Python Pandas groupby nlargest sum
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Pandas groupby nlargest sum
提问by user7102752
I am trying to use groupby, nlargest, and sumfunctions in Pandas together, but having trouble making it work.
我试图在 Pandas 中一起使用groupby、nlargest和sum函数，但无法使其正常工作。
State County Population
Alabama a 100
Alabama b 50
Alabama c 40
Alabama d 5
Alabama e 1
...
Wyoming a.51 180
Wyoming b.51 150
Wyoming c.51 56
Wyoming d.51 5
I want to use groupbyto select by state, then get the top 2 counties by population. Then use only those top 2 county population numbers to get a sum for that state.
我想使用groupby按州选择，然后按人口获取前 2 个县。然后仅使用前 2 个县的人口数字来计算该州的总和。
In the end, I'll have a list that will have the state and the population (of it's top 2 counties).
最后，我将列出一个包含州和人口（前 2 个县）的列表。
I can get the groupbyand nlargestto work, but getting the sum of the nlargest(2)is a challenge.
我可以让groupby和nlargest工作，但获得总和nlargest(2)是一个挑战。
The line I have right now is simply: df.groupby('State')['Population'].nlargest(2)
我现在的线路很简单： df.groupby('State')['Population'].nlargest(2)
回答by root
You can use applyafter performing the groupby:
您可以apply在执行以下操作后使用groupby：
df.groupby('State')['Population'].apply(lambda grp: grp.nlargest(2).sum())
I think this issue you're having is that df.groupby('State')['Population'].nlargest(2)will return a DataFrame, so you can no longer do group level operations. In general, if you want to perform multiple operations in a group, you'll need to use apply/agg.
我认为您遇到的这个问题df.groupby('State')['Population'].nlargest(2)将返回一个 DataFrame，因此您不能再进行组级操作。通常，如果要在一个组中执行多个操作，则需要使用apply/ agg。
The resulting output:
结果输出：
State
Alabama 150
Wyoming 330
EDIT
编辑
A slightly cleaner approach, as suggested by @c???s????:
正如@c???s???? 所建议的，一种更简洁的方法：
df.groupby('State')['Population'].nlargest(2).sum(level=0)
This is slightly slower than using applyon larger DataFrames though.
不过，这比apply在较大的 DataFrame 上使用稍慢。
Using the following setup:
使用以下设置：
import numpy as np
import pandas as pd
from string import ascii_letters
n = 10**6
df = pd.DataFrame({'A': np.random.choice(list(ascii_letters), size=n),
'B': np.random.randint(10**7, size=n)})
I get the following timings:
我得到以下时间：
In [3]: %timeit df.groupby('A')['B'].apply(lambda grp: grp.nlargest(2).sum())
103 ms ± 1.08 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [4]: %timeit df.groupby('A')['B'].nlargest(2).sum(level=0)
147 ms ± 3.38 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
The slower performance is potentially caused by the levelkwarg in sumperforming a second groupbyunder the hood.
较慢的性能可能是由levelkwarg 在引擎盖下sum执行一秒钟造成的groupby。
回答by aquaraga
Using agg, the grouping logic looks like:
使用agg，分组逻辑如下所示：
df.groupby('State').agg({'Population': {lambda x: x.nlargest(2).sum() }})
df.groupby('State').agg({'Population': {lambda x: x.nlargest(2).sum() }})
This results in another dataframe object; which you could query to find the most populous states, etc.
这会产生另一个数据框对象；您可以查询以查找人口最多的州等。
Population
State
Alabama 150
Wyoming 330
