pandas groupby，您可以获得一列的最大值和另一列的最小值
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pandas groupby where you get the max of one column and the min of another column
提问by lhay86
I have a dataframe as follows:
我有一个数据框如下：
user num1 num2
a 1 1
a 2 2
a 3 3
b 4 4
b 5 5
I want a dataframe which has the minimum from num1 for each user, and the maximum of num2 for each user.
我想要一个数据帧，每个用户的 num1 最小值，每个用户的 num2 最大值。
The output should be like:
输出应该是这样的：
user num1 num2
a 1 3
b 4 5
I know that if I wanted the max of both columns I could just do:
我知道如果我想要两列的最大值，我可以这样做：
a.groupby('user')['num1', 'num2'].max()
Is there some equivalent without having to do something like:
是否有一些等价物而无需执行以下操作：
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)
回答by jezrael
Use groupby+ aggby dict, so then is necessary order columns by subsetor reindex_axis. Last add reset_indexfor convert indexto columnif necessary.
使用groupby+ aggby dict，因此必须按subsetor 对列进行排序reindex_axis。如有必要，最后添加reset_index转换index为column。
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
What is same as:
什么是相同的：
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
