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https://github.com/mfarragher/appelpy

Applied Econometrics Library for Python
https://github.com/mfarragher/appelpy

econometrics economics modeling python3 regression regression-models statistical-models statistics

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Applied Econometrics Library for Python

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README 

        
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===================================================

appelpy: Applied Econometrics Library for Python

===================================================



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About 👁️

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**appelpy** is the *Applied Econometrics Library for Python*.  It seeks to bridge the gap between the software options that have a simple syntax (such as Stata) and other powerful options that use Python's object-oriented programming as part of data modelling workflows.  ⚗️



Econometric modelling and general regression analysis in Python have never been easier!



The library builds upon the functionality of the 'vanilla' Python data stack (e.g. Pandas, Numpy, etc.) and other libraries such as Statsmodels.



Documentation

==========================

See the functionality of the library at https://appelpy.readthedocs.io/



Get started with the *10 Minutes to Appelpy* tutorial:



- |binder10|_: interactive experience of the *10 Minutes to Appelpy* tutorial via Binder.

- |nbviewer10|_: static render of the *10 Minutes to Appelpy* notebook.



🥧 Why it's as easy as pie

==========================

Here is a flavour of a basic OLS regression done through appelpy, supposing you have `data `_ sitting in a Pandas dataframe ``df`` and want to model the dependent variable ``api00`` on three other variables:



    .. code-block:: python



        from appelpy.linear_model import OLS

        model1 = OLS(df, ['api00'], ['acs_k3', 'meals', 'full']).fit()

        model1.results_output  # returns summary results



The key information is sitting in the ``model1`` object, but there is much more functionality that can be done with it.  These are more things that can be done via one line of code:



* *Diagnostics* can be called from the object: e.g. produce a P-P plot via ``model1.diagnostic_plot('pp_plot')``

* *Model selection statistics*: e.g. find the root mean square error of the model from ``model1.model_selection_stats``

* *Standardized model estimates*: ``model1.results_output_standardized``



🍏 What inspired it?

====================

1) The simple syntax of software such as Stata.  With the data loaded, a regression model summary can be returned by a one-line command:



        regress api00 acs_k3 meals full



However with the simplicity comes a few disadvantages: Stata is not open-source software; the workflows are tricky with modern business problems; lacks the benefits of object-oriented programming.



2) Statsmodels is a powerful Python library that addresses some of those disadvantages, but with that power comes a considerable learning curve and clunkiness.  Here is the code for the same regression:



    .. code-block:: python



        import statsmodels.api as sm

        model1 = sm.OLS(df['api00'], sm.add_constant(df['acs_k3', 'meals', 'full'])).fit()

        results1 = model1.summary()  # returns summary results



It can get much more unwieldy than that.  The model results object is brilliant as it can be printed in different formats (plaintext, Latex, etc.)... but that is only the starting point.  How do I diagnose the regression model itself?  How do I get standardized estimates?  That's where it becomes more complicated.



**appelpy** simply aims to achieve a *sweet spot* between both approaches.



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Installation ⏲️

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``pip install appelpy``



Supported for Python 3.6 and higher versions.



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Dependencies 🖇️

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- pandas>=0.24

- jinja2

- scipy

- numpy>=1.16

- statsmodels>=0.9

- patsy

- seaborn>=0.9

- matplotlib>=3



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Licence ⚖️

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Modified BSD (3-clause)