pandas.Series.mask

Series.mask(cond, other=nan, inplace=False, axis=None, level=None, errors='raise', try_cast=<object object>)[source]

Replace values where the condition is True.

Parameters
condbool Series/DataFrame, array-like, or callable

Where cond is False, keep the original value. Where True, replace with corresponding value from other. If cond is callable, it is computed on the Series/DataFrame and should return boolean Series/DataFrame or array. The callable must not change input Series/DataFrame (though pandas doesn’t check it).

otherscalar, Series/DataFrame, or callable

Entries where cond is True are replaced with corresponding value from other. If other is callable, it is computed on the Series/DataFrame and should return scalar or Series/DataFrame. The callable must not change input Series/DataFrame (though pandas doesn’t check it).

inplacebool, default False

Whether to perform the operation in place on the data.

axisint, default None

Alignment axis if needed.

levelint, default None

Alignment level if needed.

errorsstr, {‘raise’, ‘ignore’}, default ‘raise’

Note that currently this parameter won’t affect the results and will always coerce to a suitable dtype.

  • ‘raise’ : allow exceptions to be raised.

  • ‘ignore’ : suppress exceptions. On error return original object.

try_castbool, default None

Try to cast the result back to the input type (if possible).

Deprecated since version 1.3.0: Manually cast back if necessary.

Returns
Same type as caller or None if inplace=True.

See also

DataFrame.where()

Return an object of same shape as self.

Notes

The mask method is an application of the if-then idiom. For each element in the calling DataFrame, if cond is False the element is used; otherwise the corresponding element from the DataFrame other is used.

The signature for DataFrame.where() differs from numpy.where(). Roughly df1.where(m, df2) is equivalent to np.where(m, df1, df2).

For further details and examples see the mask documentation in indexing.

Examples

>>> s = pd.Series(range(5))
>>> s.where(s > 0)
0    NaN
1    1.0
2    2.0
3    3.0
4    4.0
dtype: float64
>>> s.mask(s > 0)
0    0.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64
>>> s.where(s > 1, 10)
0    10
1    10
2    2
3    3
4    4
dtype: int64
>>> s.mask(s > 1, 10)
0     0
1     1
2    10
3    10
4    10
dtype: int64
>>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B'])
>>> df
   A  B
0  0  1
1  2  3
2  4  5
3  6  7
4  8  9
>>> m = df % 3 == 0
>>> df.where(m, -df)
   A  B
0  0 -1
1 -2  3
2 -4 -5
3  6 -7
4 -8  9
>>> df.where(m, -df) == np.where(m, df, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True
>>> df.where(m, -df) == df.mask(~m, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True