bcolz
PyMC
| bcolz | PyMC | |
|---|---|---|
| 1 | 3 | |
| 955 | 8,171 | |
| - | 0.6% | |
| 0.0 | 9.5 | |
| over 1 year ago | about 16 hours ago | |
| C | Python | |
| BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
bcolz
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Recommendation for a Database for analysis
What you need for your use case is a column-oriented store. I recommend explore bcolz or apache arrow for a column file-based systems. These are very fast, support memory mapping, uses compression and SSD speed (and even CPU architecture, in case of arrow) optimally almost out of the box, and has good interfaces to Numpy and Pandas (in case you are using Python for final data consumption and analysis). The columnar structure makes it easy to add or delete a column easily (or even dynamically). If you need a more scalable (albeit at the cost of speed) solution, you can devise a schema over a regular columnar db or an nosql db - see arctic from Man group for an example.
PyMC
- PYMC Release: v5.0.0
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An Astronomer's Introduction to NumPyro
I believe the pymc versions were resolved into developing version 4 of pymc. Development at https://github.com/pymc-devs/pymc
It still depends on theano now evolved and renamed
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What is Probabilistic Programming?
This tutorial explains what is probabilistic programming & provides a review of 5 frameworks (PPLs) using an example taken from Chapter 4 of Statistical Rethinking by Dr. Richard McElreath. Frameworks (PPLs) reviewed are - Stan (https://mc-stan.org/) PyMC3 (https://docs.pymc.io/) Tensorflow Probability (https://www.tensorflow.org/probability) Pyro/NumPyro (https://pyro.ai/) Turing.jl (https://turing.ml/stable/) I also provide the basic review of a great library called arviz (https://arviz-devs.github.io/arviz/), which can be used for all the above-mentioned PPLs to do Exploratory Data Analysis of Bayesian Models. Here is the link to the notebook in which I have implemented the example model using the above Frameworks/PPLs https://colab.research.google.com/drive/1zgR2b0j2waGi1ppnIe1rw7emkbBXtMqF?usp=sharing
What are some alternatives?
zipline - Zipline, a Pythonic Algorithmic Trading Library
statsmodels - Statsmodels: statistical modeling and econometrics in Python
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
Dask - Parallel computing with task scheduling
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
stan - Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.
Numba - NumPy aware dynamic Python compiler using LLVM
blaze - NumPy and Pandas interface to Big Data
SymPy - A computer algebra system written in pure Python
NumPy - The fundamental package for scientific computing with Python.
pyro - Deep universal probabilistic programming with Python and PyTorch