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.

Practice

1. What do NaN and None represent in a pandas DataFrame?

easy

A. Missing or null data values

B. Valid numeric data

C. Column headers

D. Index labels

Solution

1. Step 1: Understand the role of NaN and None in pandas

   Both NaN and None are used to mark missing or null data in pandas DataFrames.

2. Step 2: Differentiate from other DataFrame elements

   They do not represent valid data, headers, or indexes but indicate absence of data.

3. Final Answer:

   Missing or null data values -> Option A

4. Quick Check:

   NaN and None = Missing data [OK]

Hint: NaN and None always mean missing data in pandas [OK]

Common Mistakes:

• Thinking NaN is a valid number
• Confusing None with column names
• Assuming NaN is an index label

2. Which of the following is the correct way to check for missing values in a pandas DataFrame df?

easy

A. df.isna()

B. df.notna()

C. df.isnan()

D. df.ismissing()

Solution

1. Step 1: Recall pandas functions for missing data

   The correct function to check for missing values is isna(), which returns a boolean DataFrame indicating missing entries.

2. Step 2: Identify incorrect options

   notna() returns a boolean DataFrame indicating non-missing entries; isnan() and ismissing() do not exist in pandas.

3. Final Answer:

   df.isna() -> Option A

4. Quick Check:

   Use isna() to find missing values [OK]

Hint: Use df.isna() to find missing values in pandas [OK]

Common Mistakes:

• Using non-existent df.isnan()
• Using df.notna() which checks non-missing values
• Trying df.ismissing() which is invalid

3. Given the code:

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, np.nan, 3], 'B': [None, 2, 3]})
result = df.isna().sum().to_dict()

What is the value of result?

medium

A. {'A': 0, 'B': 0}

B. {'A': 0, 'B': 1}

C. {'A': 1, 'B': 0}

D. {'A': 1, 'B': 1}

Solution

1. Step 1: Identify missing values in each column

   Column 'A' has one np.nan (missing), column 'B' has one None (missing).

2. Step 2: Use isna() and sum() to count missing per column

   df.isna() marks missing as True, sum() counts Trues per column, resulting in {'A': 1, 'B': 1}.

3. Final Answer:

   {'A': 1, 'B': 1} -> Option D

4. Quick Check:

   Count missing with isna().sum() = {'A':1,'B':1} [OK]

Hint: Count missing with df.isna().sum() per column [OK]

Common Mistakes:

• Ignoring None as missing in column B
• Confusing sum() to count non-missing
• Assuming np.nan and None are treated differently here

4. Consider this code snippet:

import pandas as pd
df = pd.DataFrame({'X': [1, None, 3]})
df['X'] = df['X'].fillna(0)
print(df)

What is the output?

medium

A. X
0 1
1 3
2 0

B. X
0 1
1 None
2 3

C. X
0 1
1 0
2 3

D. X
0 1
1 NaN
2 3

Solution

1. Step 1: Identify missing value in column 'X'

   The second row has None, which pandas treats as missing (NaN).

2. Step 2: Use fillna(0) to replace missing with zero

   The fillna(0) replaces the missing value with 0, so the DataFrame becomes 1, 0, 3.

3. Final Answer:

   X 0 1 1 0 2 3 -> Option C

4. Quick Check:

   fillna(0) replaces None with 0 [OK]

Hint: fillna(0) replaces missing values with zero [OK]

Common Mistakes:

• Expecting None to remain after fillna
• Confusing fillna with dropna
• Assuming fillna changes non-missing values

5. You have a DataFrame df with columns 'A' and 'B' containing some NaN and None values. You want to replace all missing values in 'A' with the mean of 'A' and all missing values in 'B' with the string 'missing'. Which code correctly does this?

hard

A. df.fillna({'A': df['A'].mean(), 'B': 'missing'})

B. df['A'] = df['A'].fillna(df['A'].mean())
df['B'] = df['B'].fillna('missing')

C. df['A'].fillna(df['A'].mean())
df['B'].fillna('missing')

D. df.fillna(df.mean())
df['B'] = df['B'].fillna('missing')

Solution

1. Step 1: Replace missing in 'A' with its mean

   Use df['A'].fillna(df['A'].mean()) and assign back to 'A' to update the column.

2. Step 2: Replace missing in 'B' with 'missing'

   Use df['B'].fillna('missing') and assign back to 'B' to update that column.

3. Step 3: Check other options for correctness

   df.fillna({'A': df['A'].mean(), 'B': 'missing'}) does not assign back, so changes are not saved; df['A'].fillna(df['A'].mean()) df['B'].fillna('missing') also misses assignment; df.fillna(df.mean()) df['B'] = df['B'].fillna('missing') tries to fill numeric mean for all columns which fails for 'B'.

4. Final Answer:

   df['A'] = df['A'].fillna(df['A'].mean()) df['B'] = df['B'].fillna('missing') -> Option B

5. Quick Check:

   Assign fillna results back to columns [OK]

Hint: Assign fillna results back to columns to save changes [OK]

Common Mistakes:

• Not assigning fillna result back to DataFrame
• Using fillna without specifying columns
• Trying to fill numeric mean into string column