framecleaner 0.3.2

auto cleaning the data with easy user interface

Data Cleansing Tool This project provides a Python script for cleansing data in various file formats (CSV, Excel, JSON, XML). It offers a variety of operations to clean and process your data, including removing null values, handling duplicates, formatting column names, and transforming date columns. Users can choose cleansing operations interactively through a command-line interface.

Features File Format Support: CSV, Excel (.xls, .xlsx), JSON, XML, if you chose any operation,then want to getback to origianl data option also included. Operations: Remove rows/columns with null values. Replace null values with a custom value. Remove duplicate rows and columns. Convert column names to lowercase and remove whitespaces. Split date columns into day, month, and year. Convert categorical columns to One-Hot Encoding. And many more customizable data cleansing options.... Installation Clone the repository or download the files to your local machine.

Dependencies: Ensure that you have Python 3.x. You can install the necessary dependencies using pip:

bash Copy code pip install pandas Usage Prepare your data: Make sure you have a data file in one of the supported formats (CSV, Excel, JSON, or XML).

Run the script: In the terminal or command prompt, navigate to the directory where the script is located and run it:

bash Copy code python autocleaner.py or python3 autocleaner.py Enter file path: You will be prompted to enter the file path of the dataset you want to clean.

Choose cleansing operations: The script will display a menu of available operations. Enter the numbers of the operations you want to apply (separate multiple choices with commas). Some of the available operations include:

Remove rows/columns with null values. Replace null values with a custom value. Remove duplicate rows and columns. Convert column names to lowercase and remove whitespaces. Split date columns into day, month, and year. Convert categorical columns to One-Hot Encoding. Remove rows with null values. Replace null values with a specified value. Convert column names to lowercase. Convert a date column to 'day', 'month', and 'year'. Exit or revert to original: Type 'backtodata' to revert to the original data or 'exit' to stop the program.

Output: After performing the selected operations, the final cleaned DataFrame will be displayed.

Example Here’s a simple usage example:

bash Copy code Enter your file path: data.csv Choose cleansing operations (multiple choices allowed): Remove rows with null values Remove columns with null values Replace null values with a specified value Remove duplicate rows Convert column names to lowercase ... Enter the numbers of the operations you'd like to perform, separated by commas: 1,4,5 Final DataFrame: Column1 Column2 0 1 10 1 2 20 Notes Ensure the file path you provide is correct and accessible. The script will display statistics before and after cleansing to help you track the changes made to your dataset. You can perform multiple cleansing operations on the same dataset in a single run. License This project is licensed under the MIT License - see the LICENSE file for details.