浅谈Python及其强大的库函数pandas（1）

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　　浅谈Python及其强大的库函数——pandas（1）
　　关于Python
　　Python由荷兰数学和计算机科学研究学会的Guido van Rossum 于1990 年代初设计，作为一门叫做ABC语言的替代品。Python提供了高效的高级数据结构，还能简单有效地面向对象编程。Python语法和动态类型，以及解释型语言的本质，使它成为多数平台上写脚本和快速开发应用的编程语言，随着版本的不断更新和语言新功能的添加，逐渐被用于独立的、大型项目的开发。
　　Python强大的库函数
　　Python拥有一个庞大，且还在不断拓展的标准库。它可以帮助处理各种工作，包括正则表达式、文档生成、单元测试、线程、数据库、网页浏览器、CGI、FTP、电子邮件、XML、XML-RPC、HTML、WAV文件、密码系统、GUI（图形用户界面）、Tk和其他与系统有关的操作。这被称作Python的＂功能齐全＂理念。除了标准库以外，还有许多其他高质量的库，如wxPython、Twisted和Python图像库等等。
　　关于＂pandas库函数＂
　　pandas 是基于NumPy 的一种库，它是为解决数据分析任务而创建的。Pandas 纳入了大量库和一些标准的数据模型，提供了高效地操作大型数据集所需的工具。pandas提供了大量能使我们快速便捷地处理数据的函数和方法。你很快就会发现，它是使Python成为强大而高效的数据分析环境的重要因素之一。下面我们来看一些pandas的用法。
　　1. groupby() 函数：
　　通过传递参数对DataFrame类型的数据进行某一种自己想要的方式分类。这种分类可以更方便自己提取，观察，构建数据。
　　2.pivot_table()函数：
　　pivot_table()函数与groupby()作用大体相似，但它更类似于EXCEL中的数据透视表。
　　3.merge()函数
　　将两或多个DataFrame类型的数据合并成为一个新的DataFrame。（可以通过参数对融合的方式进行调节）
　　英文翻译
　　The English translation
　　＂Share interests, spread happiness, increase knowledge, leave beauty! Dear, this is LearningYard School. Today the editor brings you.：Python and its library functions:——Pandas
　　About the Python
　　Python was designed in the early 1990s by Guido van Rossum of the Dutch Institute for Mathematical and Computer Science research as an alternative to a language called ABC. Python provides efficient high-level data structures, as well as simple and efficient object-oriented programming. Python syntax and dynamic typing, as well as the nature of the interpreted language, make it a programming language for scripting and rapid application development on most platforms. As versions continue to be updated and new features are added to the language, it is increasingly being used for independent, large project development.
　　Python＂s powerful library functions
　　Python has a large and growing standard library. It can help with a variety of jobs, including regular expressions, document generation, unit testing, threading, databases, Web browsers, CGI, FTP, E-mail, XML, XML-RPC, HTML, WAV files, password systems, GUIs (graphical user interfaces), Tk, and other system-related operations. This is called Python＂s ＂full-featured＂ idea. In addition to the standard library, there are many other high-quality libraries, such as the wxPython, Twisted, and Python image libraries, to name a few.
　　About ＂Pandas＂
　　Pandas is a numpy-based library created to solve data analysis tasks. Pandas incorporates a large number of libraries and some standard data models to provide the tools needed to efficiently manipulate large data sets. Pandas provides a large number of functions and methods that allow us to work with data quickly and easily. You＂ll soon see that it＂s one of the things that makes Python a powerful and efficient environment for data analysis.
　　There are some expressions for pandas.
　　1. Groupby () :
　　Classify DataFrame data in a desired way by passing parameters.This classification can be more convenient to extract, observe and construct data.
　　2. Pivot_table () :
　　The pivot_table() function is similar to groupby() in general, but it is more similar to pivottables in EXCEL.
　　3. The merge () ：
　　To combine two or more DataFrame types into a new DataFrame. (The fusion mode can be adjusted through parameters)
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