Python Interview Questions with Answers Part-1: ☑️

1. What is Python and why is it popular for data analysis? 
   Python is a high-level, interpreted programming language known for simplicity and readability. It’s popular in data analysis due to its rich ecosystem of libraries like Pandas, NumPy, and Matplotlib that simplify data manipulation, analysis, and visualization.

2. Differentiate between lists, tuples, and sets in Python.
⦁ List: Mutable, ordered, allows duplicates.
⦁ Tuple: Immutable, ordered, allows duplicates.
⦁ Set: Mutable, unordered, no duplicates.

3. How do you handle missing data in a dataset? 
   Common methods: removing rows/columns with missing values, filling with mean/median/mode, or using interpolation. Libraries like Pandas provide .dropna(), .fillna() functions to do this easily.

4. What are list comprehensions and how are they useful? 
   Concise syntax to create lists from iterables using a single readable line, often replacing loops for cleaner and faster code. 
   Example: [x**2 for x in range(5)] → ``

5. Explain Pandas DataFrame and Series.
⦁ Series: 1D labeled array, like a column.
⦁ DataFrame: 2D labeled data structure with rows and columns, like a spreadsheet.

6. How do you read data from different file formats (CSV, Excel, JSON) in Python? 
   Using Pandas:
⦁ CSV: pd.read_csv('file.csv')
⦁ Excel: pd.read_excel('file.xlsx')
⦁ JSON: pd.read_json('file.json')

7. What is the difference between Python’s append() and extend() methods?
⦁ append() adds its argument as a single element to the end of a list.
⦁ extend() iterates over its argument adding each element to the list.

8. How do you filter rows in a Pandas DataFrame? 
   Using boolean indexing: 
   df[df['column'] > value] filters rows where ‘column’ is greater than value.

9. Explain the use of groupby() in Pandas with an example. 
   groupby() splits data into groups based on column(s), then you can apply aggregation. 
   Example: df.groupby('category')['sales'].sum() gives total sales per category.

10. What are lambda functions and how are they used? 
    Anonymous, inline functions defined with lambda keyword. Used for quick, throwaway functions without formally defining with def. 
    Example: df['new'] = df['col'].apply(lambda x: x*2)

React ♥️ for Part 2

Grab the deal before it over
Best deal from admin

One month premium of perplexity ai with comet browser worth 200$ for free

No payment no checkout direct redeem.
Open link, login & download comet browser.

Ask anything to comet you get best answers for your learning

https://pplx.ai/bhavinahir537

If I had to start learning data analyst all over again, I'd follow this:

1- Learn SQL:

---- Joins (Inner, Left, Full outer and Self)
---- Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
---- Group by and Having clause
---- CTE and Subquery
---- Windows Function (Rank, Dense Rank, Row number, Lead, Lag etc)

2- Learn Excel:

---- Mathematical (COUNT, SUM, AVG, MIN, MAX, etc)
---- Logical Functions (IF, AND, OR, NOT)
---- Lookup and Reference (VLookup, INDEX, MATCH etc)
---- Pivot Table, Filters, Slicers

3- Learn BI Tools:

---- Data Integration and ETL (Extract, Transform, Load)
---- Report Generation
---- Data Exploration and Ad-hoc Analysis
---- Dashboard Creation

4- Learn Python (Pandas) Optional:

---- Data Structures, Data Cleaning and Preparation
---- Data Manipulation
---- Merging and Joining Data (Merging and joining DataFrames -similar to SQL joins)
---- Data Visualization (Basic plotting using Matplotlib and Seaborn)

Hope this helps you 😊

Free Access to our premium Data Science Channel
👇👇
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y

Amazing premium resources only for my subscribers

🎁 Free Data Science Courses
🎁 Machine Learning Notes
🎁 Python Free Learning Resources
🎁 Learn AI with ChatGPT
🎁 Build Chatbots using LLM
🎁 Learn Generative AI
🎁 Free Coding Certified Courses

Join fast ❤️

ENJOY LEARNING 👍👍

📊 Core Data Analyst Interview Topics You Should Know ✅

1️⃣ Excel/Spreadsheet Skills
⦁ VLOOKUP, INDEX-MATCH, XLOOKUP (newer Excel fave)
⦁ Pivot Tables for summarizing data
⦁ Conditional Formatting to highlight trends
⦁ Data Cleaning & Validation with formulas like IFERROR

2️⃣ SQL & Databases
⦁ SELECT, JOINs (INNER, LEFT, RIGHT, FULL)
⦁ GROUP BY, HAVING, ORDER BY for aggregations
⦁ Subqueries & Window Functions (ROW_NUMBER, LAG)
⦁ CTEs for cleaner, reusable queries

3️⃣ Data Visualization
⦁ Tools: Power BI, Tableau, Excel, Google Data Studio
⦁ Best practices: Choose charts wisely (bar for comparisons, line for trends)
⦁ Dashboards & Interactivity with slicers/drill-downs
⦁ Storytelling with Data to make insights pop

4️⃣ Statistics & Probability
⦁ Mean, Median, Mode, Standard Deviation for summaries
⦁ Correlation vs. Causation (correlation doesn't imply cause!)
⦁ Hypothesis Testing (t-test, p-value for significance)
⦁ Confidence Intervals to gauge reliability

5️⃣ Python for Data Analysis
⦁ Libraries: Pandas for dataframes, NumPy for arrays, Matplotlib/Seaborn for plots
⦁ Data wrangling & cleaning (handling nulls, merging)
⦁ Basic EDA: Describe stats, visualizations, correlations

6️⃣ Business Understanding
⦁ KPI identification (e.g., conversion rate, churn)
⦁ Funnel analysis for drop-offs
⦁ A/B Testing basics to validate changes
⦁ Decision-making support with actionable recommendations

7️⃣ Problem Solving & Case Studies
⦁ Product metrics (DAU/MAU, retention)
⦁ Customer segmentation (RFM analysis)
⦁ Market trend analysis with time-series

8️⃣ ETL Concepts
⦁ Extract from sources, Transform (clean/aggregate), Load to warehouses
⦁ Data pipeline basics using tools like Airflow or dbt

9️⃣ Data Cleaning Techniques
⦁ Handling missing values (impute or drop)
⦁ Duplicates, outliers detection/removal
⦁ Data formatting (standardize dates, text)

🔟 Soft Skills & Communication
⦁ Explaining insights to non-technical stakeholders simply
⦁ Clear visualization storytelling (avoid clutter)
⦁ Collaborating with cross-functional teams for context

💬 Tap ❤️ for more!

🎯 2 Power-Packed Courses to Boost Your Tech Career! 💻🚀

Whether you're preparing for placements or starting your coding journey — we’ve got you covered!

✅ 1. DSA Self-Paced Course 
📌 Master Data Structures & Algorithms 
– Perfect for SDE interviews & competitive coding 
– Covers Arrays, Trees, Graphs, DP & more 
🔗 Join now: https://gfgcdn.com/tu/W84/

✅ 2. Python Beginner to Advanced 
📌 Learn Python from scratch to expert level 
– Covers basics, OOPs, file handling, projects & more 
🔗 Start here: https://gfgcdn.com/tu/W8D/

🎁 Use Coupon: GFGWINTERARC for 25% OFF (Limited Time!)

🎯 2 Power-Packed Courses to Boost Your Tech Career! 💻🚀

Whether you're preparing for placements or starting your coding journey — we’ve got you covered!

✅ 1. DSA Self-Paced Course
📌 Master Data Structures & Algorithms
– Perfect for SDE interviews & competitive coding
– Covers Arrays, Trees, Graphs, DP & more
🔗 Join now: https://gfgcdn.com/tu/W84/

✅ 2. Python Beginner to Advanced
📌 Learn Python from scratch to expert level
– Covers basics, OOPs, file handling, projects & more
🔗 Start here: https://gfgcdn.com/tu/W8D/

🎁 Use Coupon: GFGWINTERARC for 25% OFF (Limited Time!)

Hey guys 👋

I was working on something big from last few days.

Finally, I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634

If you go on purchasing these books, it will cost you more than 15000 but I kept the minimal price for everyone's benefit.

I hope these resources will help you in data analytics journey.

I will add more resources here in the future without any additional cost.

All the best for your career ❤️

Skills Needed To Become Data Analyst