Pandas：如何在由另一列分组的列上获取具有最大值 value_count 的行作为数据框

Question

I have three columns in a pandas dataframe, Date , Hour and Content . I want to get the hour in a day when there is the most content of that day. I am using messages.groupby(["Date", "Hour"]).Content.count().groupby(level=0).tail(1) . I don't know what groupby(level=0) is doing here. It outputs as follows-

Date        Hour
2018-04-12  23       4
2018-04-13  21      43
2018-04-14  9        1
2018-04-15  23      29
2018-04-16  17       1
                    ..
2020-04-23  20       1
2020-04-24  22       1
2020-04-25  20       1
2020-04-26  23      32
2020-04-27  23       3

This is a pandas series object, and my desired Date and Hour columns are MultiIndex here. If I try to convert the MultiIndex object to dataframe using pd.DataFrame(most_active.index) , most_active being the output of the previous code, it creates a dataframe of tuples as below-

                    0
0    (2018-04-12, 23)
1    (2018-04-13, 21)
2     (2018-04-14, 9)
3    (2018-04-15, 23)
4    (2018-04-16, 17)
..                ...
701  (2020-04-23, 20)
702  (2020-04-24, 22)
703  (2020-04-25, 20)
704  (2020-04-26, 23)
705  (2020-04-27, 23)

But I need two separate columns of Date and Hour . What is the best way for this?

Answer 1

Edit because I misunderstood your question

First, you have to count the total content by date-hour, just like you did:

df = messages.groupby(["Date", "Hour"], as_index=False).Content.count()

Here, I left the groups in their original columns by passing the parameter as_index=False .

Then, you can run the code below, provided in the original answer:

Supposing you have unique index IDs (if not, just do df.reset_index(inplace=True) ), you can use idxmax method in groupby . It will return the index with the biggest value per group, then you can use them for slicing the dataframe.

For example:

df.loc[df.groupby(['Date', 'Hour'])['Content'].idxmax()]

As an alternative (without using groupby), you can first sort the values in descending order, them remove the Date-Hour duplicates:

df.sort_values('Content', ascending=False).drop_duplicates(subset=['Date', 'Hour'])

Finally, you get a MultiIndex with the set_index() method:

df.set_index(['Date','Hour'])