Notebooks

Published

February 17, 2024

Links to Jupyter Notebooks rendered in HTML and available through nbviewer to review key concepts. I don’t work in Julia every day so I find it useful to keep a record of basic syntax until this becomes muscle memory.

Python

  • Monte Carlo is Easy and Free in Python [ipynb] [html]
  • Waterfall Charts in Plotly - Useful for Financial Planning and Analysis (FP&A) folks [ipynb] [html]
  • Simple Examples of Bayesian Networks with Python pgmpy [ipynb] [html]
  • Using pandas to create Fiscal Calendars and 52/53 (aka 4-4-5) Lookup Calendars in Python [ipynb] [html]
  • Time Series forecasting cheatsheet with the scikit-time (sktime) library [ipynb] [html]
  • Bayesian Decision Modeling with pymc4 (Notes from Ravin Kumar’s PyData Global 2020 talk) [ipynb] [html]
  • An intro to Lasso, Ridge, and ElasticNet in sklearn (bonus: support vector regression) [ipynb] [html]
  • A bare-bones intro to the statsmodels API with VAR, AR, and linear regression [ipynb] [html]
  • Auto ARIMA and ARIMAX/SARIMAX with pmdarima [html]

Learning causal impact

  • WIP collection of notebooks around probabilistic programming with numpyro, forecasting, and causal inference [Github]

Julia

  • SQL to Julia Translation for basic sorting, filtering, and aggregating of data [ipynb][html]
  • Linear Regression with GLM [ipynb] [html]
  • A Julia Project Workflow, i.e. setting up a new environment and project scaffolding [ipynb] [html]
  • Random Sampling from Distributions in Julia [ipynb] [html]
  • First Impressions of Data Visualization with Makie and AlgebraOfGraphics [ipynb] [html]
  • Animations in Julia with Plots.jl [ipynb] [html]
  • Animations in Julia with Makie.jl [ipynb] [html]
  • Cyberpunk theme for Julia plots with Makie.jl [ipynb] [html]
  • Exploring MLJ, a wrapper for lots of machine learning libraries for Julia, similar to python’s scikit-learn [ipynb] [html]
  • Example analysis workflow using TidyTuesday data using GadFly, DataFramesMeta, and DuckDB [ipynb] [html]
  • Combining Optimization with JuMP and Bayesian Decision Making with Turing [ipynb] [html]