Pandas Interview Questions: From Basics to Advanced Data Manipulation

Introduction

Pandas is a cornerstone of data manipulation in Python, making it a must-know for anyone pursuing a career in data science, data analysis, or related fields. Whether you're preparing for an interview or looking to solidify your Pandas skills, understanding common interview questions is crucial. This blog will guide you through essential Pandas questions, from basic concepts to advanced data manipulation techniques along with python best practices, helping you build the confidence to ace your interviews.

As you go through these questions, challenge yourself to not just read the answers but actively solve each one. Practice will not only deepen your understanding but also boost your confidence, preparing you to tackle Pandas-based questions in interviews with ease.

1. What is Pandas?

Pandas is a powerful, open-source Python library used for data manipulation and analysis. It provides data structures like Series (1D) and DataFrame (2D) for handling and analyzing structured data efficiently.

2. What is a Series in Pandas?

A Series is a one-dimensional labeled array in Pandas capable of holding any data type (integers, strings, floating points, etc.). It is similar to a column in an Excel spreadsheet or a database table.

3. How do you create a DataFrame in Pandas?

A DataFrame can be created using pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) with columns A and B.

4. How do you read a CSV file into a Pandas DataFrame?

By using the read_csv() function, we can read csv file with the code pd.read_csv('filename.csv')

5. What is the difference between loc and iloc in Pandas?

loc is label-based, used to select rows and columns by labels. iloc is integer position-based, used to select rows and columns by their integer index.

6. How do you handle missing values in a DataFrame?

Missing values can be handled by dropping the rows with missing values using df.dropna() or the missing values can be filled with mean, mode, median or even custom value by using df.fillna().

7. How can you select a specific column from a DataFrame?

A specific column from the dataframe can be selected using df['column_name'] or by df.column_name.

8. How do you filter rows in a DataFrame?

By using boolean indexing df[df['column_name'] > value], we can filter rows in a dataframe.

9. What is the purpose of the groupby() function in Pandas?

groupby() is used to split data into groups based on some criteria, apply a function to each group independently, and then combine the results. It’s commonly used for aggregation and analysis.

10. How do you merge two DataFrames in Pandas?

By using the merge() function with the code pd.merge(df1, df2, on='key_column'), we can merge two dataframes in pandas. We can specify the type of join using the how parameter as inner or outer.

11. How do you remove duplicates from a DataFrame?

By using df.drop_duplicates() we can remove duplicate data from a dataframe.

12. How do you rename columns in a DataFrame?

We can rename a column in a dataframe using df.rename(columns={'old_name': 'new_name'}, inplace=True).

13. What is the difference between apply() and map() in Pandas?

apply() can apply a function along an axis of the DataFrame (rows or columns). map() is used to substitute values in a Series according to a dictionary or a function.

14. How can you add a new column to a DataFrame?

By using df[‘new_column’] = [value1, value2, value3], we can add a new column to a dataframe.

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

By using df.reset_index(drop=True, inplace=True), we can reset the index of a dataframe.

16. How can you sort a DataFrame by a specific column?

By using df.sort_values(by= ‘column_name’, ascending=True, inplace=True), we can sort a dataframe by a specific column.

17. What is the use of the describe() function?

The describe() function provides summary statistics of numeric columns in a DataFrame, including count, mean, standard deviation, minimum, and maximum values.

18. How do you concatenate two DataFrames?

By using the concat() function pd.concat([df1, df2]), we can concatenate two dataframes.

19. How can you create a DataFrame from a dictionary?

Suppose we have a dictionary like data = {‘A’: [1, 2], ‘B’: [3, 4]}, then we can create a dataframe from the dictionary by using pd.DataFrame(data).

20. How do you drop a column in a DataFrame?

We can drop a column in a dataframe by using df.drop('column_name', axis=1, inplace=True).

21. How do you handle categorical data in a DataFrame?

Categorical data can be handled by converting it into a category data type using astype().By using the code df['category_column'] = df['category_column'].astype('category') we can handle the categorical data in Dataframe.

22. How do you apply a function to a DataFrame using apply()?

We can apply a function along either axis (rows or columns) of a DataFrame using apply().For example in this code df['new_column'] = df['column_name'].apply(lambda x: x**2), it squares the values of column_name and stores them in new_column.

23. Explain the difference between pivot() and pivot_table().

pivot() is used to reshape data by turning unique values from one column into multiple columns. pivot_table() is similar but allows for aggregation, handling duplicates, and missing values.

24. How can you remove rows from a DataFrame based on a condition?

We can remove rows by using Boolean indexing. By using df[df['column_name'] != value_to_remove], it keeps only the rows where the condition is True.

25. What is method chaining in Pandas, and how is it used?

Method chaining is a technique in Pandas where multiple operations are combined into a single line of code for clarity and efficiency. 

For example, If we use 

df = (df.dropna()

        .assign(new_column=lambda x: x['column_name'] * 2)

        .query('new_column > 10'))

We can drop missing values, create a new column, and filter the DataFrame, all in one chain.

26. How do you create a custom aggregation function in groupby() and apply it to multiple columns?

We can create a custom aggregation function and apply it to multiple columns using the agg() method.

def custom_agg(x):

    return x.max() - x.min()

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df_grouped = df.groupby('group_column').agg({

    'column1': 'mean',

    'column2': custom_agg,

    'column3': ['sum', 'std']

})

This code calculates the mean of column1, applies the custom aggregation function to column2, and computes the sum and standard deviation for column3.

27. How do you handle time series data with irregular intervals in Pandas?

Handling time series data with irregular intervals in Pandas involves several steps to ensure that the data is correctly aligned and analyzed. Resampling the data involves converting the time series data to a specific frequency (e.g., daily, hourly). This helps in standardizing the intervals. After resampling, we may encounter missing data due to the irregular intervals. We can handle this using various methods like Forward Fill,Backward Fill, Interpolate. Then The asfreq() method can be used to convert the data to a regular frequency while keeping or filling gaps with NaN values.

If we have multiple time series or columns with irregular intervals, you may need to align them using merge() or join() after resampling to ensure they have the same frequency. Sometimes, we may want to apply custom aggregation during resampling. Irregular intervals can sometimes result from outliers or anomalies. Identifying and handling these can be crucial before resampling.

28. What is vectorization, and how can it be used to speed up operations in Pandas?

Vectorization refers to the process of applying operations to entire arrays (or Series) of data at once, rather than looping over individual elements. This is typically faster and efficient than Python’s slower loops.

We can do the vectorization using df['new_column'] = df.apply(lambda x: x['column1'] + x['column2'], axis=1).

29. How can you perform complex transformations on DataFrame groups using groupby().apply()?

The groupby().apply() method allows for custom operations on groups. Suppose we want to standardize data within each group. 

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def standardize(group):

    return (group - group.mean()) / group.std()

df_grouped = df.groupby('group_column')['data_column'].apply(standardize)

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This method standardizes the data_column within each group_column.

30. How do you perform operations on a large DataFrame that doesn't fit into memory?

When dealing with a large DataFrame that doesn't fit into memory, we can use Dask or Vaex.These libraries offer Pandas-like DataFrame structures that allow you to work with large datasets by parallelizing operations and handling data in chunks.We can also Read data in chunks. Pandas allows you to process large files by reading them in chunks using pd.read_csv() with the chunksize parameter. Also we can use downcast data types. It reduces memory usage by converting data types to more memory-efficient formats.

Conclusion

Mastering Pandas is crucial for anyone looking to excel in data manipulation and analysis, particularly in data science and AI. The ability to handle everything from basic operations to advanced data manipulation is a key skill in today’s data-driven world. By practicing these Pandas interview questions, you can build confidence and expertise, ensuring that you’re well-prepared to tackle real-world challenges.

Whether you’re preparing for a job interview or looking to refine your skills, focusing on Pandas AI capabilities and following Python best practices will give you an edge. With Pandas, you can efficiently manipulate, analyze, and visualize data, making it an indispensable tool in your Python toolkit. Keep practicing, and you’ll be able to crack any Pandas-based interview question with ease.

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