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