https://github.com/whosgriffith/datamizer

Python package that lets you change sensitive data from a .CSV file, creating a new file with fake data. This allows the new data to be used for training, testing or analytics, without compromising private information.
https://github.com/whosgriffith/datamizer

csv pandas python

Last synced: 6 months ago
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Python package that lets you change sensitive data from a .CSV file, creating a new file with fake data. This allows the new data to be used for training, testing or analytics, without compromising private information.

• Host: GitHub
• URL: https://github.com/whosgriffith/datamizer
• Owner: whosgriffith
• License: mit
• Created: 2023-01-22T08:16:43.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2023-01-23T21:02:52.000Z (about 3 years ago)
• Last Synced: 2025-09-27T15:24:41.496Z (6 months ago)
• Topics: csv, pandas, python
• Language: Python
• Homepage:
• Size: 4.88 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          

# Datamizer for Python  

[![PyPI version](https://badge.fury.io/py/datamizer.svg)](https://badge.fury.io/py/datamizer)

This is a simple package that lets you change the sensitive data from a .CSV file creating a new file with fake data.  

  

  

This allows the new data to be used for training, testing or analytics, without compromising private information.  

  

## Installation  

  

Run the following command to install the package:  

```  

pip install datamizer  

```  

  

## Usage

1- Instanciate the Datamizer class, pass the path to the CSV file, and optionally the CSV delimiter.

```python  

from datamizer import Datamizer

csv_datamize = Datamizer('file.csv')

```  

2- Use `fake()` to anonymize the columns with sensitive data, passing the `column`,`provider`, and optionally `consistent` args.

```python

csv_datamize.fake('Username', 'user_name', consistent=True)

csv_datamize.fake('First name', 'first_name', consistent=True)

csv_datamize.fake('Last name', 'last_name', consistent=True)

csv_datamize.fake('email', 'email', consistent=True)

csv_datamize.fake('Money', 'pricetag')

```

3- Write a new CSV file with the fake data, passing the path to the new file and optionally `index=True` to include the index.

```python

csv_datamize.write_csv('users.csv')

```