Pandas count()

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Pandas DataFrame.count()

The Pandas count() is defined as a method that is used to count the number of non-NA cells for each column or row. It is also suitable to work with the non-floating data.

Syntax:

DataFrame.count(axis=0, level=None, numeric_only=False)

Parameters:

axis: {0 or 'index', 1 or 'columns'}, default value 0
0 or 'index' is used for row-wise, whereas 1 or 'columns' is used for column-wise.
level: int or str
It is an optional parameter. If an axis is hierarchical, it counts along with the particular level and collapsing into the DataFrame.
numeric_only: bool, default value False
It only includes int, float, or Boolean data.

Returns:

It returns the count of Series or DataFrame if the level is specified.

Example 1: The below example demonstrates the working of the count().

import pandas as pd
import numpy as np
info = pd.DataFrame({"Person":["Parker", "Smith", "William", "John"],
"Age": [27., 29, np.nan, 32]
info.count()

Output

Person    5
Age       3
dtype: int64

Example 2: If we want to count for each of the row, we can use the axis parameter. The below code demonstrates the working of the axis parameter.

import pandas as pd
import numpy as np
info = pd.DataFrame({"Person":["Parker", "Smith", "William", "John"],
"Age": [27., 29, np.nan, 32]
info.count(axis='columns')

Output

0       2
1       2
2       1
3       2
dtype: int64

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