python for data analysis

这篇博客介绍了Python的基础语法,包括如何运行和退出Python解释器,以及is和==的区别。接着讲解了Python的数据结构,如不可变的元组和可变的列表,详细阐述了它们的特点和操作方法。最后,提到了常见的排序操作。

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一、python基本语法

  • 运行python程序:$ python 程序名. py
  • 退出Python解释器返回终端,输入exit()或按Ctrl-D
  • 中断程序按Ctrl-C
  • 从同目录下引用另一个py程序,可以使用import+as重命名
import 程序名 as 重命名
res = 重命名.程序中函数名(函数参数)  #调用函数
  • is 、is not、==
    1)is和is not常用来判断一个变量是否为None:a is None—True
    2)是否引用同一个变量
a = [1,2,3]
b = a
c = list(a)
a is b  #True
a is c  #False,list总是创建一个新的Python列表(即复制)
a == c  #True
  • int可以存储任意大的数
  • python整除问题
3/2  #1.5
3//2 #1
  • 如果字符串中包含许多反斜杠,但没有特殊字符,可以在字符串前面加一个r,表明字符就是它自身—用于目录引用

二、python数据结构

1.元组
1)元组是一个固定长度,不可改变的序列对象,从0开始

#创建元组
res = (
这本书主要是用 pandas 连接 SciPy 和 NumPy,用pandas做数据处理是Pycon2012上一个很热门的话题。另一个功能强大的东西是Sage,它将很多开源的软件集成到统一的 Python 接口。, Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you’ll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language., Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It’s ideal for analysts new to Python and for Python programmers new to scientific computing., Use the IPython interactive shell as your primary development environment, Learn basic and advanced NumPy (Numerical Python) features, Get started with data analysis tools in the pandas library, Use high-performance tools to load, clean, transform, merge, and reshape data, Create scatter plots and static or interactive visualizations with matplotlib, Apply the pandas groupby facility to slice, dice, and summarize datasets, Measure data by points in time, whether it’s specific instances, fixed periods, or intervals, Learn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples
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