pd.DataFrame.mask

pandas.DataFrame.mask(cond, other=np.nan, inplace=False, axis=1, level=None, errors='raise', try_cast=NoDefault.no_default)

Supported Arguments

• cond: Boolean DataFrame,Boolean Series,Boolean Array
• If 1-dimensional array or Series is provided, equivalent to Pandas df.mask with axis=1.
• other: Scalar, DataFrame, Series, 1 or 2-D Array
• None, - Data types in other must match corresponding entries in DataFrame.
• None or omitting argument defaults to the respective NA value for each type.

Note

DataFrame can contain categorical data if other is a scalar.

Example Usage

>>> @bodo.jit
... def f(df, cond, other):
...   return df.mask(cond, other)
>>> df = pd.DataFrame({"A": [1,2,3], "B": [4.3, 2.4, 1.2]})
>>> cond = df > 2
>>> other = df + 100
>>> f(df, cond, other)
    A      B
0    1  104.3
1    2  102.4
2  103    1.2