Calculate new column as the mean of other columns pandas [duplicate]

Question

I have a this data frame:

and I would like to calculate a new columns as de the mean of salary_1, salary_2and 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       ... 

Thank you!

Answer 1

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 

Answer 2

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.