I am quite new to pandas, but I use python at a good level.
I have a pandas dataframe which is organized as follows
idrun idbasin time q
-192540 1 0 0
-192540 1 1 0.5
...
-192540 2 0 0
-192540 2 1 1
...
-192540 3 0 0
-192540 3 1 1
...
-192541 1 0 0
-192541 1 1 0.5
...
-192541 2 0 0
-192541 2 1 1
...
-192541 3 0 0
-192541 3 1 1
...
It is a fairly large dataframe (7 columns and ~600k rows).
What I would like to do is: given a tuple containing values referring to the idbasin column (e.g. (1,2)), if the idrun value is the same
q column of the referred idbasin values, i.e. for the example it would be (1,2)idrun value and the tuple-specified idbasin valuesidbasin equal to the first number of the tuple.Referring to my example df, the results would be
idrun idbasin time q
-192540 1 0 0
-192540 1 1 1.5
...
-192540 3 0 0
-192540 3 1 1
...
-192541 1 0 0
-192541 1 1 1.5
...
-192541 3 0 0
-192541 3 1 1
...
My solution would to use groupby to turn the df to a dict and then do the operation with one or two for loops, but I understand that iterating in pandas is not the optimal solution, so I believe there could be a "pandas" solution using the df.
You can replace values of tuple by first value of tuple in Series.mask and then aggregate sum:
tup = (1, 2)
df['idbasin'] = df['idbasin'].mask(df['idbasin'].isin(tup), tup[0])
#alternative
#df['idbasin'] = np.where(df['idbasin'].isin(tup), tup[0], df['idbasin'])
df = df.groupby(['idrun', 'idbasin','time'], as_index=False)['q'].sum()
print (df)
idrun idbasin time q
0 -192541 1 0 0.0
1 -192541 1 1 1.5
2 -192541 3 0 0.0
3 -192541 3 1 1.0
4 -192540 1 0 0.0
5 -192540 1 1 1.5
6 -192540 3 0 0.0
7 -192540 3 1 1.0