熊猫：在groupby之后重新塑造/重新转动数据框

Posted 2021-04-07

我在我的数据框的quantile列上应用duration函数：

a=df.groupby('version')[['duration']].quantile([.25, .5, .75])
a

                   duration
version     
4229        0.25    1451.00
            0.50    1451.00
            0.75    1451.00
6065        0.25     213.75
            0.50     426.50
            0.75     639.25
9209        0.25     386.50
            0.50     861.00
            0.75     866.00
2304        0.25     664.50
            0.50     669.00
            0.75     736.50
6389        0.25       1.00
            0.50     797.00
            0.75     832.00

我想知道如何重新塑造/重新调整上面的数据框，所以新数据框（是的，它必须是数据框格式）可能如下所示：

version   duration_Q1    duration_Q2    duration_Q3

4429      1451.00        1451.00        1451.00
6065      213.75         426.50         639.25
9209      386.50         861.00         866.00
2304      664.50         669.00         736.50
6389      1.00           797.00         832.00

谢谢！

答案

您可以使用unstack，然后进行一些重命名操作

a = pd.DataFrame('duration': {(2304L, 0.25): 1565.6861959516361,
  (2304L, 0.5): 446.4769649280514,
  (2304L, 0.75): 701.8254115357969,
  (4229L, 0.25): 1868.982390749203,
  (4229L, 0.5): 242.36201172579996,
  (4229L, 0.75): 789.482292226787,
  (6065L, 0.25): 1421.9585894685038,
  (6065L, 0.5): 357.04491735326343,
  (6065L, 0.75): 169.78973203074895,
  (6389L, 0.25): 1789.1550141153925,
  (6389L, 0.5): 516.9365429825862,
  (6389L, 0.75): 1830.6493228794639,
  (9209L, 0.25): 1129.853279993191,
  (9209L, 0.5): 1759.1258334115485,
  (9209L, 0.75): 1499.0498929925702}}
)

pvt = a.unstack()
pvt.columns = pvt.columns.droplevel(0)
pvt.rename(columns={0.25:'duration_Q1',0.5:'duration_Q2',0.75:'duration_Q3'},inplace=True)

---

        duration_Q1  duration_Q2  duration_Q3
version                                       
2304     1565.686196   446.476965   701.825412
4229     1868.982391   242.362012   789.482292
6065     1421.958589   357.044917   169.789732
6389     1789.155014   516.936543  1830.649323
9209     1129.853280  1759.125833  1499.049893