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()
它会发现哪些天的平均账单大于 20 美元。这是有效的，因为 groupby.filter 需要一个函数来应用于每个应该返回 True 或 False 的子帧。但是，如果我想找到 total_bill 的值大于当天的 total_bill 的每一餐(行)怎么办？例如，如果某行的 total_bill 为 22 并且是在周日，那么应该保留它，因为周日的 total_bill 平均值为 21.41.
这是我的尝试:
df.groupby('day').apply(lambda x: x['total_bill'] > x['total_bill'].mean())
然而，这会产生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
这与数据框的顺序不同，所以我不能只获取 bool 列并使用它来索引数据。
所以现在我执行以下操作:
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......必须有更简单的方法，对吧？如果有正确的方法，请告诉我。如果没有，是否至少有一种方法可以将这两个步骤合二为一？我可以执行 groupby，但我不确定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!
最佳答案
我认为最好的方法是对 groupby 和 transfrom 使用 bool 索引。首先，您按天分组以找到当天的均值，然后使用转换将该均值应用于每一行，接下来将该均值与当天的实际 total_billed 进行比较，然后使用该 bool 系列通过 bool 索引过滤您的数据框。
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]
关于python - Pandas 按组值过滤行，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/49031359/