Although we cannot predict what Python coding interview questions interviewers will ask, we can avoid interview embarrassment by being prepared with subject knowledge and doing some research on the most common Python interview questions and answers. To build a solid foundation in the Python language, you can simply enrol in our best Python courses. But for insights into the most frequently asked Python interview questions for freshers and experienced, we invite you to spend just five minutes reading this blog, where you will discover the essential Python interview questions and answers that often arise in Python interviews. Let us begin.
Here are 5 Python basic interview questions and answers that you can start with.
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.
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.
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.
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.
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. Without proper indentation, Python gets confused and throws an error.
Here are 5 Python Data Science Python interview questions for experience that can make your job ready.
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. You can use Pandas for reading data from files like CSVs, combining datasets, and even performing calculations on data quickly.
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()) or fill them in with something meaningful like the column’s average value (fillna(mean)), zeros, or even the previous value in the column. The approach depends on how critical the missing data is.
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. For example, multiplying every item in a NumPy array by 2 is super fast, while doing it for a list takes more time.
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.
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.
Here are some technical Python interview questions that you should be well-versed in to leave a strong impression.
There are various data types that can be used in Python. Here are some examples, integers (whole numbers), floats (decimals), strings (text; sequence of characters), lists (a sequence with multiple elements), tuples (like list but immutable) dictionaries type (object stores data in key-value pairs like your phonebook), and sets sets (a collection of unique items). This is important to know as these are used for storing and processing the information in your program.
Python uses a mechanism known as garbage collection to automatically manage memory. To free up memory, it records the variables you create and removes those you aren't using.
A function in Python is like a reusable recipe. You write it once and use it whenever needed. To create a function, you use the def keyword. For example:
def greet(name):
return f"Hello, {name}!"
This function takes a name as input and returns a greeting. Functions help make your code cleaner and avoid repetition.
== checks if two things have the same value, while is checks if they are the same object in memory.
Python 3 is the newer version and most people use it now. It fixed many problems from Python 2 such as better handling of Unicode (text in various languages). Python 2 no longer has official support, so it's best to use Python 3 for any new projects you start.
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The 5 primary uses of python are web development, data analysis, game development, automation and machine learning.
It totally depends on the object you pass in the function. If the object is immutable it is pass by value and if it is mutable it shows pass by reference.
Python tracks memory usage with reference counting. When something is no longer needed, garbage collection automatically frees up space.
Generators use yield to return a value and remember where they left off. Unlike regular functions, they don’t store everything in memory at once.
