From data science and machine learning to web development and automation, Python's extensive libraries and clean syntax make it an essential tool for developers across various domains. As a result, Python frequently appears in technical interviews, particularly in fields related to data science and artificial intelligence.
To help you navigate through python technical interview questions and enhance your Python interview preparation skills, we've compiled 25 frequently asked Python interview questions covering a range of topics from fundamental concepts to advanced techniques. These questions are designed to provide you with a comprehensive understanding of Python's features and python practice problems. By mastering these questions, you'll be better prepared to showcase your Python expertise and tackle complex problems effectively. Start your python interview preparation with top 25 most asked questions and practice similar questions to ace your next interview.
1. What is Python, and why is it so popular?
Python is a high-level, interpreted programming language known for its easy-to-read syntax and dynamic semantics. It's widely used because of its versatility and the large standard library, which supports modules and packages, making it ideal for rapid application development. Python's simplicity and readability make it a popular choice for both beginners and experienced developers.
2. What is the difference between ‘==’ and ‘is’ operators in Python?
== (Equality Operator): Compares the values of two objects and returns True if they are equal, irrespective of whether they are the same object.
is (Identity Operator): Compares the memory locations of two objects. It returns True if they reference the same object in memory.
3. How do you handle missing data in a dataset?
a. Removing Missing Data: If the amount of missing data is small and random, you might choose to remove rows with missing values. However, this approach can lead to loss of valuable data if the missing values are not negligible. If a column has a large number of missing values, it might be more effective to remove the entire column.
b. Imputation (Filling Missing Data): For numerical data, you can fill missing values with the mean, median, or mode of the column. Forward/Backward Fill method fills missing values with the previous or next value in the column, which can be useful for time series data. For time series or continuous data, interpolation can estimate missing values based on the surrounding data points.
c) Using Algorithms that Handle Missing Data: Certain algorithms can handle missing data internally. For example, decision trees and random forests can handle missing values in the input data without needing explicit imputation.
d) Creating a Missing Indicator Variable: This approach can be useful when the fact that a value is missing may itself be informative.
e) Predictive Modeling: You can use other features in the dataset to predict the missing values. This method is more sophisticated and may provide better estimates, but it also requires careful consideration to avoid introducing bias.
4. Explain the difference between lists and tuples in Python.
Both lists and tuples are used to store collections of items. The primary differences are:
Mutability: Lists are mutable, meaning you can modify them after creation (e.g., add, remove, or change items). Tuples are immutable, so once they are created, they cannot be altered.
Syntax: Lists are defined using square brackets [], while tuples are defined using parentheses ().
Performance: Tuples can be more performance-efficient due to their immutability.
5. How do you add elements to a list in Python and how they are different from each other?
Adding elements in a list can be done in multiple ways and each method is used by its corresponding requirements such as:
a. Using append(): The append() method adds a single element to the end of the list.
b. Using extend(): The extend() method adds all elements of an iterable (e.g., another list) to the end of the list.
c. Using insert(): The insert() method adds an element at a specified position in the list.
d. Using List Concatenation: You can concatenate lists using the + operator, which creates a new list.
e. Using List Comprehension: List comprehension allows adding elements conditionally.
6. How do you remove elements from a list in Python and how are they different from each other?
Removing elements from a list can also be done using various methods and each method is used by its corresponding requirements.
a. Using remove() Method: The remove() method removes the first occurrence of the specified element in the list.
b. Using pop() Method: The pop() method removes and returns the element at a given index. If no index is specified, it removes the last element.
c. Using del Statement: The del statement can be used to remove an element at a specific index, or to remove a slice of the list.
d. Using clear() Method: The clear() method removes all elements from the list, leaving it empty.
e. Using List Comprehension: You can use list comprehension to create a new list that excludes certain elements.
7. How to sort the list using negative indexing?
In Python, negative indexing is a way to access list elements starting from the end of the list, where -1 is the last element, -2 is the second last, and so on. We can reverse a list using negative indexing and then sort it using sorted().
8. How to separate the letters in a string?
By using a space or a specific character as a delimiter, split()can be used to break a string into parts.
9. In a list there are 3 words as elements. How can we create a dictionary with the index of the list elements?
Dictionary comprehension provides a concise way to create dictionaries. We can use enumerate() to get both the index and the value.
10. What is the difference between list.sort() and sorted(list)?
list.sort() modifies the original list (in-place), whereas sorted(list) creates and returns a new sorted list, leaving the original list unchanged. list.sort() returns None since it sorts the list in place, while sorted(list) returns a new sorted list.
11. Suppose we have a list list1 and the dataframe df, how to change the index of dataframe into the list?
To change the index of a DataFrame df to the values of a list list1, you can use the pandas library in Python. The list A should have the same length as the number of rows in the DataFrame B.
12. How do you reverse the rows of a data frame?
You can use iloc with slicing to reverse the rows like df_reversed = df.iloc[::-1].
13. What is the difference between merge and join in pandas?
In Pandas, both merge() and join() are methods used to combine data from multiple DataFrames based on common columns or indices. While they serve a similar purpose, there are subtle differences in how they operate. merge() is a more flexible and versatile method that allows you to merge DataFrames on columns or indices. Use merge() when you need to combine DataFrames horizontally (side by side) based on values of columns or indices. key is the common column name on which to join.
join() is a convenient method primarily used to join DataFrames on their indices. Use join() when you want to combine DataFrames based on their indices, which is particularly useful for combining data vertically (stacking rows). Key can be specified to indicate the column name or index on which to join.
14. How do you handle categorical data in Python?
Categorical data needs to be encoded into numerical format for most machine learning models. Common techniques include:
a. Label Encoding: Converts each category to a unique integer. This method is simple but assumes an ordinal relationship, which may not always be appropriate.
b. One-Hot Encoding: Creates a binary column for each category. This method is more suitable for nominal data with no inherent order.
c. Target Encoding: Replaces each category with the mean of the target variable for that category. This can help with high-cardinality features.
15. How do you handle outliers in a dataset?
Outliers can skew the analysis, so it’s important to handle them appropriately:
a. Removing Outliers: You can remove outliers based on statistical measures like the Z-score or the IQR (Interquartile Range).
b. Transforming Data: Apply transformations like logarithmic or square root transformations to reduce the impact of outliers.
c. Capping/Flooring: Set outliers to a specified percentile to minimize their impact.
d. Using Robust Algorithms: Some algorithms, like tree-based models, are less sensitive to outliers.
16. How do you merge two DataFrames in Python?
Use the merge() function in pandas to combine two DataFrames. The function supports different types of joins:
a. Inner Join: Returns only the rows that have matching values in both DataFrames.
b. Left Join: Returns all rows from the left DataFrame, and the matched rows from the right DataFrame.
c. Right Join: Returns all rows from the right DataFrame, and the matched rows from the left DataFrame.
d. Outer Join: Returns all rows when there is a match in either left or right DataFrame.
17. What is the purpose of the zip() function in Python?
It combines multiple iterables (like lists or tuples) into a single iterable of tuples. Each tuple contains elements from the iterables at the same position.
18. What is a pass in Python?
In Python, the pass statement is a placeholder that does nothing when executed. It is often used as a placeholder in situations where a statement is syntactically required but no action is needed. This is particularly useful in scenarios like defining an empty function, class, or loop.
19. How is Exceptional handling done in Python?
Exception handling in Python is done using the try, except, else, and finally blocks. This mechanism allows you to catch and handle exceptions (errors) that occur during the execution of your program, helping to prevent crashes and manage errors gracefully.
20. What are *args and *kwargs?
In Python, *args and **kwargs are used to pass a variable number of arguments to a function. They allow you to write more flexible and reusable code by accepting any number of positional or keyword arguments.
*args allows you to pass a variable number of positional arguments to a function. The arguments are collected into a tple. It is used when you want a function to accept any number of positional arguments without explicitly defining each one.
**kwargs allows you to pass a variable number of keyword arguments to a function. The arguments are collected into a dictionary. It is used when you want a function to accept any number of keyword arguments without explicitly defining each one.
21. What is typecasting in python?
Typecasting, also known as type conversion, is the process of converting a value from one data type to another in Python. This is often necessary when you're working with variables of different types and need them to be in the same format to perform operations on them. Python supports both implicit and explicit typecasting.
Implicit typecasting is automatically performed by Python when you perform operations that involve different data types. Python converts one data type to another without explicit instruction from the programmer.
Explicit typecasting is when you manually convert a value from one type to another using built-in functions. This is also known as type conversion or type coercion.
22. What is docstring in Python?
A docstring in Python is a special type of comment used to document a module, class, method, or function. Docstrings are enclosed in triple quotes (""" or ''') and provide a convenient way to associate documentation directly with the code. They serve as a means to explain what the code does, its parameters, and its return values.
23. What are lambda functions?
Lambda functions in Python are small, anonymous functions created using the lambda keyword. They are also known as lambda expressions or lambda abstractions. Lambda functions are useful for creating quick, throwaway functions without the need for a full function definition using def. They are often used in contexts where a short, one-time function is needed.
24. What are modules and packages in Python?
In Python, modules and packages are ways to organize and manage code. They help in structuring and reusing code efficiently. A module is a single file containing Python code. It can define functions, classes, and variables, and can also include runnable code. Modules allow you to break your code into smaller, manageable pieces and organize it logically. A package is a collection of modules organized in a directory hierarchy. Packages allow you to structure and group related modules together. Each directory in a package contains a special __init__.py file (which can be empty) that signifies that the directory should be treated as a package.
25.What is the use of self in Python?
In Python, self is a reference to the instance of the class in which it is used. It is a convention that represents the instance of the class and allows access to its attributes and methods. self is used to access instance variables (attributes) within a class. It allows you to refer to the specific instance's data attributes. Also self is used to call other methods from within the same class. This allows you to interact with other methods and attributes of the instance. It is essential for distinguishing between instance attributes and local variables, and for ensuring clarity and consistency in class definitions.
Conclusion
Mastering Python goes beyond understanding its syntax; it's about applying its features to solve real-world problems efficiently. The 25 questions discussed in this blog cover essential Python technical interview questions, including data handling, coding practices, and advanced functionalities. These questions not only test your knowledge but also your problem-solving skills and ability to use Python in practical scenarios and will help you in cracking python practice problems regularly.
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To prepare specifically for interviews, make sure to read our detailed blogs:
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