This package provides a Python package for time series classification.

pyjanitor provides a set of data cleaning routines for pandas DataFrames. These routines extend the method chaining API defined by pandas for a subset of its methods. Originally, this package was a port of the R package by the same name and it is inspired by the ease-of-use and expressiveness of the dplyr package.

This package provides a simplified scipy.signal.spectral module to do spectral analysis in Python.

Scikit-image is a collection of algorithms for image processing.

PyAMG is a Python library of Algebraic Multigrid (AMG) solvers. It features implementations of:

• Ruge-Stuben (RS) or Classical AMG

• AMG based on Smoothed Aggregation (SA)

• Adaptive Smoothed Aggregation (αSA)

• Compatible Relaxation (CR)

• Krylov methods such as CG, GMRES, FGMRES, BiCGStab, MINRES, etc.

This package provides a set of tools and Python modules for setting up, manipulating, running, visualizing and analyzing atomistic simulations.

python-pydicom is a Python library for reading and writing DICOM medical imaging data. It can read, modify and write DICOM data.

This package provides an efficient implementation of Friedman's SuperSmoother based in Python. It makes use of numpy for fast numerical computation.

Pandas 0.23 added a simple API for registering accessors with Pandas objects. Pandas-flavor extends Pandas' extension API by

• adding support for registering methods as well

• making each of these functions backwards compatible with older versions of Pandas

This package provides a Python interface to the QDLDL LDL factorization routine for quasi-definite linear system.

Modin uses Ray or Dask to provide an effortless way to speed up your pandas notebooks, scripts, and libraries. Unlike other distributed DataFrame libraries, Modin provides seamless integration and compatibility with existing pandas code.

This package provides a stable interface for interactions between Snakemake and its storage plugins.

geosketch is a Python package that implements the geometric sketching algorithm described by Brian Hie, Hyunghoon Cho, Benjamin DeMeo, Bryan Bryson, and Bonnie Berger in "Geometric sketching compactly summarizes the single-cell transcriptomic landscape", Cell Systems (2019). This package provides an example implementation of the algorithm as well as scripts necessary for reproducing the experiments in the paper.

DecayLanguage implements a language to describe and convert particle decays between digital representations, effectively making it possible to interoperate several fitting programs. Particular interest is given to programs dedicated to amplitude analyses.

Snakemake aims to reduce the complexity of creating workflows by providing a clean and modern domain specific specification language (DSL) in Python style, together with a fast and comfortable execution environment.

Scikit-survival is a Python module for survival analysis built on top of scikit-learn. It allows doing survival analysis while utilizing the power of scikit-learn, e.g., for pre-processing or doing cross-validation.

This package provides Python bindings for the Boost::Histogram library, one of the fastest libraries for histogramming.

This package provides a simple and easy-to-use PID controller.

This package provides a stable interface for interactions between Snakemake and its report plugins.

Ruffus is designed to allow scientific and other analyses to be automated with the minimum of fuss and the least effort.

Plotnine is a Python implementation of the Grammar of Graphics. It is a powerful graphics concept for creating plots and visualizations in a structured and declarative manner. It is inspired by the R package ggplot2 and aims to provide a similar API and functionality in Python.

A Snakemake executor plugin for running SLURM jobs.

paramz is a lightweight parameterization framework for parameterized model creation and handling. Its features include:

• Easy model creation with parameters.

• Fast optimized access of parameters for optimization routines.

• Memory efficient storage of parameters (only one copy in memory).

• Renaming of parameters.

• Intuitive printing of models and parameters.

• Gradient saving directly inside parameters.

• Gradient checking of parameters.

• Optimization of parameters.

• Jupyter notebook integration.

• Efficient storage of models, for reloading.

• Efficient caching.

Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with structured (tabular, multidimensional, potentially heterogeneous) and time series data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python.