Pandas Missing Data Handling Practice Questions with Solutions

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Handling missing data is one of the most important tasks in data analysis. Real-world datasets often contain missing values due to incomplete records, data entry errors, or system failures. Pandas provides several functions such as isnull(), notnull(), dropna(), and fillna() to identify, remove, and replace missing values efficiently. In this chapter, you’ll learn how to handle missing data using practical examples. Pandas Missing Data Handling practice questions with solutions help to understand the concepts.

1. Python Program to Check Missing Values Using isnull()

Problem Statement

Write a Python program to identify missing values in a DataFrame.

Python Solution

import pandas as pd
import numpy as np

data = {
    "Name": ["Rahul", "Aman", "Priya"],
    "Marks": [85, np.nan, 90]
}

df = pd.DataFrame(data)

print(df.isnull())

Sample Output

    Name  Marks
0  False  False
1  False   True
2  False  False

Explanation

The isnull() function returns True for missing values and False for non-missing values.

Concepts Covered

  • isnull()
  • Missing Values
  • Boolean Output

2. Python Program to Check Non-Missing Values Using notnull()

Problem Statement

Write a Python program to identify non-missing values in a DataFrame.

Python Solution

import pandas as pd
import numpy as np

data = {
    "Name": ["Rahul", "Aman", "Priya"],
    "Marks": [85, np.nan, 90]
}

df = pd.DataFrame(data)

print(df.notnull())

Sample Output

   Name  Marks
0  True   True
1  True  False
2  True   True

Explanation

The notnull() function returns True for available values and False for missing values.

Concepts Covered

  • notnull()
  • Missing Data Detection
  • Boolean Mask

3. Python Program to Count Missing Values

Problem Statement

Write a Python program to count the total missing values in each column.

Python Solution

import pandas as pd
import numpy as np

data = {
    "Name": ["Rahul", None, "Priya"],
    "Marks": [85, np.nan, 90]
}

df = pd.DataFrame(data)

print(df.isnull().sum())

Sample Output

Name     1
Marks    1
dtype: int64

Explanation

The sum() function counts the number of True values returned by isnull().

Concepts Covered

  • isnull()
  • sum()
  • Missing Value Count

4. Python Program to Remove Rows Containing Missing Values

Problem Statement

Write a Python program to remove rows containing missing values.

Python Solution

import pandas as pd
import numpy as np

data = {
    "Name": ["Rahul", "Aman", "Priya"],
    "Marks": [85, np.nan, 90]
}

df = pd.DataFrame(data)

result = df.dropna()

print(result)

Sample Output

    Name  Marks
0  Rahul   85.0
2  Priya   90.0

Explanation

The dropna() function removes rows that contain one or more missing values.

Concepts Covered

  • dropna()
  • Remove Missing Data
  • Data Cleaning

5. Python Program to Fill Missing Values with a Constant

Problem Statement

Write a Python program to replace missing values with 0.

Python Solution

import pandas as pd
import numpy as np

data = {
    "Name": ["Rahul", "Aman", "Priya"],
    "Marks": [85, np.nan, 90]
}

df = pd.DataFrame(data)

result = df.fillna(0)

print(result)

Sample Output

    Name  Marks
0  Rahul   85.0
1   Aman    0.0
2  Priya   90.0

Explanation

The fillna() function replaces missing values with the specified constant.

Concepts Covered

  • fillna()
  • Missing Value Replacement
  • Data Cleaning

6. Python Program to Fill Missing Values with the Column Mean

Problem Statement

Write a Python program to replace missing values in the Marks column with the average marks.

Python Solution

import pandas as pd
import numpy as np

data = {
    "Name": ["Rahul", "Aman", "Priya"],
    "Marks": [85, np.nan, 95]
}

df = pd.DataFrame(data)

df["Marks"] = df["Marks"].fillna(
    df["Marks"].mean()
)

print(df)

Sample Output

    Name  Marks
0  Rahul   85.0
1   Aman   90.0
2  Priya   95.0

Explanation

The mean() function calculates the average of available values, and fillna() replaces missing values with that average.

Concepts Covered

  • fillna()
  • mean()
  • Average Imputation

7. Python Program to Fill Missing Values with the Column Median

Problem Statement

Write a Python program to replace missing values with the median value.

Python Solution

import pandas as pd
import numpy as np

data = {
    "Marks": [80, np.nan, 90, 100]
}

df = pd.DataFrame(data)

df["Marks"] = df["Marks"].fillna(
    df["Marks"].median()
)

print(df)

Sample Output

   Marks
0   80.0
1   90.0
2   90.0
3  100.0

Explanation

The median() function returns the middle value, making it useful when the data contains outliers.

Concepts Covered

  • fillna()
  • median()
  • Missing Value Imputation

8. Python Program to Fill Missing Values with the Most Frequent Value

Problem Statement

Write a Python program to replace missing values with the most frequently occurring value.

Python Solution

import pandas as pd
import numpy as np

data = {
    "City": [
        "Delhi",
        np.nan,
        "Delhi",
        "Mumbai"
    ]
}

df = pd.DataFrame(data)

df["City"] = df["City"].fillna(
    df["City"].mode()[0]
)

print(df)

Sample Output

      City
0    Delhi
1    Delhi
2    Delhi
3   Mumbai

Explanation

The mode() function returns the most frequently occurring value in the column.

Concepts Covered

  • fillna()
  • mode()
  • Categorical Data

9. Python Program to Remove Columns Containing Missing Values

Problem Statement

Write a Python program to remove columns containing missing values.

Python Solution

import pandas as pd
import numpy as np

data = {
    "Name": ["Rahul", "Aman"],
    "Marks": [85, np.nan],
    "City": ["Delhi", "Mumbai"]
}

df = pd.DataFrame(data)

result = df.dropna(axis=1)

print(result)

Sample Output

    Name    City
0  Rahul   Delhi
1   Aman  Mumbai

Explanation

Using axis=1 tells Pandas to remove columns that contain missing values.

Concepts Covered

  • dropna()
  • axis=1
  • Remove Columns

10. Python Program to Fill Missing Values Using Forward Fill

Problem Statement

Write a Python program to fill missing values using the previous available value.

Python Solution

import pandas as pd
import numpy as np

data = {
    "Marks": [80, np.nan, np.nan, 95]
}

df = pd.DataFrame(data)

result = df.ffill()

print(result)

Sample Output

   Marks
0   80.0
1   80.0
2   80.0
3   95.0

Explanation

The ffill() (forward fill) method replaces missing values with the last valid value found above them.

Concepts Covered

  • ffill()
  • Forward Fill
  • Missing Data Handling

11. Python Program to Fill Missing Values Using Backward Fill

Problem Statement

Write a Python program to replace missing values using the next available value.

Python Solution

import pandas as pd
import numpy as np

data = {
    "Marks": [80, np.nan, np.nan, 95]
}

df = pd.DataFrame(data)

result = df.bfill()

print(result)

Sample Output

   Marks
0   80.0
1   95.0
2   95.0
3   95.0

Explanation

The bfill() (backward fill) method replaces missing values using the next valid value below them.

Concepts Covered

  • bfill()
  • Backward Fill
  • Missing Data Handling

12. Python Program to Replace Missing Values in a Specific Column

Problem Statement

Write a Python program to replace missing values only in the Salary column.

Python Solution

import pandas as pd
import numpy as np

data = {
    "Name": ["Rahul", "Aman", "Priya"],
    "Salary": [50000, np.nan, 60000]
}

df = pd.DataFrame(data)

df["Salary"] = df["Salary"].fillna(55000)

print(df)

Sample Output

    Name   Salary
0  Rahul  50000.0
1   Aman  55000.0
2  Priya  60000.0

Explanation

Selecting a specific column before using fillna() updates only that column.

Concepts Covered

  • fillna()
  • Column Selection
  • Data Cleaning

13. Python Program to Drop Rows Only When All Values Are Missing

Problem Statement

Write a Python program to remove rows where every column contains missing values.

Python Solution

import pandas as pd
import numpy as np

data = {
    "Name": ["Rahul", np.nan, "Priya"],
    "Marks": [85, np.nan, 90]
}

df = pd.DataFrame(data)

result = df.dropna(how="all")

print(result)

Sample Output

    Name  Marks
0  Rahul   85.0
2  Priya   90.0

Explanation

The how="all" parameter removes only those rows where every value is missing.

Concepts Covered

  • dropna()
  • how="all"
  • Row Filtering

14. Python Program to Drop Rows Having Missing Values in a Specific Column

Problem Statement

Write a Python program to remove rows where the Marks column contains missing values.

Python Solution

import pandas as pd
import numpy as np

data = {
    "Name": ["Rahul", "Aman", "Priya"],
    "Marks": [85, np.nan, 90]
}

df = pd.DataFrame(data)

result = df.dropna(
    subset=["Marks"]
)

print(result)

Sample Output

    Name  Marks
0  Rahul   85.0
2  Priya   90.0

Explanation

The subset parameter checks only the specified column before removing rows.

Concepts Covered

  • dropna()
  • subset
  • Selective Row Removal

15. Python Program to Replace Missing Values in Multiple Columns

Problem Statement

Write a Python program to replace missing values in multiple columns using different values.

Python Solution

import pandas as pd
import numpy as np

data = {
    "Marks": [85, np.nan, 90],
    "City": ["Delhi", np.nan, "Mumbai"]
}

df = pd.DataFrame(data)

result = df.fillna(
    {
        "Marks": 0,
        "City": "Unknown"
    }
)

print(result)

Sample Output

   Marks     City
0   85.0    Delhi
1    0.0  Unknown
2   90.0   Mumbai

Explanation

A dictionary can be passed to fillna() to specify different replacement values for different columns.

Concepts Covered

  • fillna()
  • Dictionary Mapping
  • Multiple Column Replacement

16. Python Program to Calculate the Total Missing Values in a DataFrame

Problem Statement

Write a Python program to calculate the total number of missing values in an entire DataFrame.

Python Solution

import pandas as pd
import numpy as np

data = {
    "Name": ["Rahul", None, "Priya"],
    "Marks": [85, np.nan, 90],
    "City": ["Delhi", None, "Mumbai"]
}

df = pd.DataFrame(data)

total_missing = df.isnull().sum().sum()

print("Total Missing Values:", total_missing)

Sample Output

Total Missing Values: 3

Explanation

The first sum() counts missing values column-wise, while the second sum() adds those counts to get the total missing values in the DataFrame.

Concepts Covered

  • isnull()
  • sum()
  • Missing Value Count

17. Python Program to Replace Missing Values with Different Statistical Methods

Problem Statement

Write a Python program to replace missing values using the column mean and mode.

Python Solution

import pandas as pd
import numpy as np

data = {
    "Marks": [80, np.nan, 90, 100],
    "City": ["Delhi", np.nan, "Delhi", "Mumbai"]
}

df = pd.DataFrame(data)

df["Marks"] = df["Marks"].fillna(
    df["Marks"].mean()
)

df["City"] = df["City"].fillna(
    df["City"].mode()[0]
)

print(df)

Sample Output

   Marks     City
0   80.0    Delhi
1   90.0    Delhi
2   90.0    Delhi
3  100.0   Mumbai

Explanation

Numeric columns are commonly filled using statistical values such as the mean, while categorical columns are usually filled using the mode. This approach helps preserve the overall structure of the dataset.

Concepts Covered

  • fillna()
  • mean()
  • mode()
  • Missing Data Imputation

Chapter Summary

In this chapter, you learned how to identify, count, remove, and replace missing values using Pandas. You practiced using isnull(), notnull(), dropna(), fillna(), ffill(), and bfill(). You also learned how to replace missing values using statistical methods like mean, median, and mode, remove rows or columns selectively, and calculate the total number of missing values. These techniques are essential for data cleaning and preparing datasets for analysis and machine learning.

Key Takeaways

  • isnull() identifies missing values.
  • notnull() identifies available values.
  • dropna() removes rows or columns containing missing values.
  • fillna() replaces missing values.
  • ffill() fills missing values using the previous valid value.
  • bfill() fills missing values using the next valid value.
  • Mean, median, and mode are commonly used for data imputation.
  • The subset parameter removes rows based on selected columns.
  • Dictionaries can assign different replacement values to different columns.
  • Proper missing data handling improves data quality and analysis accuracy.

Frequently Asked Questions (FAQs)

1. How do you identify missing values in Pandas?

Use the isnull() function.

df.isnull()

2. How do you count missing values in each column?

df.isnull().sum()

3. How do you remove rows containing missing values?

df.dropna()

4. How do you replace missing values with zero?

df.fillna(0)

5. What is the difference between ffill() and bfill()?

  • ffill() copies the previous valid value.
  • bfill() copies the next valid value.

6. Which statistical methods are commonly used to fill missing values?

The most commonly used methods are:

  • Mean
  • Median
  • Mode

7. How do you remove rows based on missing values in a specific column?

df.dropna(subset=["Marks"])

8. Why is handling missing data important in Pandas?

Missing data can lead to incorrect analysis, poor visualizations, and inaccurate machine learning models. Cleaning missing values ensures better data quality, reliable insights, and improved model performance.

Written by Shubhranshu Shekhar, who has trained 20000+ students in coding.

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