COOKBOOKS

Little Inference Book :: A book for the coursera statistical inference class.
General guidelines (table) for choosing a statistical analysis which was adapted from Choosing the Correct Statistic developed by James D. Leeper, Ph.D.
Rational and Irrational Thought: The Thinking that IQ Tests Miss

ACTUARIAL SCIENCE

Econometrics

HARK :: Heterogenous Agents Resources & toolKit.
NYUecondata :: This is a repository for handling certain datasets and doing simple plots. Supervised by Dave Backus.
QuantEcon.py :: A community based Python library for quantitative economics, the code is described at http://quant-econ.net/, a website dedicated to providing lectures that teach economics and programming authored by Thomas J. Sargent and John Stachurski
- Quantitative economic modelling lecture series in Python language, designed and written by Thomas J. Sargent and John Stachurski, that is freely available as a PDF file too.

Resources

QuantEcon applications :: A repository that houses example code, applications and teaching material related to QuantEcon.
Greene Econometrics :: Working through the examples in the wonderful Econometric Analysis by William Greene.
An Introduction to Stock Market Data Analysis with Python (Part 1)

Financial Accounting

autograd :: Computes derivatives of complicated numpy code.
blpapi-py :: Bloomberg Python API.
DX :: DX Analytics is a Python-based financial analytics library which allows the modeling of rather complex derivatives instruments and portfolios.
Graphical-Lasso-in-Finance :: Implementations of the graphical lasso method to estimation of covariance matrices in finance.
i3 :: Learning stochastic inverses for amortized inference in Bayesian networks.
kcbo :: A Bayesian testing framework written in Python.
Pyfin :: Basic options pricing in Python with basic Greeks calculation across valuation models, discrete dividends support in the lattice (binomial tree) and Monte Carlo simulation models.
pyfolio :: Portfolio and risk analytics in Python.
PyMC :: A python module for Bayesian statistical modeling and model fitting which focuses on advanced Markov chain Monte Carlo fitting algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.
- Talk : Bayesian Data Analysis with PyMC3 by @twiecki.
- Bayesian pymc3 europy 2014 slides
PyMC3 :: A python module for Bayesian statistical modeling and model fitting which focuses on advanced Markov chain Monte Carlo fitting algorithms.
pysdmx :: Python interface to SDMX endpoint provided by Eurostat.
Zipline :: A Pythonic Algorithmic Trading Library.
zipline-tensorboard :: TensorBoard as a Zipline dashboard.

Resources

ISLR-python :: Python code for "An Introduction to Statistical Learning", by James, Witten, Hastie, Tibshirani, 2013.
ISLR chapters from R implemented in numpy
538model - 538 Election Forecasting Model :: Python scripts that replicates some features of Nate Silver's 538 Election Forecasting Model.

OPERATIONS RESEARCH

Optimization

Mystic :: highly-constrained non-convex optimization and uncertainty quantification.
nelder-mead :: Pure Python/Numpy implementation of the Nelder-Mead algorithm.
hyperopt :: Distributed Asynchronous Hyperparameter Optimization in Python.

STATISTICS

distcan :: Probability distributions for python in their canonical form.
emcee :: The Python ensemble sampling toolkit for affine-invariant MCMC. Documentation
hypergrad :: Exploring differentiation w.r.t hyperparameters.
mcnets :: Adaptive Markov chain networks.
pandaSDMX :: An Apache 2.0-licensed Python package to retrieve and acquire statistical data and metadata disseminated in SDMX format. It works well with the SDMX services of the European statistics office (Eurostat) and the European Central Bank (ECB).
Patsy :: Describing statistical models in Python using symbolic formulas.
permute :: Permutation tests and confidence sets.
pymbar :: Statistically optimal analysis of samples from multiple equilibrium states.
PyMix :: The Python mixture package.
pystatsd :: A Python client for statsd. Documentation
Statsmodels is a Python library package for econometrics, plotting functions, statistical modeling and tests, that provides a complement to SciPy for statistical computations including descriptive statistics, and estimation and inference for statistical models. Source Code.

Bayesian

bayesian-belief-networks :: Pythonic Bayesian Belief Network Package, supporting creation of and exact inference on Bayesian Belief Networks specified as pure python functions.
Bumps :: It provides data fitting and Bayesian uncertainty modeling for inverse problems.
DBDA-python :: Python code for the book Doing Bayesian Data Analysis, 2nd Edition (Kruschke, 2015).
dora :: A library for Bayesian active sampling with non-parametric models.
PRIMO :: An implementation of a Bayesian Network in Python.
Pomegranate :: A package for graphical models and Bayesian statistics for Python, implemented in cython. Documentation.
SAMCNet :: A toolkit and demonstration for Bayesian model averaging over objective functions defined over model classes of interest using advanced MCMC techniques.
Spearmint :: Bayesian optimization codebase.
YABN :: Yet Another Bayesian Network.

Resources

Kalman-and-Bayesian-Filters-in-Python :: Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
Bayesian-Modelling-in-Python :: A python tutorial on bayesian modeling techniques (PYMC3).
Biostat 778 :: Advanced Statistical Computing.
stats_in_python_tutorial :: Material for the statistics in Python tutorial. Source on github.
Probabilistic-Programming-and-Bayesian-Methods-for-Hackers :: Bayesian Methods for Hackers using Python and PyMC.
Frequentism and Bayesianism: What's the Big Deal? by Jake Vanderplas at SciPy 2014.
Frequentism and Bayesianism IV: How to be a Bayesian in Python by @jakevdp.
Doing bayesian data analysis :: This repository contains the Python/PyMC3 version of the R programs described in the great book Doing bayesian data analysis (first edition) by John K. Kruschke, A.K.A, the puppy book.
ThinkX :: This package contains support code for books by Allen B. Downey.
- Book "thinkbayes" :: Bayesian Statistics Made Simple by Allen B.Downey.
- ThinkStats2 :: Text and supporting code for Think Stats, 2nd Edition.
- ThinkPython2 :: LaTeX source and supporting code for Think Python, 2nd edition, by Allen Downey.

TimeSeries Analysis

Time-series-classification-and-clustering :: Time series classification and clustering code written in Python. Mostly based on the work of Dr. Eamonn Keogh at University of California Riverside.

Pandas

Pandas :: A flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
pandas-finance :: High level API for access to and analysis of financial data.
Pandashells :: An attempt to marry the expressive, concise workflow of the shell pipeline with the statistical and visualization tools of the python data-stack.
pandasql :: Query pandas DataFrames using SQL syntax with SQLite.
pandas_talib :: A Python Pandas implementation of technical analysis indicators.
sandals :: Query pandas dataframes and objects using SQL.
sql4pandas :: Efficient SQL bindings for the pandas data analysis library implemented entirely in python. Compile and execute SQL queries directly on pandas data frames without copying to an external database. It uses sqlparse at the backend.

Resources

modern-pandas :: A collection of notebooks behind @TomAugspurger's series on writing idiomatic pandas.
2015-EuroScipy-pandas-tutorial :: Material for the pandas tutorial at EuroScipy 2015.
Cohort Analysis with Python.
Materials for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media.
Half day pandas tutorial at Pycon Singapore 2013 by Wes McKinney.
MultiIndex_Drop
Learn-Pandas via ipython notebooks OR use the Pandas Bootcamp App.
Pandas101 miniconf talk at pycon-au, 2014. Talk Video and the talk slides.
Baby steps in Python – Exploratory analysis in Python (using Pandas)
Baypiggies meetup on Pandas Data Analysis slides and talk video with accompanying IPython notebooks while listening to the audio in the video (that's what was on the screen):
Apache log analysis with Pandas
Common Excel Tasks Demonstrated in Pandas :: Part-ONE and Part-TWO
Data Science in Python: Pandas Cheat Sheet

RESOURCES

Book "thinkstats" :: Probability and Statistics for Programmers by Allen B.Downey.
Elements of Statistical Learning: Data Mining, Inference, and Prediction.
fpp :: Forecasting principles and practice - a comprehensive introduction to forecasting methods.
MCMC :: Testing MCMC code, part 1: unit tests
scipy-lectures :: Tutorial material on the scientific Python ecosystem
statlearning-notebooks :: IPython notebooks for exercises covered in Stanford's online StatLearning class.
Statistics and Data Mining with Open Source Tools
Talk :: IPython and Sympy to Develop a Kalman Filter for Multisensor Data Fusion by Paul Balzer at PyData-Berlin-2014 Conference. Video #StochasticDifferentialEquations.
Statistical Data Mining Tutorials
Introduction to SciPy
FIFA predictions : A notebook on World Cup Learning predictions for world cup matches results since 1950
Class repository for Fall-2013 Statistics-243 (Intro to Statistical Computing) at UC Berkeley.

MOOC - Coursera.org

Data Science
The Data Scientist’s Toolbox
Statistics: Making Sense of Data

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Uh oh!

Uh oh!