Top 20+ Pandas Interview Questions and Answers

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Pandas is a powerful Python library used for data analysis and manipulation. It was created in 2008 by Wes McKinney to make working with structured data easier. The name “Pandas” comes from “Panel Data.” Today, it is widely used in data science, finance, and machine learning. Common jobs that use Pandas include data analysts, data scientists, and Python developers. This blog covers the 20+ commonly asked Pandas interview questions and answers to help you prepare for the job roles. 

Fun Fact: Pandas was originally developed at a hedge fund to help analysts work more efficiently with financial data. It quickly grew into one of the most used data tools in the world.

Basic Pandas Interview Questions (For Freshers)

Here are some commonly asked Pandas basic interview questions to help freshers prepare. 

1. What is Pandas and how is it used in Python?

Pandas is a Python library for working with structured data. It helps clean, explore, and analyze large datasets quickly. It is built on top of NumPy and is widely used in data analysis and machine learning tasks.

2. What is the difference between a Pandas Series and DataFrame?

A Series is a one-dimensional labeled array. It looks like a single column. A DataFrame, on the other hand, is a two-dimensional table with rows and columns. Each column in a DataFrame is actually a Series.

3. How do you load a CSV file into a DataFrame?

I use pd.read_csv(“filename.csv”) to load a CSV file. It returns a DataFrame containing the file data.

4. What does the head() and tail() function do?

The head() function shows the first 5 rows of a DataFrame. tail() shows the last 5 rows. I often use them to preview the data.

5. How can you check the number of rows and columns in a DataFrame?

Use df.shape. It returns a tuple like (rows, columns). You can also use len(df) for row count and len(df.columns) for column count.

6. What is the role of an index in a DataFrame?

The index labels each row. It helps me access or align data easily. It can be numbers or strings.

7. How do you select a specific row and column from a DataFrame?

I use df.loc[row_label, column_name] for label-based selection. If I need to use position, I go with df.iloc[row_index, column_index].

Pandas Interview Questions for Experienced

These Pandas interview questions and answers are often asked to experienced professionals.

8. How do you handle large datasets efficiently using Pandas?

I try to load only required columns using usecols. I also read data in chunks with chunksize when the file is too large. Converting columns to proper data types, like category for repeated strings, helps reduce memory use. I avoid loops and use vectorized operations.

9. What is the difference between merge(), join(), and concat()?

merge() lets you join DataFrames on one or more columns. It is flexible and supports many join types like inner, outer, and left. join() is simpler and mainly joins on indexes unless you pass an on argument. concat() stacks DataFrames either vertically or horizontally.

10. What is multi-indexing and when would you use it?

Multi-indexing allows more than one index level in a DataFrame. I use it when I need to group or access data by multiple keys. It is common in time-series or hierarchical data. For example, year and month as two index levels.

11. How do you deal with SettingWithCopyWarning?

This warning shows up when modifying a slice of a DataFrame. I fix it by using .loc[] directly on the original DataFrame or making a clear .copy() before editing. That way I know the change will apply properly.

12. What is the use of groupby() and how does it work internally?

groupby() splits data into groups based on keys. It then applies a function like mean() or sum() to each group. Internally, it uses hashing or sorting depending on the operation.

13. How do you change the data type of a column and why is it important?

I use astype() to convert data types. It helps save memory and avoids type mismatch during operations.

Also Read - Top 75+ Python Interview Questions and Answers

Pandas Coding Interview Questions

Let’s go through some Pandas coding interview questions and answers that test your ability to solve problems using Python and the Pandas library.

14. How do you add a new column based on values from other columns?

I use the apply() function with a lambda. 

For example:

df[‘total’] = df.apply(lambda row: row[‘math’] + row[‘science’], axis=1)

This creates a new column total from the sum of two other columns.

15. How can you filter rows based on multiple conditions?

I use logical operators with conditions in brackets. 

For example:

filtered_df = df[(df[‘Age’] > 25) & (df[‘City’] == ‘Austin’)]

I avoid using and/or with Series objects because that throws an error.

16. How do you sort a DataFrame by more than one column?

I use the sort_values() method and pass a list:

df.sort_values(by=[‘Age’, ‘Marks’], ascending=[True, False])

This sorts by Age (ascending) and Marks (descending). The ascending list should match the columns.

17. Write code to count the frequency of each value in a column.

I use value_counts() which returns a Series:

df[‘City’].value_counts()

This helps me see how often each city appears in the column.

18. How do you replace missing values using the median of the column?

I calculate the median and use fillna() like this:

median_value = df[‘Marks’].median()

df[‘Marks’] = df[‘Marks’].fillna(median_value)

This replaces all NaN values in the Marks column with the median.

Pandas Interview Questions for Data Analyst

These questions are specially picked to match the kind of Pandas tasks and challenges faced in a data analyst role.

19. How do you handle missing values in real-world datasets?

First, I check how much data is missing using isnull().sum(). If only a few rows are affected, I might drop them with dropna(). But for larger gaps, I fill the missing values using the mean, median, or a constant depending on the column and context. Sometimes I use forward fill (method=’ffill’) if the data is time-based.

20. What is the difference between fillna() and interpolate()?

fillna() replaces missing values with a fixed number, or you can use methods like forward or backward fill.

df[‘score’].fillna(df[‘score’].mean())

interpolate() is more dynamic. It calculates values between points based on trends. I often use it with time-series data to keep the flow smooth.

df[‘score’].interpolate(method=’linear’)

21. How do you apply one-hot encoding in Pandas?

I use pd.get_dummies() to convert categorical columns into binary columns.

df_encoded = pd.get_dummies(df, columns=[‘City’])

This is helpful when preparing data for machine learning models that need numeric input.

22. How do you summarize data using describe() and what insights does it give?

The describe() function gives a quick statistical overview of numeric columns.

df.describe()

It shows count, mean, standard deviation, min, max, and percentiles. I use it to spot outliers, understand data spread, and compare distributions across columns.

Also Read - Top 35+ Data Analyst Interview Questions and Answers

Pandas Python Interview Questions

Here are some Pandas Python interview questions that test your skills in using Pandas for data analysis and manipulation with Python.

23. How do you import Pandas in Python and what alias is commonly used?

You import it like this:

import pandas as pd  

The pd alias is common and saves typing. You will see it used in almost every pandas script or notebook.

24. What is vectorization and why is it preferred over loops in Pandas?

Vectorization lets you apply operations to entire columns without using a loop. It is faster and more efficient because pandas does the work underneath using optimized code. 

For example, instead of looping over rows to multiply two columns, you just write:

df[‘total’] = df[‘price’] * df[‘quantity’]  

This is cleaner and runs faster than a for loop.

25. What is the difference between loc[] and iloc[] in Pandas?

loc[] is label-based. It selects data using row and column names.

iloc[] is index-based. It uses row and column numbers.

Example:

df.loc[2, ‘Name’]   # uses label  

df.iloc[2, 0]       # uses position  

They look similar but behave differently.

26. How do you drop duplicate rows from a DataFrame?

Use df.drop_duplicates().

It finds and removes rows that repeat. By default, it keeps the first copy and removes the rest.

You can also use subset to check for duplicates in specific columns only.

NumPy and Pandas Interview Questions

Here are important NumPy and Pandas interview questions that help you understand the differences, use cases, and core functions of both libraries in data analysis.

27. How is Pandas different from NumPy in terms of functionality?

NumPy is mainly for numerical operations on arrays. It is fast and good for scientific computing.

Pandas builds on NumPy and adds labeled axes. That means you can work with data using row and column names, not just positions. It is better for real-world tabular data like spreadsheets or SQL tables.

28. When should you use NumPy arrays over Pandas Series?

Use NumPy when you need speed and are working only with numbers. It is best for pure math operations.

I use Pandas Series when I want labeled data, like a column from a table with names or dates. Series also allows mixed types and custom labels, which NumPy arrays do not.

29. How can you convert a NumPy array to a Pandas DataFrame?

Use:

pd.DataFrame(my_array)  

This wraps your array in a DataFrame. You can also add column names like this:

pd.DataFrame(my_array, columns=[‘A’, ‘B’])  

30. How do Pandas and NumPy handle missing data differently?

Pandas has built-in support for missing values using NaN.
You can detect them with isnull(), and fill or drop them easily.

NumPy does not handle missing data as smoothly. You often have to use masked arrays or substitute with special values like np.nan.

DataFrame Interview Questions

These interview questions focus on DataFrame operations, structure, and common tasks to test your practical knowledge of working with Pandas DataFrames.

31. How do you reset the index of a DataFrame?

Use df.reset_index(drop=True).

This gives the DataFrame a new numeric index and removes the old one. It is helpful after filtering or sorting when the index gets out of order.

32. How do you rename columns in a DataFrame?

Use df.rename(columns={‘old’: ‘new’}, inplace=True).

It changes column names without affecting the data. I usually use this when cleaning messy headers from CSV files.

33. How can you delete rows with missing values from a DataFrame?

Use df.dropna().

It removes any row that has a NaN.

You can also use how=’all’ to drop rows only if all values are missing.

34. How do you get a list of all column names in a DataFrame?

Use df.columns.tolist().

This gives you a Python list of all the column headers. It is useful when looping over or selecting columns dynamically.

Tips to Prepare for Pandas Interview

Preparing for a Pandas interview takes more than just reading questions. You need hands-on practice. So, follow these tips:

• Practice with real datasets like Titanic or Sales data
• Write code daily using Jupyter or Google Colab
• Focus on filtering, groupby, and missing data handling
• Review your past data projects and explain them clearly
• Know the difference between Series and DataFrame
• Understand errors like SettingWithCopyWarning
• Use Pandas with NumPy in small projects or case studies

Wrapping Up

With these 20+ Pandas interview questions and answers, you now have a strong base to face any data role confidently. Keep practicing with real datasets and build small projects to sharpen your skills.

Looking for Pandas job? Find top IT openings on Hirist, including roles that need Pandas, Python, and data analysis skills.

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