数组聚合和分组运算

女爷i 2022-05-19 08:46 280阅读 0赞

对数据集进行分组并对各组应用一个函数（无论是聚合还是转换）， 这是数据分析工作的重要环节。 在数据集准备好之后， 通常任务就是计算分组统计或者生产透视表。pandas提供了一个灵活的高效的groupby 功能， 它使你能以一种自然的方式对数据集进行切片， 切块，摘要等操作。

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![20200423154033302.PNG][]

**goupby 技术**： split - apply - combine （拆分- 应用- 合并）。例如DataFrame 结构在其行（axis=0）或列（axis=1）上进行分组。然后， 将一个函数应用（apply）到各个分组并产生一个新值， 最后这些函数的执行结果会被合并（combine）到最终的结果对象中。

分组聚合过程：

![70][]

自定义一组dataframe结构的数据：

#coding=gbk
    #数据聚合和分组运算
    import pandas as pd
    import numpy as np
    np.random.seed(666)
    data = pd.DataFrame({'key1':['a','a','b','b','a'],
                         'key2':['one','two','one','two','one'],
                         'data1':np.random.randn(5),
                         'data2':np.random.randn(5)
                         })
    print(data)
    #       data1     data2 key1 key2
    # 0  0.824188 -0.109497    a  one
    # 1  0.479966  0.019028    a  two
    # 2  1.173468 -0.943761    b  one
    # 3  0.909048  0.640573    b  two
    # 4 -0.571721 -0.786443    a  one

group1 = data['data1'].groupby(data['key1']).mean() #依据key1 进行分组， 分为a， b 两组， 再进行聚合得到其平均值
    print(group1)
    # key1
    # a    0.244144
    # b    1.041258
    # Name: data1, dtype: float64
    #一次传入多个数组
    g2 = data['data1'].groupby([data['key1'], data['key2']]).mean()
    print(g2)
    # key1  key2
    # a     one     0.126233
    #       two     0.479966
    # b     one     1.173468
    #       two     0.909048
    # Name: data1, dtype: float64

通过两个键对数据进行分组， 得到对的series 具有一个层次化索引，（由唯一的键值对组成）

g = g2.unstack()
    print(g)
    # key2       one       two
    # key1                    
    # a     0.126233  0.479966
    # b     1.173468  0.909048
    print('----')
    print(g.loc['a'])
    # key2
    # one    0.126233
    # two    0.479966
    # Name: a, dtype: float64

### 分组键可以是任何长度的适当的数组 ###

states = np.array(['ohio','California','ohio','ohio','California'])
    years = np.array([2005,2004,2004,2004,2005])
    sy = data['data1'].groupby([states, years]).mean()
    print(sy)
    # California  2004    0.479966
    #             2005   -0.571721
    # ohio        2004    1.041258
    #             2005    0.824188
    # Name: data1, dtype: float64

### 将列名用作分组键 ###

d1 = data.groupby('key1').mean()
    print(d1)
    #          data1     data2
    # key1                    
    # a     0.244144 -0.292304
    # b     1.041258 -0.151594
    d2 = data.groupby(['key1','key2']).mean()
    print()
    print(d2)
    #               data1     data2
    # key1 key2                    
    # a    one   0.126233 -0.447970
    #      two   0.479966  0.019028
    # b    one   1.173468 -0.943761
    #      two   0.909048  0.640573

groupby 的size 用法

size = data.groupby(['key1', 'key2']).size()
    print(size)
    # key1  key2
    # a     one     2
    #       two     1
    # b     one     1
    #       two     1
    # dtype: int64

### \#对分组进行迭代   \#groupby 对象支持迭代， 可以产生一组二元元组 ###

print('对分组进行迭代')
    for name, group in data.groupby('key1'):    #key1 的属性值有2个， a 和b 
        print(name)
        print(group)
    # a
    #       data1     data2 key1 key2
    # 0  0.824188 -0.109497    a  one
    # 1  0.479966  0.019028    a  two
    # 4 -0.571721 -0.786443    a  one
    # b
    #       data1     data2 key1 key2
    # 2  1.173468 -0.943761    b  one
    # 3  0.909048  0.640573    b  two

### 将数据片段做成字典 ###

print('-----')
    pieces = dict(list(data.groupby('key1')))   #建立字典
    print(pieces['b'])
    #       data1     data2 key1 key2
    # 2  1.173468 -0.943761    b  one
    # 3  0.909048  0.640573    b  two

#groupby 默认是在axis=0 上进行分组的， 通过设置可以设置在其他的轴上进行分组。可以根据dtype 对列进行分组
    print(data.dtypes)  
    # data1    float64
    # data2    float64
    # key1      object
    # key2      object
    # dtype: object
    groupd = data.groupby(data.dtypes, axis = 1)
    print(dict(list(groupd)))   #依据dtypes进行分组
    # {dtype('O'):   key1 key2
    # 0    a  one
    # 1    a  two
    # 2    b  one
    # 3    b  two
    # 4    a  one, dtype('float64'):       data1     data2
    # 0  0.824188 -0.109497
    # 1  0.479966  0.019028
    # 2  1.173468 -0.943761
    # 3  0.909048  0.640573
    # 4 -0.571721 -0.786443}

### 选取一个或一组列 ###

对于由dataframe产生的groupby对象， 如果用一个（单个字符串）或一组（字符串数组）列名对其进行索引， 就能实现部分列进行聚合的目的。

#选取一个列或一个组
    group1 = data.groupby('key1')[['data1']].mean()    #双层中括号
    print(group1)
    #          data1
    # key1          
    # a     0.244144
    # b     1.041258

#如果只需要计算data2 列的平均值， 并以DataFrame形式得到结果
    group_d = data.groupby(['key1','key2'])[['data2']].mean()
    print(group_d)
    #               data2        #由于得到的是datafram数据结构，可以通过索引机制获得值
    # key1 key2          
    # a    one  -0.447970
    #      two   0.019028
    # b    one  -0.943761
    #      two   0.640573
    print(group_d.ix['a'])
    #          data2
    # key2          
    # one  -0.447970
    # two   0.019028

### 通过字典或者Series进行分组 ###

np.random.seed(666)
    people = pd.DataFrame(np.random.randn(5,5),
                       columns = list('abcde'),
                       index = ['Joe','Steve','Wes', 'Jim', 'Travis']
                       )
    people.ix[2:3, ['b', 'c']] = np.NaN #添加2个na值
    print(people)
    #                a         b         c         d         e
    # Joe     0.824188  0.479966  1.173468  0.909048 -0.571721
    # Steve  -0.109497  0.019028 -0.943761  0.640573 -0.786443
    # Wes     0.608870       NaN       NaN -0.736918 -0.298733
    # Jim    -0.460587 -1.088793 -0.575771 -1.682901  0.229185
    # Travis -1.756625  0.844633  0.277220  0.852902  0.194600
    
    mapping = {'a':'red', 'b':'red', 'c':'blue','d':'blue','e':'yellow','f':'orange'}  #f 中对应上述数据不存在
    by_color = people.groupby(mapping, axis=1).sum()    #依据列进行索引
    print(by_color)
    #             blue       red    yellow
    # Joe     2.082516  1.304154 -0.571721
    # Steve  -0.303188 -0.090469 -0.786443
    # Wes    -0.736918  0.608870 -0.298733
    # Jim    -2.258672 -1.549380  0.229185
    # Travis  1.130121 -0.911993  0.194600
    #Series 也有同样的功能， 他可以被看做是一个固定大小的映射
    map_series = pd.Series(mapping)
    group_series = people.groupby(mapping, axis=1).count()
    print(group_series)
    #         blue  red  yellow
    # Joe        2    2       1
    # Steve      2    2       1
    # Wes        1    1       1
    # Jim        2    2       1
    # Travis     2    2       1

### 通过函数进行分组 ###

#根据人名的长度进行分组
    name_len = people.groupby(len).sum()
    print(name_len)
    #           a         b         c         d         e
    # 3  0.972471 -0.608827  0.597697 -1.510771 -0.641269
    # 5 -0.109497  0.019028 -0.943761  0.640573 -0.786443
    # 6 -1.756625  0.844633  0.277220  0.852902  0.194600

### 根据索引级别分组 ###

#层次化索引数据集最方便的地方在于它能够根据索引级别进行聚合
    columns = pd.MultiIndex.from_arrays([['US','US','CHINA','JP','JP'],
                                         [1,3,5,1,3]],
                                         names = ['city','tenor']
                                         )
    hier_data = pd.DataFrame(np.random.randn(4,5), columns = columns)
    print(hier_data)
    # city         US               CHINA        JP          
    # tenor         1         3         5         1         3
    # 0      1.310638  1.543844 -0.529048 -0.656472 -0.201506
    # 1     -0.700616  0.687138 -0.026076 -0.829758  0.296554
    # 2     -0.312680 -0.611301 -0.821752  0.897123  0.136079
    # 3     -0.258655  1.110766 -0.188424 -0.041489 -0.984792
    city_count = hier_data.groupby(level='city', axis=1).count()
    print(city_count)
    # city  CHINA  JP  US
    # 0         1   2   2
    # 1         1   2   2
    # 2         1   2   2
    # 3         1   2   2

## 2.数据聚合 ##

data = pd.DataFrame({'key1':['a','a','b','b','a'],
                         'key2':['one','two','one','two','one'],
                         'data1':np.random.randn(5),
                         'data2':np.random.randn(5)
                         })
    print(data)
    #       data1     data2 key1 key2
    # 0  0.824188 -0.109497    a  one
    # 1  0.479966  0.019028    a  two
    # 2  1.173468 -0.943761    b  one
    # 3  0.909048  0.640573    b  two
    # 4 -0.571721 -0.786443    a  one

groups = data.groupby('key1')
    print(groups['data1'].quantile(0.9))    #quantile函数， 样本分位数，是Series 的一个方法
    # key1
    # a    0.755344
    # b    1.147026
    # Name: data1, dtype: float64

使用自己的聚合函数， 需要将其传入到aggregate 或agg 方法中;

def peak_to_peak(arr):
        return arr.max()-arr.min()
    print(groups['data2'].agg(peak_to_peak)) 
    # key1
    # a    0.805471
    # b    1.584334
    # Name: data2, dtype: float64
    print(groups.describe())    #可以传入describe 函数

### 分组集运算和转换 ###

使用transform 和 apply 方法

#       data1     data2 key1 key2
    # 0  0.824188 -0.109497    a  one
    # 1  0.479966  0.019028    a  two
    # 2  1.173468 -0.943761    b  one
    # 3  0.909048  0.640573    b  two
    # 4 -0.571721 -0.786443    a  one
    # 为dataframe 添加一个用于存放个索引分组平均值的列，一种方法是先聚合在合并
    k1_means = data.groupby('key1').mean().add_prefix('mean_')
    print(k1_means)
    #       mean_data1  mean_data2
    # key1                        
    # a       0.244144   -0.292304
    # b       1.041258   -0.151594
    merge_data = pd.merge(data, k1_means, left_on = 'key1', right_index=True)   #进行聚合
    print(merge_data)
    #       data1     data2 key1 key2  mean_data1  mean_data2
    # 0  0.824188 -0.109497    a  one    0.244144   -0.292304
    # 1  0.479966  0.019028    a  two    0.244144   -0.292304
    # 4 -0.571721 -0.786443    a  one    0.244144   -0.292304
    # 2  1.173468 -0.943761    b  one    1.041258   -0.151594
    # 3  0.909048  0.640573    b  two    1.041258   -0.151594
    
    #使用people 数据,在groupby上使用transform 
    #                a         b         c         d         e
    # Joe     0.824188  0.479966  1.173468  0.909048 -0.571721
    # Steve  -0.109497  0.019028 -0.943761  0.640573 -0.786443
    # Wes     0.608870       NaN       NaN -0.736918 -0.298733
    # Jim    -0.460587 -1.088793 -0.575771 -1.682901  0.229185
    # Travis -1.756625  0.844633  0.277220  0.852902  0.194600
    
    key = ['one','two','one','two','one']
    by_key = people.groupby(key).mean()
    print(by_key)
    #             a         b         c         d         e
    # one -0.107856  0.662299  0.725344  0.341677 -0.225285
    # two -0.285042 -0.534882 -0.759766 -0.521164 -0.278629
    transform_key = people.groupby(key).transform(np.mean)  #transform 会将一个函数应用到各个分组，然后将其放到合适位置
    print(transform_key)
    #                a         b         c         d         e
    # Joe    -0.107856  0.662299  0.725344  0.341677 -0.225285
    # Steve  -0.285042 -0.534882 -0.759766 -0.521164 -0.278629
    # Wes    -0.107856  0.662299  0.725344  0.341677 -0.225285
    # Jim    -0.285042 -0.534882 -0.759766 -0.521164 -0.278629
    # Travis -0.107856  0.662299  0.725344  0.341677 -0.225285
    
    def demean(arr):
        return arr - arr.mean()
    demeaned = people.groupby(key).transform(demean)
    print(demeaned)
    #                a         b         c         d         e
    # Joe     0.932044 -0.182333  0.448124  0.567371 -0.346437
    # Steve   0.175545  0.553911 -0.183995  1.161737 -0.507814
    # Wes     0.716726       NaN       NaN -1.078595 -0.073448
    # Jim    -0.175545 -0.553911  0.183995 -1.161737  0.507814
    # Travis -1.648770  0.182333 -0.448124  0.511224  0.419884

分位数和桶分析

print('分位数')
    frame = pd.DataFrame({'data1':np.random.randn(1000),
                          'data2':np.random.randn(1000)
                          })
    factor = pd.cut(frame.data1, 4)
    #有cut返回的factor对象可以会直接用于Groupby
    print(factor[:10])
    def get_stats(group):
        return {'max':group.max(),'min':group.min(),'count':group.count(),
                'mean':group.mean()}
    grouped = frame.data2.groupby(factor)
    gro = grouped.apply(get_stats).unstack()    #将数据集分成4份，分别计算每一份中data2 的4 个统计量
    print(gro)
    #                   count       max      mean       min
    # data1                                                
    # (-3.202, -1.548]   65.0  2.279159  0.023773 -1.777999
    # (-1.548, 0.099]   496.0  3.159843 -0.000178 -3.438710
    # (0.099, 1.746]    402.0  2.744144 -0.048409 -2.652843
    # (1.746, 3.394]     37.0  1.426442  0.051988 -2.210856
    
    grouping = pd.qcut(frame.data1, 10, labels = False )
    grouped1 = frame.data2.groupby(grouping)
    gro2 = grouped1.apply(get_stats).unstack()
    print(gro2)
    #        count       max      mean       min
    # data1                                     
    # 0      100.0  3.159843  0.024953 -1.863054
    # 1      100.0  3.006112 -0.044596 -2.744542
    # 2      100.0  2.134254  0.016900 -2.827021
    # 3      100.0  2.144788 -0.003929 -3.438710
    # 4      100.0  2.106217  0.134829 -2.465120
    # 5      100.0  1.611080 -0.215401 -2.773363
    # 6      100.0  2.205833 -0.033742 -2.652843
    # 7      100.0  2.744144  0.060229 -2.379128
    # 8      100.0  2.318960 -0.007054 -2.165005
    # 9      100.0  1.849263 -0.092987 -2.471711

一些pandas的应用
    In [63]:
    
    import numpy as np
    import pandas as pd
    np.random.seed(666)
    df = pd.DataFrame(np.random.randn(7, 3))
    df.iloc[:2,1] = np.nan
    df
    df.dropna(thresh=3)
    Out[63]:
    0	1	2
    2	0.019028	-0.943761	0.640573
    3	-0.786443	0.608870	-0.931012
    4	0.978222	-0.736918	-0.298733
    5	-0.460587	-1.088793	-0.575771
    6	-1.682901	0.229185	-1.756625
    In [64]:
    
    df.drop_duplicates()
    Out[64]:
    0	1	2
    0	0.824188	NaN	1.173468
    1	0.909048	NaN	-0.109497
    2	0.019028	-0.943761	0.640573
    3	-0.786443	0.608870	-0.931012
    4	0.978222	-0.736918	-0.298733
    5	-0.460587	-1.088793	-0.575771
    6	-1.682901	0.229185	-1.756625
    
    # 用函数和映射来转换数据
    In [71]:
    
    data = pd.DataFrame({'food':['bacon','pulled pork', 'bacon','pastrami','corned beef','Bacon','pastrami',
                                'honey ham','nova lox'
                                ],'ounces':[4,3,12,6,7.5,8,3,5,6]})
    data
    Out[71]:
    food	ounces
    0	bacon	4.0
    1	pulled pork	3.0
    2	bacon	12.0
    3	pastrami	6.0
    4	corned beef	7.5
    5	Bacon	8.0
    6	pastrami	3.0
    7	honey ham	5.0
    8	nova lox	6.0
    
     增加一列， 表示每种肉的来源的动物
    In [74]:
    
    meat_from_animal = {'bacon':'pig',
                       'pulled pork':'pig',
                        'pastrami':'cow',
                        'corned beef':'cow',
                        'honey ham':'pig',
                        'nova lox':'salmon'
                       }
    In [72]:
    
    lowercased
    lowercased = data['food'].str.lower()
    lowercased
    Out[72]:
    0          bacon
    1    pulled pork
    2          bacon
    3       pastrami
    4    corned beef
    5          bacon
    6       pastrami
    7      honey ham
    8       nova lox
    Name: food, dtype: object
    使用map的映射的方法
    In [75]:
    
    data['animal'] = lowercased.map(meat_from_animal)
    data
    Out[75]:
    food	ounces	animal
    0	bacon	4.0	pig
    1	pulled pork	3.0	pig
    2	bacon	12.0	pig
    3	pastrami	6.0	cow
    4	corned beef	7.5	cow
    5	Bacon	8.0	pig
    6	pastrami	3.0	cow
    7	honey ham	5.0	pig
    8	nova lox	6.0	salmon
    使用一个函数解决
    In [77]:
    
    data['food'].map(lambda x: meat_from_animal[x.lower()])
    data
    Out[77]:
    food	ounces	animal
    0	bacon	4.0	pig
    1	pulled pork	3.0	pig
    2	bacon	12.0	pig
    3	pastrami	6.0	cow
    4	corned beef	7.5	cow
    5	Bacon	8.0	pig
    6	pastrami	3.0	cow
    7	honey ham	5.0	pig
    8	nova lox	6.0	salmon
    替换值
    In [78]:
    
    data
    data = pd.Series([1, -999, -2,-999,-1000,3])
    data
    Out[78]:
    0       1
    1    -999
    2      -2
    3    -999
    4   -1000
    5       3
    dtype: int64
    In [83]:
    
    data.replace(-999,np.nan)
    Out[83]:
    0       1.0
    1       NaN
    2      -2.0
    3       NaN
    4   -1000.0
    5       3.0
    dtype: float64
    In [84]:
    
    data.replace([-999,-1000],[0,np.nan])
    Out[84]:
    0    1.0
    1    0.0
    2   -2.0
    3    0.0
    4    NaN
    5    3.0
    dtype: float64
    In [85]:
    
    data.replace({-999:np.nan,-1000:10})
    Out[85]:
    0     1.0
    1     NaN
    2    -2.0
    3     NaN
    4    10.0
    5     3.0
    dtype: float64
    离散化和装箱 cut , value_counts , codes, categories
    In [86]:
    
    data
    ages = [20, 22, 25, 27, 21, 23, 37, 31, 61, 45, 41, 32]
    bins = [18,25,35,60,100]
    data = pd.cut(ages, bins)
    data
    Out[86]:
    [(18, 25], (18, 25], (18, 25], (25, 35], (18, 25], ..., (25, 35], (60, 100], (35, 60], (35, 60], (25, 35]]
    Length: 12
    Categories (4, interval[int64]): [(18, 25] < (25, 35] < (35, 60] < (60, 100]]
    In [88]:
    
    data.codes
    Out[88]:
    array([0, 0, 0, 1, 0, 0, 2, 1, 3, 2, 2, 1], dtype=int8)
    In [90]:
    
    pd.value_counts(data)
    Out[90]:
    (18, 25]     5
    (35, 60]     3
    (25, 35]     3
    (60, 100]    1
    dtype: int64
    设定bin 的名字
    In [92]:
    
    data
    group_names = ['Youth','YoungAdult','Middleage','Senior']
    data = pd.cut(ages, bins , labels = group_names)
    data
    Out[92]:
    [Youth, Youth, Youth, YoungAdult, Youth, ..., YoungAdult, Senior, Middleage, Middleage, YoungAdult]
    Length: 12
    Categories (4, object): [Youth < YoungAdult < Middleage < Senior]
    In [93]:
    
    pd.value_counts(data)
    Out[93]:
    Youth         5
    Middleage     3
    YoungAdult    3
    Senior        1
    dtype: int64
    qcut 按照数据的分位数来分箱， 取决于数据的分布， 按照百分比进行切分
    In [94]:
    
    cats
    np.random.seed(666)
    data = np.random.randn(1000)
    cats = pd.qcut(data, 4)
    cats
    Out[94]:
    [(0.688, 3.394], (-0.0458, 0.688], (0.688, 3.394], (0.688, 3.394], (-0.679, -0.0458], ..., (-0.0458, 0.688], (0.688, 3.394], (-0.679, -0.0458], (-3.197, -0.679], (-0.679, -0.0458]]
    Length: 1000
    Categories (4, interval[float64]): [(-3.197, -0.679] < (-0.679, -0.0458] < (-0.0458, 0.688] < (0.688, 3.394]]
    In [95]:
    
    pd.value_counts(cats)
    Out[95]:
    (0.688, 3.394]       250
    (-0.0458, 0.688]     250
    (-0.679, -0.0458]    250
    (-3.197, -0.679]     250
    dtype: int64

### 层次化索引：（hierarchical indexing）它使我们能在一个轴上拥有多个（两个以上）的索引级别 ###

print('层次性索引')
    np.random.seed(666)
    data = pd.Series(np.random.randn(10), index = [['a','a','a','b','b','b','c','c','d','d'],
                                                   [1,2,3,1,2,3,1,2,2,3]
                                                   ])
    print(data)
    # a  1    0.824188
    #    2    0.479966
    #    3    1.173468
    # b  1    0.909048
    #    2   -0.571721
    #    3   -0.109497
    # c  1    0.019028
    #    2   -0.943761
    # d  2    0.640573
    #    3   -0.786443
    # dtype: float64
    
    #选取数据子集
    print(data['a'])
    # 1    0.824188
    # 2    0.479966
    # 3    1.173468
    # dtype: float64
    print(data.ix[['a','b']])
    print(data[:,2])    #在内层进行选择
    # a    0.479966
    # b   -0.571721
    # c   -0.943761
    # d    0.640573
    # dtype: float64
    
    #可以通过使用unstack 的方法，使其转换成一个DataFrame
    print(data.unstack())
    #           1         2         3
    # a  0.824188  0.479966  1.173468
    # b  0.909048 -0.571721 -0.109497
    # c  0.019028 -0.943761       NaN
    # d       NaN  0.640573 -0.786443
    print(data.unstack().stack()) #unstack 的逆运算是 stack
    
    #对于一个DataFrame , 每一条轴可以使用分层索引
    frame = pd.DataFrame(np.arange(12).reshape((4,3)), index = [['a','a','b','b'],[1,2,1,2]],
                         columns = [['Ohio','Ohio','Colorado'],['Green','Red','Green']]
                         )
    print(frame)
    #      Ohio     Colorado
    #     Green Red    Green
    # a 1     0   1        2
    #   2     3   4        5
    # b 1     6   7        8
    #   2     9  10       11
    frame.index.names = ['key1','key2']
    frame.columns.names = ['state','color']
    print(frame)
    # print(frame.key1)    #制定了每行每列的名称， 就会显示在控制台输出，但是不雅将索引名和轴标签混为一谈
    # color     Green Red    Green
    # key1 key2                   
    # a    1        0   1        2
    #      2        3   4        5
    # b    1        6   7        8
    #      2        9  10       11

重排分级顺序，swaplevel 接受两个级别 的编号或名称， 并返回一个互换了级别的新对象，数据不会发生变化

frame  = frame.swaplevel('key1','key2')
    print(frame)
    # state      Ohio     Colorado
    # color     Green Red    Green
    # key2 key1                   
    # 1    a        0   1        2
    # 2    a        3   4        5
    # 1    b        6   7        8
    # 2    b        9  10       11
    frame = frame.sortlevel(0)
    print(frame)
    # state      Ohio     Colorado
    # color     Green Red    Green
    # key2 key1                   
    # 1    a        0   1        2
    #      b        6   7        8
    # 2    a        3   4        5
    #      b        9  10       11
    #根据级别汇总进行统计
    sum_key2 = frame.sum(level = 'key2')
    print(sum_key2)
    # state  Ohio     Colorado
    # color Green Red    Green
    # key2                    
    # 1         6   8       10
    # 2        12  14       16
    sum_color = frame.sum(level='color', axis =1)
    print(sum_color)
    # color      Green  Red
    # key2 key1            
    # 1    a         2    1
    #      b        14    7
    # 2    a         8    4
    #      b        20   10

使用DataFrme 的列

frame = pd.DataFrame({'a':range(7),'b':range(7,0,-1),
                          'c':['one','one','one','two','two','two','two'],
                          'd':[0,1,2,0,1,2,3]
                          })
    print(frame)
    #    a  b    c  d
    # 0  0  7  one  0
    # 1  1  6  one  1
    # 2  2  5  one  2
    # 3  3  4  two  0
    # 4  4  3  two  1
    # 5  5  2  two  2
    # 6  6  1  two  3
    frame2 = frame.set_index(['c','d']) 
    #将c， d列转换成行索引， 并创建一个新的DataFrame ,c,d 列会从dataFrame上移除，
    #但是也可以使其保留下来  使用drop = False
    
    print(frame2)
    #        a  b
    # c   d      
    # one 0  0  7
    #     1  1  6
    #     2  2  5
    # two 0  3  4
    #     1  4  3
    #     2  5  2
    #     3  6  1
    
    frame3 = frame.set_index(['c','d'],drop= False) #保留c,d 行
    print(frame3)
    #        a  b    c  d
    # c   d              
    # one 0  0  7  one  0
    #     1  1  6  one  1
    #     2  2  5  one  2
    # two 0  3  4  two  0
    #     1  4  3  two  1
    #     2  5  2  two  2
    #     3  6  1  two  3

透视表：pivot\_table

![format_png][]

[20200423154033302.PNG]: /images/20220519/21c11793eed14300960f8ef7c635dec7.png
[70]: /images/20220519/849e92595ed94a4eb8ffee2e667c0a1d.png
[format_png]: /images/20220519/c4dd8246794a432ea67791a55b57cea1.png