Numpy is a foundational module for scientific computing in Python that provides high-performance multidimensional array objects and a series of functions to manipulate that array. List of high-quality authors
Installation and import.
First, make sure you have the numpy library installed. It can be installed via pip:
pip install numpy
In a Python script or interactive environment, use the following ** to import the numpy module:
import numpy as np
Creation of arrays.
numpy provides several ways to create arrays:
Define arrays directly:
arr = np.array([1, 2, 3, 4, 5])
Use npzeros creates an array padded with 0:
arr = np.zeros((3, 3)) creates a 3x3 zero matrix.
Use npones creates an array padded with 1:
arr = np.ones((2, 2)) creates a 2x2 matrix with all elements being 1
Count. Numpy supports a large number of arithmetic, such as addition, subtraction, multiplication, and division.
Here's an example of using numpy to do the math
Addition: a = nparray([1, 2, 3]) b = np.array([4, 5, 6]) c = a + b results in [5 7 9].
Subtraction: C = A - B The result is [-3 -3 -3].
Multiplication: c = a * b The result is [4 10 18].
Division: c = a b The result is [0.25 0.4 0.57142857]
Data manipulation and processing.
numpy provides a rich set of functions to handle array data, including indexing, slicing, reshaping, and sorting.
Here's an example:
Index: Gets the elements in the array by indexing. For example, get the first element: a[0].
Slicing: Gets a portion of an array by slicing. For example, get the first two elements: a[:2].
Reshape: Change the shape of an array. For example, reshape a one-dimensional array into a 2x3 two-dimensional array: areshape(2, 3)。
Summary. Numpy is a powerful library of scientific computing that provides many tools and functions to process and analyze data.
By learning and mastering NUMPY, we can perform scientific computing and data analysis more efficiently, providing strong support for our research and projects.