Pandas DataFrame: all() function

Last update on August 19 2022 21:50:33 (UTC/GMT +8 hours)

DataFrame - all() function

The all() function is used to check 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).

Syntax:

DataFrame.all(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs)

Parameters:

Name	Description	Type/Default Value	Required / Optional
axis	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.	{0 or 'index', 1 or 'columns', None} Default Value: 0	Required
bool_only	Include only boolean columns. If None, will attempt to use everything, then use only boolean data. Not implemented for Series.	bool Default Value: None	Required
skipna	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.	bool Default Value: True	Required
level	If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.	int or level name Default Value: None	Required
**kwargs	Additional keywords have no effect but might be accepted for compatibility with NumPy.	any Default Value: None	Required

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

Example:

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