Pandas: Series - all() function

Last update on September 15 2022 12:58:07 (UTC/GMT +8 hours)

Test whether all element is true over requested Pandas axis

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:

Series.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 scalar.	nt 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: scalar or Series
If level is specified, then, Series is returned; otherwise, scalar is returned.

Example - Series:

Python-Pandas Code:

import numpy as np
import pandas as pd
pd.Series([True, True]).all()

Output:

True

Python-Pandas Code:

import numpy as np
import pandas as pd
pd.Series([False, True]).all()

Output:

False

Python-Pandas Code:

import numpy as np
import pandas as pd
pd.Series([]).all()

Output:

True

Python-Pandas Code:

import numpy as np
import pandas as pd
pd.Series([np.nan]).all()

Output:

True

Python-Pandas Code:

import numpy as np
import pandas as pd
pd.Series([np.nan]).all(skipna=False)

Output:

True

Example - DataFrames:

Create a dataframe from a dictionary.

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame({'c1': [True, True], 'c2': [True, False]})
df

Output:

    c1	c2
0	True	True
1	True	False

Example - Default behaviour checks if column-wise values all return True:

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame({'c1': [True, True], 'c2': [True, False]})
df.all()

Output:

c1     True
c2    False
dtype: bool

Example - Specify axis='columns' to check if row-wise values all return True:

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame({'c1': [True, True], 'c2': [True, False]})
df.all(axis='columns')

Output:

0     True
1    False
dtype: bool

Example - Or axis=None for whether every value is True:

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame({'c1': [True, True], 'c2': [True, False]})
df.all(axis=None)

Output:

False

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