这是我正在练习的数据
import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv")
我想按分组值过滤单个行。我知道我可以执行以下操作来过滤组
df.groupby("day").filter(lambda x: x['total_bill'].mean() > 20).day.unique()
哪个日子的平均账单大于20美元。这是因为groupby.filter将函数应用于应该返回True或False的每个子帧。但是，如果我想找到total_bill的值大于当天的total_bill的每一餐（行），该怎么办？例如，如果一行有total_bill的22并且是星期日，那么它应该保留，因为周日的total_bill平均值是21.41。
这是我的尝试：
df.groupby('day').apply(lambda x: x['total_bill'] > x['total_bill'].mean())
但是，这会产生一些看起来像这样的东西（前几行）
day
Fri 90 True
91 True
92 False
93 False
94 True
Name: total_bill, dtype: bool
这与数据帧的顺序不同，所以我不能只使用布尔列并使用它来索引数据。
所以现在我做以下事情：
grouped = (df
.groupby('day')
.apply(lambda x: x['total_bill'] > x['total_bill'].mean())
.reset_index())
index_bill = (grouped
.loc[grouped.total_bill == True, 'level_1'].values)
df.loc[index_bill]
这给了我想要的结果......必须有一个更简单的方法，对吧？如果有正确的方法，请告诉我。如果没有，至少有一种方法可以将这两个步骤合二为一吗？我可以做groupby，但不知道如何在不将分组对象存储为变量然后引用它的情况下获取值。谢谢！
我认为最好的方法是使用groupby和transfrom的布尔索引。首先，您按天分组以查找当天的平均值，然后使用transform将该平均值应用于每一行，然后将该平均值与当天的实际total_billed进行比较，然后使用该布尔系列来使用布尔索引过滤您的数据帧。
df[df.groupby('day')['total_bill'].transform('mean') < df['total_bill']]
输出：
total_bill tip sex smoker day time size
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4
5 25.29 4.71 Male No Sun Dinner 4
7 26.88 3.12 Male No Sun Dinner 4
11 35.26 5.00 Female No Sun Dinner 4
15 21.58 3.92 Male No Sun Dinner 2
19 20.65 3.35 Male No Sat Dinner 3
23 39.42 7.58 Male No Sat Dinner 4
28 21.70 4.30 Male No Sat Dinner 2
33 20.69 2.45 Female No Sat Dinner 4
35 24.06 3.60 Male No Sat Dinner 3
39 31.27 5.00 Male No Sat Dinner 3
44 30.40 5.60 Male No Sun Dinner 4
46 22.23 5.00 Male No Sun Dinner 2
47 32.40 6.00 Male No Sun Dinner 4
48 28.55 2.05 Male No Sun Dinner 3
52 34.81 5.20 Female No Sun Dinner 4
54 25.56 4.34 Male No Sun Dinner 4
56 38.01 3.00 Male Yes Sat Dinner 4
57 26.41 1.50 Female No Sat Dinner 2
59 48.27 6.73 Male No Sat Dinner 4
72 26.86 3.14 Female Yes Sat Dinner 2
73 25.28 5.00 Female Yes Sat Dinner 2
77 27.20 4.00 Male No Thur Lunch 4
78 22.76 3.00 Male No Thur Lunch 2
80 19.44 3.00 Male Yes Thur Lunch 2
83 32.68 5.00 Male Yes Thur Lunch 2
85 34.83 5.17 Female No Thur Lunch 4
87 18.28 4.00 Male No Thur Lunch 2
88 24.71 5.85 Male No Thur Lunch 2
.. ... ... ... ... ... ... ...
180 34.65 3.68 Male Yes Sun Dinner 4
181 23.33 5.65 Male Yes Sun Dinner 2
182 45.35 3.50 Male Yes Sun Dinner 3
183 23.17 6.50 Male Yes Sun Dinner 4
184 40.55 3.00 Male Yes Sun Dinner 2
187 30.46 2.00 Male Yes Sun Dinner 5
189 23.10 4.00 Male Yes Sun Dinner 3
191 19.81 4.19 Female Yes Thur Lunch 2
192 28.44 2.56 Male Yes Thur Lunch 2
197 43.11 5.00 Female Yes Thur Lunch 4
200 18.71 4.00 Male Yes Thur Lunch 3
204 20.53 4.00 Male Yes Thur Lunch 4
206 26.59 3.41 Male Yes Sat Dinner 3
207 38.73 3.00 Male Yes Sat Dinner 4
208 24.27 2.03 Male Yes Sat Dinner 2
210 30.06 2.00 Male Yes Sat Dinner 3
211 25.89 5.16 Male Yes Sat Dinner 4
212 48.33 9.00 Male No Sat Dinner 4
214 28.17 6.50 Female Yes Sat Dinner 3
216 28.15 3.00 Male Yes Sat Dinner 5
219 30.14 3.09 Female Yes Sat Dinner 4
227 20.45 3.00 Male No Sat Dinner 4
229 22.12 2.88 Female Yes Sat Dinner 2
230 24.01 2.00 Male Yes Sat Dinner 4
237 32.83 1.17 Male Yes Sat Dinner 2
238 35.83 4.67 Female No Sat Dinner 3
239 29.03 5.92 Male No Sat Dinner 3
240 27.18 2.00 Female Yes Sat Dinner 2
241 22.67 2.00 Male Yes Sat Dinner 2
243 18.78 3.00 Female No Thur Dinner 2
[97 rows x 7 columns]
对于相同的输出需要transform返回相同的Series的mean每组，然后过滤boolean indexing和最后添加sort_values：
a=df[df['total_bill'] > df.groupby('day')['total_bill'].transform('mean')].sort_values('day')
print (a.head(20))
total_bill tip sex smoker day time size
90 28.97 3.00 Male Yes Fri Dinner 2
91 22.49 3.50 Male No Fri Dinner 2
94 22.75 3.25 Female No Fri Dinner 2
95 40.17 4.73 Male Yes Fri Dinner 4
96 27.28 4.00 Male Yes Fri Dinner 2
98 21.01 3.00 Male Yes Fri Dinner 2
102 44.30 2.50 Female Yes Sat Dinner 3
206 26.59 3.41 Male Yes Sat Dinner 3
229 22.12 2.88 Female Yes Sat Dinner 2
227 20.45 3.00 Male No Sat Dinner 4
219 30.14 3.09 Female Yes Sat Dinner 4
237 32.83 1.17 Male Yes Sat Dinner 2
103 22.42 3.48 Female Yes Sat Dinner 2
106 20.49 4.06 Male Yes Sat Dinner 2
107 25.21 4.29 Male Yes Sat Dinner 2
216 28.15 3.00 Male Yes Sat Dinner 5
214 28.17 6.50 Female Yes Sat Dinner 3
241 22.67 2.00 Male Yes Sat Dinner 2
212 48.33 9.00 Male No Sat Dinner 4
211 25.89 5.16 Male Yes Sat Dinner 4
编辑：
要获得days的正确排序，可以使用ordered categorical：
cats = ['Mon','Tue','Wed','Thur','Fri','Sat','Sun']
df['day'] = pd.Categorical(df['day'], categories=cats, ordered=True)
means = df.groupby('day')['total_bill'].transform('mean')
df1 = df[df['total_bill'] > means].sort_values('day')
print (df1.head(20))
total_bill tip sex smoker day time size
129 22.82 2.18 Male No Thur Lunch 3
80 19.44 3.00 Male Yes Thur Lunch 2
83 32.68 5.00 Male Yes Thur Lunch 2
85 34.83 5.17 Female No Thur Lunch 4
87 18.28 4.00 Male No Thur Lunch 2
88 24.71 5.85 Male No Thur Lunch 2
89 21.16 3.00 Male No Thur Lunch 2
119 24.08 2.92 Female No Thur Lunch 4
125 29.80 4.20 Female No Thur Lunch 6
130 19.08 1.50 Male No Thur Lunch 2
78 22.76 3.00 Male No Thur Lunch 2
131 20.27 2.83 Female No Thur Lunch 2
141 34.30 6.70 Male No Thur Lunch 6
142 41.19 5.00 Male No Thur Lunch 5
143 27.05 5.00 Female No Thur Lunch 6
146 18.64 1.36 Female No Thur Lunch 3
191 19.81 4.19 Female Yes Thur Lunch 2
192 28.44 2.56 Male Yes Thur Lunch 2
197 43.11 5.00 Female Yes Thur Lunch 4
200 18.71 4.00 Male Yes Thur Lunch 3