NaN and None in Pandas

Introduction

We use NaN and None to show missing or empty data in tables. This helps us find and handle gaps in our data.

When some data points are missing in a spreadsheet or table.

When cleaning data before analysis to spot empty values.

When combining data from different sources that may have blanks.

When calculating averages or sums but want to ignore missing values.

When preparing data for charts that need complete information.

Syntax

Pandas

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'A': [1, 2, None],
    'B': [4, np.nan, 6]
})

None is a Python object representing 'no value'.

NaN (Not a Number) is a special floating-point value used by pandas and numpy to mark missing data.

Examples

This creates a table with missing values shown as None and NaN.

Pandas

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'A': [1, None, 3],
    'B': [np.nan, 5, 6]
})
print(df)

This checks which values in column 'A' are missing (True means missing).

Pandas

df['A'].isna()

This replaces all missing values with zero.

Pandas

df.fillna(0)

Sample Program

This code creates a table with missing values using None and NaN. It then shows which values are missing and fills them with zero.

Pandas

import pandas as pd
import numpy as np

data = {
    'Name': ['Alice', 'Bob', 'Charlie', None],
    'Age': [25, np.nan, 30, 22],
    'Score': [85, 90, None, 88]
}
df = pd.DataFrame(data)

print('Original DataFrame:')
print(df)

print('\nCheck missing values:')
print(df.isna())

print('\nFill missing values with 0:')
print(df.fillna(0))

Important Notes

None works well for object (text) columns, but pandas converts it to NaN in numeric columns.

NaN is a float type, so columns with NaN become float even if original data was integer.

Use isna() or isnull() to find missing values; they work the same.

Summary

NaN and None mark missing data in pandas tables.

Use isna() to find missing values and fillna() to replace them.

Handling missing data helps keep analysis accurate and clean.