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DataFrame.where(cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True)
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Return an object of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from other.
Parameters: cond : boolean NDFrame, array or callable
If cond is callable, it is computed on the NDFrame and should return boolean NDFrame or array. The callable must not change input NDFrame (though pandas doesn?t check it).
New in version 0.18.1.
A callable can be used as cond.
other : scalar, NDFrame, or callable
If other is callable, it is computed on the NDFrame and should return scalar or NDFrame. The callable must not change input NDFrame (though pandas doesn?t check it).
New in version 0.18.1.
A callable can be used as other.
inplace : boolean, default False
Whether to perform the operation in place on the data
axis : alignment axis if needed, default None
level : alignment level if needed, default None
try_cast : boolean, default False
try to cast the result back to the input type (if possible),
raise_on_error : boolean, default True
Whether to raise on invalid data types (e.g. trying to where on strings)
Returns: wh : same type as caller
See also
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
>>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B']) >>> 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
DataFrame.where()
2017-01-12 04:46:51
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