pandas.DataFrame.mask — pandas 2.2.3 documentation (original) (raw)

DataFrame.mask(cond, other=<no_default>, *, inplace=False, axis=None, level=None)[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). If not specified, entries will be filled with the corresponding NULL value (np.nan for numpy dtypes, pd.NA for extension dtypes).

inplacebool, default False

Whether to perform the operation in place on the data.

axisint, default None

Alignment axis if needed. For Series this parameter is unused and defaults to 0.

levelint, default None

Alignment level if needed.

Returns:

Same type as caller or None if inplace=True.

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 DataFrameother is used. If the axis of other does not align with axis ofcond Series/DataFrame, the misaligned index positions will be filled with True.

The signature for DataFrame.where() differs fromnumpy.where(). Roughly df1.where(m, df2) is equivalent tonp.where(m, df1, df2).

For further details and examples see the mask documentation inindexing.

The dtype of the object takes precedence. The fill value is casted to the object’s dtype, if this can be done losslessly.

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 = pd.Series(range(5)) t = pd.Series([True, False]) s.where(t, 99) 0 0 1 99 2 99 3 99 4 99 dtype: int64 s.mask(t, 99) 0 99 1 1 2 99 3 99 4 99 dtype: int64

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