dataforgetoolkit 1.0.6

It is a library that facilitates converting CSV files to various formats (such as DataFrames or other CSV/Excel files) based on a JSON mapping

Project Title

DataForgeToolkit: Flexible Data Mapping for CSV/XLSX Files

Description

The DataForgeToolkit is a Python library designed to streamline the process of converting CSV or Excel files into customized DataFrames based on user-defined JSON mapping configurations. Whether you're working with financial reports, customer datasets, or any other structured data, this toolkit empowers you to effortlessly transform raw data into actionable insights.

Features: Versatile File Support: Seamlessly process both CSV and Excel files, providing flexibility in handling various data formats commonly encountered in data analysis tasks.

Customizable Mapping: Define transformation mappings using a JSON file, allowing for precise specification of column names, data cleaning, and value substitutions tailored to your specific data requirements.

Efficient Data Processing: Automate data preprocessing tasks such as handling missing values, standardizing column names, and applying complex value mappings with ease.

Installation Usage/Examples

  pip install dataforgetoolkit

Define Transformation Mapping:

Create a JSON file specifying the transformation mappings for your data. Define column mappings, specify new column names, and define value substitutions as needed.

Use the Toolkit:

Import the DataForgeToolkit in your Python script and utilize the map function to convert your report files:

    from dataforgetoolkit import datamapper
    datamapper.map('report file path csv / xlsx format','mapping json file path')

Access Mapped Data:

Access the transformed data as a DataFrame for further analysis or export to other formats.

Transformation Functions Available

DEFAULT_VALUE = "*"
FILTER_VALUE = "FILTER"
REPLACE_VALUE = "REPLACE_"
CONCAT_VALUE = "CONCAT"
UPPERCASE_VALUE = "UPPERCASE"
LOWERCASE_VALUE = "LOWERCASE"
REGEX_VALUE = "REGEX_"

JSON Transformation Mapping

Transformation mappings are specified using a JSON file. Example:

{ "transformation_mapping": [ { "column": "Name", "new_name": "Student Name", "value_mappings": [ { "*": "Amit Singh" } ] }, { "column": "Age_Column", "new_name": "Age", "value_mappings": [ { "FILTER": "30" } ] }, { "column": "Location", "new_name": "Country", "value_mappings": [ { "REPLACE_usa": "United state of America" } ] }, { "column": "Gender", "new_name": "Sex", "value_mappings": [ { "MALE": "M", "FEMALE": "F" } ] }, { "column": "Zipcode_Column", "new_name": "Processed_Text_regex", "value_mappings": [ { "REPLACE_hello": "hi", "REGEX_[0-9]+": "NUMBER" } ] } ] }

Authors

• @amitsingh

• Software Engineer

Contributing

Contributions are always welcome!

Please adhere to this project's code of conduct.

Suggest code and open PR/MR

Used By

'Intended Audience' :: Developers , Testers , BA