Ace Your Data Science Interview: 15 Must-Know Python Coding Questions

As a data scientist, your Python coding skills are vital, and you can expect to face coding challenges during job interviews. To help you prepare, we’ve curated 15 must-know Python coding questions that are frequently asked in data science interviews. These questions cover a range of topics, including data manipulation, analysis, and visualization, ensuring you’re well-equipped to showcase your expertise.

1. Data Aggregation and Grouping

Data aggregation and grouping are fundamental operations in data analysis, allowing you to summarize and organize data effectively. Here’s an example question:

Question: Given a dataset containing student assignment scores, write a Python function to find the largest difference in total scores between any two students.


import pandas as pddef max_score_diff(data):    # Calculate the total score for each student    data['total_score'] = data['assignment1'] + data['assignment2'] + data['assignment3']        # Find the maximum and minimum total scores    max_score = data['total_score'].max()    min_score = data['total_score'].min()        # Return the difference    return max_score - min_score

This function calculates the total score for each student, finds the maximum and minimum scores, and returns the difference between them.

2. Joining and Merging Data

In real-world scenarios, data is often spread across multiple sources, necessitating the ability to join and merge datasets. Consider the following question:

Question: Given two tables, customers and orders, write a Python function to find the lowest order cost for each customer, along with their first name.


import pandas as pddef lowest_order_cost(customers, orders):    # Merge the two tables    merged = pd.merge(customers, orders, left_on='id', right_on='cust_id')        # Group by customer ID and first name, and find the minimum order cost    result = merged.groupby(['cust_id', 'first_name'])['total_order_cost'].min().reset_index()        return result

This function merges the customers and orders tables, groups the data by customer ID and first name, and finds the minimum order cost for each group.

3. Data Filtering

Filtering data is a crucial skill for data scientists, allowing them to extract relevant information from large datasets. Here’s an example question:

Question: Given a dataset containing information about songs, write a Python function to find the top 10 ranked songs in a particular year.


import pandas as pddef top_songs(data, year):    # Filter the data for the specified year and top 10 ranks    top_10 = data[(data['year'] == year) & (data['year_rank'] <= 10)]        # Select the required columns and remove duplicates    result = top_10[['year_rank', 'group_name', 'song_name']].drop_duplicates()        return result

This function filters the data for a specific year and the top 10 ranks, selects the relevant columns, and removes any duplicate entries.

4. Text Manipulation

Working with textual data is a common task for data scientists, and proficiency in text manipulation is essential. Consider the following question:

Question: Given a dataset containing business names, write a Python function to count the number of words in each business name, excluding special symbols.


import pandas as pdimport redef word_count(data):    # Remove duplicates and special symbols from business names    data['business_name'] = data['business_name'].drop_duplicates().str.replace(r'[^a-zA-Zs]', '', regex=True)        # Split business names into words and count the words    data['word_count'] = data['business_name'].str.split().apply(len)        return data[['business_name', 'word_count']]

This function removes duplicate business names, cleans the text by removing special symbols, splits the names into words, counts the words, and returns a DataFrame with the business name and word count.

5. Datetime Manipulation

Dealing with date and time data is a common requirement in data science projects. Here’s an example question:

Question: Given a dataset of user comments, write a Python function to count the number of comments received by each user in the last 30 days, assuming today is a specific date.


import pandas as pdfrom datetime import timedeltadef recent_comments(data, today):    # Calculate the date 30 days ago    start_date = today - timedelta(days=30)        # Filter the data for comments within the last 30 days    recent_comments = data[(data['created_at'] >= start_date) & (data['created_at'] < today)]        # Group by user_id and sum the number of comments    result = recent_comments.groupby('user_id')['number_of_comments'].sum().reset_index()        return result

This function calculates the date 30 days ago, filters the data for comments within that period, groups the comments by user ID, sums the number of comments for each user, and returns a DataFrame with the user ID and comment count.

These five categories cover a wide range of data science tasks, and mastering them will help you excel in Python coding interviews for data science roles. Remember, practice is key to success, so keep solving coding challenges and honing your skills.

Programming Interview Questions And Answers For Data Science | Programming Interview | Simplilearn


Are coding questions asked in data science interview?

A data science interview involves multiple rounds and one of such rounds involves coding interview questions. The purpose of these data science coding interview questions is to check if a candidate can program and knows the required coding languages such as SQL and Python.

How much Python knowledge is required for data science?

While mastering Python for data science can take years, fundamental proficiency can be achieved in about six months. Python proficiency is crucial for roles such as Data Scientist, Data Engineer, Software Engineer, Business Analyst, and Data Analyst. Key Python libraries for data analysis are NumPy, Pandas, and SciPy.

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