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

QuTiP is a library for simulating the dynamics of closed and open quantum systems. It aims to provide numerical simulations of a wide variety of quantum mechanical problems, including those with Hamiltonians and/or collapse operators with arbitrary time-dependence, commonly found in a wide range of physics applications.

Plotly's Python graphing library makes interactive,publication-quality graphs online. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts.

pynrrd is a Python module for reading and writing NRRD files (format designed to support scientific visualization and image processing involving N-dimensional raster data) into and from numpy arrays.

pyvistaqt is a helper module for pyvista to enable you to plot using Qt by placing a vtk-widget into a background renderer. This can be quite useful when you desire to update your plot in real-time.

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.

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.

Marsilea is a Python library for creating composable visualizations in a declarative way. It is built on top of Matplotlib and provides a high-level API for you to puzzle different visualizations together like logo.

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.

Anndata is a package for simple (functional) high-level APIs for data analysis pipelines. In this context, it provides an efficient, scalable way of keeping track of data together with learned annotations and reduces the code overhead typically encountered when using a mostly object-oriented library such as scikit-learn.

python-pandera provides a flexible and expressive API for performing data validation on dataframe-like objects to make data processing pipelines more readable and robust. Dataframes contain information that python-pandera explicitly validates at runtime. This is useful in production-critical data pipelines or reproducible research settings. With python-pandera, you can:

• Define a schema once and use it to validate different dataframe types.

• Check the types and properties of columns.

• Perform more complex statistical validation like hypothesis testing.

• Seamlessly integrate with existing data pipelines via function decorators.

• Define dataframe models with the class-based API with pydantic-style syntax.

• Synthesize data from schema objects for property-based testing.

• Lazily validate dataframes so that all validation rules are executed.

• Integrate with a rich ecosystem of tools like python-pydantic, python-fastapi and python-mypy.

Often when we want to label multiple points on a graph the text will start heavily overlapping with both other labels and data points. This can be a major problem requiring manual solution. However this can be largely automated by smart placing of the labels (difficult) or iterative adjustment of their positions to minimize overlaps (relatively easy). This library implements the latter option to help with matplotlib graphs.

Anndata is a package for simple (functional) high-level APIs for data analysis pipelines. In this context, it provides an efficient, scalable way of keeping track of data together with learned annotations and reduces the code overhead typically encountered when using a mostly object-oriented library such as scikit-learn.

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

A Snakemake executor plugin for running SLURM jobs.

The fast-histogram mini-package aims to provide simple and fast histogram functions for regular bins that don't compromise on performance. It doesn't do anything complicated - it just implements a simple histogram algorithm in C and keeps it simple. The aim is to have functions that are fast but also robust and reliable. The result is a 1D histogram function here that is 7-15x faster than numpy.histogram, and a 2D histogram function that is 20-25x faster than numpy.histogram2d.

An efficient Python implementation of the Apriori algorithm, which uncovers hidden structures in categorical data

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

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.

pandarallel allows any Pandas user to take advantage of their multi-core computer, while Pandas uses only one core. pandarallel also offers nice progress bars (available on Notebook and terminal) to get an rough idea of the remaining amount of computation to be done.

This package provides Python tools for the Open Reflectometry Standards Organization (ORSO). It includes utilities for working with reflectometry data files and the ORSO file format.

Scikit-build-core is a build backend for Python that uses CMake to build extension modules. It has a simple yet powerful static configuration system in pyproject.toml, and supports almost unlimited flexibility via CMake. It was initially developed to support the demanding needs of scientific users, but can build any sort of package that uses CMake.

Xarray (formerly xray) makes working with labelled multi-dimensional arrays simple, efficient, and fun!

Xarray introduces labels in the form of dimensions, coordinates and attributes on top of raw NumPy-like arrays, which allows for a more intuitive, more concise, and less error-prone developer experience. The package includes a large and growing library of domain-agnostic functions for advanced analytics and visualization with these data structures.

This package contains public type stubs for python-pandas, following the convention of providing stubs in a separate package, as specified in PEP 561. The stubs cover the most typical use cases of python-pandas. In general, these stubs are narrower than what is possibly allowed by python-pandas, but follow a convention of suggesting best recommended practices for using python-pandas.