2        222           292        260

How can I do it in pandas in the most efficient way? Actually I have many more columns and I don't want to write this one by one.

Something like this:

      salary_1     salary_2    salary_3     salary_mean
0        230           235        210     (230+235+210)/3
1        345           375        385       ...
2        222           292        260       ...
mkrieger1
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Carmen Pérez Carrillo
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2 Answers2

82

Use .mean. By specifying the axis you can take the average across the row or the column.

df['average'] = df.mean(axis=1)
df

returns

       salary_1  salary_2  salary_3     average
0       230       235       210  225.000000
1       345       375       385  368.333333
2       222       292       260  258.000000

If you only want the mean of a few you can select only those columns. E.g.

df['average_1_3'] = df[['salary_1', 'salary_3']].mean(axis=1)
df

returns

   salary_1  salary_2  salary_3  average_1_3
0       230       235       210        220.0
1       345       375       385        365.0
2       222       292       260        241.0
Alex
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48

an easy way to solve this problem is shown below :

col = df.loc[: , "salary_1":"salary_3"]

where "salary_1" is the start column name and "salary_3" is the end column name

df['salary_mean'] = col.mean(axis=1)
df

This will give you a new dataframe with a new column that shows the mean of all the other columns This approach is really helpful when you are having a large set of columns or also helpful when you need to perform on only some selected columns not on all.

PraneetNigam
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