pandas 计算特定组的百分位数
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Calculating percentile for specific groups
提问by Anu
I have 3 columns. Product Id, Price, Group(values A, B, C, D)
我有 3 列。Product Id, Price, Group（值 A、B、C、D）
I want to get price percentile for each group and I am running the following code.
我想获得每个组的价格百分位数，我正在运行以下代码。
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.
每一组的列百分位数只有一个值 3.44。每组的预期值为2.12, 3.43, 3.65, 4.76. 8.99。
What is going wrong here, please let me know.
这里出了什么问题，请告诉我。
回答by jezrael
I think you can use in loop not all DataFramedfwith column price, but group pricewith column price:
我认为你不能在循环中DataFramedf使用 column price，而是price使用 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.percentilewhere is output Serie:
np.percentile输出的另一种解决方案是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:
解决方案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:
通过评论编辑：
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
