Patsy (PyData Berlin)

PyData Berlin / Canada Day, 2017 at 11:45am - 12:30pm

Transcript

1. @maxhumber
   Canada Day, 2017
   

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2. patsy
   

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4. r python patsy
   intro
   data
   motivation
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   summary
   

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15. type A type b
    

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19. patsy
    

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22. 1 0
    

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24. create data
    

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30. r python patsy
    

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35. wait for it
    

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47. let’s try again
    

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59. not the only one …
    

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65. Patsy is a Python library for describing statistical
    models (especially linear models, or models that have a
    linear component) and building design matrices. Patsy
    brings the convenience of R "formulas" to Python.
    https://github.com/pydata/patsy
    

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66. pip install patsy
    

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75. 
    

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81. 
    

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84. Beer: 1.5, Warm: -0.5, Family: Yes
    

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85. Beer: -0.9, Warm: 0.3, Family: No
    

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87. https://patsy.readthedocs.io/en/latest/quickstart.html
    

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88. https://patsy.readthedocs.io/en/latest/quickstart.html
    

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90. Our goal is to make Patsy the de facto standard for
    describing models in Python, regardless of the
    underlying package in use – just as formulas are the
    standard interface to all R packages. Therefore we’ve
    tried to make it as easy as possible for you to build
    Patsy support into your libraries.
    https://patsy.readthedocs.io/en/latest/library-developers.html
    

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93. PyMC3 is a Python package for Bayesian statistical
    modeling and Probabilistic Machine Learning which
    focuses on advanced Markov chain Monte Carlo and
    variational fitting algorithms. Its flexibility
    and extensibility make it applicable to a large
    suite of problems.
    https://pymc-devs.github.io/pymc3/
    

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97. summary
    

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99. patsy
    

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100. thanks!
     NJS
     https://vorpus.org/
     

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101. maxhumber
     

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102. maxhumber
     let’s talk let’s celebrate
     

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