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  • pytoLearn is a open site for scientific programming and learning through python-based coding. In other words: 'pycode it to learn'
  • Select 'Contents' (right above) to browse through material
  • Select 'About' to read what pytoLearn is all about, or
  • Select 'Links/Downloads' to see captured sessions of teaching 'pytolearn' Python material (cuurently at the University of Valladolid, Spain) and relevant material (ppt, pdf), or
  • Select from available paths below


  • P1: Data analysis and Hypothesis testing
  • This path guides you through the basics of some of the most interesting and popular packages in the Python ecosystem for data analysis and processing, namely numpy, matplotlib, pandas, and scipy.stats. It helps understand how to use and pycode some common statistical controls for hypothesis testing (such as t-test, ANOVA, ANCOVA, etc.). It has been designed to address the needs of starting researchers (especially doctoral students) in their research projects.
  • The proposed sequence of study is:
    • Basic Python
      • If not familiar with basic Python learn the basics first. Focus on: Key ideas, for-range loop (iterators), lists (emphasis on slicing), dictionaries and functions
    • Numpy and matplotlib
      • Focus on: Array object manipulation (emphasis on slicing) and plotting with matplotlib
    • pandas
      • This is important. Focus on: Series and DataFrame objects, management of DataFrame, applying scipy.stats functions on DataFrame objects
    • Hypothesis testing
      • Focus on: distributions, apply scipy.stats functions for hypothesis testing on DataFrame objects, writing your own statistical control functions


  • Main author of pytoLearn is currently: Stavros Demetriadis
  • Comments and suggestions are welcome: email me: sdemetri #at# csd.auth.gr

. Free learning material
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