RR-Custom-Python-Tools 0.0.9

Frequently used python methods/libraries.

Custom Python Library

Custom library for frequent used methods/libraries.

Installation : pip install RR-Custom-Python-Tools

License: GPLv3+

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DateManager

To use the DateManager class, you need to import it and create an instance:

from date_manager.date_manager import DateManager
date_manager = DateManager()

timestamp_to_date(dataframe: pd.DataFrame) -> pd.DataFrame

Converts datetime columns in a DataFrame to date type.

• Arguments:
  • dataframe (pd.DataFrame): The DataFrame containing the datetime columns.
• Returns:
  • pd.DataFrame: The input DataFrame with the datetime columns converted to date type.

Example:

import pandas as pd

# Create a sample DataFrame
data = {'datetime_column': ['2022-01-01', '2022-01-02', '2022-01-03']}
df = pd.DataFrame(data)
df['datetime_column'] = pd.to_datetime(df['datetime_column'])

# Convert datetime columns to date
converted_df = date_manager.timestamp_to_date(df)

timestamp_to_date_column(column: str, dataframe: pd.DataFrame) -> pd.DataFrame

Converts a specific datetime column in a DataFrame to date type.

• Arguments:
  • column (str): The name of the datetime column to convert.
  • dataframe (pd.DataFrame): The DataFrame containing the datetime column.
• Returns:
  • pd.DataFrame: The input DataFrame with the specified datetime column converted to date type.
• Raises:
  • ValueError: If the input DataFrame does not contain the specified datetime column.

Example:

import pandas as pd

# Create a sample DataFrame
data = {'datetime_column': ['2022-01-01', '2022-01-02', '2022-01-03']}
df = pd.DataFrame(data)
df['datetime_column'] = pd.to_datetime(df['datetime_column'])

# Convert specific datetime column to date
converted_df = date_manager.timestamp_to_date_column('datetime_column', df)

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ExcelManager

To use the ExcelManager class, you need to import it and create an instance:

from excel_manager.excel_manager import ExcelManager
excel_manager = ExcelManager()

get_dataframe(self, workbook: str, sheet: str) -> pd.DataFrame

Retrieve a pandas dataframe from an Excel workbook.

• Args:
  • workbook (str): The path to the Excel workbook.
  • sheet (str): The name of the sheet containing the data.
• Returns:
  • pd.DataFrame: The contents of the specified sheet as a pandas dataframe.
• Raises:
  • FileNotFoundError: If the specified workbook cannot be found.
  • Exception: If an unexpected error occurs.

Example:

workbook = "test.xlsx"
sheet = "Sheet1"
dataframe = excel_manager.get_dataframe(workbook, sheet)

delete_sheet(self, workbook: str, sheet: str) -> None

Delete a sheet from an Excel workbook.
! There should be minimum 2 sheets in the workbook to perform this operation.

• Args:
  • workbook (str): The path to the Excel workbook.
  • sheet (str): The name of the sheet to be deleted.
• Returns:
  • None: Returns nothing.
• Raises:
  • FileNotFoundError: If the specified workbook cannot be found.
  • ValueError: If the specified sheet does not exist in the workbook.
  • Exception: If an unexpected error occurs.

Example:

workbook = "test.xlsx"
sheet = "Sheet1"
excel_manager.delete_sheet(workbook, sheet)

create_sheet(self, workbook: str, sheet: str) -> None

Create a new sheet in an Excel workbook.

• Args:
  • workbook (str): The path to the Excel workbook.
  • sheet (str): The name of the sheet to be created.
• Raises:
  • FileNotFoundError: If the specified workbook cannot be found.
  • PermissionError: If the user does not have permission to write to the specified workbook.
  • Exception: If an unexpected error occurs.
• Returns:
  • None: Returns nothing.

Example:

workbook = "test.xlsx"
sheet = "Sheet1"
excel_manager.create_sheet(workbook, sheet)

overwrite_sheet(self, workbook: str, sheet: str, dataframe: pd.DataFrame) -> None

Overwrite the contents of an Excel sheet with a new dataframe.

• Args:
  • workbook (str): The path to the Excel workbook.
  • sheet (str): The name of the sheet to be overwritten.
  • dataframe (pd.DataFrame): The new contents of the sheet as a pandas dataframe.
• Returns:
  • None: Returns nothing.
• Raises:
  • FileNotFoundError: If the specified workbook cannot be found.
  • PermissionError: If the user does not have permission to write to the specified workbook.
  • ValueError: If the specified sheet does not exist in the workbook.
  • Exception: If an unexpected error occurs.

Example:

import pandas as pd

workbook = "test.xlsx"
sheet = "Sheet1"
dataframe = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
excel_manager.overwrite_sheet(workbook, sheet, dataframe)     

reposition_sheet(self, workbook: str, sheet: str) -> None

Moves a sheet at the start in the workbook and saves the changes.

• Args:
  • workbook (str): The path to the Excel workbook.
  • sheet (str): The name of the sheet to be moved.
• Raises:
  • FileNotFoundError: If the specified workbook cannot be found.
  • PermissionError: If the user does not have permission to write to the specified workbook.
  • ValueError: If the specified sheet does not exist in the workbook.
  • Exception: If an unexpected error occurs.
• Returns:
  • None: Returns nothing.

Example:

workbook = "test.xlsx"
sheet = "Sheet1"
excel_manager.reposition_sheet(workbook, sheet)

append_dataframe(self, workbook: str, sheet: str, dataframe: pd.DataFrame, password: str = None) -> None

Appends a pandas dataframe to an Excel sheet.

• Args:
  • workbook (str): The path to the Excel workbook.
  • sheet (str): The name of the sheet to which the dataframe will be appended.
  • dataframe (pd.DataFrame): The pandas dataframe to be appended to the sheet.
  • password (str, optional): The password for the Excel workbook, if it is protected. Defaults to None.
• Raises:
  • FileNotFoundError: If the specified workbook cannot be found.
  • PermissionError: If the user does not have permission to write to the specified workbook.
  • Exception: If an unexpected error occurs.
• Returns:
  • None: Returns nothing.

Example:

import pandas as pd

workbook = "test.xlsx"
sheet = "Sheet1"
dataframe = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
excel_manager.append_dataframe(workbook, sheet, dataframe)

modify_sheet_protection(self, filepath: str, sheetname: str, enable_protection: bool, password: str = None) -> None

Modifies the protection of an Excel sheet.

• Args:
  • filepath (str): The path to the Excel workbook.
  • sheetname (str): The name of the sheet to be protected.
  • enable_protection (bool): A boolean value indicating whether to enable or disable protection.
  • password (str, optional): The password for the Excel workbook, if it is protected. Defaults to None.
• Raises:
  • FileNotFoundError: If the specified workbook cannot be found.
  • PermissionError: If the user does not have permission to write to the specified workbook.
• Returns:
  • None: Returns nothing.

Example:

# to set the protection, 'abc' is password.
workbook = "test.xlsx"
sheet = "Sheet1"
excel_manager.modify_sheet_protection(workbook, sheet, True, 'abc')

# to remove the protection.
workbook = "test.xlsx"
sheet = "Sheet1"
excel_manager.modify_sheet_protection(workbook, sheet, False)

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LogManager

The LogManager class provides a customizable logging utility in Python, allowing you to log messages of different severity levels to both a file and the console.

Features

• Initialization of logging parameters such as log file name, log level, and log name.
• Logging of messages with severity levels including INFO, DEBUG, WARNING, ERROR, and CRITICAL.
• Configuration of logging to both file and console. Customizable log message format.

To use the LogManager class, follow these steps:

# Import the LogManager class:
from log_manager.log_manager import LogManager

# Create an instance of the LogManager class:
log_manager = LogManager()

By default, this initializes the logger with the following settings:

• Log file name: './Custom-Python_Tools.log'
• Log level: logging.DEBUG
• Log name: 'LogManager'

Log messages with desired severity levels:
Use the appropriate methods to log messages with different severity levels:

• info(message: str)
• debug(message: str)
• warning(message: str)
• error(message: str)
• critical(message: str)

Example:

log_manager.info("This is an informational message.")
log_manager.error("An error occurred!")

Customize LogManager settings (optional):
You can customize the LogManager settings by providing parameters during initialization:

log_manager = LogManager(log_file='my_log.log', log_level=logging.INFO, log_name='MyLogger')

Logging Format

The default logging format includes the following fields:

• Timestamp (%(asctime)s)
• Logger Name (%(name)s)
• Log Level (%(levelname)s)
• Log Message (%(message)s)

You can modify the logging format by updating the formatter string in the init method of the LogManager class.

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Logging

All of the above class utilizes a logging mechanism provided by log_manager for capturing important events during the process. Ensure you have the appropriate logger configuration to capture and manage log messages effectively.

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Dependencies

• pandas: A powerful data manipulation library in Python used extensively for data analysis and manipulation.
• logging: The Python standard library module used for logging functionality.
• openpyxl: A Python library to read/write Excel 2010 xlsx/xlsm files
• inspect: The inspect module helps in checking the objects present in the code that we have written.

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Author

• This module is maintained by Ronak Rathore.