dask_expr._collection.DataFrame.all
dask_expr._collection.DataFrame.all¶
- DataFrame.all(axis=0, skipna=True, split_every=False, **kwargs)¶
Return whether all elements are True, potentially over an axis.
This docstring was copied from pandas.core.frame.DataFrame.all.
Some inconsistencies with the Dask version may exist.
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 False (Not supported in Dask)
Include only boolean columns. 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.
- **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