10. 用连续变量分组

 In[83]: flights = pd.read_csv('data/flights.csv')
         flights.head()
Out[83]:

#  判断DIST列有无缺失值
 In[84]: flights.DIST.hasnans
Out[84]: False

#  再次删除DIST列的缺失值（原书是没有这两段的）
 In[85]: flights.dropna(subset=['DIST']).shape
Out[85]: (58492, 14)

#  使用Pandas的cut函数，将数据分成5个面元
 In[86]: bins = [-np.inf, 200, 500, 1000, 2000, np.inf]
         cuts = pd.cut(flights['DIST'], bins=bins)
         cuts.head()
Out[86]: 0     (500.0, 1000.0]
         1    (1000.0, 2000.0]
         2     (500.0, 1000.0]
         3    (1000.0, 2000.0]
         4    (1000.0, 2000.0]
         Name: DIST, dtype: category
         Categories (5, interval[float64]): [(-inf, 200.0] < (200.0, 500.0] < (500.0, 1000.0] < (1000.0, 2000.0] < (2000.0, inf]]

#  对每个面元进行统计
 In[87]: cuts.value_counts()
Out[87]: (500.0, 1000.0]     20659
         (200.0, 500.0]      15874
         (1000.0, 2000.0]    14186
         (2000.0, inf]        4054
         (-inf, 200.0]        3719
         Name: DIST, dtype: int64

#  面元Series可以用来进行分组
 In[88]: flights.groupby(cuts)['AIRLINE'].value_counts(normalize=True).round(3).head(15)
Out[88]: DIST            AIRLINE
         (-inf, 200.0]   OO         0.326
                         EV         0.289
                         MQ         0.211
                         DL         0.086
                         AA         0.052
                         UA         0.027
                         WN         0.009
         (200.0, 500.0]  WN         0.194
                         DL         0.189
                         OO         0.159
                         EV         0.156
                         MQ         0.100
                         AA         0.071
                         UA         0.062
                         VX         0.028
         Name: AIRLINE, dtype: float64

原理

 In[89]: flights.groupby(cuts)['AIRLINE'].value_counts(normalize=True)['AIRLINE'].value_counts(normalize=True).round(3).head(15)
Out[89]: 
DIST              AIRLINE
(-inf, 200.0]     OO         0.325625
                 EV         0.289325
                 MQ         0.210809
                 DL         0.086045
                 AA         0.052165
                 UA         0.027427
                 WN         0.008604
(200.0, 500.0]    WN         0.193902
                 DL         0.188736
                 OO         0.158687
                 EV         0.156293
                 MQ         0.100164
                 AA         0.071375
                 UA         0.062051
                 VX         0.028222
                 US         0.016001
                 NK         0.011843
                 B6         0.006867
                 F9         0.004914
                 AS         0.000945
(500.0, 1000.0]   DL         0.205625
                 AA         0.143908
                 WN         0.138196
                 UA         0.131129
                 OO         0.106443
                 EV         0.100683
                 MQ         0.051213
                 F9         0.038192
                 NK         0.029527
                 US         0.025316
                 AS         0.023234
                 VX         0.003582
                 B6         0.002953
(1000.0, 2000.0]  AA         0.263781
                 UA         0.199070
                 DL         0.165092
                 WN         0.159664
                 OO         0.046454
                 NK         0.045115
                 US         0.040462
                 F9         0.030664
                 AS         0.015931
                 EV         0.015579
                 VX         0.012125
                 B6         0.003313
                 MQ         0.002749
(2000.0, inf]     UA         0.289097
                 AA         0.211643
                 DL         0.171436
                 B6         0.080414
                 VX         0.073754
                 US         0.065121
                 WN         0.046374
                 HA         0.027627
                 NK         0.019240
                 AS         0.011593
                 F9         0.003700
Name: AIRLINE, dtype: float64

更多

#  求飞行时间的0.25，0.5，0.75分位数
 In[90]: flights.groupby(cuts)['AIR_TIME'].quantile(q=[.25, .5, .75]).div(60).round(2)
Out[90]: DIST                  
         (-inf, 200.0]     0.25    0.43
                           0.50    0.50
                           0.75    0.57
         (200.0, 500.0]    0.25    0.77
                           0.50    0.92
                           0.75    1.05
         (500.0, 1000.0]   0.25    1.43
                           0.50    1.65
                           0.75    1.92
         (1000.0, 2000.0]  0.25    2.50
                           0.50    2.93
                           0.75    3.40
         (2000.0, inf]     0.25    4.30
                           0.50    4.70
                           0.75    5.03
         Name: AIR_TIME, dtype: float64

#  unstack方法可以将内层的索引变为列名
 In[91]: labels=['Under an Hour', '1 Hour', '1-2 Hours', '2-4 Hours', '4+ Hours']
         cuts2 = pd.cut(flights['DIST'], bins=bins, labels=labels)
         flights.groupby(cuts2)['AIRLINE'].value_counts(normalize=True).round(3).unstack().style.highlight_max(axis=1)
Out[91]: