python - Loop through rows of dataframe at specific row values

Question

My dataframe contains three different replications for each treatment. I want to loop through both, so I want to loop through each treatment, and for each treatment calculate a model for each replication. I managed to loop through the treatments, but I need to also loop through the replications of each treatment. Ideally, the output should be saved into a new dataframe that contains 'treatment' and 'replication'. Any suggestion?

The dataframe (df) looks like this:

 treatment replication time  y
  **8          1          1   0.1**
  8          1          2   0.1 
  8          1          3   0.1
  **8          2          1   0.1**
  8          2          2   0.1 
  8          2          3   0.1
  **10         1          1   0.1**
  10         1          2   0.1 
  10         1          3   0.1
  **10         2          1   0.1**
  10         2          2   0.1 
  10         2          3   0.1

for i, g in df.groupby('treament'):
   k = g.iloc[0].y                                   
   popt, pcov = curve_fit(model, x, y)
   fit_m = popt  
   

I now apply iterrows, but then I can no longer use the index of NPQ [0] to get the initial value. Any idea how to solve this? The error message reads as:

for index, row in HL.iterrows():
  g = (index, row['filename'], row['hr'], row['time'], row['NPQ'])
  k = g.iloc[0]['NPQ'])

AttributeError: 'tuple' object has no attribute 'iloc'

Thank you in advance

question from:https://stackoverflow.com/questions/65852725/loop-through-rows-of-dataframe-at-specific-row-values

Answer 1

grouped_df = HL.groupby(["hr", "filename"])

for key, g in grouped_df:
   k = g.iloc[0].y                                   
   popt, pcov = curve_fit(model, x, y)
   fit_m = popt