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Classe « DataFrame »

Méthode pandas.DataFrame.all

Signature de la méthode all

def all(self, axis: 'Axis | None' = 0, bool_only: 'bool' = False, skipna: 'bool' = True, **kwargs) -> 'Series | bool' 

Description

help(DataFrame.all)

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_only : bool, default False
    Include only boolean columns. Not implemented for Series.
skipna : bool, 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.
**kwargs : any, 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

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