一.基本数据类型
整数:int
字符串:str(注:\t等于一个tab键)
布尔值: bool
列表:list
列表用[]
元祖:tuple
元祖用()
字典:dict
注:所有的数据类型都存在想对应的类列里,元祖和列表功能一样,列表可以修改,元祖不能修改。
二.列表所有数据类型:
基本操作:
索引,切片,长度,包含,循环
class tuple(object): """ tuple() -> empty tuple tuple(iterable) -> tuple initialized from iterable's items If the argument is a tuple, the return value is the same object. """ def count(self, value): # real signature unknown; restored from __doc__ """ T.count(value) -> integer -- return number of occurrences of value """ (T.count(价值)- >整数,返回值的出现次数) return 0 def index(self, value, start=None, stop=None): # real signature unknown; restored from __doc__ """ T.index(value, [start, [stop]]) -> integer -- return first index of value. Raises ValueError if the value is not present. """ (T。指数(价值,[开始,[不要]])- >整数,返回第一索引值。提出了ValueError如果不存在的价值。) return 0 def __add__(self, *args, **kwargs): # real signature unknown """ Return self+value. """ pass def __contains__(self, *args, **kwargs): # real signature unknown """ Return key in self. """ pass def __eq__(self, *args, **kwargs): # real signature unknown """ Return self==value. """ pass def __getattribute__(self, *args, **kwargs): # real signature unknown """ Return getattr(self, name). """ pass def __getitem__(self, *args, **kwargs): # real signature unknown """ Return self[key]. """ pass def __getnewargs__(self, *args, **kwargs): # real signature unknown pass def __ge__(self, *args, **kwargs): # real signature unknown """ Return self>=value. """ pass def __gt__(self, *args, **kwargs): # real signature unknown """ Return self>value. """ pass def __hash__(self, *args, **kwargs): # real signature unknown """ Return hash(self). """ pass def __init__(self, seq=()): # known special case of tuple.__init__ """ tuple() -> empty tuple tuple(iterable) -> tuple initialized from iterable's items If the argument is a tuple, the return value is the same object. # (copied from class doc) """ pass def __iter__(self, *args, **kwargs): # real signature unknown """ Implement iter(self). """ pass def __len__(self, *args, **kwargs): # real signature unknown """ Return len(self). """ pass def __le__(self, *args, **kwargs): # real signature unknown """ Return self<=value. """ pass def __lt__(self, *args, **kwargs): # real signature unknown """ Return self<value. """ pass def __mul__(self, *args, **kwargs): # real signature unknown """ Return self*value.n """ pass @staticmethod # known case of __new__ def __new__(*args, **kwargs): # real signature unknown """ Create and return a new object. See help(type) for accurate signature. """ pass def __ne__(self, *args, **kwargs): # real signature unknown """ Return self!=value. """ pass def __repr__(self, *args, **kwargs): # real signature unknown """ Return repr(self). """ pass def __rmul__(self, *args, **kwargs): # real signature unknown """ Return self*value. """ pass
三.所有元祖数据类型举例
#count 用于计算元素出现的个数 name_tuple = ("zhangyanlin","suoning","nick") print(name_tuple.count('zhangyanlin')) #index获取指定元素的指定位置 name_tuple = ("zhangyanlin","suoning","nick") print(name_tuple.index('zhangyanlin'))
四.索引
name_tuple = ("zhangyanlin","suoning","nick") print(name_tuple[1])
五.切片
#取出第一位到最后一位减1的元素 name_tuple = ("zhangyanlin","suoning","nick") print(name_tuple[0:len(name_tuple)-1])
六.总长度len
#取出最后一位减1的元素 name_tuple = ("zhangyanlin","suoning","nick") print(name_tuple[len(name_tuple)-1])
七.for循环
name_tuple = ("zhangyanlin","suoning","nick") for i in name_tuple: print(i)
那么使用 tuple 有什么好处呢?
Tuple 比 list 操作速度快。如果您定义了一个值的常量集,并且唯一要用它做的是不断地遍历它,请使用 tuple 代替 list。
如果对不需要修改的数据进行 “写保护”,可以使代码更安全。使用 tuple 而不是 list 如同拥有一个隐含的 assert 语句,说明这一数据是常量。如果必须要改变这些值,则需要执行 tuple 到 list 的转换 (需要使用一个特殊的函数)。
还记得我说过 dictionary keys 可以是字符串,整数和 “其它几种类型”吗?Tuples 就是这些类型之一。Tuples 可以在 dictionary 中被用做 key,但是 list 不行。实际上,事情要比这更复杂。Dictionary key 必须是不可变的。Tuple 本身是不可改变的,但是如果您有一个 list 的 tuple,那就认为是可变的了,用做 dictionary key 就是不安全的。只有字符串、整数或其它对 dictionary 安全的 tuple 才可以用作 dictionary key。
Tuples 可以用在字符串格式化中,我们会很快看到。

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

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

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

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

The article discusses popular Python libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Django, Flask, and Requests, detailing their uses in scientific computing, data analysis, visualization, machine learning, web development, and H

This tutorial builds upon the previous introduction to Beautiful Soup, focusing on DOM manipulation beyond simple tree navigation. We'll explore efficient search methods and techniques for modifying HTML structure. One common DOM search method is ex

This article guides Python developers on building command-line interfaces (CLIs). It details using libraries like typer, click, and argparse, emphasizing input/output handling, and promoting user-friendly design patterns for improved CLI usability.


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

Dreamweaver CS6
Visual web development tools

DVWA
Damn Vulnerable Web App (DVWA) is a PHP/MySQL web application that is very vulnerable. Its main goals are to be an aid for security professionals to test their skills and tools in a legal environment, to help web developers better understand the process of securing web applications, and to help teachers/students teach/learn in a classroom environment Web application security. The goal of DVWA is to practice some of the most common web vulnerabilities through a simple and straightforward interface, with varying degrees of difficulty. Please note that this software

WebStorm Mac version
Useful JavaScript development tools

Atom editor mac version download
The most popular open source editor

MinGW - Minimalist GNU for Windows
This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.
