BPt.Dataset.all#

Dataset.all(axis=0, bool_only=None, skipna=True, level=None, **kwargs)[source]#

Return whether all elements are True, potentially over an axis.

Returns True unless there at least one element within a series or along a Dataframe axis that is False or equivalent (e.g. zero or empty).

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

Indicate which axis or axes should be reduced. For Series this parameter is unused and defaults to 0.

  • 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.

bool_onlybool, default None

Include only boolean columns. If None, will attempt to use everything, then use only boolean data. Not implemented for Series.

skipnabool, default True

Exclude NA/null values. If the entire row/column is NA and skipna is True, then the result will be True, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.

levelint or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

Deprecated since version 1.3.0: The level keyword is deprecated. Use groupby instead.

**kwargsany, default None

Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns
Series or DataFrame

If level is specified, then, DataFrame is returned; otherwise, Series is returned.

See also

Series.all

Return True if all elements are True.

DataFrame.any

Return True if one (or more) elements are True.

Examples

Series

>>> pd.Series([True, True]).all()
True
>>> pd.Series([True, False]).all()
False
>>> pd.Series([], dtype="float64").all()
True
>>> pd.Series([np.nan]).all()
True
>>> pd.Series([np.nan]).all(skipna=False)
True

DataFrames

Create a dataframe from a dictionary.

>>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]})
>>> df
   col1   col2
0  True   True
1  True  False

Default behaviour checks if values in each column all return True.

>>> df.all()
col1     True
col2    False
dtype: bool

Specify axis='columns' to check if values in each row all return True.

>>> df.all(axis='columns')
0     True
1    False
dtype: bool

Or axis=None for whether every value is True.

>>> df.all(axis=None)
False