fastsetup

A Python library that has all the static repetitive code used in Python scripts, for easier and faster setup.

Installation

Use the package manager pip to install fastsetup.

pip install fastsetup

Usage and docs

import fastsetup

loggerMain, loggerInit, loggerProcess = fastsetup.main_log(log_folder)
# -----------------------------------------------------------------------
# Function: main_log(log_folder)
# Description: Creates and configures a main log and two additional logs.
# Parameters:
#   - log_folder (str): The folder path where log files will be stored.
# Returns:
#   - loggerMain (Logger): The main logger.
#   - loggerInit (Logger): The initialization logger.
#   - loggerProcess (Logger): The process logger.
# -----------------------------------------------------------------------

customLog = fastsetup.custom_log(log_folder, log_format, log_name)
# -----------------------------------------------------------------------
# Function: custom_log(log_folder, log_format, log_name)
# Description: Creates a custom log with a specified format.
# Parameters:
#   - log_folder (str): The folder path where log files will be stored.
#   - log_format (str): The format for log messages (default format used if None).
#   - log_name (str): The name for the custom log.
# Returns:
#   - customLog (Logger): The custom logger.
# -----------------------------------------------------------------------

Config_Name = fastsetup.get_config(Config_Path, SheetName, Key, Value)
# -----------------------------------------------------------------------
# Function: get_config(Config_Path, SheetName, Key, Value)
# Description: Reads a configuration file (Excel) and returns a dictionary.
# Parameters:
#   - Config_Path (str): The path to the configuration Excel file.
#   - SheetName (str): The name of the sheet within the Excel file.
#   - Key (str): The column name representing keys.
#   - Value (str): The column name representing values.
# Returns:
#   - Config (dict): A dictionary containing key-value pairs from the configuration.
# -----------------------------------------------------------------------

fastsetup.SMTP(config)
# -----------------------------------------------------------------------
# Function: SMTP(config)
# Description: Sends an email with attachments using SMTP protocol.
# Parameters:
#   - config (dict): A dictionary containing email configuration details.
#     Required keys: 'file_name', 'sender_email', 'to_email', 'email_subject',
#     'body_type', 'body', 'cc', 'attachments', 'server', 'port'.
# -----------------------------------------------------------------------

file_Name = fastsetup.SFTP_download(config)
# -----------------------------------------------------------------------
# Function: SFTP_download(config)
# Description: Downloads a file from a remote server using SFTP.
# Parameters:
#   - config (dict): A dictionary containing SFTP configuration details.
#     Required keys: 'host', 'user_name', 'password', 'SFTP_port',
#     'server_folder_name', 'server_file_name', 'local_file_path'.
# Returns:
#   - result: downloaded file.
# -----------------------------------------------------------------------

fastsetup.SFTP_upload(config)
# -----------------------------------------------------------------------
# Function: SFTP_upload(config)
# Description: Uploads a file to a remote server using SFTP.
# Parameters:
#   - config (dict): A dictionary containing SFTP configuration details.
#     Required keys: 'host', 'user_name', 'password', 'SFTP_port',
#     'local_file', 'target_location'.
# -----------------------------------------------------------------------

df = fastsetup.oracle_download(Config)
# -----------------------------------------------------------------------
# Function: oracle_download(Config)
# Description: Downloads data from an Oracle database using a provided query.
# Parameters:
#   - Config (dict): A dictionary containing Oracle database configuration details.
#     Required keys: 'host', 'port', 'data_source', 'user_ID', 'password', 'query'.
# Returns:
#   - df (DataFrame): A Pandas DataFrame containing the queried data.
# -----------------------------------------------------------------------