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Numerical experiments, Tips, Tricks and Gotchas

Numerically speaking

Python data useful references

The IPython Notebook

IPython notebook home [1], [2] and examples [3]. Video tutorial at PyCon 2014 by Fernando Perez: [4], Video tutorial at PyCon 2015 (skip to 5:44 for the beginning of the talk): [5].

The easiest way to install IPython (with all dependencies) is to install Anaconda Python distribution (no registration) [6]. Python 3 is recommended.

Beyond Python: Open source, interactive data science and scientific computing across over 40 programming languages [7], [8].

Pandas

Minimal set of essential tutorials on Pandas [9], [10], [11], [12].

Python data

PyData repositories [13].

 

References

  1. The IPython Notebook
  2. The IPython Notebook Documentation
  3. A gallery of interesting IPython Notebooks
  4. Fernando Perez, IPython in depth: high productivity interactive and parallel python - PyCon 2014
  5. Thomas Kluyver, Kyle Kelley, IPython & Jupyter in depth: high productivity interactive and parallel python - PyCon 2015
  6. Anaconda Python distribution
  7. Jupyter Notebook
  8. Try Jupyter
  9. Alfred Essa, pandas cookbook
  10. Brandon Rhodes, Pandas From The Ground Up - PyCon 2015 (Video)
  11. Brandon Rhodes, Pandas From The Ground Up - PyCon 2015 (Code - GitHub)
  12. Wes McKinney, Data analysis in Python with pandas - PyCon 2012 (Video)
  13. Python for Data

 

© Nikolai Shokhirev, 2012-2017

email: nikolai(dot)shokhirev(at)gmail(dot)com

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