Post #2236

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