pandas.DataFrame.all — pandas 0.24.0rc1 documentation (original) (raw)
DataFrame. 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. 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 None Include only boolean columns. If None, will attempt to use everything, then use only boolean data. 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. level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series. **kwargs : any, default None Additional keywords have no effect but might be accepted for compatibility with NumPy. |
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| Returns: | Series or DataFrame If level is specified, then, DataFrame is returned; otherwise, Series is returned. |
See also
Return True if all elements are True.
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([]).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 column-wise values all return True.
df.all() col1 True col2 False dtype: bool
Specify axis='columns' to check if row-wise values 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