__A. Colin Cameron: Python for Regression__

These notes are for data analysis using key
Python modules, notably statsmodels for statistics and
scikit-learn for machine learning. This requires little
knowledge of how to program in Python (a low-level language).
Instead one needs to know the commands to use the modules.

Rather than directly install Python it is convenient to install
Anaconda which also automatically installs key modules.

Anaconda installs not only base Python
plus many packages (collections of modules), including the key
ones for econometrics.

- NumPy. Short for numbers in
Python. Array data types needed for statistics and data
analysis.

- pandas. Name derived from panel data. R type data
frames and data analysis tools.

- matplotlib. Static and dynamic data
visualizations.

- SciPy. Scientific library (optimization,
integration, eigenvalue problems, random number generators, ...)

- statsmodels. Statistics using pandas dataframes
(computations and models including standard regression models).

- scikit-learn (Sklearn). Machine learning and data
mining using NumPy arrays.

- TensorFlow. Deep learning such as neural
networks.

Commands for many of these modules have similar format to R
commands.

Once Anaconda is installed you can run a Python program from within Anaconda.

For example, use the Spyder GUI interface (which is simlar to R Studio for R).

Or use the command shell.

Python
simple OLS regression example

Data for Random Forest Example

You can also run
Python within Stata.

This can be particularly useful for setting up data in Stata
and then transferring data from Stata to Python.

Python within Stata random forest
example

Kevin Sheppard, __Introduction to Python for
Econometrics, Statistics and Numerical Analysis: Fourth+
Edition__

pdf at https://www.kevinsheppard.com/teaching/python/notes/

Wes McKinney, __Python for Data Analysis: data wrangling with
pandas, NumPy, and Jupyter__, Third edition.

html web version at https://wesmckinney.com/book/
and paperback at Amazon

**Reference for machine learning with Python**

Aurelien Geron, __Hands-On Machine Learning with Scikit-Learn,
Keras & Tensor Flow__, Third Edition Amazon

Scikit-learn website
has many examples.

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani,
Jonathan Taylor, __An Introduction to Statistical Learning:
With Applications in Python__, Springer, forthcoming. https://www.statlearning.com/

A. Colin Cameron / UC-Davis Economics / http://www.econ.ucdavis.edu/faculty/cameron