熊猫:按指数值分组,然后计算分位数？

Question

我有一个在month列上索引的DataFrame (df = df.set_index('month')如果相关则设置使用):

             org_code  ratio_cost   
month
2010-08-01   1847      8.685939     
2010-08-01   1848      7.883951     
2010-08-01   1849      6.798465     
2010-08-01   1850      7.352603     
2010-09-01   1847      8.778501     

我想添加一个名为的新列quantile,它将根据该月的值为每行分配一个分位数值ratio_cost.

所以上面的例子可能如下所示:

             org_code  ratio_cost   quantile
month
2010-08-01   1847      8.685939     100 
2010-08-01   1848      7.883951     66.6 
2010-08-01   1849      6.798465     0  
2010-08-01   1850      7.352603     33.3
2010-09-01   1847      8.778501     100

我怎样才能做到这一点？我试过这个:

df['quantile'] = df.groupby('month')['ratio_cost'].rank(pct=True)

但我明白了KeyError: 'month'.

更新:我可以重现这个bug.

这是我的CSV文件:http://pastebin.com/raw/6xbjvEL0

这是重现错误的代码:

df = pd.read_csv('temp.csv')
df.month = pd.to_datetime(df.month, unit='s')
df = df.set_index('month')
df['percentile'] = df.groupby(df.index)['ratio_cost'].rank(pct=True)
print df['percentile']

我在OSX上使用Pandas 0.17.1.

Answer 1

你必须在sort_index之前rank:

import pandas as pd

df = pd.read_csv('http://pastebin.com/raw/6xbjvEL0')

df.month = pd.to_datetime(df.month, unit='s')
df = df.set_index('month')

df = df.sort_index()

df['percentile'] = df.groupby(df.index)['ratio_cost'].rank(pct=True)
print df['percentile'].head()

month
2010-08-01    0.2500
2010-08-01    0.6875
2010-08-01    0.6250
2010-08-01    0.9375
2010-08-01    0.7500
Name: percentile, dtype: float64