Python中的repr()函数
Python 有办法将任意值转为字符串:将它传入repr() 或str() 函数。
函数str() 用于将值转化为适于人阅读的形式,而repr() 转化为供解释器读取的形式。
在python的官方API中这样解释repr()函数:
aaarticlea/png;base64,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" alt="data:image/png;base64,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" />
repr()函数得到的字符串通常可以用来重新获得该对象,repr()的输入对python比较友好。通常情况下obj==eval(repr(obj))这个等式是成立的。
>>> obj='I love Python'
>>> obj==eval(repr(obj))
True
而str()函数这没有这个功能
>>> obj==eval(str(obj))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<string>", line 1
I love Python
^
repr()函数:
>>>repr([0,1,2,3])
'[0,1,2,3]'
>>> repr('Hello')
"'Hello'" >>> str(1.0/7.0)
'0.142857142857'
>>> repr(1.0/7.0)
'0.14285714285714285'
Python中的repr()函数的更多相关文章
- python --- Python中的callable 函数
python --- Python中的callable 函数 转自: http://archive.cnblogs.com/a/1798319/ Python中的callable 函数 callabl ...
- python中使用zip函数出现<zip object at 0x02A9E418>
在Python中使用zip函数,出现<zip object at 0x02A9E418>错误的原因是,你是用的是python2点多的版本,python3.0对python做了改动 zip方 ...
- [转载]python中multiprocessing.pool函数介绍
原文地址:http://blog.sina.com.cn/s/blog_5fa432b40101kwpi.html 作者:龙峰 摘自:http://hi.baidu.com/xjtukanif/blo ...
- Python 中的isinstance函数
解释: Python 中的isinstance函数,isinstance是Python中的一个内建函数 语法: isinstance(object, classinfo) 如果参数object是cla ...
- Python中的map()函数和reduce()函数的用法
Python中的map()函数和reduce()函数的用法 这篇文章主要介绍了Python中的map()函数和reduce()函数的用法,代码基于Python2.x版本,需要的朋友可以参考下 Py ...
- python中multiprocessing.pool函数介绍_正在拉磨_新浪博客
python中multiprocessing.pool函数介绍_正在拉磨_新浪博客 python中multiprocessing.pool函数介绍 (2010-06-10 03:46:5 ...
- 举例详解Python中的split()函数的使用方法
这篇文章主要介绍了举例详解Python中的split()函数的使用方法,split()函数的使用是Python学习当中的基础知识,通常用于将字符串切片并转换为列表,需要的朋友可以参考下 函数:sp ...
- python中的生成器函数是如何工作的?
以下内容基于python3.4 1. python中的普通函数是怎么运行的? 当一个python函数在执行时,它会在相应的python栈帧上运行,栈帧表示程序运行时函数调用栈中的某一帧.想要获得某个函 ...
- python中的map()函数
MapReduce的设计灵感来自于函数式编程,这里不打算提MapReduce,就拿python中的map()函数来学习一下. 文档中的介绍在这里: map(function, iterable, .. ...
随机推荐
- SQL优化-索引
(一)深入浅出理解索引结构 实际上,您可以把索引理解为一种特殊的目录.微软的SQL SERVER提供了两种索引:聚集索引(clustered index,也称聚类索引.簇集索引)和非聚集索引(nonc ...
- Python 3.x print 小结
Python 思想: “一切都是对象!” input("Press Enter") 就可以让程序运行完后停一下 输出的 print 函数总结: 1. 字符串和数值类型可以直接输出 ...
- python数据分析入门——matplotlib的中文显示问题&最小二乘法
正在学习<用python做科学计算>,在练习最小二乘法时遇到matplotlib无法显示中文的问题.查资料,感觉动态的加上几条语句是最好,这里贴上全部的代码. # -*- coding: ...
- php 安装composer
右击我的电脑 再属性 再高级 再环境变量 再系统变量里有个path 双击打开来 把你的PHP路径 加个分号再前面 添加进去就OK了 1.http://www.th7.cn/Program/php/20 ...
- “PEDIY CrackMe 2007” 下载地址
工欲善其事,必先利其器.本专辑收集了看雪论坛『CrackMe & ReverseMe』版块2004年4月-2006年12月31期间所有的CrackMe和ReverseMe,共350余个. 下载 ...
- Magento的价格去掉小数点
Magento的默认情况,价格后面是有小数点的,我们来看下如何正确的来去掉小数点. 1.复制如下路径的文件 app/code/core/Mage/Directory/Model/Currency.ph ...
- Day04_JAVA语言基础第四天
1.循环(掌握) 1.什么时候使用(理解) 如果我们发现有很多重复内容的时候就要使用循环 2.好处(理解) 让我们的代码看起来更精炼了 3.循环的组成(理解) 1 初始化条件:一般定义的是一个初始变量 ...
- python xlrd和xlwtxlutils包的使用
安装xlrd读取模块 首先去官网或者pypi下载安装包,然后解压到任意目录 在dos下进入该目录,执行python setup.py install安装 验证成功进入python,执行import 包 ...
- (实用篇)PHP缓存类完整实例
本文完整描述了一个简洁实用的PHP缓存类,可用来检查缓存文件是否在设置更新时间之内.清除缓存文件.根据当前动态文件生成缓存文件名.连续创建目录.缓存文件输出静态等功能.对于采用PHP开发CMS系统来说 ...
- Python 新手常犯错误(第一部分)
转载自:http://blog.jobbole.com/42706/ 在之前几个月里,我教一些不了解Python的孩子来慢慢熟悉这门语言.渐渐地,我发现了一些几乎所有Python初学者都会犯的错误,所 ...