Priyanshu Singh

One such area where several libraries have been created in Python is data visualization in which Matplotlib is the most often used option. Although Matplotlib was first designed to plot 2D charts like histograms, bar charts, scatter plots, line plots, etc., it has now expanded its functionality to include 3D charting modules. When displaying data […]

In our daily life when we play a ludo game and throw a dice any number can come between one to six. The number will be totally random we can not predict it. If we knew then definitely it would be no fun to us in playing those games. So that is what randomization is.

The numpy.linspace() function in Python’s NumPy library creates an array of evenly spaced values over a specified range. It’s particularly useful when we need to generate a set of data points for mathematical functions or plots, and we want precise control over the number of points within a specified interval. In this article, we will

NumPy is one of the popular Python libraries that offers a wide variety of arithmetic and numerical functions. This article aims to clarify how to perform addition operations on NumPy arrays using the numpy.add() and addition operator. We will look at both ways in detail so that you can use the one that best suits

Numpy provides us with many functions to perform different types of operations on Numpy arrays, one of them is the numpy.min() function. numpy.min() function is used to find the minimum value in arrays. In this article, we will look at three ways to find the minimum element from an array using this function. Let’s first

Whenever we deal with date and time information stored as strings, converting it to DateTime objects can prove to be very useful. For instance, when we need to import data from sources like CSV files, JSON files, or databases which may be stored as strings, converting to DateTime objects allows us to manipulate the data

An array may have one or more dimensions for storing the data and sometimes we need to manipulate or change the structure of our data, i.e., the dimension of the array containing data, for this purpose, NumPy provided us with numpy.reshape() function for reshaping an array. The most common use case of reshaping the arrays

The numpy.eye() function in the NumPy library is used to create a 2-D identity matrix with ones on the main diagonal (from the top-left to the bottom-right) and zeros elsewhere. In this article, we’ll understand look at five examples of creating an identity matrix by using numpy.eye() function. Let’s first look at this function’s syntax,

The numpy.zeros() function in Python’s NumPy library creates an array filled with zeros. This function is particularly useful when we need to initialize an array with zeros before populating it with actual data. It’s commonly used in various numerical and scientific computing tasks. In this article, we will understand Python numpy.zeros() function, its syntax, and

The numpy.arange() function in Python’s NumPy library is used to generate arrays of evenly spaced values within a specified range. It’s similar to Python’s built-in range() function but produces a NumPy array as output. In this article, we will understand Python numpy.arange() function, its syntax, and learn how to use it with the help of