Problem Statement: A manufacturing company is manufacturing a new collection of curtains. The curtains are of two colors Red(R) and black (B). The curtains color is represented as a string(string) consisting of R’s and B’s of length N.Then, they are packed (substring) into L number of curtains in each box.The box with the maximum number of ‘Red’ (R) color curtains is labeled. The task here is to find the number of ‘Red’ color curtains in the labeled box.
Tag: python
TCS NQT Coding Question-6: Calculate Price Of The Item
Problem Statement: A Mega mart maintains a pricing format for all its products. A value N(integral code) is printed on each product. When the scanner reads the value code on the item, the product of all the digits in the code is the price of the item. The task here is to design the software such that given the code of any item the price should be computed for that.
TCS NQT Coding Question-5: Count of Elements
Problem Statement: Given an integer array Arr of size S the task is to find the count of elements whose value is greater than all of its left-hand elements.
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.
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.