pandas.DataFrame.where¶
-
DataFrame.
where
(self, cond, other=nan, inplace=False, axis=None, level=None, errors='raise', try_cast=False)[source]¶ Replace values where the condition is False.
Parameters: - cond : boolean 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).
New in version 0.18.1: A callable can be used as cond.
- other : scalar, 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).
New in version 0.18.1: A callable can be used as other.
- inplace : bool, default False
Whether to perform the operation in place on the data.
- axis : int, default None
Alignment axis if needed.
- level : int, default None
Alignment level if needed.
- errors : str, {‘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_cast : bool, default False
Try to cast the result back to the input type (if possible).
Returns: - Same type as caller
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.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.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
>>> 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