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Pandas DataFrame: all() function

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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