pandas.DataFrame.where
- DataFrame.where(cond, other=nan, inplace=False, axis=None, level=None, errors='raise', try_cast=NoDefault.no_default)[source]
-
Replace values where the condition is False.
- Parameters
-
- cond:bool 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).
- 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).
- 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 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
.
- 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.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 >>> 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
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Licensed under the 3-clause BSD License.
https://pandas.pydata.org/pandas-docs/version/1.3.4/reference/api/pandas.DataFrame.where.html