45 Must-Know Pandas Interview Questions and Answers 2024

Pandas Fundamentals

1.
What is Pandas in Python and why is it used for data analysis?
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2.
Explain the difference between a Series and a DataFrame in Pandas.
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3.
How can you read and write data from and to a CSV file in Pandas?
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4.
What are Pandas indexes, and how are they used?
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5.
How do you handle missing data in a DataFrame?
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6.
Discuss the use of groupby in Pandas and provide an example.
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7.
Explain the concept of data alignment and broadcasting in Pandas.
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8.
What is data slicing in Pandas, and how does it differ from filtering?
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9.
Describe how joining and merging data works in Pandas.
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10.
How do you apply a function to all elements in a DataFrame column?
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Data Manipulation and Cleaning

11.
Demonstrate how to handle duplicate rows in a DataFrame.
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12.
Describe how you would convert categorical data into numeric format.
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13.
How can you pivot data in a DataFrame?
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14.
Show how to apply conditional logic to columns using the where() method.
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15.
What is the purpose of the apply() function in Pandas?
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16.
How do you reshape a DataFrame using stack and unstack methods?
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17.
Explain the usage and differences between astype, to_numeric, and pd.to_datetime.
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18.
Discuss how to deal with time series data in Pandas.
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Data Analysis and Exploration

19.
How can you perform statistical aggregation on DataFrame groups?
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20.
Explain the different types of data ranking available in Pandas.
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21.
How do you use window functions in Pandas for running calculations?
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22.
What is a crosstab in Pandas, and when would you use it?
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23.
Describe how to perform a multi-index query on a DataFrame.
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24.
Provide an example of how to normalize data within a DataFrame column.
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Visualization and Representation

25.
Show how to create simple plots from a DataFrame using Pandas’ visualization tools.
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26.
Discuss how Pandas integrates with Matplotlib and Seaborn for data visualization.
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27.
Explain how you would export a DataFrame to different file formats for reporting purposes.
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Pandas Performance and Scaling

28.
What techniques can you use to improve the performance of Pandas operations?
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29.
Compare and contrast the memory usage in Pandas for categories vs. objects.
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30.
How does one use Dask or Modin to handle larger-than-memory data in Pandas?
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Coding Challenges

31.
Write a Pandas script to filter rows in a DataFrame based on a column’s value being higher than a specified percentile.
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32.
Code a function that concatenates two DataFrames and handles overlapping indices correctly.
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33.
Implement a data cleaning function that drops columns with more than 50% missing values and fills the remaining ones with column mean.
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34.
Create a Pandas pipeline that ingests, processes, and summarizes time-series data from a CSV file.
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35.
Write a Python function that takes a DataFrame and computes the correlation matrix, then visualizes it using Seaborn’s heatmap.
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Scenario-Based Data Manipulation

36.
If you have a DataFrame with multiple datetime columns, detail how you would create a new column combining them into the earliest datetime.
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37.
Describe how you could use Pandas to preprocess data for a machine learning model.
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38.
Develop a routine in Pandas to detect and flag rows that deviate by more than three standard deviations from the mean of specific columns.
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39.
How would you use Pandas to prepare and clean ecommerce sales data for better insight into customer purchasing patterns?
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40.
Outline how to merge multiple time series datasets effectively in Pandas, ensuring correct alignment and handling missing values.
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Advanced Topics and Optimization

41.
Discuss the advantages of vectorized operations in Pandas over iteration.
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42.
How do you manage memory usage when working with large DataFrames?
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43.
What are some strategies for optimizing Pandas code performance?
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44.
How can you use chunking to process large CSV files with Pandas?
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45.
Explain the importance of using categorical data types, especially when working with a large number of unique values.
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45 Pandas interview questions

What is Pandas in Python and why is it used for data analysis?

Explain the difference between a Series and a DataFrame in Pandas.

How can you read and write data from and to a CSV file in Pandas?

What are Pandas indexes, and how are they used?

How do you handle missing data in a DataFrame?

Discuss the use of groupby in Pandas and provide an example.

Explain the concept of data alignment and broadcasting in Pandas.

What is data slicing in Pandas, and how does it differ from filtering?

Describe how joining and merging data works in Pandas.

How do you apply a function to all elements in a DataFrame column?

Demonstrate how to handle duplicate rows in a DataFrame.

Describe how you would convert categorical data into numeric format.

How can you pivot data in a DataFrame?

Show how to apply conditional logic to columns using the where() method.

What is the purpose of the apply() function in Pandas?

How do you reshape a DataFrame using stack and unstack methods?

Explain the usage and differences between astype, to_numeric, and pd.to_datetime.

Discuss how to deal with time series data in Pandas.

How can you perform statistical aggregation on DataFrame groups?

Explain the different types of data ranking available in Pandas.

How do you use window functions in Pandas for running calculations?

What is a crosstab in Pandas, and when would you use it?

Describe how to perform a multi-index query on a DataFrame.

Provide an example of how to normalize data within a DataFrame column.

Show how to create simple plots from a DataFrame using Pandas’ visualization tools.

Discuss how Pandas integrates with Matplotlib and Seaborn for data visualization.

Explain how you would export a DataFrame to different file formats for reporting purposes.

What techniques can you use to improve the performance of Pandas operations?

Compare and contrast the memory usage in Pandas for categories vs. objects.

How does one use Dask or Modin to handle larger-than-memory data in Pandas?

Write a Pandas script to filter rows in a DataFrame based on a column’s value being higher than a specified percentile.

Code a function that concatenates two DataFrames and handles overlapping indices correctly.

Implement a data cleaning function that drops columns with more than 50% missing values and fills the remaining ones with column mean.

Create a Pandas pipeline that ingests, processes, and summarizes time-series data from a CSV file.

Write a Python function that takes a DataFrame and computes the correlation matrix, then visualizes it using Seaborn’s heatmap.

If you have a DataFrame with multiple datetime columns, detail how you would create a new column combining them into the earliest datetime.

Describe how you could use Pandas to preprocess data for a machine learning model.

Develop a routine in Pandas to detect and flag rows that deviate by more than three standard deviations from the mean of specific columns.

How would you use Pandas to prepare and clean ecommerce sales data for better insight into customer purchasing patterns?

Outline how to merge multiple time series datasets effectively in Pandas, ensuring correct alignment and handling missing values.

Discuss the advantages of vectorized operations in Pandas over iteration.

How do you manage memory usage when working with large DataFrames?

What are some strategies for optimizing Pandas code performance?

How can you use chunking to process large CSV files with Pandas?

Explain the importance of using categorical data types, especially when working with a large number of unique values.

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Discuss the use of `groupby` in Pandas and provide an example.

Show how to apply conditional logic to columns using the `where()` method.

What is the purpose of the `apply()` function in Pandas?

Explain the usage and differences between `astype`, `to_numeric`, and `pd.to_datetime`.