Home Backend Development Python Tutorial How to install Python 3.7 on Debian 9?

How to install Python 3.7 on Debian 9?

Apr 09, 2019 am 10:17 AM

Python is one of the most popular programming languages ​​in the world and is a versatile programming language; it is simple and easy to learn, and we can use it to complete any operation we want, write small scripts, build games, develop websites, etc. wait. Python 3.7 is the latest major version of the Python language. The following article will introduce how to install the Python 3.7 version on the Debian system. I hope it will be helpful to everyone.

How to install Python 3.7 on Debian 9?

Building Python 3.7 on Debian is a relatively simple process and shouldn't take long.

1. First install the packages required to build the Python source:

$ sudo apt update
$ sudo apt install build-essential zlib1g-dev libncurses5-dev libgdbm-dev libnss3-dev libssl-dev libreadline-dev libffi-dev wget
Copy after login

2. Use the curl command from python Download the source code of the required version on the download page

The following command is to download the Python 3.7.3 version:

$ curl -O https://www.python.org/ftp/python/3.7.3/Python-3.7.3.tar.xz
Copy after login

3. After the download is completed , use the tar command to decompress:

$ tar -xf Python-3.7.3.tar.xz
Copy after login

4. Navigate to the python source directory and run the configure script

The configureThe script will perform a number of checks to ensure that all dependencies on the system are present.

$ cd Python-3.7.3
$ ./configure --enable-optimizations
Copy after login

Description: The --enable-optimizations option will optimize the Python binary by running multiple tests, which will make the build process slower.

5. Run make to start the build process:

$ make -j 8
Copy after login

In order to shorten the build time, you need to modify it according to the processor -j flag. If you don't know the core number of your processor, you can find it by typing nproc. This article takes 8 cores as an example, and uses the -j8 flag.

6. Install the python binary file

After the build is completed, run the following command as a user with sudo access to install the python binary file :

$ sudo make altinstall
Copy after login

Note: Do not use the standard make install as it will overwrite the default python3 system d binary.

7. Verification

At this point, Python 3.7 has been installed on the Debian system and can be used. We can enter the following command to verify it:

$ python3.7 --version
Copy after login

Output

Python 3.7.3
Copy after login

Recommended related video tutorials: "Python3 Tutorial"

The above is The entire content of this article is hoped to be helpful to everyone's study. For more exciting content, you can pay attention to the relevant tutorial columns of the PHP Chinese website! ! !

The above is the detailed content of How to install Python 3.7 on Debian 9?. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Article

R.E.P.O. Energy Crystals Explained and What They Do (Yellow Crystal)
2 weeks ago By 尊渡假赌尊渡假赌尊渡假赌
Repo: How To Revive Teammates
1 months ago By 尊渡假赌尊渡假赌尊渡假赌
Hello Kitty Island Adventure: How To Get Giant Seeds
4 weeks ago By 尊渡假赌尊渡假赌尊渡假赌

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

How to Use Python to Find the Zipf Distribution of a Text File How to Use Python to Find the Zipf Distribution of a Text File Mar 05, 2025 am 09:58 AM

This tutorial demonstrates how to use Python to process the statistical concept of Zipf's law and demonstrates the efficiency of Python's reading and sorting large text files when processing the law. You may be wondering what the term Zipf distribution means. To understand this term, we first need to define Zipf's law. Don't worry, I'll try to simplify the instructions. Zipf's Law Zipf's law simply means: in a large natural language corpus, the most frequently occurring words appear about twice as frequently as the second frequent words, three times as the third frequent words, four times as the fourth frequent words, and so on. Let's look at an example. If you look at the Brown corpus in American English, you will notice that the most frequent word is "th

How Do I Use Beautiful Soup to Parse HTML? How Do I Use Beautiful Soup to Parse HTML? Mar 10, 2025 pm 06:54 PM

This article explains how to use Beautiful Soup, a Python library, to parse HTML. It details common methods like find(), find_all(), select(), and get_text() for data extraction, handling of diverse HTML structures and errors, and alternatives (Sel

Image Filtering in Python Image Filtering in Python Mar 03, 2025 am 09:44 AM

Dealing with noisy images is a common problem, especially with mobile phone or low-resolution camera photos. This tutorial explores image filtering techniques in Python using OpenCV to tackle this issue. Image Filtering: A Powerful Tool Image filter

How to Perform Deep Learning with TensorFlow or PyTorch? How to Perform Deep Learning with TensorFlow or PyTorch? Mar 10, 2025 pm 06:52 PM

This article compares TensorFlow and PyTorch for deep learning. It details the steps involved: data preparation, model building, training, evaluation, and deployment. Key differences between the frameworks, particularly regarding computational grap

Introduction to Parallel and Concurrent Programming in Python Introduction to Parallel and Concurrent Programming in Python Mar 03, 2025 am 10:32 AM

Python, a favorite for data science and processing, offers a rich ecosystem for high-performance computing. However, parallel programming in Python presents unique challenges. This tutorial explores these challenges, focusing on the Global Interprete

How to Implement Your Own Data Structure in Python How to Implement Your Own Data Structure in Python Mar 03, 2025 am 09:28 AM

This tutorial demonstrates creating a custom pipeline data structure in Python 3, leveraging classes and operator overloading for enhanced functionality. The pipeline's flexibility lies in its ability to apply a series of functions to a data set, ge

Serialization and Deserialization of Python Objects: Part 1 Serialization and Deserialization of Python Objects: Part 1 Mar 08, 2025 am 09:39 AM

Serialization and deserialization of Python objects are key aspects of any non-trivial program. If you save something to a Python file, you do object serialization and deserialization if you read the configuration file, or if you respond to an HTTP request. In a sense, serialization and deserialization are the most boring things in the world. Who cares about all these formats and protocols? You want to persist or stream some Python objects and retrieve them in full at a later time. This is a great way to see the world on a conceptual level. However, on a practical level, the serialization scheme, format or protocol you choose may determine the speed, security, freedom of maintenance status, and other aspects of the program

Mathematical Modules in Python: Statistics Mathematical Modules in Python: Statistics Mar 09, 2025 am 11:40 AM

Python's statistics module provides powerful data statistical analysis capabilities to help us quickly understand the overall characteristics of data, such as biostatistics and business analysis. Instead of looking at data points one by one, just look at statistics such as mean or variance to discover trends and features in the original data that may be ignored, and compare large datasets more easily and effectively. This tutorial will explain how to calculate the mean and measure the degree of dispersion of the dataset. Unless otherwise stated, all functions in this module support the calculation of the mean() function instead of simply summing the average. Floating point numbers can also be used. import random import statistics from fracti

See all articles