Pandas DataFrame: where() function

Last update on August 19 2022 21:50:51 (UTC/GMT +8 hours)

DataFrame - where() function

The where() function is used to replace values where the condition is False.

Syntax:

DataFrame.where(self, cond, other=nan, inplace=False, axis=None, level=None, errors='raise', try_cast=False)

Parameters:

Name	Description	Type/Default Value	Required / Optional
cond	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).	boolean Series/DataFrame, array-like, or callable	Required
other	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).	scalar, Series/DataFrame, or callable	Required
inplace	Whether to perform the operation in place on the data.	bool Default Value: False	Required
axis	Alignment axis if needed.	int Default Value: None	Required
level	Alignment level if needed.	int Default Value: None	Required
errors	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.	str, {‘raise’, ‘ignore’} Default Value: ‘raise’	Required
try_cast	Try to cast the result back to the input type (if possible).	bool Default Value: False	Required

Returns: Same type as caller

Notes

The where method is an application of the if-then 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).

Example:

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