十分钟掌握pandas
文档版本：0.20.3

这是一个对pandas简短的介绍，适合新用户。你可以在Cookbook中查看更详细的内容。 

通常，我们要像下面一样导入一些包。

In [1]: import pandas as pd
 
In [2]: import numpy as np
 
In [3]: import matplotlib.pyplot as plt
创建对象
用一个包含值的序列创建一个Series，pandas会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

用numpy数值创建一个带有datetime索引和列标签的数据框

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

用包含对象的字典创建一个数据框，该方法与创建Series的方法相似。

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

该数据框有特殊的dtypes

In [12]: df2.dtypes
Out[12]: 
A           float64
B    datetime64[ns]
C           float32
D             int32
E          category
F            object
dtype: object

如果你是使用IPython，tab键可以自动激活可选列名（包括其它的属性）。下边就有一个可以被实现的属性的集合。

In [13]: df2.<TAB>
df2.A                  df2.bool
df2.abs                df2.boxplot
df2.add                df2.C
df2.add_prefix         df2.clip
df2.add_suffix         df2.clip_lower
df2.align              df2.clip_upper
df2.all                df2.columns
df2.any                df2.combine
df2.append             df2.combine_first
df2.apply              df2.compound
df2.applymap           df2.consolidate
df2.as_blocks          df2.convert_objects
df2.asfreq             df2.copy
df2.as_matrix          df2.corr
df2.astype             df2.corrwith
df2.at                 df2.count
df2.at_time            df2.cov
df2.axes               df2.cummax
df2.B                  df2.cummin
df2.between_time       df2.cumprod
df2.bfill              df2.cumsum
df2.blocks             df2.D

就像你所见到的列A,B,C和D的自动弹出都可以由tab完成。列E也是一样的；剩下的属性为了简短起见都省略了。

查看数据
查看整个数据的头部或尾部

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

显示数据框的索引，列名和值。

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 ]])

描述性显示关于数据的简短统计摘要

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

通过轴来分类你的数据（相当于排序，axis=1可以理解为分类列名，=0则为索引名）

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

通过值来分类

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
小记：对于选择数据和设置数据来说，标准的python和numpy表达式非常直观而且对于交互式 
工作来说很难进行的，对于应用性代码来说，我们比较推荐最优化的pandas数据获取方法， 
例如.at, .iat, .loc, .iloc and .ix。

获取
在方括号中输入这个单一的列名，来获得一个Series，该操作相当于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

通过对行切片来获取数据

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

由标签获取数据
用标签来截取一行数据

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

在多个轴上通过标签来选取数据

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

同时用标签切片和标签名索引来获取数据

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

对返回的对象的维度进行减少维度

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

仅仅获取标量值的方法

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

更快地获取标量值（效果相当于前一个方法）

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

通过位置进行索引
通过适合的整数来代表位置进行索引

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
与numpy/python相似的操作，整数切片来获取数据

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

通过含有代表位置的整数列表来获取数据，与numpy/python的风格相似

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

显式切片索引行

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

显式切片索引列

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

显式索引数据值

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

使系统快速地获取标量值（结果与前一个方法相等）

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

布尔值索引
使用单一的列的值来选取数据

In [39]: df[df.A > 0]
Out[39]: 
               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
2013-01-04  0.721555 -0.706771 -1.039575  0.271860

从给出布尔条件的数据框来获取数据

In [40]: df[df > 0]
Out[40]: 
               A         B         C         D
2013-01-01  0.469112       NaN       NaN       NaN
2013-01-02  1.212112       NaN  0.119209       NaN
2013-01-03       NaN       NaN       NaN  1.071804
2013-01-04  0.721555       NaN       NaN  0.271860
2013-01-05       NaN  0.567020  0.276232       NaN
2013-01-06       NaN  0.113648       NaN  0.524988

使用isin()方法来过滤数据

In [41]: df2 = df.copy()
 
In [42]: df2['E'] = ['one', 'one','two','three','four','three']
 
In [43]: df2
Out[43]: 
               A         B         C         D      E
2013-01-01  0.469112 -0.282863 -1.509059 -1.135632    one
2013-01-02  1.212112 -0.173215  0.119209 -1.044236    one
2013-01-03 -0.861849 -2.104569 -0.494929  1.071804    two
2013-01-04  0.721555 -0.706771 -1.039575  0.271860  three
2013-01-05 -0.424972  0.567020  0.276232 -1.087401   four
2013-01-06 -0.673690  0.113648 -1.478427  0.524988  three
 
In [44]: df2[df2['E'].isin(['two','four'])]
Out[44]: 
               A         B         C         D     E
2013-01-03 -0.861849 -2.104569 -0.494929  1.071804   two
2013-01-05 -0.424972  0.567020  0.276232 -1.087401  four

安插
在安插新的列时通过索引值自动排列

In [45]: s1 = pd.Series([1,2,3,4,5,6], index=pd.date_range('20130102', periods=6))
 
In [46]: s1
Out[46]: 
2013-01-02    1
2013-01-03    2
2013-01-04    3
2013-01-05    4
2013-01-06    5
2013-01-07    6
Freq: D, dtype: int64
 
In [47]: df['F'] = s1

通过标签安插值

In [48]: df.at[dates[0],'A'] = 0

通过位置安插值

In [49]: df.iat[0,1] = 0
通过分配numpy数组来安插新的列

In [50]: df.loc[:,'D'] = np.array([5] * len(df))

前面安插值的操作的结果

In [51]: df
Out[51]: 
               A         B         C  D    F
2013-01-01  0.000000  0.000000 -1.509059  5  NaN
2013-01-02  1.212112 -0.173215  0.119209  5  1.0
2013-01-03 -0.861849 -2.104569 -0.494929  5  2.0
2013-01-04  0.721555 -0.706771 -1.039575  5  3.0
2013-01-05 -0.424972  0.567020  0.276232  5  4.0
2013-01-06 -0.673690  0.113648 -1.478427  5  5.0

用一个where操作来安插数据

In [52]: df2 = df.copy()
 
In [53]: df2[df2 > 0] = -df2
 
In [54]: df2
Out[54]: 
               A         B         C  D    F
2013-01-01  0.000000  0.000000 -1.509059 -5  NaN
2013-01-02 -1.212112 -0.173215 -0.119209 -5 -1.0
2013-01-03 -0.861849 -2.104569 -0.494929 -5 -2.0
2013-01-04 -0.721555 -0.706771 -1.039575 -5 -3.0
2013-01-05 -0.424972 -0.567020 -0.276232 -5 -4.0
2013-01-06 -0.673690 -0.113648 -1.478427 -5 -5.0

缺失值
早先的pandas使用 np.nan的值来代表缺失值。缺失值默认不会进行计算。

重新排列索引操作允许你在指定的轴上改变/增加/删除索引。下面返回一个前面数据的复制结果

In [55]: df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])
 
In [56]: df1.loc[dates[0]:dates[1],'E'] = 1
 
In [57]: df1
Out[57]: 
               A         B         C  D    F    E
2013-01-01  0.000000  0.000000 -1.509059  5  NaN  1.0
2013-01-02  1.212112 -0.173215  0.119209  5  1.0  1.0
2013-01-03 -0.861849 -2.104569 -0.494929  5  2.0  NaN
2013-01-04  0.721555 -0.706771 -1.039575  5  3.0  NaN

删除所有含有缺失值的行

In [58]: df1.dropna(how='any')
Out[58]: 
               A         B         C  D    F    E
2013-01-02  1.212112 -0.173215  0.119209  5  1.0  1.0
替换缺失值

In [59]: df1.fillna(value=5)
Out[59]: 
               A         B         C  D    F    E
2013-01-01  0.000000  0.000000 -1.509059  5  5.0  1.0
2013-01-02  1.212112 -0.173215  0.119209  5  1.0  1.0
2013-01-03 -0.861849 -2.104569 -0.494929  5  2.0  5.0
2013-01-04  0.721555 -0.706771 -1.039575  5  3.0  5.0

通过判断缺失值来获取布尔值

In [60]: pd.isnull(df1)
Out[60]: 
            A      B      C      D      F      E
2013-01-01  False  False  False  False   True  False
2013-01-02  False  False  False  False  False  False
2013-01-03  False  False  False  False  False   True
2013-01-04  False  False  False  False  False   True

运算
统计表
该操作一般不包含缺失值 

呈现一个描述性的统计表

In [61]: df.mean()
Out[61]: 
A   -0.004474
B   -0.383981
C   -0.687758
D    5.000000
F    3.000000
dtype: float64

在其他轴上进行相同的操作

In [62]: df.mean(1)
Out[62]: 
2013-01-01    0.872735
2013-01-02    1.431621
2013-01-03    0.707731
2013-01-04    1.395042
2013-01-05    1.883656
2013-01-06    1.592306
Freq: D, dtype: float64

对有不同的维度和需要排列的对象进行运算。另外，pandas自动沿着指定的维度进行运算。

应用
对数据进行函数的应用

In [66]: df.apply(np.cumsum)# 同一列前面元素的和
Out[66]: 
               A         B         C   D     F
2013-01-01  0.000000  0.000000 -1.509059   5   NaN
2013-01-02  1.212112 -0.173215 -1.389850  10   1.0
2013-01-03  0.350263 -2.277784 -1.884779  15   3.0
2013-01-04  1.071818 -2.984555 -2.924354  20   6.0
2013-01-05  0.646846 -2.417535 -2.648122  25  10.0
2013-01-06 -0.026844 -2.303886 -4.126549  30  15.0
 
In [67]: df.apply(lambda x: x.max() - x.min())
Out[67]: 
A    2.073961
B    2.671590
C    1.785291
D    0.000000
F    4.000000
dtype: float64

统计值的频数
In [68]: s = pd.Series(np.random.randint(0, 7, size=10))
 
In [69]: s
Out[69]: 
0    4
1    2
2    1
3    2
4    6
5    4
6    4
7    6
8    4
9    4
dtype: int64
 
In [70]: s.value_counts()
Out[70]: 
4    5
6    2
2    2
1    1
dtype: int64


字符串操作
Series拥有像对字符串集合处理方法的能力，在str属性中可以对数组的每一个元素进行便捷的操作，就像下面的一小片字段中显示的那样。

In [71]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
 
In [72]: s.str.lower()
Out[72]: 
0       a
1       b
2       c
3    aaba
4    baca
5     NaN
6    caba
7     dog
8     cat
dtype: object

聚合
组合
pandas提供了不同的工具为了简便地用不同的方式来对索引设置逻辑和相关的代数功能结合Series，DataFrame和Panel对象，例如join/merge-type操作

用concat（）函数来连接pandas对象

In [73]: df = pd.DataFrame(np.random.randn(10, 4))
 
In [74]: df
Out[74]: 
      0         1         2         3
0 -0.548702  1.467327 -1.015962 -0.483075
1  1.637550 -1.217659 -0.291519 -1.745505
2 -0.263952  0.991460 -0.919069  0.266046
3 -0.709661  1.669052  1.037882 -1.705775
4 -0.919854 -0.042379  1.247642 -0.009920
5  0.290213  0.495767  0.362949  1.548106
6 -1.131345 -0.089329  0.337863 -0.945867
7 -0.932132  1.956030  0.017587 -0.016692
8 -0.575247  0.254161 -1.143704  0.215897
9  1.193555 -0.077118 -0.408530 -0.862495
 
# break it into pieces
In [75]: pieces = [df[:3], df[3:7], df[7:]]
 
In [76]: pd.concat(pieces)
Out[76]: 
      0         1         2         3
0 -0.548702  1.467327 -1.015962 -0.483075
1  1.637550 -1.217659 -0.291519 -1.745505
2 -0.263952  0.991460 -0.919069  0.266046
3 -0.709661  1.669052  1.037882 -1.705775
4 -0.919854 -0.042379  1.247642 -0.009920
5  0.290213  0.495767  0.362949  1.548106
6 -1.131345 -0.089329  0.337863 -0.945867
7 -0.932132  1.956030  0.017587 -0.016692
8 -0.575247  0.254161 -1.143704  0.215897
9  1.193555 -0.077118 -0.408530 -0.862495

Join
SQL风格的聚合方式

In [77]: left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})
 
In [78]: right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})
 
In [79]: left
Out[79]: 
    key  lval
0  foo     1
1  foo     2
 
In [80]: right
Out[80]: 
    key  rval
0  foo     4
1  foo     5
 
In [81]: pd.merge(left, right, on='key')
Out[81]: 
    key  lval  rval
0  foo     1     4
1  foo     1     5
2  foo     2     4
3  foo     2     5

WARN: 不明白呀,这俩啥区别
该方法的另一个例子

In [82]: left = pd.DataFrame({'key': ['foo', 'bar'], 'lval': [1, 2]})
 
In [83]: right = pd.DataFrame({'key': ['foo', 'bar'], 'rval': [4, 5]})
 
In [84]: left
Out[84]: 
    key  lval
0  foo     1
1  bar     2
 
In [85]: right
Out[85]: 
    key  rval
0  foo     4
1  bar     5
 
In [86]: pd.merge(left, right, on='key')
Out[86]: 
     key  lval  rval
0  foo     1     4
1  bar     2     5

附加
对数据框附加行

In [87]: df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])
 
In [88]: df
Out[88]: 
      A         B         C         D
0  1.346061  1.511763  1.627081 -0.990582
1 -0.441652  1.211526  0.268520  0.024580
2 -1.577585  0.396823 -0.105381 -0.532532
3  1.453749  1.208843 -0.080952 -0.264610
4 -0.727965 -0.589346  0.339969 -0.693205
5 -0.339355  0.593616  0.884345  1.591431
6  0.141809  0.220390  0.435589  0.192451
7 -0.096701  0.803351  1.715071 -0.708758
 
In [89]: s = df.iloc[3]
 
In [90]: df.append(s, ignore_index=True)
Out[90]: 
      A         B         C         D
0  1.346061  1.511763  1.627081 -0.990582
1 -0.441652  1.211526  0.268520  0.024580
2 -1.577585  0.396823 -0.105381 -0.532532
3  1.453749  1.208843 -0.080952 -0.264610
4 -0.727965 -0.589346  0.339969 -0.693205
5 -0.339355  0.593616  0.884345  1.591431
6  0.141809  0.220390  0.435589  0.192451
7 -0.096701  0.803351  1.715071 -0.708758
8  1.453749  1.208843 -0.080952 -0.264610

分组运算
在”group by”中我们提及一个操作过程，该过程涉及到一个或多个下列步骤

基于一个标准分割数据到各个组中
在每个组中独立地应用函数
结合结果到数据结构中

In [91]: df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar','foo', 'bar', 'foo', 'foo'],
                        'B' : ['one', 'one', 'two', 'three','two', 'two', 'one', 'three'],
                        'C' : np.random.randn(8),
                        'D' : np.random.randn(8)})
 
 
In [92]: df
Out[92]: 
    A      B         C         D
0  foo    one -1.202872 -0.055224
1  bar    one -1.814470  2.395985
2  foo    two  1.018601  1.552825
3  bar  three -0.595447  0.166599
4  foo    two  1.395433  0.047609
5  bar    two -0.392670 -0.136473
6  foo    one  0.007207 -0.561757
7  foo  three  1.928123 -1.623033
分组然后应用sum函数到分组的结果中

In [93]: df.groupby('A').sum()
Out[93]: 
         C        D
A                     
bar -2.802588  2.42611
foo  3.146492 -0.63958

通过多列形式分组获得多重索引进行应用函数

In [94]: df.groupby(['A','B']).sum()
Out[94]: 
              C         D
A   B                        
bar one   -1.814470  2.395985
three -0.595447  0.166599
two   -0.392670 -0.136473
foo one   -1.195665 -0.616981
three  1.928123 -1.623033
two    2.414034  1.600434

重塑
有堆叠
WARN:不理解
In [95]: tuples = list(zip(*[['bar', 'bar', 'baz', 'baz', 
....:                      'foo', 'foo', 'qux',     'qux'],
....:                     ['one', 'two', 'one',     'two',
....:                      'one', 'two', 'one', 'two']]))
....: 
 
In [96]: index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
 
In [97]: df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])
 
In [98]: df2 = df[:4]
 
In [99]: df2
Out[99]: 
                 A         B
first second                    
bar   one     0.029399 -0.542108
  two     0.282696 -0.087302
baz   one    -1.575170  1.771208
  two     0.816482  1.100230

stack（）方法”压缩”DataFrame的列

In [100]: stacked = df2.stack()
 
In [101]: stacked
Out[101]: 
first  second   
bar    one     A    0.029399
               B   -0.542108
       two     A    0.282696
               B   -0.087302
baz    one     A   -1.575170
               B    1.771208
       two     A    0.816482
               B    1.100230
dtype: float64

对于堆叠的数据库，相反的stack()操作是unstack(),unstack()默认解除最后一个索引的堆叠状态。

In [102]: stacked.unstack()
Out[102]: 
                 A         B
first second                    
bar   one     0.029399 -0.542108
      two     0.282696 -0.087302
baz   one    -1.575170  1.771208
      two     0.816482  1.100230
 
In [103]: stacked.unstack(1)
Out[103]: 
second        one       two
first                      
bar   A  0.029399  0.282696
      B -0.542108 -0.087302
baz   A -1.575170  0.816482
      B  1.771208  1.100230
 
In [104]: stacked.unstack(0)
Out[104]: 
first          bar       baz
second                      
one    A  0.029399 -1.575170
       B -0.542108  1.771208
two    A  0.282696  0.816482
       B -0.087302  1.100230

数据透视表
WARN:没看
In [105]: df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3,
.....:                    'B' :     ['A', 'B', 'C'] * 4,
.....:                    'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,
.....:                    'D' : np.random.randn(12),
.....:                    'E' : np.random.randn(12)})
.....: 
 
In [106]: df
Out[106]: 
        A  B    C         D         E
0     one  A  foo  1.418757 -0.179666
1     one  B  foo -1.879024  1.291836
2     two  C  foo  0.536826 -0.009614
3   three  A  bar  1.006160  0.392149
4     one  B  bar -0.029716  0.264599
5     one  C  bar -1.146178 -0.057409
6     two  A  foo  0.100900 -1.425638
7   three  B  foo -1.035018  1.024098
8     one  C  foo  0.314665 -0.106062
9     one  A  bar -0.773723  1.824375
10    two  B  bar -1.170653  0.595974
11  three  C  bar  0.648740  1.167115

我们可以从这个数据中轻松地制作出数据透视表

In [107]: pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])
Out[107]: 
C             bar       foo
A     B                    
one   A -0.773723  1.418757
      B -0.029716 -1.879024
      C -1.146178  0.314665
three A  1.006160       NaN
      B       NaN -1.035018
      C  0.648740       NaN
two   A       NaN  0.100900
      B -1.170653       NaN
      C       NaN  0.536826

呈现时区

In [111]: rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D')
 
In [112]: ts = pd.Series(np.random.randn(len(rng)), rng)
 
In [113]: ts
Out[113]: 
2012-03-06    0.464000
2012-03-07    0.227371
2012-03-08   -0.496922
2012-03-09    0.306389
2012-03-10   -2.290613
Freq: D, dtype: float64
 
In [114]: ts_utc = ts.tz_localize('UTC')
 
In [115]: ts_utc
Out[115]: 
2012-03-06 00:00:00+00:00    0.464000
2012-03-07 00:00:00+00:00    0.227371
2012-03-08 00:00:00+00:00   -0.496922
2012-03-09 00:00:00+00:00    0.306389
2012-03-10 00:00:00+00:00   -2.290613
Freq: D, dtype: float64

转换到另一个时区

In [116]: ts_utc.tz_convert('US/Eastern')
Out[116]: 
2012-03-05 19:00:00-05:00    0.464000
2012-03-06 19:00:00-05:00    0.227371
2012-03-07 19:00:00-05:00   -0.496922
2012-03-08 19:00:00-05:00    0.306389
2012-03-09 19:00:00-05:00   -2.290613
Freq: D, dtype: float64

在时间区间内转化

In [117]: rng = pd.date_range('1/1/2012', periods=5, freq='M')
 
In [118]: ts = pd.Series(np.random.randn(len(rng)), index=rng)
 
In [119]: ts
Out[119]: 
2012-01-31   -1.134623
2012-02-29   -1.561819
2012-03-31   -0.260838
2012-04-30    0.281957
2012-05-31    1.523962
Freq: M, dtype: float64
 
In [120]: ps = ts.to_period()
 
In [121]: ps
Out[121]: 
2012-01   -1.134623
2012-02   -1.561819
2012-03   -0.260838
2012-04    0.281957
2012-05    1.523962
Freq: M, dtype: float64
 
In [122]: ps.to_timestamp()
Out[122]: 
2012-01-01   -1.134623
2012-02-01   -1.561819
2012-03-01   -0.260838
2012-04-01    0.281957
2012-05-01    1.523962
Freq: MS, dtype: float64

在时间段和时间戳之间进行转换可以使用便捷的算术函数。在下面的例子中，我们把在十一月结束的季度频率转化为在月末的九点的季度频率：

In [123]: prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')
 
In [124]: ts = pd.Series(np.random.randn(len(prng)), prng)
 
In [125]: ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9
 
In [126]: ts.head()
Out[126]: 
1990-03-01 09:00   -0.902937
1990-06-01 09:00    0.068159
1990-09-01 09:00   -0.057873
1990-12-01 09:00   -0.368204
1991-03-01 09:00   -1.144073
Freq: H, dtype: float64

分类
从0.15版本开始，pandas就可以在数据框内包含分类数据。

In [127]: df = pd.DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})

把 raw_grade转变为分类数据类型。

In [128]: df["grade"] = df["raw_grade"].astype("category")
 
In [129]: df["grade"]
Out[129]: 
0    a
1    b
2    b
3    a
4    a
5    e
Name: grade, dtype: category
Categories (3, object): [a, b, e]

将分类数据重命名为更有意义的名字。

In [130]: df["grade"].cat.categories = ["very good", "good", "very bad"]

重新排列分类数据，同时添加缺失的分类数据。

In [131]: df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"])
 
In [132]: df["grade"]
Out[132]: 
0    very good
1         good
2         good
3    very good
4    very good
5     very bad
Name: grade, dtype: category
Categories (5, object): [very bad, bad, medium, good, very good]

对分类数据进行排序会作用于每列而不是指定的列。

In [133]: df.sort_values(by="grade")
Out[133]: 
    id raw_grade      grade
5   6         e   very bad
1   2         b       good
2   3         b       good
0   1         a  very good
3   4         a  very good
4   5         a  very good

对分类数据列那列进行分组也会显示出空的分类数据。

In [134]: df.groupby("grade").size()
Out[134]: 
grade
very bad     1
bad          0
medium       0
good         2
very good    3
dtype: int64

画图
在数据框中，plot()是一个非常方便的把所有列作为标签绘制在图标上的函数。
In [135]: ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
 
In [136]: ts = ts.cumsum()
 
In [137]: ts.plot()
Out[137]: <matplotlib.axes._subplots.AxesSubplot at 0x1187d7278>

输入/输出数据
CSV
把数据输出为csv文件

In [141]: df.to_csv('foo.csv')

读取csv文件

In [142]: pd.read_csv('foo.csv')

HDF5
写出一个HDF5存储单元

In [143]: df.to_hdf('foo.h5','df')

读入一个HDF5存储单元

In [144]: pd.read_hdf('foo.h5','df')

Excel
写出一个excel文件

In [145]: df.to_excel('foo.xlsx', sheet_name='Sheet1')

读入一个excel文件

In [146]: pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])
