十分钟搞定pandas

﹏ヽ暗。殇╰゛Y 2022-06-08 14:06 263阅读 0赞

转自：[http://www.cnblogs.com/chaosimple/p/4153083.html][http_www.cnblogs.com_chaosimple_p_4153083.html]  
本文是对pandas官方网站上《10 Minutes to pandas》的一个简单的翻译，原文在这里。这篇文章是对pandas的一个简单的介绍，详细的介绍请参考：Cookbook 。习惯上，我们会按下面格式引入所需要的包：

In [1]: import pandas as pd
    
    In [2]: import numpy as np
    
    In [3]: import matplotlib.pyplot as plt

一、 创建对象  
可以通过 Data Structure Intro Setion 来查看有关该节内容的详细信息。

1、可以通过传递一个list对象来创建一个Series，pandas会默认创建整型索引：

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

2、通过传递一个numpy array，时间索引以及列标签来创建一个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

3、通过传递一个能够被转换成类似序列结构的字典对象来创建一个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

4、查看不同列的数据类型：

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

5、如果你使用的是IPython，使用Tab自动补全功能会自动识别所有的属性以及自定义的列，下图中是所有能够被自动识别的属性的一个子集：

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

二、 查看数据  
详情请参阅：Basics Section

1、 查看frame中头部和尾部的行：

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

2、 显示索引、列和底层的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 ]])

3、 describe()函数对于数据的快速统计汇总：

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

4、 对数据的转置：

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

5、 按轴进行排序

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

6、 按值进行排序

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

三、 选择  
虽然标准的Python/Numpy的选择和设置表达式都能够直接派上用场，但是作为工程使用的代码，我们推荐使用经过优化的pandas数据访问方式： .at, .iat, .loc, .iloc 和 .ix详情请参阅Indexing and Selecing Data 和 MultiIndex / Advanced Indexing。

l 获取

1、 选择一个单独的列，这将会返回一个Series，等同于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

2、 通过\[\]进行选择，这将会对行进行切片

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

l 通过标签选择

1、 使用标签来获取一个交叉的区域

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

2、 通过标签来在多个轴上进行选择

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

3、 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

4、 对于返回的对象进行维度缩减

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

5、 获取一个标量

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

6、 快速访问一个标量（与上一个方法等价）

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

l 通过位置选择

1、 通过传递数值进行位置选择（选择的是行）

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

2、 通过数值进行切片，与numpy/python中的情况类似

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

3、 通过指定一个位置的列表，与numpy/python中的情况类似

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

4、 对行进行切片

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

5、 对列进行切片

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

6、 获取特定的值

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

l 布尔索引

1、 使用一个单独列的值来选择数据：

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

2、 使用where操作来选择数据：

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

3、 使用isin()方法来过滤：

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

l 设置

1、 设置一个新的列：

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

2、 通过标签设置新的值：

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

3、 通过位置设置新的值：

In [49]: df.iat[0,1] = 0

4、 通过一个numpy数组设置一组新值：

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

上述操作结果如下：

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

5、 通过where操作来设置新的值：

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

四、 缺失值处理  
在pandas中，使用np.nan来代替缺失值，这些值将默认不会包含在计算中，详情请参阅：Missing Data Section。

1、 reindex()方法可以对指定轴上的索引进行改变/增加/删除操作，这将返回原始数据的一个拷贝：、

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

2、 去掉包含缺失值的行：

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

3、 对缺失值进行填充：

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

4、 对数据进行布尔填充：

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

五、 相关操作  
详情请参与 Basic Section On Binary Ops

l 统计（相关操作通常情况下不包括缺失值）

1、 执行描述性统计：

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

2、 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

3、 对于拥有不同维度，需要对齐的对象进行操作。Pandas会自动的沿着指定的维度进行广播：

In \[63\]: s = pd.Series(\[1,3,5,np.nan,6,8\], index=dates).shift(2)

In \[64\]: s  
Out\[64\]:  
2013-01-01 NaN  
2013-01-02 NaN  
2013-01-03 1.0  
2013-01-04 3.0  
2013-01-05 5.0  
2013-01-06 NaN  
Freq: D, dtype: float64

In \[65\]: df.sub(s, axis=’index’)  
Out\[65\]:  
A B C D F  
2013-01-01 NaN NaN NaN NaN NaN  
2013-01-02 NaN NaN NaN NaN NaN  
2013-01-03 -1.861849 -3.104569 -1.494929 4.0 1.0  
2013-01-04 -2.278445 -3.706771 -4.039575 2.0 0.0  
2013-01-05 -5.424972 -4.432980 -4.723768 0.0 -1.0  
2013-01-06 NaN NaN NaN NaN NaN

l Apply

1、 对数据应用函数：

In [66]: df.apply(np.cumsum)
    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

l 直方图

具体请参照：Histogramming and Discretization

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

l 字符串方法

Series对象在其str属性中配备了一组字符串处理方法，可以很容易的应用到数组中的每个元素，如下段代码所示。更多详情请参考：Vectorized String Methods.

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

六、 合并  
Pandas提供了大量的方法能够轻松的对Series，DataFrame和Panel对象进行各种符合各种逻辑关系的合并操作。具体请参阅：Merging section

l Concat

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

l Join 类似于SQL类型的合并，具体请参阅：Database style joining

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

l Append 将一行连接到一个DataFrame上，具体请参阅Appending：

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

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

七、 分组  
对于”group by”操作，我们通常是指以下一个或多个操作步骤：

l （Splitting）按照一些规则将数据分为不同的组；

l （Applying）对于每组数据分别执行一个函数；

l （Combining）将结果组合到一个数据结构中；

详情请参阅：Grouping section

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

1、 分组并对每个分组执行sum函数：

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

2、 通过多个列进行分组形成一个层次索引，然后执行函数：

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

八、 Reshaping  
详情请参阅 Hierarchical Indexing 和 Reshaping。

l Stack

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

l 数据透视表，详情请参阅：Pivot Tables.

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

可以从这个数据中轻松的生成数据透视表：

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

Pivot Tables
    
    See the section on Pivot Tables.
    
    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
    We can produce pivot tables from this data very easily:
    
    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

九、 时间序列  
Pandas在对频率转换进行重新采样时拥有简单、强大且高效的功能（如将按秒采样的数据转换为按5分钟为单位进行采样的数据）。这种操作在金融领域非常常见。具体参考：Time Series section。

In [108]: rng = pd.date_range('1/1/2012', periods=100, freq='S')
    
    In [109]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
    
    In [110]: ts.resample('5Min').sum()
    Out[110]: 
    2012-01-01    25083
    Freq: 5T, dtype: int64

1、 时区表示：

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

2、 时区转换：

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

3、 时间跨度转换：

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

4、 时期和时间戳之间的转换使得可以使用一些方便的算术函数。

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

十、 Categorical  
从0.15版本开始，pandas可以在DataFrame中支持Categorical类型的数据，详细 介绍参看：categorical introduction和API documentation。

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

1、 将原始的grade转换为Categorical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]

2、 将Categorical类型数据重命名为更有意义的名称：

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

3、 对类别进行重新排序，增加缺失的类别：

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]

4、 排序是按照Categorical的顺序进行的而不是按照字典顺序进行：

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

5、 对Categorical列进行排序时存在空的类别：

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

十一、 画图  
具体文档参看：Plotting docs

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>

![这里写图片描述][SouthEast]  
对于DataFrame来说，plot是一种将所有列及其标签进行绘制的简便方法：

In [138]: df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,
       .....:                   columns=['A', 'B', 'C', 'D'])
       .....: 
    
    In [139]: df = df.cumsum()
    
    In [140]: plt.figure(); df.plot(); plt.legend(loc='best')
    Out[140]: <matplotlib.legend.Legend at 0x11b5dea20>

![这里写图片描述][SouthEast 1]

十二、 导入和保存数据  
l CSV，参考：Writing to a csv file

1、 写入csv文件：

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

2、 从csv文件中读取：

In [142]: pd.read_csv('foo.csv')
    Out[142]: 
         Unnamed: 0          A          B         C          D
    0    2000-01-01   0.266457  -0.399641 -0.219582   1.186860
    1    2000-01-02  -1.170732  -0.345873  1.653061  -0.282953
    2    2000-01-03  -1.734933   0.530468  2.060811  -0.515536
    3    2000-01-04  -1.555121   1.452620  0.239859  -1.156896
    4    2000-01-05   0.578117   0.511371  0.103552  -2.428202
    5    2000-01-06   0.478344   0.449933 -0.741620  -1.962409
    6    2000-01-07   1.235339  -0.091757 -1.543861  -1.084753
    ..          ...        ...        ...       ...        ...
    993  2002-09-20 -10.628548  -9.153563 -7.883146  28.313940
    994  2002-09-21 -10.390377  -8.727491 -6.399645  30.914107
    995  2002-09-22  -8.985362  -8.485624 -4.669462  31.367740
    996  2002-09-23  -9.558560  -8.781216 -4.499815  30.518439
    997  2002-09-24  -9.902058  -9.340490 -4.386639  30.105593
    998  2002-09-25 -10.216020  -9.480682 -3.933802  29.758560
    999  2002-09-26 -11.856774 -10.671012 -3.216025  29.369368
    
    [1000 rows x 5 columns]

l HDF5，参考：HDFStores

1、 写入HDF5存储：

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

2、 从HDF5存储中读取：

In [144]: pd.read_hdf('foo.h5','df')
    Out[144]: 
                        A          B         C          D
    2000-01-01   0.266457  -0.399641 -0.219582   1.186860
    2000-01-02  -1.170732  -0.345873  1.653061  -0.282953
    2000-01-03  -1.734933   0.530468  2.060811  -0.515536
    2000-01-04  -1.555121   1.452620  0.239859  -1.156896
    2000-01-05   0.578117   0.511371  0.103552  -2.428202
    2000-01-06   0.478344   0.449933 -0.741620  -1.962409
    2000-01-07   1.235339  -0.091757 -1.543861  -1.084753
    ...               ...        ...       ...        ...
    2002-09-20 -10.628548  -9.153563 -7.883146  28.313940
    2002-09-21 -10.390377  -8.727491 -6.399645  30.914107
    2002-09-22  -8.985362  -8.485624 -4.669462  31.367740
    2002-09-23  -9.558560  -8.781216 -4.499815  30.518439
    2002-09-24  -9.902058  -9.340490 -4.386639  30.105593
    2002-09-25 -10.216020  -9.480682 -3.933802  29.758560
    2002-09-26 -11.856774 -10.671012 -3.216025  29.369368
    
    [1000 rows x 4 columns]

l Excel，参考：MS Excel

1、 写入excel文件：

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

2、 从excel文件中读取：

In [146]: pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])
    Out[146]: 
                        A          B         C          D
    2000-01-01   0.266457  -0.399641 -0.219582   1.186860
    2000-01-02  -1.170732  -0.345873  1.653061  -0.282953
    2000-01-03  -1.734933   0.530468  2.060811  -0.515536
    2000-01-04  -1.555121   1.452620  0.239859  -1.156896
    2000-01-05   0.578117   0.511371  0.103552  -2.428202
    2000-01-06   0.478344   0.449933 -0.741620  -1.962409
    2000-01-07   1.235339  -0.091757 -1.543861  -1.084753
    ...               ...        ...       ...        ...
    2002-09-20 -10.628548  -9.153563 -7.883146  28.313940
    2002-09-21 -10.390377  -8.727491 -6.399645  30.914107
    2002-09-22  -8.985362  -8.485624 -4.669462  31.367740
    2002-09-23  -9.558560  -8.781216 -4.499815  30.518439
    2002-09-24  -9.902058  -9.340490 -4.386639  30.105593
    2002-09-25 -10.216020  -9.480682 -3.933802  29.758560
    2002-09-26 -11.856774 -10.671012 -3.216025  29.369368
    
    [1000 rows x 4 columns]

[http_www.cnblogs.com_chaosimple_p_4153083.html]: http://www.cnblogs.com/chaosimple/p/4153083.html
[SouthEast]: /images/20220608/4024894b7dd14073b94ee3b1dbdfe85f.png
[SouthEast 1]: /images/20220608/52c81050f5c4405c91dd096db2575cf4.png