Python Pandas DataFrame组通过选择列 [英] Python pandas dataframe groupby selecting columns

问题描述

我有以下detaframe bb:

I have the following detaframe bb:

bq_selection_id bq_balance  bq_market_id  bq_back_price
0         45094462     185.04       7278437           1.97
1         45094462     185.04       7278437           1.97
2         45094463     185.04       7278437           3.05
3         45094463     185.04       7278437           3.05
4         45094464     185.04       7278437           5.80
5         45094464     185.04       7278437           5.80
6         45094466     185.04       7278437         200.00
7         45094466     185.04       7278437         200.00
8         45094465     185.04       7278437            NaN
9         45094465     185.04       7278437            NaN

我想按"market_id"分组并采用前两个最低的"bq_back_price".我设法用

I would like to group by "market_id" and take first two lowest "bq_back_price". I managed to do this with

bb.groupby('bq_market_id')['bq_back_price'].nsmallest(2)

问题是我缺少某些列，例如"bq_selection_id"，"bq_balance"和"bq_back_price"列没有名称.那就是我得到的

The problem is that I am missing some of the columns such as "bq_selection_id", "bq_balance" and column "bq_back_price" does not have name. That is what I get

bq_market_id   
7278437       0    1.97
7278437       1    1.97

我想得到这样的东西

bq_selection_id bq_balance  bq_market_id  bq_back_price
0         45094462     185.04       7278437           1.97
1         45094462     185.04       7278437           1.97

你能帮我吗?

推荐答案

您可以先在bq_back_price上对值进行排序，然后在每个组中使用head(2).

You can first sort values on bq_back_price, and then take head(2) in each groups.

In [218]: df.sort_values('bq_back_price').groupby('bq_market_id').head(2)
Out[218]:
   bq_selection_id  bq_balance  bq_market_id  bq_back_price
0         45094462      185.04       7278437           1.97
1         45094462      185.04       7278437           1.97

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