Row-wise average for a subset of columns with missing values

Question

I've got a 'DataFrame` which has occasional missing values, and looks something like this:

          Monday         Tuesday         Wednesday        ================================================ Mike        42             NaN               12 Jenna       NaN            NaN               15 Jon         21              4                 1 

I'd like to add a new column to my data frame where I'd calculate the average across all columns for every row.

Meaning, for Mike, I'd need (df['Monday'] + df['Wednesday'])/2, but for Jenna, I'd simply use df['Wednesday amt.']/1

Does anyone know the best way to account for this variation that results from missing values and calculate the average?

Answer 1

You can simply:

df['avg'] = df.mean(axis=1)         Monday  Tuesday  Wednesday        avg Mike       42      NaN         12  27.000000 Jenna     NaN      NaN         15  15.000000 Jon        21        4          1   8.666667 

because .mean() ignores missing values by default: see docs.

To select a subset, you can:

df['avg'] = df[['Monday', 'Tuesday']].mean(axis=1)         Monday  Tuesday  Wednesday   avg Mike       42      NaN         12  42.0 Jenna     NaN      NaN         15   NaN Jon        21        4          1  12.5 

Answer 2

Alternative - using iloc (can also use loc here):

df['avg'] = df.iloc[:,0:2].mean(axis=1)