Problem Statement: Airport security team have confiscated several items of the passengers at the security check point. All the items have been dropped into a box (array). Each item possesses a certain amount of risk items [0,1,2]. Here, the risk severity of the items represents an array [] of N number of integral values. The task here is to sort the items based on their levels of risk in the array. The risk values range from 0 to 2
Month: May 2024
TCS NQT Coding Question-3: Number of Sundays
Problem Statement: Ankit is always excited about sunday. It is favourite day, when he used to play all day And goes to cycling with his group. So every time when the months starts he counts the number of sundays he will get to enjoy. Considering the month can start with any day, be it Sun, Mon…. Or so on. Count the number of Sundays Ankit will get within n number of days.
TCS NQT Coding Question-2: toggling all bits
Problem Statement: Joseph is learning digital logic subject which will be for his next semester. He usually tries to solve unit assignment problems before the lecture. Today he got one tricky question. The problem statement is “A positive integer has been given as an input.Convert decimal value to binary representation. Print the positive integer value after toggling all bits”.
TCS NQT Coding Question-1: Factory empty packets
Problem Statement: A factory is packing candies into the packets. The candies packets here represent an array of N number of integer values. The task is to find the empty packets(0) of candies and push it to the end of the conveyor belt(array).
Python | How to read a csv file using pandas?
CSV (Comma-Separated Values) files are a popular format for storing tabular data, and Python’s Pandas library provides powerful tools for reading, manipulating, and analyzing such data. In this article, we’ll explore how to read CSV files using Pandas, covering different cases and options to efficiently load data into a pandas DataFrame.
Python | Introduction to numpy
NumPy, short for Numerical Python, is a open-source library in Python for numerical computation. It provides a powerful array object and a collection of functions for manipulating and processing arrays efficiently. NumPy is widely used in fields such as data science, machine learning, signal processing, and scientific computing. In this article, we’ll explore the basics of NumPy, its key features, and how it revolutionizes numerical computation in Python.
Python | Introduction to pandas
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.