dtoolkit.accessor.dataframe.boolean#

dtoolkit.accessor.dataframe.boolean(df: DataFrame, /, how: Literal['any', 'all'] = 'any', complement: bool = False, **kwargs) → Series[source]#

Return whether any (or all) element is True, potentially over an axis.

An API to gather any() and all() to one.

Parameters:

how{‘any’, ‘all’}, default ‘any’

Choose a method to get mask.

‘any’ : Do any() function to DataFrame.
‘all’ : Do all() function to DataFrame.

axis{0 or ‘index’, 1 or ‘columns’, None}, default 0

Indicate which axis or axes should be reduced.

0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.
1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.
None : reduce all axes, return a scalar.

complementbool, default False

If True does logical ‘not’ operator to values firstly.

**kwargs

See the documentation for any() and all for complete details on the keyword arguments.

Returns:

Series(bool)

Raises:

ValueError: If how isn’t “any” or “all”.

See also

pandas.DataFrame.any
pandas.DataFrame.all

Examples

>>> import dtoolkit
>>> import pandas as pd
>>> df = pd.DataFrame({"a": [True, True, False], "b": [False, True, False]})
>>> df
       a      b
0   True  False
1   True   True
2  False  False

Get the bool value from each column.

>>> df.boolean(how="any")
a    True
b    True
dtype: bool

Get the bool value from each row. And require each element is True.

>>> df.boolean(how="all", axis=1)
0    False
1     True
2    False
dtype: bool

Do a ‘~’ (logical not) operation then get the bool value.

>>> df.boolean(how="all", axis=1, complement=True)
0    False
1    False
2     True
dtype: bool

Get the bool value from the entire data.

>>> df.boolean(how="all", axis=None)
False