I am working with panel time-series data and am struggling with creating a fast for loop, to sum up, the past 50 numbers at the current i. The data is like 600k rows, and it starts to churn around 30k. Is there a way to use pandas or Numpy to do the same at a fraction of the time?
The change column is of type float, with 4 decimals.
Index Change 0 0.0410 1 0.0000 2 0.1201 ... ... 74327 0.0000 74328 0.0231 74329 0.0109 74330 0.0462
SEQ_LEN = 50 for i in range(SEQ_LEN, len(df)): df.at[i, 'Change_Sum'] = sum(df['Change'][i-SEQ_LEN:i])
Any help would be highly appreciated! Thank you!