taminur :
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
Quang Hoang :
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