翻译 10 Minutes to pandas
原文档：http://pandas.pydata.org/pandas-docs/stable/10min.html#getting
导入模块：
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: import matplotlib.pyplot as plt数据结构介绍
Series
类似一维数组的对象，由一组数据和一组索引组成。
DateFrame
具有标记轴（行和列）的二维大小可变，可以异构的表格数据结构。 算术运算在行标签和列标签上对齐。 可以被认为是Series对象的类似dict的容器。
创建对象
通过传递值列表来创建Series，让pandas创建一个默认的整数索引：
In [4]: s = pd.Series([1,3,5,np.nan,6,8])
In [5]: s
Out[5]:
0 1.0
1 3.0
2 5.0
3 NaN
4 6.0
5 8.0
dtype: float64通过传递带有日期时间索引和标记列的NumPy数组来创建DataFrame：
In [6]: dates = pd.date_range('20130101', periods=6)
In [7]: dates
Out[7]:
DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
'2013-01-05', '2013-01-06'],
dtype='datetime64[ns]', freq='D')
In [8]: df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
In [9]: df
Out[9]:
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
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
2013-01-05 -0.424972 0.567020 0.276232 -1.087401
2013-01-06 -0.673690 0.113648 -1.478427 0.524988通过dict字典方式来创建DataFrame。
In [10]: df2 = pd.DataFrame({ 'A' : 1.,
....: 'B' : pd.Timestamp('20130102'),
....: 'C' : pd.Series(1,index=list(range(4)),dtype='float32'),
....: 'D' : np.array([3] * 4,dtype='int32'),
....: 'E' : pd.Categorical(["test","train","test","train"]),
....: 'F' : 'foo' })
....:
In [11]: df2
Out[11]:
A B C D E F
0 1.0 2013-01-02 1.0 3 test foo
1 1.0 2013-01-02 1.0 3 train foo
2 1.0 2013-01-02 1.0 3 test foo
3 1.0 2013-01-02 1.0 3 train foo生成的DataFrame的列具有不同的dtypes。
In [12]: df2.dtypes
Out[12]:
A float64
B datetime64[ns]
C float32
D int32
E category
F object
dtype: object可视化数据
head（）查看顶层，tail（）查看底层数据
In [14]: df.head()
Out[14]:
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
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
2013-01-05 -0.424972 0.567020 0.276232 -1.087401
In [15]: df.tail(3)
Out[15]:
A B C D
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
2013-01-05 -0.424972 0.567020 0.276232 -1.087401
2013-01-06 -0.673690 0.113648 -1.478427 0.524988显示索引，列和基础NumPy数据：
In [16]: df.index
Out[16]:
DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
'2013-01-05', '2013-01-06'],
dtype='datetime64[ns]', freq='D')
In [17]: df.columns
Out[17]: Index(['A', 'B', 'C', 'D'], dtype='object')
In [18]: df.values
Out[18]:
array([[ 0.4691, -0.2829, -1.5091, -1.1356],
[ 1.2121, -0.1732, 0.1192, -1.0442],
[-0.8618, -2.1046, -0.4949, 1.0718],
[ 0.7216, -0.7068, -1.0396, 0.2719],
[-0.425 , 0.567 , 0.2762, -1.0874],
[-0.6737, 0.1136, -1.4784, 0.525 ]])describe（）显示了数据的快速统计摘要：
In [19]: df.describe()
Out[19]:
A B C D
count 6.000000 6.000000 6.000000 6.000000
mean 0.073711 -0.431125 -0.687758 -0.233103
std 0.843157 0.922818 0.779887 0.973118
min -0.861849 -2.104569 -1.509059 -1.135632
25% -0.611510 -0.600794 -1.368714 -1.076610
50% 0.022070 -0.228039 -0.767252 -0.386188
75% 0.658444 0.041933 -0.034326 0.461706
max 1.212112 0.567020 0.276232 1.071804转置数据：
In [20]: df.T
Out[20]:
2013-01-01 2013-01-02 2013-01-03 2013-01-04 2013-01-05 2013-01-06
A 0.469112 1.212112 -0.861849 0.721555 -0.424972 -0.673690
B -0.282863 -0.173215 -2.104569 -0.706771 0.567020 0.113648
C -1.509059 0.119209 -0.494929 -1.039575 0.276232 -1.478427
D -1.135632 -1.044236 1.071804 0.271860 -1.087401 0.524988按轴排序：
axis = 0 代表对横轴操作，在运算的过程中其运算的方向表现为纵向运算；
axis = 1 代表对纵轴操作，在运算的过程中其运算的方向表现为横向运算；
In [21]: df.sort_index(axis=1, ascending=False)
Out[21]:
D C B A
2013-01-01 -1.135632 -1.509059 -0.282863 0.469112
2013-01-02 -1.044236 0.119209 -0.173215 1.212112
2013-01-03 1.071804 -0.494929 -2.104569 -0.861849
2013-01-04 0.271860 -1.039575 -0.706771 0.721555
2013-01-05 -1.087401 0.276232 0.567020 -0.424972
2013-01-06 0.524988 -1.478427 0.113648 -0.673690指定列按值排序：
In [22]: df.sort_values(by='B')
Out[22]:
A B C D
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
2013-01-06 -0.673690 0.113648 -1.478427 0.524988
2013-01-05 -0.424972 0.567020 0.276232 -1.087401选择数据
Getting数据
选择一列数据，返回Series数据类型，相当于df.A：
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[]选择，对行进行切片
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按标签选择
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按标签选择多轴：
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：
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针对切片对象精简维度：
In [29]: df.loc['20130102',['A','B']]
Out[29]:
A 1.212112
B -0.173215
Name: 2013-01-02 00:00:00, dtype: float64获取标量值：
In [30]: df.loc[dates[0],'A']
Out[30]: 0.46911229990718628快速访问标量：
In [31]: df.at[dates[0],'A']
Out[31]: 0.46911229990718628按位置选择