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

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