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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!

13. Thank You
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