A library for biological computation in Python

What is Python?

Python is a high-level, interpreted programming language created by Guido van Rossum and first released in 1991. It is known for its simplicity, readability, and extensive standard library.

1. Python for data manipulation

   • NumPy is one of the best Python libraries for numerical operations and calculations.
   • Pandas: An excellent Python library for data analysis and manipulation

2. Python for statistical analysis

   • SciPy: A library for scientific computing in Python
   • Statsmodels: A library for statistical modeling in Python
   • Seaborn: A library for data visualization in Python

3. Python for machine learning

   • Scikit-learn: A library for machine learning in Python
   • TensorFlow: It’s one of the most  open-source machine learning library developed by Google
   • Keras: A deep learning library for Python, running on top of TensorFlow

4. Python for web development

   • Django: is another high-level Python web framework it promotes and aids in quick development as well as clear, practical design, among other things.
   • Flask: is a lightweight web framework for Python that is easy to learn and use

5. Python for bioinformatics

   • Biopython: A library for biological computation in Python
   • Pysam: A library for reading and writing genomic data in Python

6. Python for data visualization

   • Matplotlib: A plotting library for Python
   • Plotly: An interactive, publication-ready graphing library for Python-quality graphs
   • Seaborn: A library for data visualization in Python

7. Python also for text analysis and natural language(NL) processing

   • NLTK: A library for natural language processing in Python
   • SpaCy: A library for advanced natural language processing in Python
   • TextBlob: A library for processing textual data in Python

By learning these topics, medical students can enhance their analytical skills and stay updated with the latest developments in data science and artificial intelligence.

For beginners, Python is recommended due to its simplicity and readability. Bioinformatics is also a field that uses other languages, such as R, Julia, and MATLAB. However, Python has become the standard choice due to its extensive range of libraries and resources available for learning.

Python for data manipulation can be particularly useful for working with electronic health records (EHRs). It can also be employed for processing genomic data, a rapidly growing field in the medical sector.

Python is extensively utilized not only in bioinformatics but also in data analysis and visualization. Its integration with various web development frameworks and its ability to interact with databases make it an indispensable tool for web developers and data engineers.

About Author

SURF

See author's posts