使用Pandas 首先要import 相关模块

In [1]: import numpy as np
In [2]: import pandas as pd

对象创建

Series 是一维的标签化的数组，能够存储任何数据类型(integers, strings, floating point numbers, Python objects, etc.)。 轴标签（ is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). axis标签统称为索引，创建Series的基本方法

>>> s = pd.Series(data, index=index)

数据选取

选取单列会生成一个Series

In [23]: df['A']
Out[23]: 
2013-01-01    0.469112
2013-01-02    1.212112
2013-01-03   -0.861849
2013-01-04    0.721555
2013-01-05   -0.424972
2013-01-06   -0.673690
Freq: D, Name: A, dtype: float64

通过[]切片选取多行

In [24]: df[0:3]
Out[24]: 
                   A         B         C         D
2013-01-01  0.469112 -0.282863 -1.509059 -1.135632
2013-01-02  1.212112 -0.173215  0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929  1.071804

In [25]: df['20130102':'20130104']
Out[25]: 
                   A         B         C         D
2013-01-02  1.212112 -0.173215  0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929  1.071804
2013-01-04  0.721555 -0.706771 -1.039575  0.271860

通过标签选取

使用标签获取横截面

In [26]: df.loc[dates[0]]
Out[26]: 
A    0.469112
B   -0.282863
C   -1.509059
D   -1.135632
Name: 2013-01-01 00:00:00, dtype: float64

通过标签在多轴上选取

In [27]: df.loc[:, ['A', 'B']]
Out[27]: 
                   A         B
2013-01-01  0.469112 -0.282863
2013-01-02  1.212112 -0.173215
2013-01-03 -0.861849 -2.104569
2013-01-04  0.721555 -0.706771
2013-01-05 -0.424972  0.567020
2013-01-06 -0.673690  0.113648

显示标签切片，包括两个端点

In [28]: df.loc['20130102':'20130104', ['A', 'B']]
Out[28]: 
                   A         B
2013-01-02  1.212112 -0.173215
2013-01-03 -0.861849 -2.104569
2013-01-04  0.721555 -0.706771

缩小返回对象的尺寸

In [29]: df.loc['20130102', ['A', 'B']]
Out[29]: 
A    1.212112
B   -0.173215
Name: 2013-01-02 00:00:00, dtype: float64

获取标量

In [30]: df.loc[dates[0], 'A']
Out[30]: 0.46911229990718628

为了快速访问标量(等价于之前的方法)

In [31]: df.at[dates[0], 'A']
Out[31]: 0.46911229990718628

通过位置选取

通过传递的整数位置选择

In [32]: df.iloc[3]
Out[32]: 
A    0.721555
B   -0.706771
C   -1.039575
D    0.271860
Name: 2013-01-04 00:00:00, dtype: float64

通过整数切片，作用类似于numpy/python

In [33]: df.iloc[3:5, 0:2]
Out[33]: 
                   A         B
2013-01-04  0.721555 -0.706771
2013-01-05 -0.424972  0.567020

通过整数位置位置列表，类似于numpy/python风格:

In [34]: df.iloc[[1, 2, 4], [0, 2]]
Out[34]: 
                   A         C
2013-01-02  1.212112  0.119209
2013-01-03 -0.861849 -0.494929
2013-01-05 -0.424972  0.276232

准确地切分行

In [35]: df.iloc[1:3, :]
Out[35]: 
                   A         B         C         D
2013-01-02  1.212112 -0.173215  0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929  1.071804

准确地切分列

In [36]: df.iloc[:, 1:3]
Out[36]: 
                   B         C
2013-01-01 -0.282863 -1.509059
2013-01-02 -0.173215  0.119209
2013-01-03 -2.104569 -0.494929
2013-01-04 -0.706771 -1.039575
2013-01-05  0.567020  0.276232
2013-01-06  0.113648 -1.478427

准确地得到一个数据

In [37]: df.iloc[1, 1]
Out[37]: -0.17321464905330858

为了快速访问标量(等价于之前的方法)

In [38]: df.iat[1, 1]
Out[38]: -0.17321464905330858

还有布尔索引暂时先不写