sop-deutils 0.3.0

A utils package for Yes4All SOP

Yes4All SOP Utils Packages

This is a utils package served for SOP Data Analytics team at Yes4All. It contains various modules to work with PostgreSQL, MinIO, Google API, Airflow, Telegram...

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User Guide Documentation

Install this package

$ pip install --upgrade sop-deutils

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Modules usage

Airflow

Use case: when having a new scheduled task file on Airflow.

Functional:

Auto naming DAG ID and alerting failed DAG to Telegram:

• Sample code of base config Airflow dag file:

   from airflow import DAG
   from airflow.decorators import task
   from sop_deutils.y4a_airflow import auto_dag_id, telegram_alert
  
   default_args = {
       "retries":  20,			# number times to retry when the task is failed
       "retry_delay": timedelta(minutes=7),			# time delay among retries
       "start_date": datetime(2023, 7, 14, 0, 0, 0),			# date that the DAG start to run 
       "owner": 'liuliukiki',			# telegram user name of DAG owner
       "on_failure_callback": telegram_alert,			# this contains function to alert to Telegram when the DAG/task is failed
       "execution_timeout": timedelta(hours=4),			# limit time the DAG run
   }
  
   dag = DAG(
       dag_id=auto_dag_id(),			# this contains function to name the DAG based on the file directory
       description='Sample DAG',			# description about the DAG
       default_args=default_args,			# default arguments contains dictionary of predefined params above
       catchup=False,			# If True, the DAG will backfill tasks from the start_date to current date
   )
  
   with dag:
       @task
       def function_1():
           ...
  
       @task
       def function_2():
           ...
  
       function_1() >> function_2()
  

---

GoogleSheet

Use case: when interacting with Google Sheet.

Functional:

Firstly, import GoogleSheet utils module class. If want to use personal credentials, provide the dictionary of credentials as value of parameter user_creds.

from sop_deutils.gg_api.y4a_sheet import GGSheetUtils

sheet_utils = GGSheetUtils()

To create a new spread sheet, using create_spread_sheet method, it has three parameters:

• sheet_name (required): name of the sheet to create. (str)

• folder_id (optional): id of the folder contains spreadsheet. The default value is None. (str)

• share_to (optional): list of email to share the spreadsheet. The default value is []. (list)

  The method will return the created spreadsheet id.

   spread_sheet_id = sheet_utils.create_spread_sheet(
       sheet_name='my-sheet-name',
       folder_id='my-folder-id',
       share_to=['[e-mail]'],
   )
  
   print(spread_sheet_id)
  

  Output:

   1vTjZOcRfd5eiF5Qo8DCha29Vdt0zvYP11XPbq54eCMg
  

To get all available worksheet of spreadsheet, using list_all_work_sheets method, it has one parameter:

• sheet_id (required): spreadsheet id. (str)

  The method will return list all worksheets of spreadsheet.

   work_sheets = sheet_utils.list_all_work_sheets(
       sheet_id='my-sheet-id',
   )
  
   print(work_sheets)
  

  Output:

   ['Sheet1']
  

To delete specific worksheet of spreadsheet, using delete_work_sheet method, it has two parameters:

• sheet_id (required): spreadsheet id. (str)

• sheet_name (optional): worksheet name. The default value is 'Sheet1'. (str)

   sheet_utils.delete_work_sheet(
       sheet_id='my-sheet-id',
       sheet_name='my-sheet-name',
   )
  

To clear all data of specific worksheet of spreadsheet, using clear_work_sheets method, it has two parameters:

• sheet_id (required): spreadsheet id. (str)

• sheet_name (optional): worksheet name. The default value is 'Sheet1'. (str)

   sheet_utils.clear_work_sheet(
       sheet_id='my-sheet-id',
       sheet_name='my-sheet-name',
   )
  

To get data from the given sheet, using get_data method, it has five parameters:

• sheet_id (required): spreadsheet id. (str)

• sheet_name (optional): worksheet name. The default value is 'Sheet1'. (str)

• range_from (optional): the begining of the range of data from sheet to get. The default value is 'A'. (str)

• range_to (optional): the end of the range of data from sheet to get. The default value is 'Z'. (str)

• columns_first_row (optional): whether to convert the first row to columns. The default value is False. (bool)

   df = sheet_utils.get_data(
       sheet_id='my-sheet-id',
       columns_first_row=True,
   )
  
   print(df)
  

  Output:

   | Column1 Header | Column2 Header | Column3 Header |
   | ---------------| ---------------| ---------------|
   | Row1 Value1    | Row1 Value2    | Row1 Value3    |
   | Row2 Value1    | Row2 Value2    | Row2 Value3    |
   | Row3 Value1    | Row3 Value2    | Row3 Value3    |
  

To insert data to the given sheet, using insert_data method, it has five parameters:

• data (required): dataframe contains data to insert. (pd.DataFrame)

• sheet_id (required): spreadsheet id. (str)

• sheet_name (optional): worksheet name. The default value is 'Sheet1'. (str)

• from_row_index (optional): the index of the row beginning to insert. The default value is 1. (int)

• insert_column_names (optional): whether to insert column names. The default value is False. (bool)

   sheet_utils.insert_data(
       data=df,
       sheet_id='my-sheet-id',
       from_row_index=2,
       insert_column_names=False,
   )
  

To update data of the given sheet, using update_data method, it has five parameters:

• data (required): dataframe contains data to update. (pd.DataFrame)

• sheet_id (required): spreadsheet id. (str)

• sheet_name (optional): worksheet name. The default value is 'Sheet1'. (str)

• range_from (optional): the begining of the range of data from sheet to update. The default value is 'A'. (str)

• range_to (optional): the end of the range of data from sheet to update. The default value is 'Z'. (str)

   sheet_utils.update_data(
       data=new_df,
       sheet_id='my-sheet-id',
       range_from='A4',
       range_to='E7',
   )
  

To remove data from specific range of the given sheet, using remove_data method, it has three parameters:

• sheet_id (required): spreadsheet id. (str)

• sheet_name (optional): worksheet name. The default value is 'Sheet1'. (str)

• list_range (optional): list of data ranges to remove. The default value is ['A1:Z1', 'A4:Z4']. (list)

   sheet_utils.remove_data(
       sheet_id='my-sheet-id',
       list_range=[
           'A2:D5',
           'E5:G6',
       ],
   )
  

---

MinIO

MinIO is an object storage, it is API compatible with the Amazon S3 cloud storage service. MinIO can be used as a datalake to store unstructured data (photos, videos, log files, backups, and container images) and structured data.

Use case: when need to store raw data or get raw data from datalake. Notes that the stored data extension must be .parquet .

Notes about how to determine the file_path parameter in minIO when using this module:

For example, if the directory to the data file in minIO is as above, then the file_path is "/scraping/amazon_vendor/avc_bulk_buy_request/2023/9/24/batch_1695525619" (after removing bucket name, data storage mode, and data file extension).

Functional:

Firstly, import minIO utils module class.

from sop_deutils.datalake.y4a_minio import MinioUtils

minio_utils = MinioUtils()

To check whether data exists in a storage directory, using data_exist method, it has three parameters:

• mode (required): the data storage mode, the value must be either 'prod' or 'stag'. (str)

• file_path (required): the data directory to check. (str)

• bucket_name (optional): the name of the bucket to check. The default value is 'sc-bucket'. (str)

  The method will return True if data exists otherwise False.

   minio_utils.data_exist(
       mode='stag',
       file_path='your-data-path',
       bucket_name='sc-bucket',
   )
  

  Output:

   True
  

To get the distinct values of a specified column of data in a data directory, using get_data_value_exist method, it has four parameters:

• mode (required): the data storage mode, the value must be either 'prod' or 'stag'. (str)

• file_path (required): the data directory to get distinct values. (str)

• column_key (required): the column name to get distinct values. (str)

• bucket_name (optional): the name of the bucket to get distinct values. The default value is 'sc-bucket'. (str)

  The method will return list of distinct values.

   minio_utils.get_data_value_exist(
       mode='stag',
       file_path='your-data-path',
       column_key='your-chosen-column',
       bucket_name='sc-bucket',
   )
  

  Output:

   ['value_1', 'value_2']
  

To load data from dataframe to storage, using load_data method, it has four parameters:

• data (required): dataframe contains data to load. (pd.DataFrame)

• mode (required): the data storage mode, the value must be either 'prod' or 'stag'. (str)

• file_path (required): the directory to load the data. (str)

• bucket_name (optional): the name of the bucket to load the data. The default value is 'sc-bucket'. (str)

   minio_utils.load_data(
       data=df,
       mode='stag',
       file_path='your-data-path',
       bucket_name='sc-bucket',
   )
  

To get data from a single file of directory of storage, using get_data method, it has three parameters:

• mode (required): the data storage mode, the value must be either 'prod' or 'stag'. (str)

• file_path (required): the data directory to get data. (str)

• bucket_name (optional): the name of the bucket to get data. The default value is 'sc-bucket'. (str)

  The method will return dataframe contains data to get.

   df = minio_utils.get_data(
       mode='stag',
       file_path='your-data-path',
       bucket_name='sc-bucket',
   )
  
   print(df)
  

  Output:

   | Column1 Header | Column2 Header | Column3 Header |
   | ---------------| ---------------| ---------------|
   | Row1 Value1    | Row1 Value2    | Row1 Value3    |
   | Row2 Value1    | Row2 Value2    | Row2 Value3    |
   | Row3 Value1    | Row3 Value2    | Row3 Value3    |
  

To get data from multiple files of directories of storage, using get_data_wildcard method, it has three parameters:

• mode (required): the data storage mode, the value must be either 'prod' or 'stag'. (str)

• file_path (required): the parent data directory to get the data. (str)

• bucket_name (optional): the name of the bucket to get data. The default value is 'sc-bucket'. (str)

  The method will return dataframe contains data to get.

   df = minio_utils.get_data_wildcard(
       mode='stag',
       file_path='your-parent-data-path',
       bucket_name='sc-bucket',
   )
  
   print(df)
  

  Output:

   | Column1 Header | Column2 Header | Column3 Header |
   | ---------------| ---------------| ---------------|
   | Row1 Value1    | Row1 Value2    | Row1 Value3    |
   | Row2 Value1    | Row2 Value2    | Row2 Value3    |
   | Row3 Value1    | Row3 Value2    | Row3 Value3    |
  

---

PostgreSQL

Use case: when interacting with Postgres database.

Functional:

Firstly, import PostgreSQL utils module class. This class requires three parameters:

• account_name: the client account name to connect to PostgreSQL. The value can be used as DA member name. (str)

• db_host: host db name to connect. Available values are 'raw_master', 'raw_repl', 'serving_master', 'serving_repl'. (str)

• db: database to connect. The default value is 'serving'. (str)

   from sop_deutils.sql.y4a_postgresql import PostgreSQLUtils
  
   pg_utils = PostgreSQLUtils(
       account_name='your-account-name',
       db_host='serving_master',
       db='serving',
   )
  

To get the SQL query given by SQL file, using read_sql_file method, it has one parameter:

• sql_file_path (required): the located path of SQL file. (str)

  The method will return the string of SQL query.

   sql = pg_utils.read_sql_file(
       sql_file_path: 'your-path/select_all.sql',
   )
  
   print(sql)
  

  Output:

   "SELECT * FROM your_schema.your_table"
  

To insert data to PostgreSQL table, using insert_data method, it has six parameters:

• data (required): a dataframe contains data to insert. (pd.DataFrame)

• schema (required): schema contains table to insert. (str)

• table (required): table name to insert. (str)

• ignore_errors (optional): whether to ignore errors when inserting data. The default value is False. (bool)

• commit_every (optional): number rows of data to commit each time. The default value is 1000. (int)

• db_pool_conn (optional): connection pool to connect to database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)

   pg_utils.insert_data(
       data=your_df,
       schema='your-schema',
       table='your-table',
       ignore_errors=True,
       commit_every=1000,
       db_pool_conn=pool,
   )
  

To insert large data to PostgreSQL table, using bulk_insert_data method, it has five parameters:

• data (required): a dataframe contains data to insert. (pd.DataFrame)

• schema (required): schema contains table to insert. (str)

• table (required): table name to insert. (str)

• commit_every (optional): number rows of data to commit each time. The default value is 1000. (int)

• db_pool_conn (optional): connection pool to connect to database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)

   pg_utils.bulk_insert_data(
       data=your_df,
       schema='your-schema',
       table='your-table',
       commit_every=1000,
       db_pool_conn=pool,
   )
  

To upsert data to PostgreSQL table, using upsert_data method, it has five parameters:

• data (required): a dataframe contains data to upsert. Notes that if dataframe contains duplicated rows, it will be dropped. (pd.DataFrame)

• schema (required): schema contains table to upsert. (str)

• table (required): table name to upsert. (str)

• commit_every (optional): number rows of data to commit each time. The default value is 1000. (int)

• db_pool_conn (optional): connection pool to connect to database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)

   pg_utils.upsert_data(
       data=your_df,
       schema='your-schema',
       table='your-table',
       commit_every=1000,
       db_pool_conn=pool,
   )
  

To upsert large data to PostgreSQL table, using bulk_upsert_data method, it has five parameters:

• data (required): a dataframe contains data to upsert. Notes that if dataframe contains duplicated rows, it will be dropped. (pd.DataFrame)

• schema (required): schema contains table to upsert. (str)

• table (required): table name to upsert. (str)

• commit_every (optional): number rows of data to commit each time. The default value is 1000. (int)

• db_pool_conn (optional): connection pool to connect to database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)

   pg_utils.bulk_upsert_data(
       data=your_df,
       schema='your-schema',
       table='your-table',
       commit_every=1000,
       db_pool_conn=pool,
   )
  

To update new data of specific columns in the table based on primary keys, using update_table method, it has six parameters:

• data (required): a dataframe contains data to update, including primary keys and columns to update. (pd.DataFrame)

• schema (required): schema contains table to update data. (str)

• table (required): table to update data. (str)

• columns (required): list of column names to update data. (list)

• commit_every (optional): number rows of data to commit each time. The default value is 1000. (int)

• db_pool_conn (optional): connection pool to connect to database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)

   pg_utils.update_table(
       data=your_df,
       schema='your-schema',
       table='your-table',
       columns=['col1', 'col2'],
       commit_every=1000,
       db_pool_conn=pool,
   )
  

To get data from PostgreSQL database given by a SQL query, using get_data method, it has two parameters:

• sql (required): SQL query to get data. (str)

• db_pool_conn (optional): connection pool to connect to database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)

  The method will return dataframe contains data extracted by the given SQL query.

   df = pg_utils.get_data(
       sql='your-query',
       db_pool_conn=pool,
   )
  
   print(df)
  

  Output:

   | Column1 Header | Column2 Header | Column3 Header |
   | ---------------| ---------------| ---------------|
   | Row1 Value1    | Row1 Value2    | Row1 Value3    |
   | Row2 Value1    | Row2 Value2    | Row2 Value3    |
   | Row3 Value1    | Row3 Value2    | Row3 Value3    |
  

To get the distinct values of a specified column in a PostgreSQL table, using select_distinct method, it has four parameters:

• col (required): column name to get the distinct data. (str)

• schema (required): schema contains table to get data. (str)

• table (required): table to get data. (str)

• db_pool_conn (optional): connection pool to connect to database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)

  The method will return list of distinct values.

   distinct_values = pg_utils.select_distinct(
       col='chosen-column',
       schema='your-schema',
       table='your-table',
       db_pool_conn=pool,
   )
  
   print(distinct_values)
  

  Output:

   ['val1', 'val2', 'val3']
  

To get list of columns name of a specific PostgreSQL table, using show_columns method, it has three parameters:

• schema (required): schema contains table to get columns. (str)

• table (required): table to get columns. (str)

• db_pool_conn (optional): connection pool to connect to database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)

  The method will return list of column names of the table.

   col_names = pg_utils.show_columns(
       schema='your-schema',
       table='your-table',
       db_pool_conn=pool,
   )
  
   print(col_names)
  

  Output:

   ['col1', 'col2', 'col3']
  

To execute the given SQL query, using execute method, it has three parameters:

• sql (required): SQL query to execute. (str)

• fetch_output (optional): whether to fetch the results of the query. The default value is False. (bool)

• db_pool_conn (optional): connection pool to connect to database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)

  The method will return list of query output if fetch_output is True, otherwise None.

   sql = """
       UPDATE
           sales_order_avc_di o,
           (
               SELECT
                   DISTINCT po_name, 
                   asin,
                   CASE
                       WHEN o.status LIKE '%cancel%' AND a.status IS NULL THEN ''
                       WHEN o.status LIKE '%cancel%' THEN CONCAT(a.status,' ',cancel_date) 
                       ELSE o.status END po_asin_amazon_status
               FROM
                   sales_order_avc_order_status o
                   LEFT JOIN
                       sales_order_avc_order_asin_status a USING (updated_at, po_name)
               WHERE updated_at > DATE_SUB(NOW(), INTERVAL 1 DAY)
           ) s
       SET
           o.po_asin_amazon_status = s.po_asin_amazon_status
       WHERE
           o.po_name = s.po_name
           AND o.asin = s.asin
   """
  
   pg_utils.execute(
       sql=sql,
       fetch_output=False,
       db_pool_conn=pool,
   )
  

To create new column for a specific PostgreSQL table, using add_column method, it has six parameters:

• schema (required): schema contains table to create column. (str)

• table (required): table to create column. (str)

• column_name (optional): name of the column to create available when creating single column. The default value is None (str)

• dtype (optional): data type of the column to create available when creating single column. The default value is None (str)

• muliple_columns (optional): dictionary contains columns name as key and data type of columns as value respectively. The default value is {} (dict)

• db_pool_conn (optional): connection pool to connect to database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)

   pg_utils.add_column(
       schema='my-schema',
       table='my-table',
       muliple_columns={
           'col1': 'int',
           'col2': 'varchar(50)',
       },
       db_pool_conn=pool,
   )
  

To create new table in PostgreSQL database, using create_table method, it has seven parameters:

• schema (required): schema contains table to create. (str)

• table (required): table name to create. (str)

• columns_with_dtype (required): dictionary contains column names as key and the data type of column as value respectively. (dict)

• columns_primary_key (optional): list of columns to set primary keys. The default value is []. (list)

• columns_not_null (optional): list of columns to set constraints not null. The default value is []. (list)

• columns_with_default (optional): dictionary contains column names as key and the default value of column as value respectively. The default value is {}. (dict)

• db_pool_conn (optional): connection pool to connect to database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)

   pg_utils.create_table(
       schema='my-schema',
       table='my-new-table',
       columns_with_dtype={
           'col1': 'int',
           'col2': 'varchar(50)',
           'col3': 'varchar(10)',
       },
       columns_primary_key=[
           'col1',
       ],
       columns_not_null=[
           'col2',
       ],
       columns_with_default={
           'col3': 'USA',
       },
       db_pool_conn=pool,
   )
  

To grant table privileges to users in PostgreSQL, using grant_table method, it has five parameters:

• schema (required): schema contains table to grant. (str)

• table (required): table name to grant. (str)

• list_users (required): list of users to grant. If want to grant for all members of DA team, provide ['da']. (list)

• privileges (optional): list of privileges to grant. The default value is ['SELECT']. The accepted values in privileges list are 'SELECT', 'INSERT', 'UPDATE', 'DELETE', 'TRUNCATE', 'REFERENCES', 'TRIGGER'. (list)

• all_privileges (optional): whether to grant all privileges. The default value is False. (bool)

   pg_utils.grant_table(
       schema='my-schema',
       table='my-new-table',
       list_users=[
           'linhvk',
           'trieuna',
       ],
       privileges=[
           'SELECT',
           'INSERT',
           'UPDATE',
       ],
   )
  

To remove all the data of PostgreSQL table, using truncate_table method, it has four parameters:

• schema (required): schema contains table to truncate. (str)

• table (required): table name to truncate. (str)

• reset_identity (optional): whether to reset identity of the table. The defaults value is False. (bool)

• db_pool_conn (optional): connection pool to connect to database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)

   pg_utils.truncate_table(
       schema='my-schema',
       table='my-table',
       db_pool_conn=pool,
   )
  

To check if the PostgreSQL table exists in database, using table_exists method, it has three parameters:

• schema (required): schema contains table to check. (str)

• table (required): table name to check. (str)

• db_pool_conn (optional): connection pool to connect to database. The default value is None. If the value is None, a new connection will be created and automatically closed after being used. (callable)

  The method will return True if table exists and False if not.

   pg_utils.table_exists(
       schema='my-schema',
       table='my-exists-table',
       db_pool_conn=pool,
   )
  

  Output:

   True
  

---

Telegram

Use case: when need to send messages to Telegram by using bot

Functional:

To send messages to Telegram, using send_message method, it has three parameters:

• text (required): message to send. (str)

• bot_token (optional): token of the bot which send the message. The default value is None. If the value is None, the bot sleep at 9pm will be used to send messages. (str)

• chat_id (optional): id of group chat where the message is sent. The default value is None. If the value is None, the group chat Airflow Status Alert will be used. (str)

   from sop_deutils.y4a_telegram import send_message
  
   send_message(
       text='Hello liuliukiki'
   )
  

---

All in one

Use case: so far, there are a lot of platforms that needs to access frequently, in order not to import lots of modules, users can inherit all of above modules as simplest way.

Functional:

Firstly, import DAConfig class. This class requires one parameters:

• account_name: the client account name to access platforms. The value can be used as DA member name. (str)

   from sop_deutils.base.y4a_da_cfg import DAConfig
  
   da_cfg = DAConfig(
       account_name='your-account-name',
   )
  

This class will have its attributes as all above modules (PostgreSQL, MinIO, Google API, Airflow, Telegram) that users don't need to import and config to connect individually to each platform, each platform attributes will have the its own methods that listed above. List of attributes are:

• minio_utils

• pg_raw_r_utils (connected to PostgreSQL raw read - repl)

• pg_raw_w_utils (connected to PostgreSQL raw write - master)

• pg_serving_r_utils (connected to PostgreSQL serving read - repl)

• pg_serving_w_utils (connected to PostgreSQL serving write - master)

• sheet_utils

   print(da_cfg.minio_utils)
   print(da_cfg.pg_raw_r_utils)
   print(da_cfg.pg_raw_w_utils)
   print(da_cfg.pg_serving_r_utils)
   print(da_cfg.pg_serving_w_utils)
   print(da_cfg.sheet_utils)
  

  Output:

   <sop_deutils.datalake.y4a_minio.MinioUtils object at 0x7fe6e704d6f0>
   <sop_deutils.sql.y4a_postgresql.PostgreSQLUtils object at 0x7fe6e704d9f0>
   <sop_deutils.sql.y4a_postgresql.PostgreSQLUtils object at 0x7fe6e704dae0>
   <sop_deutils.sql.y4a_postgresql.PostgreSQLUtils object at 0x7fe6e704e170>
   <sop_deutils.sql.y4a_postgresql.PostgreSQLUtils object at 0x7fe6e704e0b0>
   <sop_deutils.gg_api.y4a_sheet.GGSheetUtils object at 0x7fe72c65e1d0>
  

---

Workflow example

Without using DAConfig class:

# Begining of workflow

from sop_deutils.gg_api.y4a_sheet import GGSheetUtils
from sop_deutils.datalake.y4a_minio import MinioUtils
from sop_deutils.sql.y4a_postgresql import PostgreSQLUtils
import pandas as pd


minio_utils = MinioUtils()
sheet_utils = GGSheetUtils()
pg_serving_r_utils = PostgreSQLUtils(
    account_name='your-account-name',
    db_host='serving_repl'
)
pg_serving_w_utils = PostgreSQLUtils(
    account_name='your-account-name',
    db_host='serving_master'
)

# Create new spreadsheet id
spread_sheet_id = sheet_utils.create_spread_sheet(
    sheet_name='test_sheet_20231015',
    share_to=['[e-mail]'],
)

# Have a predefined dataframe
df = pd.DataFrame(
    [[1, 2, 3, 4]]*20,
    columns=['col1', 'col2', 'col3', 'col4']
)

# Insert dataframe to spreadsheet
sheet_utils.insert_data(
    data=df,
    sheet_id=spread_sheet_id,
    from_row_index=1,
    insert_column_names=True,
)

# Process data in the spreadsheet
sheet_utils.remove_data(
    sheet_id=spread_sheet_id,
    list_range=[
        'A3:D3',
        'A15:D15',
    ],
)

# Get data from spreadsheet
df_from_sheet = sheet_utils.get_data(
    sheet_id=spread_sheet_id,
    columns_first_row=True,
)

# Load data to minIO storage
minio_utils.load_data(
    data=df_from_sheet,
    mode='stag',
    file_path='/test_flow/20131015',
    bucket_name='sc-bucket',
)

# Get data from minIO
df_from_lake = minio_utils.get_data(
    mode='stag',
    file_path='/test_flow/20131015',
    bucket_name='sc-bucket',
)

# Process data
df_from_lake['total'] = df_from_lake['col1'] + df_from_lake['col2']\
    + df_from_lake['col3'] + df_from_lake['col4']
df_from_lake.dropna(inplace=True)
for col in df_from_lake.columns:
    df_from_lake[col] = df_from_lake[col].astype('int')

# Create new table and load processed data to database
pool_serving_w = pg_serving_w_utils.create_pool_conn()

pg_serving_w_utils.create_table(
    schema='sop_da_tmp',
    table='test_20131015',
    columns_with_dtype={
        'col1': 'int',
        'col2': 'int',
        'col3': 'int',
        'col4': 'int',
        'total': 'int',
    },
    db_pool_conn=pool_serving_w,
)

pg_serving_w_utils.insert_data(
    data=df_from_lake,
    schema='sop_da_tmp',
    table='test_20131015',
    db_pool_conn=pool_serving_w,
)

pg_serving_w_utils.close_pool_conn(pool_serving_w)

# Get data from database
pool_serving_r = pg_serving_r_utils.create_pool_conn()

df_from_db = pg_serving_r_utils.get_data(
    sql='SELECT * FROM sop_da_tmp.test_20131015'
)

pg_serving_r_utils.close_pool_conn(pool_serving_r)

# Update data from database to spreadsheet
sheet_utils.update_data(
    data=df_from_db,
    sheet_id=spread_sheet_id,
    range_from='A2',
    range_to='E22',
)

# End of workflow

With using DAConfig class:

# Begining of the workflow

from sop_deutils.base.y4a_da_cfg import DAConfig
import pandas as pd

da_cfg = DAConfig(
    account_name='your-account-name',
)

# Create new spreadsheet id
spread_sheet_id = da_cfg.sheet_utils.create_spread_sheet(
    sheet_name='test_sheet_20231015_new',
    share_to=['[e-mail]'],
)

# Have a predefined dataframe
df = pd.DataFrame(
    [[1, 2, 3, 4]]*20,
    columns=['col1', 'col2', 'col3', 'col4']
)

# Insert dataframe to spreadsheet
da_cfg.sheet_utils.insert_data(
    data=df,
    sheet_id=spread_sheet_id,
    from_row_index=1,
    insert_column_names=True,
)

# Process data in the spreadsheet
da_cfg.sheet_utils.remove_data(
    sheet_id=spread_sheet_id,
    list_range=[
        'A3:D3',
        'A15:D15',
    ],
)

# Get data from spreadsheet
df_from_sheet = da_cfg.sheet_utils.get_data(
    sheet_id=spread_sheet_id,
    columns_first_row=True,
)

# Load data to minIO storage
da_cfg.minio_utils.load_data(
    data=df_from_sheet,
    mode='stag',
    file_path='/test_flow/20131015_new',
    bucket_name='sc-bucket',
)

# Get data from minIO
df_from_lake = da_cfg.minio_utils.get_data(
    mode='stag',
    file_path='/test_flow/20131015_new',
    bucket_name='sc-bucket',
)

# Process data
df_from_lake['total'] = df_from_lake['col1'] + df_from_lake['col2']\
    + df_from_lake['col3'] + df_from_lake['col4']
df_from_lake.dropna(inplace=True)
for col in df_from_lake.columns:
    df_from_lake[col] = df_from_lake[col].astype('int')

# Create new table and load processed data to database
pool_serving_w = da_cfg.pg_serving_w_utils.create_pool_conn()

da_cfg.pg_serving_w_utils.create_table(
    schema='sop_da_tmp',
    table='test_20131015_new',
    columns_with_dtype={
        'col1': 'int',
        'col2': 'int',
        'col3': 'int',
        'col4': 'int',
        'total': 'int',
    },
    db_pool_conn=pool_serving_w,
)

da_cfg.pg_serving_w_utils.insert_data(
    data=df_from_lake,
    schema='sop_da_tmp',
    table='test_20131015_new',
    db_pool_conn=pool_serving_w,
)

da_cfg.pg_serving_w_utils.close_pool_conn(pool_serving_w)

# Get data from database
pool_serving_r = da_cfg.pg_serving_r_utils.create_pool_conn()

df_from_db = da_cfg.pg_serving_r_utils.get_data(
    sql='SELECT * FROM sop_da_tmp.test_20131015_new'
)

da_cfg.pg_serving_r_utils.close_pool_conn(pool_serving_r)

# Update data from database to spreadsheet
da_cfg.sheet_utils.update_data(
    data=df_from_db,
    sheet_id=spread_sheet_id,
    range_from='A2',
    range_to='E22',
)

# End of the workflow

---

provided by liuliukiki

and special thank to duiikha for contributing api method to get and secure account credentials.

---