Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
The problem statement includes counting the odd numbers in N−th row of Pascal’s triangle. A pascal’s triangle is a triangular array where each row represents the binomial coefficients in the expansion of binomial expression. The Pascal’s triangle is demonstrated as below: 1 1 ... Read More
The problem statement includes printing the first N terms of the Moser−de Bruijn Sequence where N will be given in the user input. The Moser−de Bruijn sequence is a sequence consisting of integers which are nothing but the sum of the different powers of 4 i.e. 1, 4, 16, 64 and so on. The first few numbers of the sequence include 0, 1, 4, 5, 16, 17, 20, 21, 64....... The sequence always starts with zero followed by the sum of different powers of 4 such as $\mathrm{4^{0}}$ i.e $\mathrm{4^{1}\:i.e\:4, }$ then sum of $\mathrm{4^{0}\:and\:4^{1}\:i.e\:5}$ and so on. In this ... Read More
The acquisition of integer input holds immense significance in various programming tasks, and the Python programming language offers a plethora of techniques to achieve this goal. This article embarks on an insightful journey to explore diverse methodologies for acquiring integer input in Python, focusing on the following strategies: Unveiling the Potential of the `input()` Function and `int()` Type Conversion Harnessing the Versatility of the `map()` Function Unearthing Integer Input from File Sources Attaining Integer Input via Command Line Arguments Method 1: ... Read More
When it comes to manipulating NumPy arrays, there might be instances where you want to interchange the positions of two columns. In this article, we delve into four distinct techniques to exchange columns in a given NumPy array: utilizing advanced indexing, employing NumPy indexing, harnessing the np.swapaxes function, and leveraging direct assignment. We will comprehend these methods through illustrative examples. Method 1: Exploiting Advanced Indexing Unleashing the potential of advanced indexing, you open the capability to reshape the course of action of measurements inside a NumPy cluster, much obliged to a choosy curated program of column indices. ... Read More
When working with NumPy arrays, you may encounter small numbers represented in scientific notation. Although this compact representation is advantageous, deciphering or comparing values can be arduous. This guide delves into four distinct techniques to abate scientific notation usage for diminutive numbers in NumPy arrays: employing numpy.vectorize alongside string formatting, utilizing numpy.ndarray.round, leveraging string formatting, and harnessing numpy.set_printoptions. Examples will elucidate these methodologies, discussing pros and cons, and providing an all-encompassing comprehension of each approach. Method 1: Using numpy.vectorize with string formatting numpy.vectorize function, when combined with string formatting, can suppress scientific notation in NumPy arrays. This approach ... Read More
XML (eXtensible Markup Language) stands tall as a widely embraced format for storing and exchanging structured information. In the realm of efficient data storage and retrieval, MySQL has earned its reputation as a go-to relational database management system (RDBMS). Python, blessed with its versatile libraries, presents an exquisite union for seamlessly handling XML and MySQL. Embark on a journey with us as we dive into the art of storing XML data in a MySQL database using Python, unraveling each step with intricacy and flair. Step 1: Importing the Essential Libraries Let us kickstart our endeavor by importing the ... Read More
While working with Pandas DataFrames, situations may arise where arithmetic operations between attributes are necessary. One such operation is deducting two attributes. In this guide, we will delve into three distinct techniques to deduct two attributes in a Pandas DataFrame: employing the `sub` method, utilizing the `apply` method combined with a lambda function, and leveraging the `subtract` function. Examples will aid in understanding these approaches. Method 1: Employing the `sub` method The `sub` method is an intrinsic Pandas function that facilitates direct deduction of one attribute from another. This technique is straightforward and effective for performing deductions between ... Read More
In the vast expanse of data exploration, the art of standardization, sometimes referred to as feature scaling, assumes a paramount role as a preparatory step. It involves the transformation of disparate data elements into a harmonized range or scale, enabling fair analysis and comparison. Python's extraordinary library, Pandas, seamlessly facilitates this endeavor. Picture Pandas DataFrames as two-dimensional, ever-shifting, heterogeneous tabular data arrays, meticulously crafted to streamline the manipulation of data. With intuitive syntax and dynamic capabilities, it has emerged as the structure of choice for data enthusiasts worldwide. Let us delve deeper into the methods we can employ to ... Read More
The vast universe of Python includes a shining constellation named Pandas. Recognized globally for its might in data management and manipulation, it empowers data analysts with tools that act as an extension of their thoughts, transforming ideas into reality. The crux of this discussion lies in a particular feature of Pandas, the fusion of DataFrames along an axis. When the challenge is to blend information from diverse origins or conglomerate data for a comprehensive analysis, Pandas offers a basket of functions like concat(), append(), and merge(). The onus is on us to pick the tool that aligns with our ... Read More
Embarking on the vast domains of machine learning and data science, one encounters tasks that might appear inconsequential but hold a crucial position in the broader perspective. One such vital task is the division of data into training and validation sets - a foundational step for creating an effective predictive model. Scikit-learn, a prominent Python library for machine learning, boasts a versatile function, train_test_split(), crafted to address this task with remarkable ease. This treatise aims to steer you through the process of partitioning your data using scikit-learn's train_test_split() function. Syntax from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = ... Read More