Python in Digital Scholarship

Open Source and Community

Python is open-source software, meaning it is free to use, modify, and distribute. It is developed and maintained by a global community under the auspices of the Python Software Foundation. Python’s open-source nature has fostered a vibrant ecosystem of users and contributors. This encourages widespread adoption and continuous improvement of the language by the community. Researchers and students benefit from this collaborative environment—there is extensive documentation, tutorials, and forums (such as Stack Overflow and the PyPI repository) available for troubleshooting and learning. The open-source license also means Python and its libraries can be installed on any system (Windows, macOS, Linux) at no cost, which is ideal for academic settings. The community regularly updates Python to add new features and optimize performance, and it has established a tradition of sharing knowledge through conferences (e.g., PyCon) and user groups. Overall, Python’s open-source, community-driven development ensures transparency and accessibility, aligning well with academic and research values.

Python Libraries and Ecosystem

One of Python’s greatest strengths is its rich ecosystem of libraries (also known as packages) that extend its functionality. The Python Standard Library (included with Python) provides modules for diverse tasks such as file I/O, data serialization, regular expressions, math, and more, embodying Python’s “batteries-included” philosophy. In addition, a vast collection of third-party libraries is available via the Python Package Index (PyPI). As of March 2025, PyPI hosts over 614,000 packages for Python. covering nearly every computational problem domain. These libraries enable specialized tasks and accelerate development by providing pre-built, tested functionality.

Key categories of Python libraries relevant to academic work include:

• Scientific Computing and Data Analysis: Libraries like NumPy, SciPy, and Pandas provide fast array computations, linear algebra, statistical functions, and DataFrame structures for manipulating and analyzing datasets.

• Visualization: Libraries such as Matplotlib and Seaborn (and others like Plotly or Bokeh) allow creation of publication-quality graphs and interactive plots (see the Visualization section below for details).

• Web Development: Frameworks like Django and Flask enable building web applications and RESTful APIs for research data portals or interactive tools.

• Automation and Scripting: Packages like Requests (for web requests), BeautifulSoup or Scrapy (for web scraping), and OS/Python’s pathlib (for file system operations) help automate data collection and processing tasks.

• Machine Learning and AI: Python is the de facto language for machine learning. Libraries such as scikit-learn (for classical ML algorithms) and deep learning frameworks like TensorFlow and PyTorch provide tools to develop and deploy models (covered in Machine Learning and LLMs sections).

• Text Processing and NLP: Libraries like NLTK and spaCy facilitate natural language processing and text analysis (see the Text Analysis section).

• Others: Python’s ecosystem also includes tools for database access (e.g., SQLAlchemy for SQL databases, PyMongo for MongoDB), image processing (OpenCV, Pillow), and domain-specific libraries (for bioinformatics, digital humanities, etc.).

This extensive library ecosystem means researchers rarely need to reinvent the wheel – for most tasks, there are existing packages or examples available. Installing libraries is typically done via the pip package manager (or conda, if using Anaconda). For example, to install the popular data analysis library Pandas, one would run: pip install pandas. (It is best practice to install packages within a virtual environment – discussed below – to avoid conflicts.)