How to use the all() method in pandas

The all() function

The pandas all() function returns a single boolean value for each row or column. The function returns True if all the values in the given axis are true or False if any value is false.

Syntax

all(axis=0, bool_only=None, skipna=True, level=None)

Parameters

  • axis: This represents the index to perform the operation. The value 0 indicates rows while 1 indicates the columns.

  • bool_only: This is an optional parameter that specifies whether or not to check for only boolean columns. The default value is None.

  • skipna: This is an optional parameter. The default is True. If set to False, it won’t skip null values. Instead, it will return True for NaN values.

  • level: This is an optional parameter. The default is None. It specifies the level (in the case of multilevel) to count along.

Return type

It returns true or false for each row/column.

Example

The following code will demonstrate how to use the any() function in pandas:

import pandas as pd
import numpy as np
# Create a DataFrame
df = pd.DataFrame({'A': [1, np.nan, 0, 2, 0, np.nan, 4],
       'B': [1, 1, 3, 5, 0, 0, 5],
       'C': [np.nan, 0, np.nan, 0, 1, 0, 0]})
# finds if value is true or not using all()
print(df.all(axis=1))

Explanation

In the code above:

  • Lines 1–2: We import the needed libraries.

  • Lines 5–7: We create a DataFrame, df, from a dictionary.

  • Line 13: We use the any() function to return a True value if any value in the column axis is true. Otherwise, False is returned.

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