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Python Lab Summary and Resources

Python Lab Summary and Resources. Proteomics Informatics, Spring 2014 Week 12 22 nd Apr, 2014 Himanshu.Grover@nyumc.org. Covered a lot of ground. Fundamentals: Basic data types and operations Core data structures Concept of variables, statements, expressions Flow control statements

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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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