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Python Lab Summary and Resources

This week’s session focused on fundamental Python programming concepts, covering basic data types, core data structures, variables, flow control statements, functions, and file handling. We explored libraries vital for data analytics, including Pandas and Matplotlib, and engaged in practical examples that emphasize project development and algorithm implementation. Resources for further learning, such as official documentation, recommended books, and online platforms, were also provided to enhance your Python skills in the context of proteomics informatics.

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Python Lab Summary and Resources

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  1. Python LabSummary and Resources Proteomics Informatics, Spring 2014 Week 12 22nd Apr, 2014 Himanshu.Grover@nyumc.org

  2. Covered a lot of ground • Fundamentals: • Basic data types and operations • Core data structures • Concept of variables, statements, expressions • Flow control statements • If/else conditional blocks • Repetitive computation (for loops) • Functions & design guidelines • Modules (import statements) • Little bit of file-handling • Toolbox: • IDEs (Eclipse, IPython) • Code exploration and debugging (ipdb) • Getting help (read code documentation through ipython)

  3. Covered a lot of ground • Several libraries (data analytics ecosystem): • Pandas • Matplotlib • Little bit of scipy/numpy/math • Several Examples: • Interactive vs. batch processing • Logically breaking down your computational goal into code units • Design choices • What functions • What data structures • Etc.

  4. Practice makes Perfect • Develop your own project; implement some algorithms • Move day-to-day work to python • Learn new libraries and functionality • Dig deeper into sourcecode. Ex. Pandas, Matplotlib, BioPython • Join their team, contribute to code and/or documentation • Experiment with different design choices • Focus on “correctness”, “elegance” and “performance”

  5. Resources • Python official documentation (http://docs.python.org) • Books • Learning Python (5th Edition) • Python for Data Analysis (Pandas Book) • Think Series (Think Python, Think Stats, Think Complexity) • Fundamentals of Python – From First Programs Through Data Structures • Clean Code; Design Patterns (more general) • Conferences (videos): PyCon, PyData, SciPy, EuroSciPy, EuroPython • Videos on various topics (Ipython, Pandas, web frameworks, databases, matplotlib, debugging, profiling, many, many more) • Online education & other web resources: • Khan’s Academy, Coursera (??) • http://code.activestate.com/ (Python cookbook recipies) • Documentation: • Matplotlib user guide (http://matplotlib.org/contents.html) • Also has a lot of code for various examples • Numpyuser guide (http://docs.scipy.org/doc/numpy/user/) • Scipy reference (http://docs.scipy.org/doc/scipy/reference/) • Sackler Programming Club - Pamela Wu • Fall term Python course (??)

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