This Function uses the pandas python module to convert csv files produced by akta pure machines into dictionaries of dataframes.

akta_pure_csv_to_dataframe.py

# AKTA Pure 
def akta_pure_csv_to_dataframe(csv_file_name):
  """
  DESCRIPTION:
    This Function uses the pandas python module to convert csv files produced by akta pure machines into dictionaries of dataframes.
  ARGUMENTS:
    csv_file_name = str: path to csv file produced by akta pure machine.
  ERRORS:
    None?
  RETURN:
    dataframe_dictionary ={
      "UV": <dataframe>,
      ...
      }
  EXAMPLE:
    dataframe_dictionary = akta_pure_csv_to_dataframe("my_chromatogram.csv")
  """
  original_df = pd.read_csv(csv_file_name, encoding='utf-16',sep='\t',skiprows=[0])

  # Dictionary for better names.
  UNIT_TO_DIMENSION = {
    "ml":"Volume (mL)",
    "mAU":"Absorbance (arb. mAU)",
    "mAU (1 cm)":"Absorbance (mAU at 1 cm)",
    "mS/cm":"Conductivimty (mS/cm)",
    "%":"Percent B (%)",
    }

  # Split Dataframe
  dataframe_dictionary = {}
  num_columns = original_df.shape[1]
  for start_col in range(0, num_columns, 2):
    # Split Dataframe by Columns
    end_col = min(start_col + 2, num_columns)
    df = original_df.iloc[:, start_col:end_col]
    # Get the header of the first column
    df_name = df.columns[0]
    # Rename the columns using the first row as header units
    df.columns = [ (UNIT_TO_DIMENSION.get(head) if UNIT_TO_DIMENSION.get(head) else head)  for head in df.iloc[0]]
    # Drop the first row since it's now the column headers
    df = df[1:]
    # Correct Values
    try:
      df = df.dropna(how='all')
      df = df.replace(r'\n',' ', regex=True) 
      df = df.replace('Waste', 0)
      df = df.replace('Frac', 0)
      df = df.astype(float)
    except ValueError:
      print(f"Failed to cast database ({df_name}) to floats.")
      pass
    # Append to Dictionary 
    dataframe_dictionary[df_name] = df
  # Return Dataframe Dictionary
  return dataframe_dictionary