text: import pandas as pd
  
  df = pd.read_csv("data_raw.csv")
  
  all_features = df.columns
  
  # Let's drop some features
  names = [feat for feat in all_features if "net_name" in feat] # excluded for privacy reasons
  useless = ["info_gew","info_resul","interviewtime","id","date"] # features that we expect are uninformative
  drop_list = names + useless 
  
  # Remove the questionnaire about agricultural practices until I can better understand it
  practice_list = ["legum","conc","add","lact","breed","covman","comp","drag","cov","plow","solar","biog","ecodr"]
  for feat in all_features:
      if any(x in feat for x in practice_list):
          drop_list.append(feat)
  
  
  df = df.drop(columns=drop_list)
  
  # Convert non-numeric features to numeric
  non_numeric = list(df.select_dtypes(include=['O']).columns)
  for col in non_numeric:
      codes,uniques=pd.factorize(df[col])
      df[col] = codes
  
  df.to_csv("data_processed.csv")