10+ Python Numpy 使い方 Ideas

Python NumPy Tutorial NumPy ndarray & NumPy Array DataFlair from data-flair.training

Introduction

Python Numpy is a powerful numerical computing library that allows users to perform complex mathematical operations with ease. Numpy stands for Numerical Python and is widely used in data science, machine learning, and scientific computing. In this article, we will explore the basic usage of Python Numpy and how it can be used to simplify mathematical operations.

Installation

Before we dive into the usage of Numpy, we first need to install it. The easiest way to install Numpy is by using pip, a package manager for Python. To install Numpy using pip, simply open your terminal and type the following command:

pip install numpy

Numpy Arrays

One of the main features of Numpy is its ability to handle arrays. Arrays are similar to lists in Python, but with the added functionality of being able to perform mathematical operations on them. To create an array in Numpy, we simply use the following code:

import numpy as np

my_array = np.array([1, 2, 3])

Numpy Operations

Once we have created our Numpy array, we can perform various mathematical operations on it. For example, we can add, subtract, multiply, and divide arrays. To add two arrays together, we simply use the following code:

import numpy as np

array_1 = np.array([1, 2, 3])

array_2 = np.array([4, 5, 6])

result = array_1 + array_2

Numpy Functions

In addition to basic mathematical operations, Numpy also provides a variety of mathematical functions that can be used to perform more complex operations. For example, we can use the sin function to calculate the sine of an array. To use the sin function, we simply use the following code:

import numpy as np

my_array = np.array([0, 1, 2, 3])

result = np.sin(my_array)

Numpy Indexing

Numpy arrays can also be indexed, which allows us to access specific elements in the array. To access a specific element in the array, we simply use the following code:

import numpy as np

my_array = np.array([1, 2, 3])

result = my_array[1]

Numpy Slicing

In addition to indexing, Numpy also allows us to slice arrays. Slicing allows us to extract a specific portion of an array. To slice an array, we simply use the following code:

import numpy as np

my_array = np.array([1, 2, 3, 4, 5])

result = my_array[1:3]

Numpy Broadcasting

Another useful feature of Numpy is broadcasting. Broadcasting allows us to perform mathematical operations on arrays of different shapes. For example, if we have an array with a shape of (3, 3) and another array with a shape of (1, 3), we can add them together using broadcasting. To perform broadcasting, we simply use the following code:

import numpy as np

array_1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

array_2 = np.array([1, 2, 3])

result = array_1 + array_2

Conclusion

In this article, we have explored the basic usage of Python Numpy and how it can be used to simplify mathematical operations. We have covered topics such as creating arrays, performing mathematical operations, using mathematical functions, indexing and slicing arrays, and broadcasting. With these tools at your disposal, you can perform complex mathematical operations with ease in Python.