pandas groupby，您将获得一列的最大值和另一列的最小值

本文介绍了pandas groupby，您将获得一列的最大值和另一列的最小值的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我有一个数据框，如下所示:

I have a dataframe as follows:

user num1 num2 a 1 1 a 2 2 a 3 3 b 4 4 b 5 5

我想要一个数据帧，该数据帧的每个用户的编号均应为num1起的最小值，每个用户的最大编号应为num2.

I want a dataframe which has the minimum from num1 for each user, and the maximum of num2 for each user.

输出应类似于:

user num1 num2 a 1 3 b 4 5

我知道，如果我想同时获得两列的最大值，就可以这样做:

I know that if I wanted the max of both columns I could just do:

a.groupby('user')['num1', 'num2'].max()

是否存在一些等效项，而不必执行以下操作:

Is there some equivalent without having to do something like:

series_1 = a.groupby('user')['num1'].min() series_2 = a.groupby('user')['num2'].max() # converting from series to df so I can do a join on user df_1 = pd.DataFrame(np.array([series_1]).transpose(), index=series_1.index, columns=['num1']) df_2 = pd.DataFrame(np.array([series_2]).transpose(), index=series_2.index, columns=['num2']) df_1.join(df_2)

推荐答案

使用 groupby + agg (dict)，因此必须按subset或 reindex_axis .最后添加 reset_index 进行转换到column.

Use groupby + agg by dict, so then is necessary order columns by subset or reindex_axis. Last add reset_index for convert index to column if necessary.

df = a.groupby('user').agg({'num1':'min', 'num2':'max'})[['num1','num2']].reset_index() print (df) user num1 num2 0 a 1 3 1 b 4 5

与什么相同:

df = a.groupby('user').agg({'num1':'min', 'num2':'max'}) .reindex_axis(['num1','num2'], axis=1) .reset_index() print (df) user num1 num2 0 a 1 3 1 b 4 5