Pandas Series: any() function
Test whether any element is true over requested Pandas axis
The any() function is used to check whether any element is True, potentially over an axis.
Returns False unless there at least one element within a series or along a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty).
Syntax:
Series.any(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 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:
For Series input, the output is a scalar indicating whether any element is True.
Python-Pandas Code:
import numpy as np
import pandas as pd
pd.Series([False, False]).any()
Output:
False
Python-Pandas Code:
import numpy as np
import pandas as pd
pd.Series([True, False]).any()
Output:
True
Python-Pandas Code:
import numpy as np
import pandas as pd
pd.Series([]).any()
Output:
False
Python-Pandas Code:
import numpy as np
import pandas as pd
pd.Series([np.nan]).any()
Output:
False
Python-Pandas Code:
import numpy as np
import pandas as pd
pd.Series([np.nan]).any(skipna=False)
Output:
True
Example - DataFrame:
Whether each column contains at least one True element (the default).
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame({"P": [2, 3], "Q": [0, 4], "R": [0, 0]})
df
Output:
P Q R 0 2 0 0 1 3 4 0
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame({"P": [2, 3], "Q": [0, 4], "R": [0, 0]})
df.any()
Output:
P True Q True R False dtype: bool
Example - Aggregating over the columns:
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame({"P": [True, False], "Q": [2, 3]})
df
Output:
P Q 0 True 2 1 False 3
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame({"P": [True, False], "Q": [2, 3]})
df.any(axis='columns')
Output:
0 True 1 True dtype: bool
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame({"P": [True, False], "Q": [2, 0]})
df
Output:
P Q 0 True 2 1 False 0
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame({"P": [True, False], "Q": [2, 0]})
df.any(axis='columns')
Output:
0 True 1 False dtype: bool
Example - Aggregating over the entire DataFrame with axis=None:
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame({"P": [True, False], "Q": [2, 3]})
df.any(axis=None)
Output:
True
Example - any for an empty DataFrame is an empty Series:
Python-Pandas Code:
import numpy as np
import pandas as pd
pd.DataFrame([]).any()
Output:
Series([], dtype: bool)
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