Panel.where(cond, other=nan, inplace=False, axis=None, level=None, errors='raise', try_cast=False, raise_on_error=None)
[source]
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, arraylike, or callable Where New in version 0.18.1: A callable can be used as cond. other : scalar, NDFrame, or callable Entries where 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
errors : str, {‘raise’, ‘ignore’}, default ‘raise’
Note that currently this parameter won’t affect the results and will always coerce to a suitable dtype. 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) Deprecated since version 0.21.0. 

Returns: 

See also
The where method is an application of the ifthen idiom. For each element in the calling DataFrame, if cond
is True
the element is used; otherwise the corresponding element from the DataFrame other
is used.
The signature for DataFrame.where()
differs from numpy.where()
. Roughly df1.where(m, df2)
is equivalent to np.where(m, df1, df2)
.
For further details and examples see the where
documentation in indexing.
>>> s = pd.Series(range(5)) >>> s.where(s > 0) 0 NaN 1 1.0 2 2.0 3 3.0 4 4.0
>>> s.mask(s > 0) 0 0.0 1 NaN 2 NaN 3 NaN 4 NaN
>>> s.where(s > 1, 10) 0 10.0 1 10.0 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
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http://pandas.pydata.org/pandasdocs/version/0.23.4/generated/pandas.Panel.where.html