Privacy is critical when dealing with user-generated text, as common in Natural Language Processing (NLP) and Information Retrieval (IR) tasks. Documents, queries, posts, and reviews might pose a risk of inadvertently disclosing sensitive information. Such exposure of private data is a significant threat to user privacy, as it may reveal information that users prefer to keep confidential. The leading framework to protect user privacy when handling textual information is represented by the ε-Differential Privacy (DP). However, the research community lacks a unified framework for comparing different DP mechanisms. This study introduces pyPANTERA, an open-source Python package developed for text obfuscation. The package is designed to incorporate State-of-the-Art DP mechanisms within a unified framework for obfuscating data. pyPANTERA is not only designed as a modular and extensible library for enriching DP techniques, thereby enabling the integration of new DP mechanisms in future research, but also to allow reproducible comparison of the current State-of-the-Art mechanisms. Through extensive evaluation, we demonstrate the effectiveness of pyPANTERA, making it an essential resource for privacy researchers and practitioners. The source code of the library and for the experiments is available at: https://github.com/Kekkodf/pypantera∗∗REMOVE 2nd URL∗∗://github.com/Kekkodf/pypantera.