60 Most Asked Pandas Interview Questions and Answers [ANSWERED + CODE]

Did you know Python Pandas powers almost every major data analysis task today? Imagine being able to manipulate thousands of rows of messy data with a few commands. That's exactly what Python Pandas delivers. 

It is indispensable in data analysis, machine learning, and almost any real-world data application. Its powerful functions let you clean, transform, and analyze data effortlessly.

Pandas interview questions are no joke. They’re designed to test your analytical prowess and your ability to manipulate data like a pro. This article is your complete Pandas cheatsheet to acing Python Pandas interview questions—beginner to advanced, plus coding challenges. 

So, buckle up; you’re about to uncover everything recruiters might ask in your next interview.

Pandas Interview Questions for Freshers

This section is your starting point, packed with fundamental python pandas interview questions designed for freshers and entry-level professionals. These questions lay the groundwork, helping you understand core concepts that are essential to building confidence in tackling real-world problems. 

Interviewers often ask these to test your familiarity with Python Pandas basics and ensure you can handle simple data tasks.

Get ready to dive into freshers pandas interview questions, each designed to strengthen your grasp on this indispensable library.

Also Read: Pandas vs NumPy in Data Science: Top 15 Differences

1. What are pandas in Python, and why is it used?

Interviewers start with this to test your basic understanding of the library’s purpose. It’s the foundation of every pandas-related discussion.

Direct Answer: Pandas is an open-source Python library for data manipulation and analysis. It’s used for handling structured data efficiently.

Here’s why pandas stand out:

• Handles structured data
• Tools for cleaning
• Analyzes large datasets
• Simplifies file I/O

Example: You use pandas to clean messy data before feeding it into a machine learning model.

Code Snippet:

import pandas as pd
# Creating a DataFrame
data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data)
print(df)

Output:

Name    Age
Alice   25
Bob     30

2. What are the primary data structures in pandas?

Expect this question to check your familiarity with the backbone of pandas.

Direct Answer: Pandas has two main data structures: Series (1D) and DataFrame (2D).

Here’s how they’re used:

• Series: 1D labeled data
• DataFrame: 2D tabular data
• Integration: Works with NumPy
• Indexing: Rich support

Also read: 4 Built-in Data Structures in Python: Dictionaries, Lists, Sets, Tuples

3. How do you create a Series in pandas?

This question tests your hands-on knowledge of pandas basics.

Direct Answer: You can create a Series using lists, NumPy arrays, or Python dictionaries.

Here are the options:

• Use a list or tuple.
• Use a dictionary (keys become labels).
• Specify an index for custom labels.
• Create directly from NumPy arrays.

Example: A Series is perfect for representing a single-column dataset like temperatures.

Code Snippet:

import pandas as pd
# Creating a Series
temps = pd.Series([72, 75, 78], index=['Monday', 'Tuesday', 'Wednesday'])
print(temps)

Output:

Monday       72
Tuesday      75
Wednesday    78
dtype:      int64

4. How do you create a DataFrame in pandas?

This question checks your ability to work with two-dimensional data.

Direct Answer: A DataFrame can be created from dictionaries, lists, or even existing pandas objects.

Ways to create a DataFrame:

• Use a dictionary of lists.
• Convert a NumPy array.
• Load data from files like CSV.
• Create from another DataFrame or Series.

Example: Use a DataFrame to represent customer details like name, age, and purchase history.

Code Snippet:

import pandas as pd
# Creating a DataFrame
data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data)
print(df)

Output:

Name   Age
Alice  25
Bob    30

5. How do you read data from a CSV file into a DataFrame?

This is a go-to question for understanding file handling with pandas.

Direct Answer: Use read_csv() to load a CSV file into a DataFrame.

Steps involved:

• Specify the file path.
• Customize separators or delimiters.
• Handle headers and column names.
• Manage missing values during import.

Example: Loading sales data from a CSV file for analysis.

Method to read data:

pandas.read_csv(file_name)

6. How can you view the first few rows of a DataFrame?

This question tests your ability to inspect datasets.

Direct Answer: Use the head() method to preview the top rows.

Here’s why it’s useful:

• Quickly understand dataset structure.
• Check if data is loaded correctly.
• Spot obvious issues like missing values.
• Customize the number of rows displayed.

Example: Checking the top 5 rows of customer data after loading a file.

Two Methods:

• df.head(n)
• df.iloc[:n]

7. How do you check the data types of columns in a DataFrame?

This question ensures you can identify and handle various data types in your dataset.

Direct Answer: Use the dtypes attribute to view data types of all columns in a DataFrame.

Here’s what it helps with:

• Verify data types
• Ensure compatibility
• Detect errors
• Guide conversions

Example: Checking if a column intended for numbers mistakenly contains strings.

Syntax: df = pd.read_csv('data.csv')

8. How do you select a single column or multiple columns in pandas?

This question tests your understanding of data selection methods.

Direct Answer: Use square brackets [] for single columns or a list of column names for multiple.

Options include:

• Use df['column_name'] for a single column.
• Use df[['col1', 'col2']] for multiple columns.
• Select with .loc[] or .iloc[].
• Chain operations for complex selections.

Example: Extracting a "Salary" column or selecting "Name" and "Age" together.

9. What is the difference between loc and iloc in pandas?

Interviewers use this question to test your grasp on indexing methods.

Direct Answer: 

• loc: Selects by label (index or column names).
• iloc: Selects by integer positions (like arrays).

Key differences:

• loc: Label-based. Use row/column names.
• iloc: Integer-based. Use numerical positions.
• Flexible slicing with both.
• loc allows boolean indexing.

Syntax:

iloc: DataFrame.iloc[row_index, column_index]
loc: DataFrame.loc[row_label, column_label]

Also Read: LOC vs ILOC in Pandas: Difference Between LOC and ILOC in Pandas

10. How do you add a new column to an existing DataFrame?

This question assesses your ability to manipulate a DataFrame.

Direct Answer: Assign a new column directly using the bracket notation: df.apply() or df.map().

Methods include:

• Direct assignment: df['new_col'] = value.
• Use functions or calculations.
• Add dynamically using existing columns.
• Fill with default or computed values.

11. How do you delete a column or row in a DataFrame?

This question tests your ability to remove unwanted data.

Direct Answer: Use the drop() method to delete rows or columns.

To delete a column:

DataFrame.drop(['Column_Name'], axis=1)

To delete a row:

DataFrame.drop([Row_Index_Number], axis=0)

12. How do you handle missing data in pandas?

This question checks your ability to clean datasets effectively.

Direct Answer: Use methods like fillna(), dropna(), or isna().

Common approaches:

• Use dropna() to remove missing rows/columns.
• Use fillna() to replace missing values.
• Interpolate missing data.
• Detect using isna() or notna().

Also Read: Data Preprocessing in Machine Learning: 7 Easy Steps To Follow

13. How do you rename columns in a DataFrame?

Interviewers ask this to test your ability to improve.

Direct Answer: Use the rename() method to change column names.

Ways to Rename a column:

• DataFrame.rename(columns={'column1': 'COLUMN_1', 'column2':'COLUMN_2'}, inplace=True)
• DataFrame.set_axis(labels=['COLUMN_1','COLUMN_2'], axis=1, inplace=True)

14. What is reindexing in pandas, and how is it used?

This question tests your understanding of aligning data.

Direct Answer: Reindexing changes the row/column labels of a DataFrame which can be done using reindex() method.

Example: df.reindex(new_index)

15. How do you sort data in a DataFrame by a specific column?

This question focuses on ordering datasets.

Direct Answer: Use the sort_values() method to sort by a specific column.

Example: DataFrame.sort_values(by='Age',ascending=True)

Don’t know where to begin with Python? Join upGrad’s Free Certificate Programming with Python Course today!

Intermediate Pandas Interview Questions

With the basics out of the way, it’s time to raise the bar. This section covers python pandas interview questions that focus on intermediate concepts like indexing, grouping, merging, and transforming data. These are essential for anyone with experience working with Python pandas in real-world scenarios.

Now, dive into these important questions to expand your skill set.

16. What is a pandas Index, and how does it work?

Interviewers ask this question to check your understanding of data organization in pandas.

Direct Answer: An Index in pandas is a label or key that uniquely identifies rows or columns in a DataFrame or Series.

Key points to remember:

• Ensures fast lookups and slicing.
• Supports hierarchical structures (multi-indexing).
• Can be customized for complex datasets.
• Impacts operations like reindexing or merging.

Adding Index: 

df.set_index(keys, drop=True, append=False, inplace=False, verify_integrity=False)

17. How do you set or reset the index of a DataFrame?

This question tests your ability to manipulate row labels dynamically.

Direct Answer: Use set_index() to define a column as the index and reset_index() to convert the index back to a column.

How it’s done:

• set_index: Customizes row labels.
• reset_index: Restores default integer labels.
• Both support inplace=True.
• Works with hierarchical indexing.

Setting Index: DataFrame.set_index('Column_Name')

Resetting Index: DataFrame.reset_index(inplace = True)

18. What is multi-indexing in pandas, and how do you create it?

This question evaluates your grasp of advanced indexing techniques.

Direct Answer: Multi-indexing allows hierarchical indexing with multiple levels for rows or columns.

Ways to create:

• From arrays or tuples using MultiIndex.from_tuples().
• Directly set multiple columns as index.
• Combine groupby() or pivot tables.
• Reindex existing data for hierarchy.

19. How do you filter rows based on a condition in pandas?

This question checks if you can extract meaningful subsets of data.

Direct Answer: Use Boolean indexing to filter rows based on a condition.

How it’s done:

• Use comparison operators (>, <, ==).
• Combine multiple conditions with & or |.
• Apply query() for complex filtering.
• Chain filters for specific results.

20. How do you handle duplicate data in a DataFrame?

This question tests your ability to clean and optimize datasets.

Direct Answer: Use duplicated() to find duplicates and drop_duplicates() to remove them.

Steps to manage duplicates:

• Detects duplicates using duplicated().
• Drop duplicates by rows or columns.
• Customize with keep='first' or 'last'.
• Update inplace or return a new DataFrame.

Checking Duplicate Value: DataFrame.duplicated()

Removing Duplicate Value: DataFrame.drop_duplicates()

21. How do you group data using the groupby() function?

Interviewers ask this question to test your data aggregation skills.

Direct Answer: The groupby() function groups data by a specific column or index for aggregation.

Steps to use groupby():

• Define a column for grouping.
• Apply aggregation functions like sum(), mean().
• Iterate over grouped data for custom operations.
• Combine with other pandas methods for advanced analysis.

Syntax: DataFrame.groupby(by=['Col_name'])

22. What are pivot tables in pandas, and how do you create them?

This question tests your ability to reshape and summarize data.

Direct Answer: Pivot tables reorganize data by aggregating values across specified dimensions.

Steps to create:

• Use pivot_table() method.
• Define index (rows) and columns.
• Specify aggregation function (sum, mean).
• Handle missing values with fill_value.

23. How do you concatenate or append DataFrames?

This question assesses your knowledge of combining datasets.

Direct Answer: Use concat() to combine DataFrames along rows or columns, and append() to add one DataFrame to another.

Key methods to know:

• concat(): Combines multiple DataFrames.
• append(): Adds rows from another DataFrame.
• Handle axis with axis=0 (rows) or axis=1 (columns).

24. What is the difference between merge() and join() in pandas?

This question tests your understanding of merging datasets.

Direct Answer: 

• merge(): Combines DataFrames based on common columns or indices.
• join(): Combines DataFrames on index by default.

Key distinctions:

• merge() is column-focused, join() is index-focused.
• merge() requires explicit column matching.
• join() is simpler for index-aligned data.

25. How do you perform different types of joins (inner, outer, left, right) in pandas?

This question checks your ability to handle relational data.

Direct Answer: Use the merge() method and specify the how parameter.

Join types explained:

• Inner: Keeps matching rows only.
• Outer: Includes all rows from both DataFrames.
• Left: Keeps all rows from the left DataFrame.
• Right: Keeps all rows from the right DataFrame.

26. How do you apply a function to every element in a DataFrame using applymap()?

This question evaluates your ability to perform element-wise transformations.

Direct Answer: Use applymap() to apply a function to every element in a DataFrame.

Steps to apply:

• Define or pass a function.
• Apply it to all elements.
• Use for element-wise numeric or string operations.
• Works on DataFrames only (not Series).

27. What is the difference between apply(), map(), and applymap() methods?

This question tests your understanding of pandas transformation methods.

Direct Answer: 

• map(): Works on Series for element-wise operations.
• apply(): Works on Series or DataFrames for row/column-wise operations.
• applymap(): Works on DataFrames for element-wise transformations.

Here are the key differences between apply(), map(), and applymap() methods.

Key points to note:

• map() is simpler but limited to Series.
• apply() is versatile for rows/columns.
• applymap() is specialized for DataFrames.
• Choose based on data structure and transformation scope.

28. How do you handle categorical data in pandas?

This question checks your ability to work with non-numeric data types.

Direct Answer: Use pandas’ Categorical data type to optimize storage and analysis.

How to manage categorical data:

• Convert using pd.Categorical().
• Use astype('category') for DataFrame columns.
• Leverage .cat accessor for operations.
• Ideal for reducing memory usage.

29. How do you perform one-hot encoding using pandas?

This question tests your ability to preprocess categorical data.

Direct Answer: Use pd.get_dummies() to create binary columns for each category.

Steps for one-hot encoding:

• Select the categorical column.
• Use get_dummies() to encode.
• Concatenate back to the original DataFrame.
• Drop the original column if required.

30. How do you change the data type of a column in a DataFrame?

This question evaluates your ability to manage data consistency.

Direct Answer: Use astype() to change the column’s data type.

Steps involved:

• Specify the target data type.
• Convert to numeric, categorical, or string.
• Handle errors with errors='ignore'.
• Verify results with dtypes.

Also Read: 12 Amazing Real-World Applications of Python

Advanced Pandas Interview Questions

With intermediate concepts mastered, it's time to tackle the tough stuff. These pandas interview questions are designed for experienced professionals and dive deep into optimization, time series data, advanced transformations, and file handling. 

This section provides comprehensive answers with practical examples and efficient code snippets to solidify your expertise.

Get ready to sharpen your skills further with these advanced topics.

31. How do you optimize pandas performance with large datasets?

This question tests your ability to handle memory-intensive tasks.

Direct Answer: Optimize pandas performance by using efficient data types, chunking, and vectorized operations.

Optimization strategies include:

• Use dtype to reduce memory usage.
• Process data in chunks using chunksize.
• Leverage NumPy for vectorized computations.
• Filter and clean data early in the pipeline.

32. What is the SettingWithCopyWarning in pandas, and how can you avoid it?

This question tests your understanding of common pandas pitfalls.

Direct Answer: The SettingWithCopyWarning arises when modifying a slice of a DataFrame rather than the original object.

How to avoid it:

• Use .loc[] for explicit assignments.
• Avoid chained indexing.
• Assign back to the original DataFrame.
• Use copy() for independent subsets.

33. How do you work with time series data in pandas?

This question evaluates your ability to handle date-based data.

Direct Answer: Use pandas’ datetime functionality to work with time series data effectively.

Time series handling includes:

• Convert columns to datetime using pd.to_datetime().
• Set datetime as the index for time-based operations.
• Use resample() for aggregations.
• Handle time zones with .dt accessor.

34. How do you resample time series data in pandas?

This question tests your ability to aggregate or downsample time series data.

Direct Answer: Use resample() to group data by specific time intervals (e.g., daily, monthly).

Resampling steps:

• Use resample('D') for daily frequency.
• Apply aggregation like sum() or mean().
• Downsample for lower frequencies (e.g., weeks).
• Upsample and interpolate missing values.

Pandas Built-in Function: DataFrame.resample('H').sum()

35. What is the rolling() function, and how do you use it?

This question tests your ability to compute moving averages or rolling statistics.

Direct Answer: The rolling() function calculates metrics over a sliding window.

Key features:

• Specify the window size (e.g., 7 days).
• Apply aggregations like mean() or sum().
• Handle missing values in the window.
• Combine with time series for advanced analysis.

36. How do you interpolate missing data in a DataFrame?

This question focuses on handling gaps in datasets.

Direct Answer: Use interpolate() to estimate and fill missing data based on patterns.

How it works:

• Fill using linear interpolation.
• Apply polynomial or spline methods.
• Customize axis for row/column interpolation.
• Handle time-based data seamlessly.

Pandas Built-in Function: DataFrame.interpolate()

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

This question highlights your understanding of missing value strategies.

Direct Answer: 

• fillna(): Fills missing values with a constant or method (ffill, bfill).
• interpolate(): Estimates values based on patterns (linear, spline).

Here are the key differences between fillna() and interpolate() methods.

38. How do you use the pivot() and melt() functions in pandas?

This question tests your ability to reshape datasets.

Direct Answer: 

• pivot(): Converts rows into columns for better structure.
• melt(): Converts columns into rows for long-format data.

When to use them:

• Use pivot() for summarizing data.
• Use melt() for preparing data for visualization.
• Both are useful for reshaping efficiently.

39. How do you perform vectorized operations in pandas?

This question assesses your efficiency with operations on large datasets.

Direct Answer: Vectorized operations in pandas apply functions to entire Series or DataFrames without explicit loops.

Benefits of vectorized operations:

• Faster than traditional Python loops.
• Simplifies code readability.
• Leverages pandas’ optimized backend.
• Works seamlessly on columns or rows.

40. How do you read data from SQL databases using pandas?

This question evaluates your ability to integrate pandas with relational databases.

Direct Answer: Use read_sql() or read_sql_query() to fetch data directly from a SQL database into a pandas DataFrame.

Steps to connect:

• Use a Python database library like sqlite3.
• Write a SQL query or fetch an entire table.
• Leverage pandas for further data manipulation.
• Ensure proper indexing for large datasets.

41. How do you export a DataFrame to different file formats (CSV, Excel, JSON)?

This question ensures you know how to share processed data.

Direct Answer: Pandas provides functions like to_csv(), to_excel(), and to_json() for exporting DataFrames.

Exporting formats include:

• CSV: Common format for data sharing.
• Excel: Used in business reporting.
• JSON: Suitable for APIs or web applications.
• Customize file paths, delimiters, or headers.

42. How do you perform advanced indexing and selection using query()?

This question tests your ability to write clean and efficient filters.

Direct Answer: The query() method allows advanced row filtering using expressions.

Features of query():

• Simplifies complex filtering conditions.
• Supports logical operators (and, or).
• Handles column names with spaces easily.
• Avoids the verbosity of traditional indexing.

Example: Filter products with sales greater than 250.

Code Snippet:

import pandas as pd
df = pd.DataFrame({'Product': ['A', 'B', 'C'], 'Sales': [200, 300, 150]})
filtered = df.query('Sales > 250')
print(filtered)

Output:

  Product  Sales
1       B    300

43. How do you create custom aggregations with groupby()?

This question checks your ability to perform tailored data analysis.

Direct Answer: Use groupby() with custom functions to aggregate data based on specific needs.

Steps for custom aggregation:

• Group data by a column or index.
• Apply predefined or custom aggregation functions.
• Combine multiple aggregations with .agg().
• Reset index for flat output.

Example: Group sales by region and calculate total and average sales.

Code Snippet:

import pandas as pd
data = {'Region': ['East', 'West', 'East'], 'Sales': [200, 300, 150]}
df = pd.DataFrame(data)
result = df.groupby('Region')['Sales'].agg(['sum', 'mean'])
print(result)

Output:

Region  sum   mean
East    350  175.0
West    300  300.0

44. How do you use window functions like expanding() and ewm() in pandas?

This question evaluates your ability to compute cumulative or exponentially weighted statistics.

Direct Answer: 

• expanding(): Calculates cumulative metrics across all data points.
• ewm(): Calculates exponentially weighted moving averages.

Key points to know:

• Use expanding() for cumulative sums or averages.
• Use ewm() to prioritize recent data in time series.
• Both support custom aggregation functions.

upGrad’s Exclusive Data Science Webinar for you –

How to Build Digital & Data Mindset

45. How do you serialize and deserialize pandas objects?

This question tests your knowledge of saving and loading pandas objects efficiently.

Direct Answer: Serialization converts a DataFrame into a storable format, while deserialization restores it.

Serialization methods:

• Pickle: Use .to_pickle() and pd.read_pickle().
• Parquet: Efficient for large datasets with .to_parquet().
• JSON: Suitable for lightweight storage.

Ready to dive into Python and unlock new career opportunities? Enroll in upGrad’s comprehensive Python courses today and gain hands-on experience in data science and programming.

Pandas Coding Interview Questions

Ready to flex those coding muscles? This section dives into practical challenges that often appear in pandas interview questions. You’ll learn how to handle real-world scenarios like data transformations, outlier removal, and SQL-like operations in Python pandas. 

These tasks not only test your technical skills but also your problem-solving approach. Now, it’s time to tackle these coding scenarios one by one.

46. How do you split a DataFrame into multiple DataFrames based on a condition?

This question tests your ability to filter and manage subsets of data.

Direct Answer: Use conditional filtering to split a DataFrame into subsets.

Here’s how to approach it:

• Apply conditions using Boolean indexing.
• Create separate DataFrames for each subset.
• Use functions for dynamic splitting.
• Combine with query() for cleaner syntax.

47. How do you calculate the correlation between columns in a DataFrame?

This question evaluates your understanding of statistical relationships.

Direct Answer: Use the corr() method to compute pairwise correlations between columns.

Steps to calculate correlations:

• Select numeric columns only.
• Apply corr() for Pearson correlation.
• Use Spearman or Kendall if needed.
• Visualize correlations with heatmaps.

48. How do you create a DataFrame from a dictionary of lists or a list of dictionaries?

This question tests your ability to construct DataFrames from diverse structures.

Direct Answer: Use pd.DataFrame() to create a DataFrame from dictionaries.

Steps to create:

• Pass a dictionary of lists for column-based structure.
• Pass a list of dictionaries for row-based data.
• Specify index or columns if needed.
• Combine multiple dictionaries for dynamic creation.

Example: Create a DataFrame with student details.

Code Snippet:

import pandas as pd
# Dictionary of lists
data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data)
print(df)

Output:

Name    Age
Alice   25
Bob     30

49. How do you convert a DataFrame to a NumPy array?

This question tests your ability to transition between pandas and NumPy.

Direct Answer: Use the .values attribute or .to_numpy() method to convert a DataFrame to a NumPy array.

Key points to consider:

• Ensure consistent data types in the DataFrame.
• Use to_numpy() for compatibility.
• Retain only values, without labels.
• Combine with NumPy operations for speed.

Syntax: Dataframe.to_numpy()

50. How do you apply a lambda function to transform DataFrame columns?

This question evaluates your ability to implement custom transformations.

Direct Answer: Use the apply() method with a lambda function to modify column values.

Steps to use:

• Select the column to transform.
• Pass a lambda function to apply().
• Apply to multiple columns if needed.
• Combine with map() for Series-level changes.

51. How do you detect and remove outliers in a DataFrame?

This question tests your ability to clean and standardize data.

Direct Answer: Use statistical methods like the IQR or z-scores to identify and filter outliers.

Steps to remove outliers:

• Calculate IQR (Interquartile Range).
• Identify data outside 1.5x IQR bounds.
• Drop rows with extreme values.
• Visualize outliers with boxplots.

52. How do you merge DataFrames with different shapes or indexes?

This question tests your merging and alignment skills.

Direct Answer: Use merge() or join() with specific parameters to handle mismatched DataFrames.

Key approaches:

• Align on common columns or indexes.
• Use how='outer' for all data points.
• Handle mismatched shapes with fillna().
• Use concat() for appending rows.

Example: Merge customer data with sales data by Customer ID.

Code Snippet:

import pandas as pd
customers = pd.DataFrame({'ID': [1, 2], 'Name': ['Alice', 'Bob']})
sales = pd.DataFrame({'ID': [2, 3], 'Amount': [300, 400]})
merged = pd.merge(customers, sales, on='ID', how='outer')
print(merged)

Output:

  ID   Name  Amount
0   1  Alice     NaN
1   2    Bob   300.0
2   3    NaN   400.0

53. How do you randomize the order of rows in a DataFrame?

This question evaluates your ability to manipulate row arrangements.

Direct Answer: Use sample(frac=1) to shuffle rows in a DataFrame.

Steps to randomize rows:

• Set frac=1 to shuffle all rows.
• Use random_state for reproducibility.
• Reset index after shuffling.
• Combine with filters for random sampling.

54. How do you read and write JSON data with pandas?

This question tests your ability to handle JSON file formats.

Direct Answer: Use read_json() to load JSON data and to_json() to export DataFrames.

JSON handling includes:

• Read JSON strings or files.
• Specify orient for structure.
• Export in nested or flat formats.
• Combine with APIs for integration.

55. How do you perform SQL-like operations using pandas?

This question evaluates your ability to mimic database operations in pandas.

Direct Answer: Use query(), merge(), and group by methods to replicate SQL operations.

SQL-like operations include:

• Filtering with query().
• Joining tables with merge().
• Aggregations with groupby().
• Sorting with sort_values().

Pandas Interview Questions for Data Scientists and ML Engineers

As coding skills meet real-world data challenges, pandas interview questions for data scientists and ML engineers focus on advanced preprocessing, feature scaling, and model integration. 

These specialized topics are vital for deploying robust machine learning workflows. Expect questions that test your ability to transform raw data into model-ready formats.

Dive into how Python pandas fits into ML workflows with these key topics.

56. How do you preprocess data for machine learning using pandas?

This question  tests your ability to prepare raw data for machine learning pipelines.

Direct Answer: Preprocess data using pandas by handling missing values, encoding categorical variables, and normalizing numerical data.

Key preprocessing steps include:

• Handle missing values with fillna() or dropna().
• Encode categorical data using get_dummies().
• Scale features for consistency.
• Detecting and removing outliers.

Different Functions to Preprocess data:

• PandasSeries.str.extract()
• apply()

Also Read: Data Scientist Job Description – Job Guide

57. How do you handle imbalanced datasets in pandas?

This question evaluates your ability to address class imbalance issues in datasets.

Direct Answer: Handle imbalanced datasets by resampling techniques like oversampling minority classes or undersampling majority classes.

Techniques to balance data include:

• Use sample() to oversample minority classes.
• Drop excess rows for undersampling.
• Combine with synthetic methods like SMOTE.
• Visualize distributions to validate results.

58. How do you perform feature scaling and normalization in pandas?

This question tests your ability to prepare data for algorithms sensitive to scales.

Direct Answer: Use pandas with MinMaxScaler or StandardScaler from scikit-learn to scale and normalize features.

Steps for scaling:

• Use StandardScaler for standardization (mean=0, std=1).
• Use MinMaxScaler for normalization (range [0, 1]).
• Scale selected columns only.
• Save scaling parameters for test data.

59. How do you integrate pandas with scikit-learn for model training?

This question tests your ability to connect data handling with machine learning workflows.

Direct Answer: Convert pandas DataFrames to NumPy arrays and use them in scikit-learn models for seamless integration.

Integration steps include:

• Split data using train_test_split.
• Pass feature arrays and target columns to models.
• Use pandas for pre-splitting validation sets.
• Combine predictions back into pandas for evaluation.

60. How do you perform cross-validation using pandas and scikit-learn?

This question tests your ability to evaluate models robustly.

Direct Answer: Use scikit-learn’s cross_val_score() function to perform cross-validation, while pandas manages data preparation.

Steps for cross-validation:

• Define the model and scoring metric.
• Pass features and targets from pandas DataFrame.
• Use stratified splits for classification tasks.
• Analyze scores for consistency.

Looking to write optimized code? upGrad’s Online Software Development Courses provide expert tips and strategies.

Tips for Preparing for Pandas Interviews

After diving into the core pandas interview questions, it’s time to gear up for the actual interview. Preparation is everything. Mastering Python pandas interview questions requires strategic practice, hands-on projects, and staying current with the latest updates. 

Here’s a roadmap to get you interview-ready:

• Review pandas documentation and tutorials:

  Dive into the official documentation and explore in-depth tutorials for clarity and precision.

• Practice coding problems regularly:

  Use platforms like LeetCode or HackerRank to solve pandas coding challenges daily.

• Work on real-world data projects: 

  Explore datasets from Kaggle or other sources to gain practical experience.

• Understand integration with other data science tools:

  Learn how pandas works with libraries like NumPy, scikit-learn, and Matplotlib.

• Stay updated with the latest pandas features:

  Follow pandas release notes to keep up with new functions and optimizations.

Ready to level up your preparation? upGrad’s data science courses can guide you through pandas, machine learning, and data analysis in structured, easy-to-follow formats. Gain industry-relevant skills while learning from top educators.

Dive into these courses and gain a competitive edge for your pandas interview questions!

Conclusion

Cracking pandas interview questions requires a mix of technical expertise and practical experience. By mastering data manipulation, integration, and coding challenges, you can confidently tackle even the toughest Python pandas interview questions. Consistent practice, real-world projects, and staying updated will keep you ahead.

For structured learning, enroll in upGrad's specialized courses. They provide hands-on training and industry insights tailored to ace data science interviews. Invest in your growth and make every opportunity count!

Take the next step in your career with our Popular Data Science Courses, where innovation meets expertise.

Stay informed and inspired with our Popular Data Science Articles, featuring expert insights, trends, and practical tips!

Learn the Top Data Science Skills to solve complex problems, optimize solutions, and lead in the era of big data.