Pandas Data Cleaning Practice Questions with Solutions

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Data cleaning is one of the most important steps in data analysis. Real-world datasets often contain missing values, duplicate records, incorrect data types, inconsistent formatting, and invalid entries. Pandas provides powerful functions to identify, clean, and transform dirty data into a structured format suitable for analysis. In this chapter, you’ll learn how to handle missing values, remove duplicates, replace incorrect values, and clean datasets using practical examples. Pandas Data Cleaning practice questions with solutions help to understand the concepts.

1. Python Program to Check Missing Values in a DataFrame

Problem Statement

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

Python Solution

import pandas as pd
import numpy as np

data = {
    "Name": ["Rahul", "Aman", "Priya", "Sneha"],
    "Marks": [85, np.nan, 78, np.nan]
}

df = pd.DataFrame(data)

print(df.isnull())

Sample Output

    Name  Marks
0  False  False
1  False   True
2  False  False
3  False   True

Explanation

The isnull() function checks every value in the DataFrame and returns True where a value is missing (NaN).

Concepts Covered

  • isnull()
  • Missing Values
  • Data Cleaning

2. Python Program to Count Missing Values in Each Column

Problem Statement

Write a Python program to count the total number of missing values in every column.

Python Solution

import pandas as pd
import numpy as np

data = {
    "Name": ["Rahul", "Aman", "Priya", "Sneha"],
    "Age": [20, 21, np.nan, 22],
    "Marks": [85, np.nan, 78, np.nan]
}

df = pd.DataFrame(data)

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

Sample Output

Name     0
Age      1
Marks    2
dtype: int64

Explanation

The sum() function counts the number of True values returned by isnull(), giving the total missing values in each column.

Concepts Covered

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

3. Python Program to Remove Rows Containing Missing Values

Problem Statement

Write a Python program to remove all rows containing missing values.

Python Solution

import pandas as pd
import numpy as np

data = {
    "Name": ["Rahul", "Aman", "Priya", "Sneha"],
    "Marks": [85, np.nan, 78, 92]
}

df = pd.DataFrame(data)

clean_df = df.dropna()

print(clean_df)

Sample Output

    Name  Marks
0  Rahul   85.0
2  Priya   78.0
3  Sneha   92.0

Explanation

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

Concepts Covered

  • dropna()
  • Missing Values
  • Row Removal

4. Python Program to Fill Missing Values with a Constant

Problem Statement

Write a Python program to replace all missing values with 0.

Python Solution

import pandas as pd
import numpy as np

data = {
    "Name": ["Rahul", "Aman", "Priya", "Sneha"],
    "Marks": [85, np.nan, 78, np.nan]
}

df = pd.DataFrame(data)

df["Marks"] = df["Marks"].fillna(0)

print(df)

Sample Output

     Name  Marks
0   Rahul   85.0
1    Aman    0.0
2   Priya   78.0
3   Sneha    0.0

Explanation

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

Concepts Covered

  • fillna()
  • Missing Value Replacement
  • Data Cleaning

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

Problem Statement

Write a Python program to replace missing values with the average of the column.

Python Solution

import pandas as pd
import numpy as np

data = {
    "Marks": [85, np.nan, 78, 92]
}

df = pd.DataFrame(data)

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

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

print(df)

Sample Output

       Marks
0  85.000000
1  85.000000
2  78.000000
3  92.000000

Explanation

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

Concepts Covered

  • fillna()
  • mean()
  • Missing Value Imputation

6. Python Program to Fill Missing Values with the Median

Problem Statement

Write a Python program to replace missing values in a DataFrame with the median value of the column.

Python Solution

import pandas as pd
import numpy as np

data = {
    "Marks": [85, np.nan, 78, 92, 88]
}

df = pd.DataFrame(data)

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

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

print(df)

Sample Output

   Marks
0   85.0
1   86.5
2   78.0
3   92.0
4   88.0

Explanation

The median() function calculates the middle value of the column. It is often preferred over the mean when the dataset contains outliers.

Concepts Covered

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

7. Python Program to Fill Missing Values with the Mode

Problem Statement

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

Python Solution

import pandas as pd
import numpy as np

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

df = pd.DataFrame(data)

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

df["City"] = df["City"].fillna(mode_value)

print(df)

Sample Output

      City
0    Delhi
1    Mumbai
2    Delhi
3    Delhi
4    Delhi

Explanation

The mode() function returns the most frequently occurring value. Missing values are replaced using fillna().

Concepts Covered

  • mode()
  • fillna()
  • Categorical Data Cleaning

8. Python Program to Remove Duplicate Rows

Problem Statement

Write a Python program to remove duplicate rows from a DataFrame.

Python Solution

import pandas as pd

data = {
    "Name": ["Rahul", "Aman", "Rahul", "Sneha"],
    "Marks": [85, 90, 85, 88]
}

df = pd.DataFrame(data)

clean_df = df.drop_duplicates()

print(clean_df)

Sample Output

     Name  Marks
0   Rahul     85
1    Aman     90
3   Sneha     88

Explanation

The drop_duplicates() function removes duplicate rows while keeping the first occurrence.

Concepts Covered

  • drop_duplicates()
  • Duplicate Records
  • Data Cleaning

9. Python Program to Check Duplicate Rows

Problem Statement

Write a Python program to identify duplicate rows in a DataFrame.

Python Solution

import pandas as pd

data = {
    "Name": ["Rahul", "Aman", "Rahul", "Sneha"],
    "Marks": [85, 90, 85, 88]
}

df = pd.DataFrame(data)

print(df.duplicated())

Sample Output

0    False
1    False
2     True
3    False
dtype: bool

Explanation

The duplicated() function returns True for rows that are duplicates of previous rows.

Concepts Covered

  • duplicated()
  • Duplicate Detection
  • Boolean Output

10. Python Program to Replace Specific Values in a DataFrame

Problem Statement

Write a Python program to replace all occurrences of “Delhi” with “New Delhi” in a DataFrame.

Python Solution

import pandas as pd

data = {
    "City": ["Delhi", "Mumbai", "Delhi", "Pune"]
}

df = pd.DataFrame(data)

df["City"] = df["City"].replace("Delhi", "New Delhi")

print(df)

Sample Output

         City
0  New Delhi
1      Mumbai
2  New Delhi
3       Pune

Explanation

The replace() function substitutes one value with another throughout the DataFrame or selected columns.

Concepts Covered

  • replace()
  • Value Replacement
  • Data Cleaning

11. Python Program to Remove Leading and Trailing Spaces from a Column

Problem Statement

Write a Python program to remove leading and trailing spaces from the Name column.

Python Solution

import pandas as pd

data = {
    "Name": [" Rahul ", " Aman", "Priya ", " Sneha "]
}

df = pd.DataFrame(data)

df["Name"] = df["Name"].str.strip()

print(df)

Sample Output

     Name
0   Rahul
1    Aman
2   Priya
3   Sneha

Explanation

The str.strip() method removes extra spaces from the beginning and end of each string.

Concepts Covered

  • str.strip()
  • String Cleaning
  • Data Cleaning

12. Python Program to Convert Text to Lowercase

Problem Statement

Write a Python program to convert all values in the City column to lowercase.

Python Solution

import pandas as pd

data = {
    "City": ["Delhi", "MUMBAI", "Jaipur", "PUNE"]
}

df = pd.DataFrame(data)

df["City"] = df["City"].str.lower()

print(df)

Sample Output

      City
0    delhi
1   mumbai
2   jaipur
3     pune

Explanation

The str.lower() method converts every string in the selected column to lowercase, ensuring consistency in textual data.

Concepts Covered

  • str.lower()
  • String Operations
  • Data Standardization

13. Python Program to Convert Text to Uppercase

Problem Statement

Write a Python program to convert all values in the Course column to uppercase.

Python Solution

import pandas as pd

data = {
    "Course": ["Python", "Java", "Pandas", "NumPy"]
}

df = pd.DataFrame(data)

df["Course"] = df["Course"].str.upper()

print(df)

Sample Output

   Course
0  PYTHON
1     JAVA
2  PANDAS
3   NUMPY

Explanation

The str.upper() method converts every string in the selected column to uppercase.

Concepts Covered

  • str.upper()
  • Text Formatting
  • Data Cleaning

14. Python Program to Rename Multiple Columns

Problem Statement

Write a Python program to rename multiple columns in a DataFrame.

Python Solution

import pandas as pd

data = {
    "Name": ["Rahul", "Aman"],
    "Marks": [85, 90]
}

df = pd.DataFrame(data)

df.rename(
    columns={
        "Name": "Student Name",
        "Marks": "Score"
    },
    inplace=True
)

print(df)

Sample Output

  Student Name  Score
0        Rahul     85
1         Aman     90

Explanation

The rename() function changes one or more column names without affecting the underlying data.

Concepts Covered

  • rename()
  • Column Renaming
  • DataFrame Modification

15. Python Program to Change the Data Type of a Column

Problem Statement

Write a Python program to convert the Marks column from string to integer.

Python Solution

import pandas as pd

data = {
    "Marks": ["85", "90", "78", "88"]
}

df = pd.DataFrame(data)

df["Marks"] = df["Marks"].astype(int)

print(df)
print(df.dtypes)

Sample Output

   Marks
0     85
1     90
2     78
3     88

Marks    int64
dtype: object

Explanation

The astype() function converts the data type of a column. It is commonly used to convert strings into numeric values before performing calculations.

Concepts Covered

  • astype()
  • Data Type Conversion
  • Data Cleaning

16. Python Program to Remove Rows with Empty Strings

Problem Statement

Write a Python program to remove rows where the Name column contains an empty string.

Python Solution

import pandas as pd

data = {
    "Name": ["Rahul", "", "Priya", "Sneha"],
    "Marks": [85, 90, 78, 88]
}

df = pd.DataFrame(data)

clean_df = df[df["Name"] != ""]

print(clean_df)

Sample Output

     Name  Marks
0   Rahul     85
2   Priya     78
3   Sneha     88

Explanation

This program filters out rows where the Name column contains an empty string, keeping only valid records.

Concepts Covered

  • Boolean Indexing
  • Empty String Removal
  • Data Cleaning

17. Python Program to Remove Outliers Using a Condition

Problem Statement

Write a Python program to remove records where the Marks value is greater than 100.

Python Solution

import pandas as pd

data = {
    "Name": ["Rahul", "Aman", "Priya", "Sneha"],
    "Marks": [85, 120, 78, 95]
}

df = pd.DataFrame(data)

clean_df = df[df["Marks"] <= 100]

print(clean_df)

Sample Output

     Name  Marks
0   Rahul     85
2   Priya     78
3   Sneha     95

Explanation

Sometimes datasets contain invalid values that fall outside an acceptable range. Boolean indexing can be used to remove such outlier records before analysis.

Concepts Covered

  • Boolean Filtering
  • Outlier Removal
  • Data Validation

Chapter Summary

In this chapter, you learned how to clean datasets using Pandas. You explored techniques for identifying and handling missing values, filling missing data with mean, median, and mode, removing duplicate records, replacing incorrect values, trimming extra spaces, standardizing text using uppercase and lowercase conversion, renaming columns, changing data types, removing empty strings, and filtering invalid records. These data cleaning techniques are essential for preparing high-quality datasets before performing analysis or building machine learning models.

Key Takeaways

  • Use isnull() and isnull().sum() to detect missing values.
  • Remove missing data using dropna().
  • Replace missing values using fillna().
  • Mean, median, and mode can be used for missing value imputation.
  • Use drop_duplicates() to remove duplicate records.
  • Detect duplicates using duplicated().
  • Replace incorrect values using replace().
  • Clean text using str.strip(), str.lower(), and str.upper().
  • Convert data types using astype().
  • Filter invalid records using Boolean indexing.

Frequently Asked Questions (FAQs)

1. What is data cleaning in Pandas?

Data cleaning is the process of identifying and correcting missing, duplicate, inconsistent, or invalid data before analysis.

2. How do you identify missing values?

Use the isnull() function.

print(df.isnull())

3. How do you remove rows containing missing values?

Use the dropna() function.

clean_df = df.dropna()

4. Which function replaces missing values?

Use the fillna() function.

df["Marks"] = df["Marks"].fillna(0)

5. How do you remove duplicate rows?

Use the drop_duplicates() function.

clean_df = df.drop_duplicates()

6. How do you convert a column to a different data type?

Use the astype() function.

df["Marks"] = df["Marks"].astype(int)

7. How do you remove extra spaces from text?

Use the str.strip() method.

df["Name"] = df["Name"].str.strip()

8. Why is data cleaning important?

Data cleaning improves data quality by removing errors, inconsistencies, duplicates, and invalid values. Clean data leads to more accurate analysis, reliable reports, and better machine learning model performance.

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

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