dpkits 1.6.4
A small package for data processing
Requires Python: >=3.9

# 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
        ```
        # Define input/output files name
        str_file_name = 'APDataTest'
        str_tbl_file_name = f'{str_file_name}_Topline.xlsx'
        
        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)
    ```
    # Use pandas's functions to clean/process data

    df_data['Gender_new'] = df_data['Gender']

    df_data.replace({
        'Q1_SP1': {1: 5, 2: 4, 3: 3, 4: 2, 5: 1},
        'Q1_SP2': {1: 5, 2: 4, 3: 3, 4: 2, 5: 1},
    }, inplace=True)

    df_data.loc[(df_data['Gender_new'] == 2) & (df_data['Age'] == 5),  ['Gender_new']] = [np.nan]
    df_info.loc[df_info['var_name'] == 'Q1_SP1', ['val_lbl']] = [{'1': 'a', '2': 'b', '3': 'c', '4': 'd', '5': 'e'}]

    df_info = pd.concat([df_info, pd.DataFrame(
        columns=['var_name', 'var_lbl', 'var_type', 'val_lbl'],
        data=[
            ['Gender_new', 'Please indicate your gender', 'SA', {'1': 'aaa', '2': 'bb', '3': 'cc'}]
        ]
    )], ignore_index=True)
    ```

- Step 5: Transpose data (if needed)
    - class DataTranspose.to_stack(df_data, df_info, dict_stack_structure)
        - df_data: pandas.Dataframe
        - df_info: pandas.Dataframe
        - dict_stack_structure: dict
        - Returns:
            - df_data_stack: pandas.Dataframe
            - df_info_stack: pandas.Dataframe
        ```
        dict_stack_structure = {
            'id_col': 'ResID',
            'sp_col': 'Ma_SP',
            'lst_scr': ['Gender', 'Age', 'City', 'HHI'],
            'dict_sp': {
                1: {
                    'Ma_SP1': 'Ma_SP',
                    'Q1_SP1': 'Q1',
                    'Q2_SP1': 'Q2',
                    'Q3_SP1': 'Q3',
                },
                2: {
                    'Ma_SP2': 'Ma_SP',
                    'Q1_SP2': 'Q1',
                    'Q2_SP2': 'Q2',
                    'Q3_SP2': 'Q3',
                },
            },
            'lst_fc': ['Awareness1', 'Frequency', 'Awareness2', 'Perception']
        }

        df_data_stack, df_info_stack = DataTranspose.to_stack(df_data, df_info, dict_stack_structure)
        ```
    - class DataTranspose.to_unstack(df_data_stack, df_info_stack, dict_unstack_structure)
        - df_data_stack: pandas.Dataframe which transpose from stack
        - df_info_stack: pandas.Dataframe which transpose from stack
        - dict_unstack_structure: dict
        - Returns:
            - df_data_unstack: pandas.Dataframe
            - df_info_unstack: pandas.Dataframe
        ```
        dict_unstack_structure = {
            'id_col': 'ResID',
            'sp_col': 'Ma_SP',
            'lst_col_part_head': ['Gender', 'Age', 'City', 'HHI'],
            'lst_col_part_body': ['Q1', 'Q2', 'Q3'],
            'lst_col_part_tail': ['Awareness1', 'Frequency', 'Awareness2', 'Perception']
        }

        df_data_unstack, df_info_unstack = DataTranspose.to_unstack(df_data_stack, df_info_stack, dict_unstack_structure)
        ```
        
- Step 6: OE Running
    ```
    
    ```

- Step 7: Export *.sav & *.xlsx
    - class converter.generate_multiple_data_files(dict_dfs=dict_dfs, is_md=False, is_export_sav=True, is_export_xlsx=True, is_zip=True)
        - df_data: pandas.Dataframe
            - dict_dfs: dict
            - is_md: bool default False
            - is_export_sav: bool default True
            - is_export_xlsx: bool default True
            - is_zip: bool default True
            - Returns: NONE
            ```
            dict_dfs = {
                1: {
                    'data': df_data,
                    'info': df_info,
                    'tail_name': 'ByCode',
                    'sheet_name': 'ByCode',
                    'is_recode_to_lbl': False,
                },
                2: {
                    'data': df_data,
                    'info': df_info,
                    'tail_name': 'ByLabel',
                    'sheet_name': 'ByLabel',
                    'is_recode_to_lbl': True,
                },
                3: {
                    'data': df_data_stack,
                    'info': df_info_stack,
                    'tail_name': 'Stack',
                    'sheet_name': 'Stack',
                    'is_recode_to_lbl': False,
                },
                4: {
                    'data': df_data_unstack,
                    'info': df_info_unstack,
                    'tail_name': 'Unstack',
                    'sheet_name': 'Unstack',
                    'is_recode_to_lbl': False,
                },
            }

            converter.generate_multiple_data_files(dict_dfs=dict_dfs, is_md=False, is_export_sav=True, is_export_xlsx=True, is_zip=True)
            ```

- Step 8: Export data tables
    - init DataTableGenerator(df_data=df_data, df_info=df_info, xlsx_name=str_tbl_file_name)
        - df_data: pandas.Dataframe
        - df_info: pandas.Dataframe
        - xlsx_name: str
        - Returns: NONE
    - class DataTableGenerator.run_tables_by_js_files(lst_func_to_run)
        - lst_func_to_run: list
        - Returns: NONE
    - init TableFormatter(xlsx_name=str_tbl_file_name)
        - xlsx_name: str
        - Returns: NONE
    - class TableFormatter.format_sig_table()
        - Returns: NONE
    ```
    lst_side_qres = [
        {"qre_name": "CC1", "sort": "des"},
        {"qre_name": "$CC3", "sort": "asc"},
        {"qre_name": "$CC4", "sort": "des"},
        {"qre_name": "$CC6"},
        {"qre_name": "$CC10"},
        {"qre_name": "LSM"},
        {"qre_name": "Gender"},
        {"qre_name": "Age"},
        {"qre_name": "City"},
        {"qre_name": "HHI"},
        
        # MA Question with net/combine (can apply to SA questions)
        {"qre_name": "$Q15", "cats": {
            'net_code': {
                '900001|combine|Group 1 + 2': {
                    '1': 'Yellow/dull teeth',
                    '3': 'Dental plaque',
                    '5': 'Bad breath',
                    '7': 'Aphthousulcer',
                    '2': 'Sensitive teeth',
                    '4': 'Caries',
                    '6': 'Gingivitis (bleeding, swollen gums)',
                },
                '900002|net|Group 1': {
                    '1': 'Yellow/dull teeth',
                    '3': 'Dental plaque',
                    '5': 'Bad breath',
                    '7': 'Aphthousulcer',
                },
                '900003|net|Group 2': {
                    '2': 'Sensitive teeth',
                    '4': 'Caries',
                    '6': 'Gingivitis (bleeding, swollen gums)',
                },
            },
            '8': 'Other (specify)',
            '9': 'No problem',
        }},

        # Scale question with full properties
        {
            "qre_name": "Perception",
            "cats": {
                '1': 'Totally disagree', '2': 'Disagree', '3': 'Neutral', '4': 'Agree', '5': 'Totally agree',
                'net_code': {
                    '900001|combine|B2B': {'1': 'Totally disagree', '2': 'Disagree'},
                    '900002|combine|Medium': {'3': 'Neutral'},
                    '900003|combine|T2B': {'4': 'Agree', '5': 'Totally agree'},
                }
            },
            "mean": {1: 1, 2: 2, 3: 3, 4: 4, 5: 5}
        },
    ]

    lst_header_qres = [
        [
            {
                "qre_name": "Age",
                "qre_lbl": "Age",
                "cats": {
                    'TOTAL': 'TOTAL',
                    '2': '18 - 24', '3': '25 - 30', '4': '31 - 39', '5': '40 - 50', '6': 'Trên 50'
                }
            },
            {
                "qre_name": "@City2",
                "qre_lbl": "Location",
                "cats": {
                    'City.isin([1, 5, 10, 11, 12])': 'All South',
                    'City.isin([2, 4, 16, 17, 18])': 'All North',
                }
            },
        ],
    ]

    lst_func_to_run = [
        {
            'func_name': 'run_standard_table_sig',
            'tables_to_run': [
                'Tbl_1_Pct',  # this table use df_data & df_info to run
                'Tbl_1_Count',  # this table use df_data & df_info to run
            ],
            'tables_format': {

                "Tbl_1_Pct": {
                    "tbl_name": "Table 1 - Pct",
                    "tbl_filter": "City > 0",
                    "is_count": 0,
                    "is_pct_sign": 1,
                    "is_hide_oe_zero_cats": 1,
                    "sig_test_info": {
                        "sig_type": "",  # ind / rel
                        "sig_cols": [],
                        "lst_sig_lvl": []
                    },
                    "lst_side_qres": lst_side_qres,
                    "lst_header_qres": lst_header_qres
                },

                "Tbl_1_Count": {
                    "tbl_name": "Table 1 - Count",
                    "tbl_filter": "City > 0",
                    "is_count": 1,
                    "is_pct_sign": 0,
                    "is_hide_oe_zero_cats": 1,
                    "sig_test_info": {
                        "sig_type": "",
                        "sig_cols": [],
                        "lst_sig_lvl": []
                    },
                    "lst_side_qres": lst_side_qres,
                    "lst_header_qres": lst_header_qres
                },
            },

        },
    ]

    dtg = DataTableGenerator(df_data=df_data, df_info=df_info, xlsx_name=str_tbl_file_name)
    dtg.run_tables_by_js_files(lst_func_to_run)

    dtf = TableFormatter(xlsx_name=str_tbl_file_name)
    dtf.format_sig_table()
    ```





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