Pandas DataFrame.count: Count Non-NA Values in a DataFrame

Pandas DataFrame.count

The DataFrame.count method in pandas counts the number of non-NA/null values along the specified axis of a DataFrame. It is a helpful method for analyzing data completeness.

Syntax

The syntax for DataFrame.count is:

DataFrame.count(axis=0, numeric_only=False)

Here, DataFrame refers to the pandas DataFrame on which the operation is performed.

Parameters

ParameterDescription
axisSpecifies the axis along which the counting is performed. Use 0 or 'index' for columns and 1 or 'columns' for rows. Defaults to 0.
numeric_onlyIf True, only counts numeric data types. If False, counts all data types. Defaults to False.

Returns

A Series with counts of non-NA/null values along the specified axis. The index of the Series corresponds to the column or row labels of the DataFrame.

Examples

Counting Non-NA Values in a DataFrame

This example demonstrates how to use DataFrame.count to count non-NA values in each column.

Python Program

import pandas as pd

# Create a DataFrame with missing values
df = pd.DataFrame({
    'A': [1, 2, None, 4, 5],
    'B': [None, 2, 3, 4, None],
    'C': [1, 2, 3, 4, 5]
})

# Count non-NA values along columns
result = df.count(axis=0)
print(result)

Output

A    4
B    3
C    5
dtype: int64

Counting Non-NA Values Along Rows in a DataFrame

This example demonstrates counting non-NA values along the rows of a DataFrame using axis=1.

Python Program

import pandas as pd

# Create a DataFrame with missing values
df = pd.DataFrame({
    'A': [1, None, 3],
    'B': [4, 5, None],
    'C': [None, 8, 9]
})

# Count non-NA values along rows
result = df.count(axis=1)
print(result)

Output

0    2
1    2
2    2
dtype: int64

Counting Numeric Data Only in a DataFrame

This example shows how to use the numeric_only parameter to count numeric data types only.

Python Program

import pandas as pd

# Create a DataFrame with mixed data types
df = pd.DataFrame({
    'A': [1, 2, None],
    'B': [4, None, 6],
    'C': ['x', 'y', 'z']
})

# Count numeric data only
result = df.count(numeric_only=True)
print(result)

Output

A    2
B    2
dtype: int64

Counting Non-NA Values in an Empty DataFrame

This example demonstrates counting non-NA values in an empty DataFrame.

Python Program

import pandas as pd

# Create an empty DataFrame
df = pd.DataFrame()

# Count non-NA values
result = df.count()
print(result)

Output

Series([], dtype: int64)

Summary

In this tutorial, we explored the DataFrame.count method in pandas. Key takeaways include:

  • Using count to compute the number of non-NA values in DataFrame columns or rows.
  • Specifying the axis for counting (axis=0 for columns, axis=1 for rows).
  • Using the numeric_only parameter to count numeric data types only.
  • Handling empty DataFrames gracefully.

Python Libraries