Python数据分析_第08课：数据聚合与分组处理_笔记

朱雀 2021-11-02 11:26 340阅读 0赞

### 文章目录 ###

*  数据聚合、分组运算与转换、透视表与交叉表
 *  GroupBy
 *  对分组进行迭代
 *  选择一个或一组列
 *  通过字典或Series进行分组
 *  通过函数进行分组
 *  通过索引进行分组
 *  数据聚合
 *  面向列的多函数应用
 *  分组及运算和转换
 *  apply方法
 *  禁止分组键
 *  分位数和桶分析
 *  用特定于分组的值填充缺失值
 *  随机采样和排列
 *  不显示分组关键字
 *  分组加权平均数和相关系数
 *  透视表
 *  交叉表
 *  2012联邦选举委员会数据分析
 *  根据职业和雇主统计赞助信息
 *  对出资额分组
 *  根据州统计赞助信息

GitHub: [https://github.com/RealEmperor/Python-for-Data-Analysis][https_github.com_RealEmperor_Python-for-Data-Analysis]

# 数据聚合、分组运算与转换、透视表与交叉表 #

import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd
    from pandas import Series, DataFrame
    
    np.random.seed(12345)
    plt.rc('figure', figsize=(10, 6))
    
    np.set_printoptions(precision=4)
    
    pd.options.display.notebook_repr_html = False
    get_ipython().magic(u'matplotlib inline')

# GroupBy #

df = DataFrame({
        'key1': ['a', 'a', 'b', 'b', 'a'],
                    'key2': ['one', 'two', 'one', 'two', 'one'],
                    'data1': np.random.randn(5),
                    'data2': np.random.randn(5)})
    print(df)

data1     data2 key1 key2
    0 -0.204708  1.393406    a  one
    1  0.478943  0.092908    a  two
    2 -0.519439  0.281746    b  one
    3 -0.555730  0.769023    b  two
    4  1.965781  1.246435    a  one

grouped = df['data1'].groupby(df['key1'])
    print(list(grouped))

[('a', 0   -0.204708
    1    0.478943
    4    1.965781
    Name: data1, dtype: float64), ('b', 2   -0.519439
    3   -0.555730
    Name: data1, dtype: float64)]

grouped.mean()

key1
    a    0.746672
    b   -0.537585
    Name: data1, dtype: float64

means = df['data1'].groupby([df['key1'], df['key2']]).mean()
    print(means)

key1  key2
    a     one     0.880536
          two     0.478943
    b     one    -0.519439
          two    -0.555730
    Name: data1, dtype: float64

means.unstack()

key2       one       two
    key1                    
    a     0.880536  0.478943
    b    -0.519439 -0.555730

states = np.array(['Ohio', 'California', 'California', 'Ohio', 'Ohio'])
    years = np.array([2005, 2005, 2006, 2005, 2006])
    df['data1'].groupby([states, years]).mean()

California  2005    0.478943
                2006   -0.519439
    Ohio        2005   -0.380219
                2006    1.965781
    Name: data1, dtype: float64

df.groupby('key1').mean()

data1     data2
    key1                    
    a     0.746672  0.910916
    b    -0.537585  0.525384

df.groupby(['key1', 'key2']).mean()

data1     data2
    key1 key2                    
    a    one   0.880536  1.319920
         two   0.478943  0.092908
    b    one  -0.519439  0.281746
         two  -0.555730  0.769023

df.groupby(['key1', 'key2']).size()

key1  key2
    a     one     2
          two     1
    b     one     1
          two     1
    dtype: int64

# 对分组进行迭代 #

for name, group in df.groupby('key1'):
        print(name)
        print(group)

a
          data1     data2 key1 key2
    0 -0.204708  1.393406    a  one
    1  0.478943  0.092908    a  two
    4  1.965781  1.246435    a  one
    b
          data1     data2 key1 key2
    2 -0.519439  0.281746    b  one
    3 -0.555730  0.769023    b  two

list(df.groupby('key1'))

[('a',       data1     data2 key1 key2
      0 -0.204708  1.393406    a  one
      1  0.478943  0.092908    a  two
      4  1.965781  1.246435    a  one), ('b',       data1     data2 key1 key2
      2 -0.519439  0.281746    b  one
      3 -0.555730  0.769023    b  two)]

for (k1, k2), group in df.groupby(['key1', 'key2']):
        print((k1, k2))
        print(group)

('a', 'one')
          data1     data2 key1 key2
    0 -0.204708  1.393406    a  one
    4  1.965781  1.246435    a  one
    ('a', 'two')
          data1     data2 key1 key2
    1  0.478943  0.092908    a  two
    ('b', 'one')
          data1     data2 key1 key2
    2 -0.519439  0.281746    b  one
    ('b', 'two')
         data1     data2 key1 key2
    3 -0.55573  0.769023    b  two

pieces = dict(list(df.groupby('key1')))
    pieces['b']

data1     data2 key1 key2
    2 -0.519439  0.281746    b  one
    3 -0.555730  0.769023    b  two

df.dtypes

data1    float64
    data2    float64
    key1      object
    key2      object
    dtype: object

grouped = df.groupby(df.dtypes, axis=1)
    dict(list(grouped))

{dtype('float64'):       data1     data2
     0 -0.204708  1.393406
     1  0.478943  0.092908
     2 -0.519439  0.281746
     3 -0.555730  0.769023
     4  1.965781  1.246435, dtype('O'):   key1 key2
     0    a  one
     1    a  two
     2    b  one
     3    b  two
     4    a  one}

# 选择一个或一组列 #

list(df.groupby('key1')['data1'])

[('a', 0   -0.204708
      1    0.478943
      4    1.965781
      Name: data1, dtype: float64), ('b', 2   -0.519439
      3   -0.555730
      Name: data1, dtype: float64)]

list(df.groupby('key1')[['data2']])

list(df['data1'].groupby(df['key1']))

[('a', 0   -0.204708
      1    0.478943
      4    1.965781
      Name: data1, dtype: float64), ('b', 2   -0.519439
      3   -0.555730
      Name: data1, dtype: float64)]

list(df[['data2']].groupby(df['key1']))

[('a',       data2
      0  1.393406
      1  0.092908
      4  1.246435), ('b',       data2
      2  0.281746
      3  0.769023)]

df.groupby(['key1', 'key2'])[['data2']].mean()

data2
    key1 key2          
    a    one   1.319920
         two   0.092908
    b    one   0.281746
         two   0.769023

s_grouped = df.groupby(['key1', 'key2'])['data2']
    list(s_grouped)

[(('a', 'one'), 0    1.393406
      4    1.246435
      Name: data2, dtype: float64), (('a', 'two'), 1    0.092908
      Name: data2, dtype: float64), (('b', 'one'), 2    0.281746
      Name: data2, dtype: float64), (('b', 'two'), 3    0.769023
      Name: data2, dtype: float64)]

s_grouped.mean()

key1  key2
    a     one     1.319920
          two     0.092908
    b     one     0.281746
          two     0.769023
    Name: data2, dtype: float64

# 通过字典或Series进行分组 #

people = DataFrame(np.random.randn(5, 5),
                       columns=['a', 'b', 'c', 'd', 'e'],
                       index=['Joe', 'Steve', 'Wes', 'Jim', 'Travis'])
    people.loc[2:3, ['b', 'c']] = np.nan  # Add a few NA values
    people

mapping = {
        'a': 'red', 'b': 'red', 'c': 'blue',
               'd': 'blue', 'e': 'red', 'f': 'orange'}
    
    by_column = people.groupby(mapping, axis=1)
    by_column.sum()

blue       red
    Joe     0.503905  1.063885
    Steve   1.297183 -1.553778
    Wes    -1.021228 -1.116829
    Jim     0.524712  1.770545
    Travis -4.230992 -2.405455

map_series = Series(mapping)
    map_series

a       red
    b       red
    c      blue
    d      blue
    e       red
    f    orange
    dtype: object

people.groupby(map_series, axis=1).count()

blue  red
    Joe        2    3
    Steve      2    3
    Wes        1    2
    Jim        2    3
    Travis     2    3

# 通过函数进行分组 #

people.groupby(len).sum()

a         b         c         d         e
    3  0.591569 -0.993608  0.798764 -0.791374  2.119639
    5  0.886429 -2.001637 -0.371843  1.669025 -0.438570
    6 -0.713544 -0.831154 -2.370232 -1.860761 -0.860757

key_list = ['one', 'one', 'one', 'two', 'two']
    people.groupby([len, key_list]).min()

a         b         c         d         e
    3 one -0.539741 -1.296221  0.274992 -1.021228 -0.577087
      two  0.124121  0.302614  0.523772  0.000940  1.343810
    5 one  0.886429 -2.001637 -0.371843  1.669025 -0.438570
    6 two -0.713544 -0.831154 -2.370232 -1.860761 -0.860757

# 通过索引进行分组 #

columns = pd.MultiIndex.from_arrays([['US', 'US', 'US', 'JP', 'JP'],
                                         [1, 3, 5, 1, 3]], names=['cty', 'tenor'])
    hier_df = DataFrame(np.random.randn(4, 5), columns=columns)
    hier_df

cty          US                            JP          
    tenor         1         3         5         1         3
    0      0.560145 -1.265934  0.119827 -1.063512  0.332883
    1     -2.359419 -0.199543 -1.541996 -0.970736 -1.307030
    2      0.286350  0.377984 -0.753887  0.331286  1.349742
    3      0.069877  0.246674 -0.011862  1.004812  1.327195

hier_df.groupby(level='cty', axis=1).count()

cty  JP  US
    0     2   3
    1     2   3
    2     2   3
    3     2   3

# 数据聚合 #

print(df)

grouped = df.groupby('key1')
    grouped['data1'].quantile(0.9)

key1
    a    1.668413
    b   -0.523068
    Name: data1, dtype: float64

def peak_to_peak(arr):
        return arr.max() - arr.min()
    
    
    grouped.agg(peak_to_peak)

data1     data2
    key1                    
    a     2.170488  1.300498
    b     0.036292  0.487276

grouped.describe()

data1                                                              \
         count      mean       std       min       25%       50%       75%   
    key1                                                                     
    a      3.0  0.746672  1.109736 -0.204708  0.137118  0.478943  1.222362   
    b      2.0 -0.537585  0.025662 -0.555730 -0.546657 -0.537585 -0.528512   
    
                   data2                                                    \
               max count      mean       std       min       25%       50%   
    key1                                                                     
    a     1.965781   3.0  0.910916  0.712217  0.092908  0.669671  1.246435   
    b    -0.519439   2.0  0.525384  0.344556  0.281746  0.403565  0.525384   
    
                              
               75%       max  
    key1                      
    a     1.319920  1.393406  
    b     0.647203  0.769023

# 面向列的多函数应用 #

tips = pd.read_csv('data/tips.csv')
    
    tips['tip_pct'] = tips['tip'] / tips['total_bill']
    tips[:6]

total_bill   tip     sex smoker  day    time  size   tip_pct
    0       16.99  1.01  Female     No  Sun  Dinner     2  0.059447
    1       10.34  1.66    Male     No  Sun  Dinner     3  0.160542
    2       21.01  3.50    Male     No  Sun  Dinner     3  0.166587
    3       23.68  3.31    Male     No  Sun  Dinner     2  0.139780
    4       24.59  3.61  Female     No  Sun  Dinner     4  0.146808
    5       25.29  4.71    Male     No  Sun  Dinner     4  0.186240

grouped = tips.groupby(['sex', 'smoker'])
    
    grouped_pct = grouped['tip_pct']
    grouped_pct.agg('mean')

sex     smoker
    Female  No        0.156921
            Yes       0.182150
    Male    No        0.160669
            Yes       0.152771
    Name: tip_pct, dtype: float64

grouped_pct.agg(['mean', 'std', peak_to_peak])

mean       std  peak_to_peak
    sex    smoker                                  
    Female No      0.156921  0.036421      0.195876
           Yes     0.182150  0.071595      0.360233
    Male   No      0.160669  0.041849      0.220186
           Yes     0.152771  0.090588      0.674707

grouped_pct.agg([('foo', 'mean'), ('bar', np.std)])

foo       bar
    sex    smoker                    
    Female No      0.156921  0.036421
           Yes     0.182150  0.071595
    Male   No      0.160669  0.041849
           Yes     0.152771  0.090588

functions = ['count', 'mean', 'max']
    result = grouped['tip_pct', 'total_bill'].agg(functions)
    result

tip_pct                     total_bill                  
                    count      mean       max      count       mean    max
    sex    smoker                                                         
    Female No          54  0.156921  0.252672         54  18.105185  35.83
           Yes         33  0.182150  0.416667         33  17.977879  44.30
    Male   No          97  0.160669  0.291990         97  19.791237  48.33
           Yes         60  0.152771  0.710345         60  22.284500  50.81

ftuples = [('Durchschnitt', 'mean'), ('Abweichung', np.var)]
    grouped['tip_pct', 'total_bill'].agg(ftuples)

tip_pct              total_bill           
                  Durchschnitt Abweichung Durchschnitt Abweichung
    sex    smoker                                                
    Female No         0.156921   0.001327    18.105185  53.092422
           Yes        0.182150   0.005126    17.977879  84.451517
    Male   No         0.160669   0.001751    19.791237  76.152961
           Yes        0.152771   0.008206    22.284500  98.244673

grouped.agg({
        'tip': np.max, 'size': 'sum'})

tip  size
    sex    smoker            
    Female No       5.2   140
           Yes      6.5    74
    Male   No       9.0   263
           Yes     10.0   150

grouped.agg({
        'tip_pct': ['min', 'max', 'mean', 'std'],
                 'size': 'sum'})

tip_pct                               size
                        min       max      mean       std  sum
    sex    smoker                                             
    Female No      0.056797  0.252672  0.156921  0.036421  140
           Yes     0.056433  0.416667  0.182150  0.071595   74
    Male   No      0.071804  0.291990  0.160669  0.041849  263
           Yes     0.035638  0.710345  0.152771  0.090588  150

# 分组及运算和转换 #

print(df)

k1_means = df.groupby('key1').mean().add_prefix('mean_')
    k1_means

mean_data1  mean_data2
    key1                        
    a       0.746672    0.910916
    b      -0.537585    0.525384

pd.merge(df, k1_means, left_on='key1', right_index=True)

data1     data2 key1 key2  mean_data1  mean_data2
    0 -0.204708  1.393406    a  one    0.746672    0.910916
    1  0.478943  0.092908    a  two    0.746672    0.910916
    4  1.965781  1.246435    a  one    0.746672    0.910916
    2 -0.519439  0.281746    b  one   -0.537585    0.525384
    3 -0.555730  0.769023    b  two   -0.537585    0.525384

print(people)

a         b         c         d         e
    Joe     1.007189 -1.296221  0.274992  0.228913  1.352917
    Steve   0.886429 -2.001637 -0.371843  1.669025 -0.438570
    Wes    -0.539741       NaN       NaN -1.021228 -0.577087
    Jim     0.124121  0.302614  0.523772  0.000940  1.343810
    Travis -0.713544 -0.831154 -2.370232 -1.860761 -0.860757

key = ['one', 'two', 'one', 'two', 'one']
    people.groupby(key).mean()

a         b         c         d         e
    one -0.082032 -1.063687 -1.047620 -0.884358 -0.028309
    two  0.505275 -0.849512  0.075965  0.834983  0.452620

people.groupby(key).transform(np.mean)

a         b         c         d         e
    Joe    -0.082032 -1.063687 -1.047620 -0.884358 -0.028309
    Steve   0.505275 -0.849512  0.075965  0.834983  0.452620
    Wes    -0.082032 -1.063687 -1.047620 -0.884358 -0.028309
    Jim     0.505275 -0.849512  0.075965  0.834983  0.452620
    Travis -0.082032 -1.063687 -1.047620 -0.884358 -0.028309

def demean(arr):
        return arr - arr.mean()
    
    
    demeaned = people.groupby(key).transform(demean)
    demeaned

a         b         c         d         e
    Joe     1.089221 -0.232534  1.322612  1.113271  1.381226
    Steve   0.381154 -1.152125 -0.447807  0.834043 -0.891190
    Wes    -0.457709       NaN       NaN -0.136869 -0.548778
    Jim    -0.381154  1.152125  0.447807 -0.834043  0.891190
    Travis -0.631512  0.232534 -1.322612 -0.976402 -0.832448

demeaned.groupby(key).mean()

a             b    c             d    e
    one  0.000000e+00 -1.110223e-16  0.0  7.401487e-17  0.0
    two -2.775558e-17  0.000000e+00  0.0  0.000000e+00  0.0

# apply方法 #

def top(df, n=5, column='tip_pct'):
        return df.sort_values(by=column)[-n:]
    
    
    top(tips, n=6)

total_bill   tip     sex smoker  day    time  size   tip_pct
    109       14.31  4.00  Female    Yes  Sat  Dinner     2  0.279525
    183       23.17  6.50    Male    Yes  Sun  Dinner     4  0.280535
    232       11.61  3.39    Male     No  Sat  Dinner     2  0.291990
    67         3.07  1.00  Female    Yes  Sat  Dinner     1  0.325733
    178        9.60  4.00  Female    Yes  Sun  Dinner     2  0.416667
    172        7.25  5.15    Male    Yes  Sun  Dinner     2  0.710345

tips.groupby('smoker').apply(top)

total_bill   tip     sex smoker   day    time  size   tip_pct
    smoker                                                                   
    No     88        24.71  5.85    Male     No  Thur   Lunch     2  0.236746
           185       20.69  5.00    Male     No   Sun  Dinner     5  0.241663
           51        10.29  2.60  Female     No   Sun  Dinner     2  0.252672
           149        7.51  2.00    Male     No  Thur   Lunch     2  0.266312
           232       11.61  3.39    Male     No   Sat  Dinner     2  0.291990
    Yes    109       14.31  4.00  Female    Yes   Sat  Dinner     2  0.279525
           183       23.17  6.50    Male    Yes   Sun  Dinner     4  0.280535
           67         3.07  1.00  Female    Yes   Sat  Dinner     1  0.325733
           178        9.60  4.00  Female    Yes   Sun  Dinner     2  0.416667
           172        7.25  5.15    Male    Yes   Sun  Dinner     2  0.710345

tips.groupby(['smoker', 'day']).apply(top, n=1, column='total_bill')

total_bill    tip     sex smoker   day    time  size  \
    smoker day                                                              
    No     Fri  94        22.75   3.25  Female     No   Fri  Dinner     2   
           Sat  212       48.33   9.00    Male     No   Sat  Dinner     4   
           Sun  156       48.17   5.00    Male     No   Sun  Dinner     6   
           Thur 142       41.19   5.00    Male     No  Thur   Lunch     5   
    Yes    Fri  95        40.17   4.73    Male    Yes   Fri  Dinner     4   
           Sat  170       50.81  10.00    Male    Yes   Sat  Dinner     3   
           Sun  182       45.35   3.50    Male    Yes   Sun  Dinner     3   
           Thur 197       43.11   5.00  Female    Yes  Thur   Lunch     4   
    
                      tip_pct  
    smoker day                 
    No     Fri  94   0.142857  
           Sat  212  0.186220  
           Sun  156  0.103799  
           Thur 142  0.121389  
    Yes    Fri  95   0.117750  
           Sat  170  0.196812  
           Sun  182  0.077178  
           Thur 197  0.115982

result = tips.groupby('smoker')['tip_pct'].describe()
    result

count      mean       std       min       25%       50%       75%  \
    smoker                                                                      
    No      151.0  0.159328  0.039910  0.056797  0.136906  0.155625  0.185014   
    Yes      93.0  0.163196  0.085119  0.035638  0.106771  0.153846  0.195059   
    
                 max  
    smoker            
    No      0.291990  
    Yes     0.710345

result.unstack('smoker')

smoker
    count  No        151.000000
           Yes        93.000000
    mean   No          0.159328
           Yes         0.163196
    std    No          0.039910
           Yes         0.085119
    min    No          0.056797
           Yes         0.035638
    25%    No          0.136906
           Yes         0.106771
    50%    No          0.155625
           Yes         0.153846
    75%    No          0.185014
           Yes         0.195059
    max    No          0.291990
           Yes         0.710345
    dtype: float64

# 禁止分组键 #

tips.groupby('smoker', group_keys=False).apply(top)

total_bill   tip     sex smoker   day    time  size   tip_pct
    88        24.71  5.85    Male     No  Thur   Lunch     2  0.236746
    185       20.69  5.00    Male     No   Sun  Dinner     5  0.241663
    51        10.29  2.60  Female     No   Sun  Dinner     2  0.252672
    149        7.51  2.00    Male     No  Thur   Lunch     2  0.266312
    232       11.61  3.39    Male     No   Sat  Dinner     2  0.291990
    109       14.31  4.00  Female    Yes   Sat  Dinner     2  0.279525
    183       23.17  6.50    Male    Yes   Sun  Dinner     4  0.280535
    67         3.07  1.00  Female    Yes   Sat  Dinner     1  0.325733
    178        9.60  4.00  Female    Yes   Sun  Dinner     2  0.416667
    172        7.25  5.15    Male    Yes   Sun  Dinner     2  0.710345

# 分位数和桶分析 #

frame = DataFrame({
        'data1': np.random.randn(1000),
                       'data2': np.random.randn(1000)})
    factor = pd.cut(frame.data1, 4)
    factor[:10]

0     (-1.23, 0.489]
    1    (-2.956, -1.23]
    2     (-1.23, 0.489]
    3     (0.489, 2.208]
    4     (-1.23, 0.489]
    5     (0.489, 2.208]
    6     (-1.23, 0.489]
    7     (-1.23, 0.489]
    8     (0.489, 2.208]
    9     (0.489, 2.208]
    Name: data1, dtype: category
    Categories (4, interval[float64]): [(-2.956, -1.23] < (-1.23, 0.489] < (0.489, 2.208] < (2.208, 3.928]]

def get_stats(group):
        return {
        'min': group.min(), 'max': group.max(),
                'count': group.count(), 'mean': group.mean()}
    
    
    grouped = frame.data2.groupby(factor)
    grouped.apply(get_stats).unstack()

count       max      mean       min
    data1                                               
    (-2.956, -1.23]   95.0  1.670835 -0.039521 -3.399312
    (-1.23, 0.489]   598.0  3.260383 -0.002051 -2.989741
    (0.489, 2.208]   297.0  2.954439  0.081822 -3.745356
    (2.208, 3.928]    10.0  1.765640  0.024750 -1.929776

grouping = pd.qcut(frame.data1, 10, labels=False)
    
    grouped = frame.data2.groupby(grouping)
    grouped.apply(get_stats).unstack()

count       max      mean       min
    data1                                     
    0      100.0  1.670835 -0.049902 -3.399312
    1      100.0  2.628441  0.030989 -1.950098
    2      100.0  2.527939 -0.067179 -2.925113
    3      100.0  3.260383  0.065713 -2.315555
    4      100.0  2.074345 -0.111653 -2.047939
    5      100.0  2.184810  0.052130 -2.989741
    6      100.0  2.458842 -0.021489 -2.223506
    7      100.0  2.954439 -0.026459 -3.056990
    8      100.0  2.735527  0.103406 -3.745356
    9      100.0  2.377020  0.220122 -2.064111

# 用特定于分组的值填充缺失值 #

s = Series(np.random.randn(6))
    s[::2] = np.nan
    print(s)

0         NaN
    1   -0.125921
    2         NaN
    3   -0.884475
    4         NaN
    5    0.227290
    dtype: float64

s.fillna(s.mean())

0   -0.261035
    1   -0.125921
    2   -0.261035
    3   -0.884475
    4   -0.261035
    5    0.227290
    dtype: float64

states = ['Ohio', 'New York', 'Vermont', 'Florida',
              'Oregon', 'Nevada', 'California', 'Idaho']
    group_key = ['East'] * 4 + ['West'] * 4
    data = Series(np.random.randn(8), index=states)
    data[['Vermont', 'Nevada', 'Idaho']] = np.nan
    print(data)

Ohio          0.922264
    New York     -2.153545
    Vermont            NaN
    Florida      -0.375842
    Oregon        0.329939
    Nevada             NaN
    California    1.105913
    Idaho              NaN
    dtype: float64

data.groupby(group_key).mean()

East   -0.535707
    West    0.717926
    dtype: float64

fill_mean = lambda g: g.fillna(g.mean())
    data.groupby(group_key).apply(fill_mean)

Ohio          0.922264
    New York     -2.153545
    Vermont      -0.535707
    Florida      -0.375842
    Oregon        0.329939
    Nevada        0.717926
    California    1.105913
    Idaho         0.717926
    dtype: float64

fill_values = {
        'East': 0.5, 'West': -1}
    fill_func = lambda g: g.fillna(fill_values[g.name])
    
    data.groupby(group_key).apply(fill_func)

Ohio          0.922264
    New York     -2.153545
    Vermont       0.500000
    Florida      -0.375842
    Oregon        0.329939
    Nevada       -1.000000
    California    1.105913
    Idaho        -1.000000
    dtype: float64

# 随机采样和排列 #

suits = ['H', 'S', 'C', 'D']
    card_val = (list(range(1, 11)) + [10] * 3) * 4
    base_names = ['A'] + list(range(2, 11)) + ['J', 'K', 'Q']
    cards = []
    for suit in ['H', 'S', 'C', 'D']:
        cards.extend(str(num) + suit for num in base_names)
    
    deck = Series(card_val, index=cards)
    
    deck[:13]

AH      1
    2H      2
    3H      3
    4H      4
    5H      5
    6H      6
    7H      7
    8H      8
    9H      9
    10H    10
    JH     10
    KH     10
    QH     10
    dtype: int64

def draw(deck, n=5):
        return deck.take(np.random.permutation(len(deck))[:n])
    
    
    draw(deck)

AD     1
    8C     8
    5H     5
    KC    10
    2C     2
    dtype: int64

get_suit = lambda card: card[-1]  # 只要最后一个字母
    deck.groupby(get_suit).apply(draw, n=2)

C  2C     2
       3C     3
    D  KD    10
       8D     8
    H  KH    10
       3H     3
    S  2S     2
       4S     4
    dtype: int64

# 不显示分组关键字 #

deck.groupby(get_suit, group_keys=False).apply(draw, n=2)

KC    10
    JC    10
    AD     1
    5D     5
    5H     5
    6H     6
    7S     7
    KS    10
    dtype: int64

# 分组加权平均数和相关系数 #

df = DataFrame({
        'category': ['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b'],
                    'data': np.random.randn(8),
                    'weights': np.random.rand(8)})
    print(df)

category      data   weights
    0        a  1.561587  0.957515
    1        a  1.219984  0.347267
    2        a -0.482239  0.581362
    3        a  0.315667  0.217091
    4        b -0.047852  0.894406
    5        b -0.454145  0.918564
    6        b -0.556774  0.277825
    7        b  0.253321  0.955905

grouped = df.groupby('category')
    get_wavg = lambda g: np.average(g['data'], weights=g['weights'])
    grouped.apply(get_wavg)

category
    a    0.811643
    b   -0.122262
    dtype: float64

close_px = pd.read_csv('data/stock_px.csv', parse_dates=True, index_col=0)
    close_px.info()

<class 'pandas.core.frame.DataFrame'>
    DatetimeIndex: 2214 entries, 2003-01-02 to 2011-10-14
    Data columns (total 4 columns):
    AAPL    2214 non-null float64
    MSFT    2214 non-null float64
    XOM     2214 non-null float64
    SPX     2214 non-null float64
    dtypes: float64(4)
    memory usage: 86.5 KB

close_px[-4:]

AAPL   MSFT    XOM      SPX
    2011-10-11  400.29  27.00  76.27  1195.54
    2011-10-12  402.19  26.96  77.16  1207.25
    2011-10-13  408.43  27.18  76.37  1203.66
    2011-10-14  422.00  27.27  78.11  1224.58

rets = close_px.pct_change().dropna()
    spx_corr = lambda x: x.corrwith(x['SPX'])
    by_year = rets.groupby(lambda x: x.year)
    by_year.apply(spx_corr)

AAPL      MSFT       XOM  SPX
    2003  0.541124  0.745174  0.661265  1.0
    2004  0.374283  0.588531  0.557742  1.0
    2005  0.467540  0.562374  0.631010  1.0
    2006  0.428267  0.406126  0.518514  1.0
    2007  0.508118  0.658770  0.786264  1.0
    2008  0.681434  0.804626  0.828303  1.0
    2009  0.707103  0.654902  0.797921  1.0
    2010  0.710105  0.730118  0.839057  1.0
    2011  0.691931  0.800996  0.859975  1.0

# 苹果公司和微软的年度相关系数
    by_year.apply(lambda g: g['AAPL'].corr(g['MSFT']))

2003    0.480868
    2004    0.259024
    2005    0.300093
    2006    0.161735
    2007    0.417738
    2008    0.611901
    2009    0.432738
    2010    0.571946
    2011    0.581987
    dtype: float64

# 透视表 #

tips.pivot_table(index=['sex', 'smoker'])

size       tip   tip_pct  total_bill
    sex    smoker                                          
    Female No      2.592593  2.773519  0.156921   18.105185
           Yes     2.242424  2.931515  0.182150   17.977879
    Male   No      2.711340  3.113402  0.160669   19.791237
           Yes     2.500000  3.051167  0.152771   22.284500

tips.pivot_table(['tip_pct', 'size'], index=['sex', 'day'],
                     columns='smoker')

size             tip_pct          
    smoker             No       Yes        No       Yes
    sex    day                                         
    Female Fri   2.500000  2.000000  0.165296  0.209129
           Sat   2.307692  2.200000  0.147993  0.163817
           Sun   3.071429  2.500000  0.165710  0.237075
           Thur  2.480000  2.428571  0.155971  0.163073
    Male   Fri   2.000000  2.125000  0.138005  0.144730
           Sat   2.656250  2.629630  0.162132  0.139067
           Sun   2.883721  2.600000  0.158291  0.173964
           Thur  2.500000  2.300000  0.165706  0.164417

tips.pivot_table(['tip_pct', 'size'], index=['sex', 'day'],
                     columns='smoker', margins=True)

size                       tip_pct                    
    smoker             No       Yes       All        No       Yes       All
    sex    day                                                             
    Female Fri   2.500000  2.000000  2.111111  0.165296  0.209129  0.199388
           Sat   2.307692  2.200000  2.250000  0.147993  0.163817  0.156470
           Sun   3.071429  2.500000  2.944444  0.165710  0.237075  0.181569
           Thur  2.480000  2.428571  2.468750  0.155971  0.163073  0.157525
    Male   Fri   2.000000  2.125000  2.100000  0.138005  0.144730  0.143385
           Sat   2.656250  2.629630  2.644068  0.162132  0.139067  0.151577
           Sun   2.883721  2.600000  2.810345  0.158291  0.173964  0.162344
           Thur  2.500000  2.300000  2.433333  0.165706  0.164417  0.165276
    All          2.668874  2.408602  2.569672  0.159328  0.163196  0.160803

tips.pivot_table('tip_pct', index=['sex', 'smoker'], columns='day',
                     aggfunc=len, margins=True)

day             Fri   Sat   Sun  Thur    All
    sex    smoker                               
    Female No       2.0  13.0  14.0  25.0   54.0
           Yes      7.0  15.0   4.0   7.0   33.0
    Male   No       2.0  32.0  43.0  20.0   97.0
           Yes      8.0  27.0  15.0  10.0   60.0
    All            19.0  87.0  76.0  62.0  244.0

tips.pivot_table('size', index=['time', 'sex', 'smoker'],
                     columns='day', aggfunc='sum', fill_value=0)

day                   Fri  Sat  Sun  Thur
    time   sex    smoker                     
    Dinner Female No        2   30   43     2
                  Yes       8   33   10     0
           Male   No        4   85  124     0
                  Yes      12   71   39     0
    Lunch  Female No        3    0    0    60
                  Yes       6    0    0    17
           Male   No        0    0    0    50
                  Yes       5    0    0    23

# 交叉表 #

from io import StringIO
    
    data = """Sample    Gender    Handedness
    1    Female    Right-handed
    2    Male    Left-handed
    3    Female    Right-handed
    4    Male    Right-handed
    5    Male    Left-handed
    6    Male    Right-handed
    7    Female    Right-handed
    8    Female    Left-handed
    9    Male    Right-handed
    10    Female    Right-handed"""
    data = pd.read_table(StringIO(data), sep='\s+')
    print(data)

Sample  Gender    Handedness
    0       1  Female  Right-handed
    1       2    Male   Left-handed
    2       3  Female  Right-handed
    3       4    Male  Right-handed
    4       5    Male   Left-handed
    5       6    Male  Right-handed
    6       7  Female  Right-handed
    7       8  Female   Left-handed
    8       9    Male  Right-handed
    9      10  Female  Right-handed

pd.crosstab(data.Gender, data.Handedness, margins=True)

Handedness  Left-handed  Right-handed  All
    Gender                                    
    Female                1             4    5
    Male                  2             3    5
    All                   3             7   10

pd.crosstab([tips.time, tips.day], tips.smoker, margins=True)

smoker        No  Yes  All
    time   day                
    Dinner Fri     3    9   12
           Sat    45   42   87
           Sun    57   19   76
           Thur    1    0    1
    Lunch  Fri     1    6    7
           Thur   44   17   61
    All          151   93  244

# 2012联邦选举委员会数据分析 #

# P00000001-ALL.csv 文件太大，压缩到了P00000001-ALL.zip，需要调用的同学自行解压
    fec = pd.read_csv('data/P00000001-ALL.csv')
    
    fec.info()

C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py:2717: DtypeWarning: Columns (6) have mixed types. Specify dtype option on import or set low_memory=False.
      interactivity=interactivity, compiler=compiler, result=result)
    
    
    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 1001731 entries, 0 to 1001730
    Data columns (total 16 columns):
    cmte_id              1001731 non-null object
    cand_id              1001731 non-null object
    cand_nm              1001731 non-null object
    contbr_nm            1001731 non-null object
    contbr_city          1001712 non-null object
    contbr_st            1001727 non-null object
    contbr_zip           1001620 non-null object
    contbr_employer      988002 non-null object
    contbr_occupation    993301 non-null object
    contb_receipt_amt    1001731 non-null float64
    contb_receipt_dt     1001731 non-null object
    receipt_desc         14166 non-null object
    memo_cd              92482 non-null object
    memo_text            97770 non-null object
    form_tp              1001731 non-null object
    file_num             1001731 non-null int64
    dtypes: float64(1), int64(1), object(14)
    memory usage: 122.3+ MB

fec.loc[123456]

cmte_id                             C00431445
    cand_id                             P80003338
    cand_nm                         Obama, Barack
    contbr_nm                         ELLMAN, IRA
    contbr_city                             TEMPE
    contbr_st                                  AZ
    contbr_zip                          852816719
    contbr_employer      ARIZONA STATE UNIVERSITY
    contbr_occupation                   PROFESSOR
    contb_receipt_amt                          50
    contb_receipt_dt                    01-DEC-11
    receipt_desc                              NaN
    memo_cd                                   NaN
    memo_text                                 NaN
    form_tp                                 SA17A
    file_num                               772372
    Name: 123456, dtype: object

unique_cands = fec.cand_nm.unique()
    unique_cands

array(['Bachmann, Michelle', 'Romney, Mitt', 'Obama, Barack',
           "Roemer, Charles E. 'Buddy' III", 'Pawlenty, Timothy',
           'Johnson, Gary Earl', 'Paul, Ron', 'Santorum, Rick', 'Cain, Herman',
           'Gingrich, Newt', 'McCotter, Thaddeus G', 'Huntsman, Jon',
           'Perry, Rick'], dtype=object)

parties = {
        'Bachmann, Michelle': 'Republican',
               'Cain, Herman': 'Republican',
               'Gingrich, Newt': 'Republican',
               'Huntsman, Jon': 'Republican',
               'Johnson, Gary Earl': 'Republican',
               'McCotter, Thaddeus G': 'Republican',
               'Obama, Barack': 'Democrat',
               'Paul, Ron': 'Republican',
               'Pawlenty, Timothy': 'Republican',
               'Perry, Rick': 'Republican',
               "Roemer, Charles E. 'Buddy' III": 'Republican',
               'Romney, Mitt': 'Republican',
               'Santorum, Rick': 'Republican'}
    
    fec.cand_nm[123456:123461]

123456    Obama, Barack
    123457    Obama, Barack
    123458    Obama, Barack
    123459    Obama, Barack
    123460    Obama, Barack
    Name: cand_nm, dtype: object

fec.cand_nm[123456:123461].map(parties)

123456    Democrat
    123457    Democrat
    123458    Democrat
    123459    Democrat
    123460    Democrat
    Name: cand_nm, dtype: object

fec['party'] = fec.cand_nm.map(parties)
    fec['party'].value_counts()

Democrat      593746
    Republican    407985
    Name: party, dtype: int64

(fec.contb_receipt_amt > 0).value_counts()

True     991475
    False     10256
    Name: contb_receipt_amt, dtype: int64

fec = fec[fec.contb_receipt_amt > 0]
    fec.head()

cmte_id    cand_id             cand_nm           contbr_nm  \
    0  C00410118  P20002978  Bachmann, Michelle     HARVEY, WILLIAM   
    1  C00410118  P20002978  Bachmann, Michelle     HARVEY, WILLIAM   
    2  C00410118  P20002978  Bachmann, Michelle       SMITH, LANIER   
    3  C00410118  P20002978  Bachmann, Michelle    BLEVINS, DARONDA   
    4  C00410118  P20002978  Bachmann, Michelle  WARDENBURG, HAROLD   
    
              contbr_city contbr_st   contbr_zip        contbr_employer  \
    0              MOBILE        AL   3.6601e+08                RETIRED   
    1              MOBILE        AL   3.6601e+08                RETIRED   
    2              LANETT        AL  3.68633e+08  INFORMATION REQUESTED   
    3             PIGGOTT        AR  7.24548e+08                   NONE   
    4  HOT SPRINGS NATION        AR  7.19016e+08                   NONE   
    
           contbr_occupation  contb_receipt_amt contb_receipt_dt receipt_desc  \
    0                RETIRED              250.0        20-JUN-11          NaN   
    1                RETIRED               50.0        23-JUN-11          NaN   
    2  INFORMATION REQUESTED              250.0        05-JUL-11          NaN   
    3                RETIRED              250.0        01-AUG-11          NaN   
    4                RETIRED              300.0        20-JUN-11          NaN   
    
      memo_cd memo_text form_tp  file_num       party  
    0     NaN       NaN   SA17A    736166  Republican  
    1     NaN       NaN   SA17A    736166  Republican  
    2     NaN       NaN   SA17A    749073  Republican  
    3     NaN       NaN   SA17A    749073  Republican  
    4     NaN       NaN   SA17A    736166  Republican

fec_mrbo = fec[fec.cand_nm.isin(['Obama, Barack', 'Romney, Mitt'])]
    fec_mrbo.head()

cmte_id    cand_id       cand_nm           contbr_nm contbr_city  \
    411  C00431171  P80003353  Romney, Mitt  ELDERBAUM, WILLIAM         DPO   
    412  C00431171  P80003353  Romney, Mitt  ELDERBAUM, WILLIAM         DPO   
    413  C00431171  P80003353  Romney, Mitt    CARLSEN, RICHARD         APO   
    414  C00431171  P80003353  Romney, Mitt      DELUCA, PIERRE         APO   
    415  C00431171  P80003353  Romney, Mitt    SARGENT, MICHAEL         APO   
    
        contbr_st   contbr_zip                   contbr_employer  \
    411        AA   3.4023e+08                     US GOVERNMENT   
    412        AA   3.4023e+08                     US GOVERNMENT   
    413        AE    9.128e+07       DEFENSE INTELLIGENCE AGENCY   
    414        AE    9.128e+07                             CISCO   
    415        AE  9.01201e+07  RAYTHEON TECHNICAL SERVICES CORP   
    
                 contbr_occupation  contb_receipt_amt contb_receipt_dt  \
    411    FOREIGN SERVICE OFFICER               25.0        01-FEB-12   
    412    FOREIGN SERVICE OFFICER              110.0        01-FEB-12   
    413       INTELLIGENCE ANALYST              250.0        13-APR-12   
    414                   ENGINEER               30.0        21-AUG-11   
    415  COMPUTER SYSTEMS ENGINEER              100.0        07-MAR-12   
    
        receipt_desc memo_cd memo_text form_tp  file_num       party  
    411          NaN     NaN       NaN   SA17A    780124  Republican  
    412          NaN     NaN       NaN   SA17A    780124  Republican  
    413          NaN     NaN       NaN   SA17A    785689  Republican  
    414          NaN     NaN       NaN   SA17A    760261  Republican  
    415          NaN     NaN       NaN   SA17A    780128  Republican

# 根据职业和雇主统计赞助信息 #

fec.contbr_occupation.value_counts()[:10]

RETIRED                                   233990
    INFORMATION REQUESTED                      35107
    ATTORNEY                                   34286
    HOMEMAKER                                  29931
    PHYSICIAN                                  23432
    INFORMATION REQUESTED PER BEST EFFORTS     21138
    ENGINEER                                   14334
    TEACHER                                    13990
    CONSULTANT                                 13273
    PROFESSOR                                  12555
    Name: contbr_occupation, dtype: int64

occ_mapping = {
        
        'INFORMATION REQUESTED PER BEST EFFORTS': 'NOT PROVIDED',
        'INFORMATION REQUESTED': 'NOT PROVIDED',
        'INFORMATION REQUESTED (BEST EFFORTS)': 'NOT PROVIDED',
        'C.E.O.': 'CEO'
    }
    
    # If no mapping provided, return x
    f = lambda x: occ_mapping.get(x, x)
    fec.contbr_occupation = fec.contbr_occupation.map(f)
    fec.contbr_occupation.head()

0         RETIRED
    1         RETIRED
    2    NOT PROVIDED
    3         RETIRED
    4         RETIRED
    Name: contbr_occupation, dtype: object

emp_mapping = {
        
        'INFORMATION REQUESTED PER BEST EFFORTS': 'NOT PROVIDED',
        'INFORMATION REQUESTED': 'NOT PROVIDED',
        'SELF': 'SELF-EMPLOYED',
        'SELF EMPLOYED': 'SELF-EMPLOYED',
    }
    
    # If no mapping provided, return x
    f = lambda x: emp_mapping.get(x, x)
    fec.contbr_employer = fec.contbr_employer.map(f)
    fec.contbr_employer.head()

0         RETIRED
    1         RETIRED
    2    NOT PROVIDED
    3            NONE
    4            NONE
    Name: contbr_employer, dtype: object

by_occupation = fec.pivot_table('contb_receipt_amt',
                                    index='contbr_occupation',
                                    columns='party', aggfunc='sum')
    
    over_2mm = by_occupation[by_occupation.sum(1) > 2000000]
    over_2mm

party                 Democrat    Republican
    contbr_occupation                           
    ATTORNEY           11141982.97  7.477194e+06
    CEO                 2074974.79  4.211041e+06
    CONSULTANT          2459912.71  2.544725e+06
    ENGINEER             951525.55  1.818374e+06
    EXECUTIVE           1355161.05  4.138850e+06
    HOMEMAKER           4248875.80  1.363428e+07
    INVESTOR             884133.00  2.431769e+06
    LAWYER              3160478.87  3.912243e+05
    MANAGER              762883.22  1.444532e+06
    NOT PROVIDED        4866973.96  2.056547e+07
    OWNER               1001567.36  2.408287e+06
    PHYSICIAN           3735124.94  3.594320e+06
    PRESIDENT           1878509.95  4.720924e+06
    PROFESSOR           2165071.08  2.967027e+05
    REAL ESTATE          528902.09  1.625902e+06
    RETIRED            25305116.38  2.356124e+07
    SELF-EMPLOYED        672393.40  1.640253e+06

over_2mm.plot(kind='barh')

<matplotlib.axes._subplots.AxesSubplot at 0x1e3333d3198>

\[外链图片转存失败(img-A7QwOTtI-1565690498552)(data/output\_118\_1.png)\]

def get_top_amounts(group, key, n=5):
        totals = group.groupby(key)['contb_receipt_amt'].sum()
    
        # Order totals by key in descending order
        return totals.sort_values(ascending=False)[-n:]
    
    
    grouped = fec_mrbo.groupby('cand_nm')
    grouped.apply(get_top_amounts, 'contbr_occupation', n=7)

cand_nm        contbr_occupation                     
    Obama, Barack  COMPUTER ASSISTANT                        3.0
                   SPRINKLER FITTER FIRE PROTECTION SPECI    3.0
                   ADMINISTRATION/INSTRUCTOR                 3.0
                   LEAD UI/UX DEVELOPER                      3.0
                   POLICY/ LAWYER                            3.0
                   LAN/WAN ANALYST                           3.0
                   SR MGR                                    3.0
    Romney, Mitt   MD - UROLOGIST                            5.0
                   DISTRICT REPRESENTATIVE                   5.0
                   INDEPENDENT PROFESSIONAL                  3.0
                   REMODELER & SEMI RETIRED                  3.0
                   AFFORDABLE REAL ESTATE DEVELOPER          3.0
                   IFC CONTRACTING SOLUTIONS                 3.0
                   3RD GENERATION FAMILY BUSINESS OWNER      3.0
    Name: contb_receipt_amt, dtype: float64

grouped.apply(get_top_amounts, 'contbr_employer', n=10)

cand_nm        contbr_employer                   
    Obama, Barack  SOLIYA                                3.0
                   CARR ENTERPRISES                      3.0
                   PENN STATE DICKINSON SCHOOL OF LAW    3.0
                   CADUCEUS OCCUPATIONAL MEDICINE        3.0
                   N.A.                                  3.0
                   REAL ENERGY CONSULTING SERVICES       3.0
                   JPDSYSTEMS, LLC                       3.0
                   CASS REGIONAL MED. CENTER             2.5
                   ARCON CORP                            2.0
                   THE VICTORIA GROUP, INC.              2.0
    Romney, Mitt   EASTHAM CAPITAL                       5.0
                   GREGORY GALLIVAN                      5.0
                   DIRECT LENDERS LLC                    5.0
                   LOUGH INVESTMENT ADVISORY LLC         4.0
                   WATERWORKS INDUSRTIES                 3.0
                   WILL MERRIFIELD                       3.0
                   HONOLD COMMUNICTAIONS                 3.0
                   INDEPENDENT PROFESSIONAL              3.0
                   UPTOWN CHEAPSKATE                     3.0
                   UN                                    3.0
    Name: contb_receipt_amt, dtype: float64

# 对出资额分组 #

bins = np.array([0, 1, 10, 100, 1000, 10000, 100000, 1000000, 10000000])
    labels = pd.cut(fec_mrbo.contb_receipt_amt, bins)
    labels

411           (10, 100]
    412         (100, 1000]
    413         (100, 1000]
    414           (10, 100]
    415           (10, 100]
    416           (10, 100]
    417         (100, 1000]
    418           (10, 100]
    419         (100, 1000]
    420           (10, 100]
    421           (10, 100]
    422         (100, 1000]
    423         (100, 1000]
    424         (100, 1000]
    425         (100, 1000]
    426         (100, 1000]
    427       (1000, 10000]
    428         (100, 1000]
    429         (100, 1000]
    430           (10, 100]
    431       (1000, 10000]
    432         (100, 1000]
    433         (100, 1000]
    434         (100, 1000]
    435         (100, 1000]
    436         (100, 1000]
    437           (10, 100]
    438         (100, 1000]
    439         (100, 1000]
    440           (10, 100]
                  ...      
    701356        (10, 100]
    701357          (1, 10]
    701358        (10, 100]
    701359        (10, 100]
    701360        (10, 100]
    701361        (10, 100]
    701362      (100, 1000]
    701363        (10, 100]
    701364        (10, 100]
    701365        (10, 100]
    701366        (10, 100]
    701367        (10, 100]
    701368      (100, 1000]
    701369        (10, 100]
    701370        (10, 100]
    701371        (10, 100]
    701372        (10, 100]
    701373        (10, 100]
    701374        (10, 100]
    701375        (10, 100]
    701376    (1000, 10000]
    701377        (10, 100]
    701378        (10, 100]
    701379      (100, 1000]
    701380    (1000, 10000]
    701381        (10, 100]
    701382      (100, 1000]
    701383          (1, 10]
    701384        (10, 100]
    701385      (100, 1000]
    Name: contb_receipt_amt, Length: 694282, dtype: category
    Categories (8, interval[int64]): [(0, 1] < (1, 10] < (10, 100] < (100, 1000] < (1000, 10000] < (10000, 100000] < (100000, 1000000] < (1000000, 10000000]]

grouped = fec_mrbo.groupby(['cand_nm', labels])
    grouped.size()

cand_nm        contb_receipt_amt  
    Obama, Barack  (0, 1]                    493
                   (1, 10]                 40070
                   (10, 100]              372280
                   (100, 1000]            153991
                   (1000, 10000]           22284
                   (10000, 100000]             2
                   (100000, 1000000]           3
                   (1000000, 10000000]         4
    Romney, Mitt   (0, 1]                     77
                   (1, 10]                  3681
                   (10, 100]               31853
                   (100, 1000]             43357
                   (1000, 10000]           26186
                   (10000, 100000]             1
    dtype: int64

grouped.size().unstack(0)

cand_nm              Obama, Barack  Romney, Mitt
    contb_receipt_amt                               
    (0, 1]                       493.0          77.0
    (1, 10]                    40070.0        3681.0
    (10, 100]                 372280.0       31853.0
    (100, 1000]               153991.0       43357.0
    (1000, 10000]              22284.0       26186.0
    (10000, 100000]                2.0           1.0
    (100000, 1000000]              3.0           NaN
    (1000000, 10000000]            4.0           NaN

bucket_sums = grouped.contb_receipt_amt.sum().unstack(0)
    bucket_sums

cand_nm              Obama, Barack  Romney, Mitt
    contb_receipt_amt                               
    (0, 1]                      318.24         77.00
    (1, 10]                  337267.62      29819.66
    (10, 100]              20288981.41    1987783.76
    (100, 1000]            54798531.46   22363381.69
    (1000, 10000]          51753705.67   63942145.42
    (10000, 100000]           59100.00      12700.00
    (100000, 1000000]       1490683.08           NaN
    (1000000, 10000000]     7148839.76           NaN

normed_sums = bucket_sums.div(bucket_sums.sum(axis=1), axis=0)
    normed_sums

cand_nm              Obama, Barack  Romney, Mitt
    contb_receipt_amt                               
    (0, 1]                    0.805182      0.194818
    (1, 10]                   0.918767      0.081233
    (10, 100]                 0.910769      0.089231
    (100, 1000]               0.710176      0.289824
    (1000, 10000]             0.447326      0.552674
    (10000, 100000]           0.823120      0.176880
    (100000, 1000000]         1.000000           NaN
    (1000000, 10000000]       1.000000           NaN

normed_sums[:-2].plot(kind='barh', stacked=True)

<matplotlib.axes._subplots.AxesSubplot at 0x1e3347a3fd0>

\[外链图片转存失败(img-tM1DbKhU-1565690498553)(data/output\_127\_1.png)\]

# 根据州统计赞助信息 #

grouped = fec_mrbo.groupby(['cand_nm', 'contbr_st'])
    totals = grouped.contb_receipt_amt.sum().unstack(0).fillna(0)
    totals = totals[totals.sum(1) > 100000]
    totals[:10]

cand_nm    Obama, Barack  Romney, Mitt
    contbr_st                             
    AK             281840.15      86204.24
    AL             543123.48     527303.51
    AR             359247.28     105556.00
    AZ            1506476.98    1888436.23
    CA           23824984.24   11237636.60
    CO            2132429.49    1506714.12
    CT            2068291.26    3499475.45
    DC            4373538.80    1025137.50
    DE             336669.14      82712.00
    FL            7318178.58    8338458.81

percent = totals.div(totals.sum(1), axis=0)
    percent[:10]

cand_nm    Obama, Barack  Romney, Mitt
    contbr_st                             
    AK              0.765778      0.234222
    AL              0.507390      0.492610
    AR              0.772902      0.227098
    AZ              0.443745      0.556255
    CA              0.679498      0.320502
    CO              0.585970      0.414030
    CT              0.371476      0.628524
    DC              0.810113      0.189887
    DE              0.802776      0.197224
    FL              0.467417      0.532583

参考资料：炼数成金Python数据分析课程

[https_github.com_RealEmperor_Python-for-Data-Analysis]: https://github.com/RealEmperor/Python-for-Data-Analysis