Sort by both index and value in Multi-indexed data of Pandas dataframe

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Suppose, I have a dataframe as below:

    year    month   message
0   2018    2   txt1
1   2017    4   txt2
2   2019    5   txt3
3   2017    5   txt5
4   2017    5   txt4
5   2020    4   txt3
6   2020    6   txt3
7   2020    6   txt3
8   2020    6   txt4

I want to figure out top three number of messages in each year. So, I grouped the data as below:

df.groupby(['year','month']).count()

which results:

            message
year    month   
2017    4   1
        5   2
2018    2   1
2019    5   1
2020    4   1
        6   3

The data is in ascending order for both indexes. But how to find the results as shown below where the data is sorted by year (ascending) and count (descending) for top n values. 'month' index will be free.

            message
year    month   
2017    5   2
        4   1
2018    2   1
2019    5   1
2020    6   3
        4   1
multi-index pandas python-3.x sorting python
5 Answers

This will sort by year (ascending) and count (descending).

df = df.groupby(['year', 'month']).count().sort_values(['year', 'message'], ascending=[True, False])
user_128215890

value_counts gives you sort by default:

df.groupby('year')['month'].value_counts()

Output:

year  month
2017  5        2
      4        1
2018  2        1
2019  5        1
2020  6        3
      4        1
Name: month, dtype: int64

If you want only 2 top values for each year, do another groupby:

(df.groupby('year')['month'].value_counts()
   .groupby('year').head(2)
)

Output:

year  month
2017  5        2
      4        1
2018  2        1
2019  5        1
2020  6        3
      4        1
Name: month, dtype: int64
user_42384080

You can use sort_index, specifying ascending=[True,False] so that only the second level is sorted in descending order:

df = df.groupby(['year','month']).count().sort_index(ascending=[True,False])

              message
year month         
2017 5            2
     4            1
2018 2            1
2019 5            1
2020 6            3
     4            1
user_96986840

here you go

df.groupby(['year', 'month']).count().sort_values(axis=0, ascending=False, by='message').sort_values(axis=0, ascending=True, by='year')
user_130322570

you can use this code for it.

df.groupby(['year', 'month']).count().sort_index(axis=0, ascending=False).sort_values(by="year", ascending=True)
user_130322570
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