Pandas Interview Questions

This document provides a curated list of Pandas interview questions commonly asked in technical interviews for Data Science, Data Analysis, Machine Learning, and Python Developer roles. It covers fundamental concepts to advanced data manipulation techniques, including rigorous "brutally difficult" questions for senior roles.

This is updated frequently but right now this is the most exhaustive list of type of questions being asked.

Code Examples

1. Memory Optimization

import pandas as pd
import numpy as np

# Typical large dataframe creation
df = pd.DataFrame({
    'category': np.random.choice(['A', 'B', 'C'], size=1000000),
    'value': np.random.randn(1000000)
})

# Memory usage before optimization
print(df.memory_usage(deep=True).sum() / 1024**2, "MB")

# Optimize by converting object to category
df['category'] = df['category'].astype('category')

# Memory usage after optimization
print(df.memory_usage(deep=True).sum() / 1024**2, "MB")

2. Method Chaining for Clean Code

# Instead of multiple intermediate variables
df = (
    pd.read_csv('data.csv')
    .query('status == "active"')
    .assign(
        year=lambda x: pd.to_datetime(x['date']).dt.year,
        total_cost=lambda x: x['price'] * x['quantity']
    )
    .groupby(['year', 'region'])
    .agg(total_revenue=('total_cost', 'sum'))
    .reset_index()
    .sort_values('total_revenue', ascending=False)
)

3. Parallel Processing with Swifter

import pandas as pd
import swifter

df = pd.DataFrame({'text': ['some text'] * 100000})

def heavy_processing(text):
    # Simulate heavy work
    return text.upper()[::-1]

# Automatic parallelization
df['processed'] = df['text'].swifter.apply(heavy_processing)

Questions asked in Google interview

• How would you optimize a Pandas operation running slowly on large dataset?
• Explain the difference between merge() and join()
• Write code to calculate rolling averages with different window sizes
• How would you handle a DataFrame with 100 million rows?
• Explain memory optimization techniques
• Write code to perform complex GroupBy with multiple aggregations
• Explain the internal data structure of DataFrame
• How would you implement feature engineering pipelines?
• Write code to calculate year-over-year growth
• Explain vectorized operations and their importance
• How to handle SettingWithCopyWarning?
• Write code to perform window functions similar to SQL

Questions asked in Amazon interview

• Write code to merge multiple DataFrames with different schemas
• How would you calculate year-over-year growth?
• Explain how to handle time series data with irregular intervals
• Write code to identify and remove duplicate records
• How would you implement a moving average crossover strategy?
• Explain the difference between transform() and apply()
• Write code to pivot data for sales analysis
• How would you handle categorical variables with high cardinality?
• Explain how to optimize for memory efficiency
• Write code to perform cohort analysis

Questions asked in Meta interview

• Write code to analyze user engagement data
• How would you calculate conversion funnels?
• Explain how to handle large-scale data processing
• Write code to resample time series data
• How would you implement A/B testing analysis?
• Explain method chaining and its benefits
• Write code to calculate retention metrics
• How would you handle hierarchical data structures?
• Explain vectorization benefits over loops
• Write code to analyze network data

Questions asked in Microsoft interview

• Explain the SettingWithCopyWarning and how to avoid it
• Write code to perform window functions similar to SQL
• How would you handle timezone conversions?
• Explain the difference between views and copies
• Write code to implement custom aggregation functions
• How would you optimize Pandas for production?
• Explain multi-level indexing use cases
• Write code to compare two DataFrames
• How would you handle missing data in time series?
• Explain eval() and query() methods

Questions asked in Netflix interview

• Write code to analyze viewing patterns and user behavior
• How would you calculate streaming quality metrics?
• Explain how to handle messy data from multiple sources
• Write code to implement collaborative filtering preprocessing
• How would you analyze content performance across regions?
• Explain time series decomposition
• Write code to calculate customer lifetime value
• How would you handle data for recommendation systems?
• Explain rolling window calculations for real-time analytics
• Write code to analyze A/B test results

Questions asked in Apple interview

• Write code to perform data validation on imported data
• How would you implement data quality checks?
• Explain how to handle multi-format data imports
• Write code to analyze product performance metrics
• How would you implement data anonymization?
• Explain best practices for production Pandas code
• Write code to create automated data reports
• How would you handle data versioning?
• Explain memory management for large DataFrames
• Write code to implement time-based partitioning

Questions asked in Flipkart interview

• Write code to analyze e-commerce transaction data
• How would you calculate GMV metrics?
• Explain handling high-cardinality categorical data
• Write code to analyze customer purchase patterns
• How would you implement product recommendation preprocessing?
• Explain data aggregation for dashboard analytics

Questions asked in LinkedIn interview

• Write code to analyze professional network connections
• How would you calculate engagement metrics for posts?
• Explain how to handle user activity data
• Write code to implement skill-based matching
• How would you analyze job posting performance?
• Explain data preprocessing for NLP tasks

Additional Resources

• Official Pandas Documentation
• Minimally Sufficient Pandas
• Modern Pandas
• Python Data Science Handbook
• Effective Pandas (Book)