Python Interview Questions with Answers Part-2: ☑️

11. How do you merge or join two DataFrames? 
    Use pd.merge(df1, df2, on='key_column', how='inner') with options:
⦁ how='inner' (default) for intersection,
⦁ left, right, or outer for other joins.

12. What is the difference between .loc[] and .iloc[] in Pandas?
⦁ .loc[] selects data by label (index names).
⦁ .iloc[] selects data by integer position (0-based).

13. How do you handle duplicates in a DataFrame? 
    Use df.duplicated() to find duplicates and df.drop_duplicates() to remove them.

14. Explain how to deal with outliers in data. 
    Detect outliers using statistical methods like IQR or Z-score, then either remove, cap, or transform them depending on context.

15. What is data normalization and how can it be done in Python? 
    Scaling data to a standard range (e.g., 0 to 1). Can be done using sklearn’s MinMaxScaler or manually using (x - min) / (max - min).

16. Describe different data types in Python. 
    Common types: int, float, str, bool, list, tuple, dict, set, NoneType.

17. How do you convert data types in Pandas? 
    Use df['col'].astype(new_type) to convert columns, e.g., astype('int') or astype('category').

18. What are Python dictionaries and how are they useful? 
    Unordered collections of key-value pairs useful for fast lookups, mapping, and structured data storage.

19. How do you write efficient loops in Python? 
    Use list comprehensions, generator expressions, and built-in functions instead of traditional loops, or leverage libraries like NumPy for vectorization.

20. Explain error handling in Python with try-except. 
    Wrap code that might cause errors in try: block and handle exceptions in except: blocks to prevent crashes and manage errors gracefully.

React ♥️ for Part 3