Top Python Interview Questions and Answers for Success.pptx
- 1. Top Python Interview Questions and Answers for Success Hey, are you ready for your next Python interview?
- 2. What are the benefits of using Python language as a tool in the present scenario? It is easy to learn, works on almost everything, and has a huge community. Perfectly fitted for data science, web development, automation, and even AI.
- 3. Explain the difference between lists and tuples. Both lists and tuples allow you to store multiple items- grocery lists-but they are not the same. Lists are a flexible option-a list lets you add, remove, or change its items anytime. Tuples, on the other hand, are like sealed envelopes-you cannot change them once you have created them. If you need something permanent, use a tuple; otherwise, go for a list.
- 4. What are the common built-in data types in Python? Python comes with several built-in data types to make your life easy. For example, there’s int for whole numbers, float for decimals, and str for text. If you want to store a bunch of items, you can use list, tuple, or set. For key-value pairs, there’s dict.
- 5. Explain the difference between a shallow and a deep copy. Think of a shallow copy as copying the cover of a book— you only copy the outer layer. If the original changes inside, your copy changes too. A deep copy is like photocopying every page of the book; it’s a complete, independent copy, so changes to the original don’t affect it. In Python, you can use the copy module for this.
- 6. Is Indentation Required in Python? Yes, indentation is a big deal in Python. It’s like the way you organise paragraphs in a story. Instead of using braces {} like some other languages, Python uses spaces or tabs to show where a block of code starts and ends.
- 7. What is Pandas, and why is it used? Pandas is a super helpful Python library used for working with data. It lets you handle large datasets easily by offering tools to clean, analyse, and manipulate data in rows and columns, just like in Excel.
- 8. How do you handle missing data in a dataset? Missing data can mess up your analysis, so fixing it is important. In Python, you can use Pandas to either remove rows/columns with missing values (dropna())
- 9. What’s the difference between a list and a NumPy array? Lists are Python's way of storing a collection of items, and they’re pretty flexible—you can put anything in them. But NumPy arrays are like a super-efficient list just for numbers, and they let you do math on the whole array at once.
- 10. What is the purpose of Panda's groupby() function? groupby() is a useful Pandas function that divides a dataset into groups based on one or more columns and then computes for each group. So, if you have sales data, you can use the groupby() function to calculate total sales by product or region. It's similar to generating and analysing smaller data sets from a larger dataset.
- 11. What are some common Python libraries used for Data Visualization? For making data look good in graphs or charts, the most popular libraries are Matplotlib, Seaborn, and Plotly. Matplotlib is like your base tool for creating any kind of chart. Seaborn makes prettier charts with less work, and Plotly is awesome for interactive, zoomable charts that you can share online.
- 12. Conclusion Analytics Shiksha’s Super30 Analytics course, part of their data analytics courses, offers top-tier training in Python and data analysis. By preparing for common Python interview questions, you’ll showcase your skills, quick thinking, and problem-solving attitude. Keep practicing, stay curious, and book your seat today—only 30 spots available!