pandas.DataFrame.where#
- DataFrame.where(cond, other=_NoDefault.no_default, *, inplace=False, axis=None, level=None)[source]#
Replace values where the condition is False.
- Parameters
- condbool Series/DataFrame, array-like, or callable
Where cond is True, keep the original value. Where False, 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 False 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
.
- Same type as caller or None if
See also
DataFrame.mask()
Return an object of same shape as self.
Notes
The where method is an application of the if-then idiom. For each element in the calling DataFrame, if
cond
isTrue
the element is used; otherwise the corresponding element from the DataFrameother
is used. If the axis ofother
does not align with axis ofcond
Series/DataFrame, the misaligned index positions will be filled with False.The signature for
DataFrame.where()
differs fromnumpy.where()
. Roughlydf1.where(m, df2)
is equivalent tonp.where(m, df1, df2)
.For further details and examples see the
where
documentation in indexing.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