dpkits 0.0.19

A small package for data processing

Data processing Package

• Requirements

  • pandas
  • pyreadstat
  • numpy
  • zipfile
  • fastapi[UploadFile]

• Step 1: import classes

  # Convert data to pandas dataframe
  from dpkits.ap_data_converter import APDataConverter
  
  # Calculate LSM score
  from dpkits.calculate_lsm import LSMCalculation
  
  # Transpose data to stack and untack
  from dpkits.data_transpose import DataTranspose
  
  # Create the tables from converted dataframe 
  from dpkits.table_generator import DataTableGenerator
  
  # Format data tables 
  from dpkits.table_formater import TableFormatter
  

• Step 2: Convert data files to dataframe

  • class APDataConverter(files=None, file_name='', is_qme=True)
    • input 1 of files or file_name
    • files: list[UploadFile] default = None
    • file_name: str default = ''
    • is_qme: bool default = True
    • Returns:
      • df_data: pandas.Dataframe
      • df_info: pandas.Dataframe

    converter = APDataConverter(file_name='APDataTesting.xlsx')
    
    df_data, df_info = converter.convert_df_mc() 
    
    # Use 'converter.convert_df_md()' if you need md data
    

• Step 3: Calculate LSM classificate (only for LSM projects)

  • class LSMCalculation.cal_lsm_6(df_data, df_info)
    • df_data: pandas.Dataframe
    • df_info: pandas.Dataframe
    • Returns:
      • df_data: pandas.Dataframe
      • df_info: pandas.Dataframe

    df_data, df_info = LSMCalculation.cal_lsm_6(df_data, df_info)
    
    # df_data, df_info will contains the columns CC1_Score to CC6_Score & LSM_Score
    

• Step 4: Data cleaning (if needed)

• Step 5: Transpose data (if needed)

• Step 6: Export *.sav & *.xlsx

• Step 7: Export data tables

This is a simple example package. You can use Github-flavored Markdown to write your content.