pandas DataFrame set value on boolean mask

Question

I'm trying to set a number of different in a pandas DataFrame all to the same value. I thought I understood boolean indexing for pandas, but I haven't found any resources on this specific error.

import pandas as pd  df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'f']}) mask = df.isin([1, 3, 12, 'a']) df[mask] = 30 Traceback (most recent call last): ... TypeError: Cannot do inplace boolean setting on mixed-types with a non np.nan value 

Above, I want to replace all of the True entries in the mask with the value 30.

I could do df.replace instead, but masking feels a bit more efficient and intuitive here. Can someone explain the error, and provide an efficient way to set all of the values?

Answer 1

You can't use the boolean mask on mixed dtypes for this unfortunately, you can use pandas where to set the values:

In [59]: df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'f']}) mask = df.isin([1, 3, 12, 'a']) df = df.where(mask, other=30) df  Out[59]:     A   B 0   1   a 1  30  30 2   3  30 

Note: that the above will fail if you do inplace=True in the where method, so df.where(mask, other=30, inplace=True) will raise:

TypeError: Cannot do inplace boolean setting on mixed-types with a non np.nan value

EDIT

OK, after a little misunderstanding you can still use where y just inverting the mask:

In [2]:     df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'f']}) mask = df.isin([1, 3, 12, 'a']) df.where(~mask, other=30)  Out[2]:     A   B 0  30  30 1   2   b 2  30   f