I have a dataset where I want to make a new variable everytime 'Recording' number changes. I want the new variable to include the 'Duration' data for the specific 'Recording' and the previous data. So for the below table it would be:
Var1 = (3, 3, 3)
Var2 = (3, 3, 3, 4, 6)
Var2 = (3, 3, 3, 4, 6, 4, 3, 1, 4)
And so on. I have several dataset that can have different number of recordings (but always starting from 1) and different number of durations for each recording. Any help is greatly appreciated.
Recording | Duration |
---|---|
1 | 3 |
1 | 3 |
1 | 3 |
2 | 4 |
2 | 6 |
3 | 4 |
3 | 3 |
3 | 1 |
3 | 4 |
CodePudding user response:
You can aggregate list
with cumualative sum for lists, then convert to tuples and dictionary:
d = df.groupby('Recording')['Duration'].agg(list).cumsum().apply(tuple).to_dict()
print (d)
{1: (3, 3, 3), 2: (3, 3, 3, 4, 6), 3: (3, 3, 3, 4, 6, 4, 3, 1, 4)}
print (d[1])
print (d[2])
print (d[3])
Your ouput is possible, but not recommended:
s = df.groupby('Recording')['Duration'].agg(list).cumsum().apply(tuple)
for k, v in s.items():
globals()[f'Var{k}'] = v
CodePudding user response:
@jezrael's answer is beautiful and definately better :). But if you really wanted to do this as a loop, (perhaps in future you might want to modify the logic further), then you might:
import pandas as pd
df = pd.DataFrame({
"Recording": [1,1,1,2,2,3,3,3,3],
"Duration": [3,3,3,4,6,4,3,1,4]
}) # your example data
records = {}
record = []
last_recording = None # flag to track change in recording
for r, d in zip(df.Recording, df.Duration):
if record and not r == last_recording:
records[last_recording] = (tuple(record))
record.append(d)
last_recording = r
records[last_recording] = (tuple(record)) # capture final group
print(records)
modified to provide a dict (which seems sensible). This will be slow for large datasets.