Calculating the percentile for specific groups

I have 3 columns. Product Id, Price, Group (values A, B, C, D)

I want to get price percentile for each group and I am running the following code.

for group, price in df.groupby(['group']):
    df['percentile'] = np.percentile(df['price'],60)

the column percentile has only one value 3.44 for each group. The expected values for each group were 2.12, 3.43, 3.65, 4.76. 8.99.

What is going wrong here, please let me know.

I think you can use in loop not all DataFrame df with column price, but group price with column price:

import pandas as pd
import numpy as np

np.random.seed(1)
df = pd.DataFrame(np.random.randint(10, size=(5,3)))
df.columns = ['Product Id','group','price']
print df
   Product Id  group  price
0           5      8      9
1           5      0      0
2           1      7      6
3           9      2      4
4           5      2      4

for group, price in df.groupby(['group']):
    print np.percentile(df['price'],60)
4.8
4.8
4.8
4.8
group   

for group, price in df.groupby(['group']):
    print np.percentile(price['price'],60)
0.0
4.0
6.0
9.0

Another solution for np.percentile where is output Serie:

print df.groupby(['group'])['price'].apply(lambda x: np.percentile(x,60))
group
0    0.0
2    4.0
7    6.0
8    9.0
Name: price, dtype: float64

Solution with DataFrameGroupBy.quantile:

print df.groupby(['group'])['price'].quantile(.6)
group
0    0.0
2    4.0
7    6.0
8    9.0
Name: price, dtype: float64

EDIT by comment:

If you need new column use transform, docs:

>>> np.random.seed(1)
>>> df = pd.DataFrame(np.random.randint(10,size=(20,3)))
>>> df.columns = ['Product Id','group','price']
>>> df
    Product Id  group  price
0            5      8      9
1            5      0      0
2            1      7      6
3            9      2      4
4            5      2      4
5            2      4      7
6            7      9      1
7            7      0      6
8            9      9      7
9            6      9      1
10           0      1      8
11           8      3      9
12           8      7      3
13           6      5      1
14           9      3      4
15           8      1      4
16           0      3      9
17           2      0      4
18           9      2      7
19           7      9      8
>>> df['percentil'] = df.groupby(['group'])['price'].transform(lambda x: x.quantile(.6))

>>> df
    Product Id  group  price  percentil
0            5      8      9        9.0
1            5      0      0        4.4
2            1      7      6        4.8
3            9      2      4        4.6
4            5      2      4        4.6
5            2      4      7        7.0
6            7      9      1        5.8
7            7      0      6        4.4
8            9      9      7        5.8
9            6      9      1        5.8
10           0      1      8        6.4
11           8      3      9        9.0
12           8      7      3        4.8
13           6      5      1        1.0
14           9      3      4        9.0
15           8      1      4        6.4
16           0      3      9        9.0
17           2      0      4        4.4
18           9      2      7        4.6
19           7      9      8        5.8