Variable is a new knowledge point that you will come into contact with when you first learn python, and it is also a concept that you need to be familiar with. Python is a dynamically typed language. Different types of values can be bound during the execution of assignment. This process is called variable assignment operation. The assignment also determines the variable type.
What is a variable
A lot of data will be used in the python learning process. In order to facilitate the operation, these data need to be represented by a simple name for convenience. Referenced in the following procedure. A variable is a name that represents a certain data (value).
How to define the operation of python variable assignment
The variable definition method is very simple:
>>> x = 5
This operation The process is assignment, which means assigning the integer number 5 to the variable x, and using the equal sign to connect the variable name and value. You can then use this new variable in expressions.
>>> References its type.
1. Variable names can include letters, numbers, and underscores, but numbers cannot be the beginning. For example: name1 is a legal variable name, but 1name is not. 2. System keywords cannot be used as variable names.
3. Except for underscores, other symbols cannot be used as variable names.4. Python variable names are case-sensitive, for example: name and Name are two variable names, not the same variable.
Related learning recommendations:
The above is the detailed content of How to define variables in python. For more information, please follow other related articles on the PHP Chinese website!

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

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

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

PDF files are popular for their cross-platform compatibility, with content and layout consistent across operating systems, reading devices and software. However, unlike Python processing plain text files, PDF files are binary files with more complex structures and contain elements such as fonts, colors, and images. Fortunately, it is not difficult to process PDF files with Python's external modules. This article will use the PyPDF2 module to demonstrate how to open a PDF file, print a page, and extract text. For the creation and editing of PDF files, please refer to another tutorial from me. Preparation The core lies in using external module PyPDF2. First, install it using pip: pip is P

This tutorial demonstrates how to leverage Redis caching to boost the performance of Python applications, specifically within a Django framework. We'll cover Redis installation, Django configuration, and performance comparisons to highlight the bene

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

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

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


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

SublimeText3 Linux new version
SublimeText3 Linux latest version

Notepad++7.3.1
Easy-to-use and free code editor

Atom editor mac version download
The most popular open source editor

WebStorm Mac version
Useful JavaScript development tools

ZendStudio 13.5.1 Mac
Powerful PHP integrated development environment
