用python/pandas 的数据聚合 groupby 函数分

数据集链接：
https://github.com/kojoidrissa/pydata-book

# 定义最大差值 的函数
In [49]: def PeakToPeak(arr):
    ...:     return arr.max() - arr.min()

In [51]: tips = pd.read_csv('ch08/tips.csv')

# tip_pct 小费占订单的百分比

In [52]: tips['tip_pct'] = tips['tip'] / tips['total_bill']

In [53]: tips[:6]
Out[53]:
   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

# 键值对设为['sex','smoker'], 处理数据为 ['tip_pct']

In [54]: grouped = tips.groupby(['sex','smoker'])

In [55]: grouped_pct = grouped['tip_pct']

In [56]: grouped_pct.agg('mean')
Out[56]:
sex     smoker
Female  No        0.156921
        Yes       0.182150
Male    No        0.160669
        Yes       0.152771
Name: tip_pct, dtype: float64

# 计算平均值那一列命名为 'foo'， 仅是改名作用

In [59]: grouped_pct.agg([('foo','mean'),('bar',np.std)])
Out[59]:
                    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


# 批量计算

In [60]: functions = ['count','mean','max']

In [61]: result = grouped['tip_pct','total_bill'].agg(functions)

In [62]: result
Out[62]:
              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

In [63]: result['total_bill']
Out[63]:
               count       mean    max
sex    smoker
Female No         54  18.105185  35.83
       Yes        33  17.977879  44.30
Male   No         97  19.791237  48.33
       Yes        60  22.284500  50.81

2018.8.22

省略看图说话的数据分析.