diego.aves...@gmail.com wrote: > Dear all, > > I am trying to apply a mask to my dataframe: > > mask = (df['datatime'] > start_date) & (df['datatime'] <= end_date) > df = df.loc[mask] > > > It seems to work pretty well. > > After that I crate the cumulative of its element as: > > > PP_cumPP = np.cumsum(df[PP_station]) > > > However, I am not able to compute PP_cumPP last element. Indeed, when I do > > len(PP_cumPP) > > I get > > 8783 > > and when I try to do: > > PP_cumPP[len(PP_cumPP)-1] > > I get an error.
The ultimate problem description ;) You can certainly do better than that. Please do always try to make your example self-contained so that a reader can run it and see the same error as you do. At the very least cut and paste the traceback and the error message you are getting. > What I am doing wrong? My steps to reproduce what is probably your problem: >>> df = pd.DataFrame([[1],[2],[3]], columns=["foo"]) >>> odd = df.loc[df["foo"] & 1] >>> odd foo 0 1 2 3 [2 rows x 1 columns] >>> odd["foo"][1] Traceback (most recent call last): [snip] KeyError: 1 OK, let's try something else: >>> odd["foo"][2] 3 It looks like you have to use the original indices. But google sure can find a way to get rid of these: >>> odd = odd.reset_index(drop=True) >>> odd["foo"][1] 3 > Thanks a lot for any kind of help > > Diedro -- https://mail.python.org/mailman/listinfo/python-list
