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Numpy Articles
Page 2 of 81
Finding the Number of Weekdays of a Given Month in NumPy
Numpy is an open-source library in Python used for numerical computing, scientific computing, data analysis, and machine learning applications. This library has powerful array methods and tools to perform vector and matrix operations. You must install the NumPy module manually since it is not a component of the default Python library. The Python package manager pip can be used to accomplish this by executing the following command − pip install numpy We will use NumPy’s busday_count() function to calculate the required count of weekdays. This will calculate the number of business days (Monday to Friday) between 2 specific dates. ...
Read MoreFind the size of numpy Array
Numpy is often used along with packages such as SciPy and matplotlib in python. Additionally, arrays in Numpy are faster compared to lists in Python due to which this module is extensively used for complex mathematical algorithms and problems. It has various functions, and methods, to make our task easy and simple for matrix operations. In Numpy arrays there are two types of array − Single Dimensional Array Multi-Dimensional Array Single Dimensional Array (1D array) The datatypes of the elements stored in the array are - strings, integers, boolean or floating points. These arrays are called single or ...
Read MoreGenerate a Hermite_e series with given roots using NumPy in Python
Hermite polynomials are a set of orthogonal polynomials that are useful in a variety of mathematical applications. They are commonly used in the solution of differential equations, probability theory, and quantum mechanics. The Hermite_e series is a variation of the Hermite polynomial that is used to represent a function in terms of its roots. In this article, we will discuss how to generate a Hermite_e series with given roots using NumPy in Python. Installation and Syntax NumPy is a Python library that provides support for numerical operations and it can be installed using pip and imported into Python using the ...
Read MoreFinding the k smallest values of a NumPy array
Finding the k smallest values of a NumPy arra Installation and Syntax Pip, Python's package installer, is often used to install NumPy. pip install numpy The following function may be used to identify the k NumPy array elements with the smallest values − np.sort(array)[:k] This returns the first k items of the sorted array after sorting it in ascending order. The array may be be sorted using the alternative syntax shown below, which will retrieve the last k entries and sort the array in descending order − np.sort(array)[::-1][:k] Algorithm The algorithm for finding the k smallest ...
Read MoreFind the sum and product of a NumPy array elements
A Python package called NumPy is employed in scientific computing and to handle large-scale numerical data. It provides support for multi-dimensional arrays and matrices, as well as a large library of mathematical functions to manipulate them. In this tutorial, we will focus on two of the most commonly used NumPy functions: sum() and prod(). These functions are used to calculate the sum and product of all the elements in a NumPy array, respectively. Installation Get the numpy package installed using pip inside your terminal pip install numpy Import it as highlighted after successful installation − import numpy as np ...
Read MoreWhat does -1 Mean in Numpy Reshape?
NumPy is a Python library for numerical computing that provides efficient array operations, and numpy.reshape() is a function used to change the shape of an array, with -1 indicating an inferred dimension. When working with arrays, we frequently get into instances where we need to modify the shape of the array, but doing so requires copying the data first, then arranging it into the required shape which is time taking. Fortunately, Python has a function called reshape() that may help with this. Example 1: Find Unknown Dimensions in Numpy We are only allowed to have one "unknown" dimension while we ...
Read MoreHow to Conduct a Paired Samples T-Test
Introduction In machine learning and data science, many statistical tests are used to compare and find the differences between variables or the features of the data. These tests are mainly hypothesis tests where the conditions are defined, and according to the different tests being conducted, the relationship between variables is assumed. The t-test is also a type of statistical test that is used to compare the means of different groups of the categorical variable. In this article, we will discuss the paired t-test, which is an extension or a type of t-test used in statistics, and we will ...
Read MoreHow to iterate over Columns in Numpy
The name Numpy stands for Numerical Python. It helps to solve the mathematical operation on an array. In Python, we have some built-in functions such as nditor(), T(), array(), shape[], and apply_along_axis() that will be used to iterate over columns in Numpy. Syntax The following syntax is used in the examples- nditer() The NumPy module contains this built-in function for iterator objects. T() This function refers to transposing of the index in a column dataframe. array() This is a built-in function in Python that creates the array. An array is defined by collecting the items of ...
Read MoreHow to normalize a NumPy array so the values range exactly between 0 and 1?
NumPy is a powerful library in Python for numerical computing that provides an array object for the efficient handling of large datasets. Often, it is necessary to normalize the values of a NumPy array to ensure they fall within a specific range. One common normalization technique is to scale the values between 0 and 1. In this article, we will learn how to normalize a NumPy array so the values range exactly between 0 and 1. We will see the different approaches that can be used to achieve this using NumPy, along with syntax and complete examples. Approaches There are ...
Read MoreHow to Create a Sequence of Linearly Increasing Values with NumPy Arrange?
NumPy is a Python library widely used for numerical computations and scientific data analysis. One of the most commonly used functions of NumPy is ‘numpy.arange()’, which creates a sequence of linearly increasing values with a given start, stop, and step size. In this tutorial, we'll examine how to use ‘numpy.arange()’ to produce a sequence of linearly increasing values. We will illustrate three examples of linearly arranged values with different steps. In this tutorial, we will learn to create a sequence of linearly increasing values with a NumPy arrange. We will be using NumPy, which is a famous python library. ...
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