Pandas is an open-source data manipulation and analysis library for Python. It provides easy-to-use data structures and functions to efficiently manipulate structured data, making it an essential tool for data scientists, analysts, and developers alike. In this article, we’ll provide a comprehensive introduction to Pandas, covering its key features, data structures, and basic operations, along with practical examples to get you started on your data analysis journey.
Author: Ankit Rai
Python | map() function
Introduction: The map() is a type of higer order function which is used to apply a given function to each item of an iterable (such as a list, tuple, or set) and returns an iterator that yields the results. In this article, we’ll explore the concept of map() function in Python, explore its syntax, usage, and provide practical examples to illustrate its functionality.
Python | *args and **kwargs
In Python, *args and **kwargs are special syntax used in function definitions to accept an arbitrary number of positional arguments and keyword arguments, respectively. They provide flexibility and convenience in function design, allowing developers to create functions that can handle a variable number of inputs. In this article, we’ll explore the concept of *args and **kwargs in Python functions, explore their syntax, features, and demonstrate their usage through practical examples.
Python | Dictionary Comprehension
Python’s dictionary comprehension is an elegant way to create dictionaries from iterable objects such as lists, tuples, or even other dictionaries. It offers a simple syntax for generating dictionaries with ease and flexibility. In this article, we’ll explore the concept of dictionary comprehension in Python, explore its syntax, features, and demonstrate its usage through practical examples.
Python | List Comprehension
Introduction: List comprehension is a powerful and simple technique in Python for creating lists in a more readable way. It provides a short syntax to generate lists by applying an expression to each item in an iterable. In this article, we’ll explore the concept of list comprehension in Python, explore its syntax, features, and demonstrate its usage through practical examples.
Big Data Engineer Interview Questions
Preparing for an interview in the Big Data field can be challenging, given the diverse range of technologies and methodologies involved. To help you excel in your career, I’ve compiled an extensive collection of Big Data interview questions asked by different companies in the industry
Python | f-strings
Introduction: In the world of Python programming, string manipulation plays a crucial role in crafting readable and efficient code. Python offers various methods for string formatting, and one of the most powerful and simple approaches is using f-strings. In this article, we’ll explore the concept of f-strings in Python, explore their syntax, features, and demonstrate their usage through practical examples.
Common Python Interview Questions for Data Engineers
Python is a crucial language for Data Engineers, widely used in data processing, ETL workflows, and big data frameworks like Apache Spark. In this article, we will cover some commonly asked Python interview questions that will help you prepare for your Data Engineering interviews.
KPMG | Big Data Engineer Interview Questions
In this article, we will see the list of questions asked in KPMG India Company Interview for 2+ year of experience candidate in big data field.
Let’s see the Questions:
Python | How to Setup Snowpark Environment in Local Machine
Setting up a Snowpark environment on your local machine allows you to leverage the power of Snowflake for data processing and analytics. Whether you’re a data engineer, data scientist, or data analyst, having a local Snowpark environment can significantly enhance your productivity and facilitate experimentation. In this post, we’ll walk you through the steps to set up a Snowpark environment on your local machine.