Asked By – Lee
Consider a csv file:
string,date,number a string,2/5/11 9:16am,1.0 a string,3/5/11 10:44pm,2.0 a string,4/22/11 12:07pm,3.0 a string,4/22/11 12:10pm,4.0 a string,4/29/11 11:59am,1.0 a string,5/2/11 1:41pm,2.0 a string,5/2/11 2:02pm,3.0 a string,5/2/11 2:56pm,4.0 a string,5/2/11 3:00pm,5.0 a string,5/2/14 3:02pm,6.0 a string,5/2/14 3:18pm,7.0
I can read this in, and reformat the date column into datetime format:
b=pd.read_csv('b.dat') b['date']=pd.to_datetime(b['date'],format='%m/%d/%y %I:%M%p')
I have been trying to group the data by month. It seems like there should be an obvious way of accessing the month and grouping by that. But I can’t seem to do it. Does anyone know how?
What I am currently trying is re-indexing by the date:
I can access the month like so:
However I can’t seem to find a function to lump together by month.
Now we will see solution for issue: pandas dataframe groupby datetime month
Managed to do it:
b = pd.read_csv('b.dat') b.index = pd.to_datetime(b['date'],format='%m/%d/%y %I:%M%p') b.groupby(by=[b.index.month, b.index.year])
b.groupby(pd.Grouper(freq='M')) # update for v0.21+