Why using Python for Data Science applied for Veterinary Medicine and AnimalScience?

Python was created in 1989 by Guido van Rossum, with its first official release in 1991 (Python 0.9.0). Python 2.0 was released in 2000 which included list comprehensions and garbage collection. In 2008, the modernized version of Python was launched (Python 3.0). It was between 2006-2008 that libraries like NumPy and SciPy became available excelling its data science capabilities, since the language had been used primarily for scripting, automation, and web development up to that point.

In 2009 and thereafter, other libraries like Pandas, Scikit-learn, TensorFlow, PyTorch, including Jupyter Notebooks have been launched make it a premier language for computational biology and data science. Since 2015, Python is considered among the top language of choice for data science, AI, and machine learning. Here are some advantages of using it for Veterinary Medicine and Animal Science:

1. Ease of learning and use: o Python is written in clear human readable syntax that is easy to learn o Allows for rapid prototyping of ideas without the need for complex understanding of the language architecture o Default functions are easy to learn with a short well define list of keywords (36 keywords in Python 3.12)

2. Rich library for data science: o NumPy and Pandas -> manage and analyze animal health records, farm production data, clinical and genetic datasets o Matplotlib, Seaborn, Plotly, Pandas, and Altair -> comprehensive libraries for data visualization o SciPy, Statsmodels, and Pingouin -> classical statistics o Scikit-learn and PyCaret -> Predictive machine learning o PyMC, PyStan, and TensorFlow Probability -> Bayesian statistics o Statsmodels, Prophet, and pmdarima -> Time series analysis o Lifelines (survival analysis) and linearmodels (econometrics)

3. Applications in Veterinary Medicine and Animal Science: o Epidemiology: Modeling disease outbreaks in livestock or zoonoses, molecular epidemiology using genomics data o Genetics/Genomics: Analyzing DNA/RNA sequencing data to improve breeding programs o Farm Management: Forecasting milk production, feed efficiency across livestock, and price modeling/forecasting o Imaging: X-rays, ultrasounds, or pathology slides o Sensors & ioT: Process real-time data from collars, GPS, or barn sensors

4. Reproducibility and Research collaboration: o Widely used in academia and industry, especially with the advantage of using Jupyter Notebooks for sharing models and code o Jupyter Notebook -> an open-source, interactive coding environment allowing the data scientist to combine code, results, text, equations, and visualizations in one document o JupyterLab -> next-generation interface (tabbed workspace, multiple files) o Google Colab -> cloud-based, free GPU access, and sharable links where one can run Jupyter Notebooks o nbconvert -> export notebooks to PDF, HTML, and slides

5. Cost-effective and scalable: o Open-source (free) with global community support o Can run on a laptop or scale up to cloud computing for big data

Reference:

OpenAI, 2025. ChatGPT version 5, accessed on September 27 th , 2025, generated responses that contributed to the content of this blog.