NumPy Contributor Spotlight: Mukulika Pahari
Our first Contributor Spotlight interview is with Mukulika Pahari, our “go-to” person for Numpy documentation. Mukulika is a Computer Science student at Mumbai University. Her passions outside of computing involve things with paper, including reading books (fiction!), folding origami, and journaling. During our interview she discussed why she joined NumPy, what keeps her motivated, and how likely she would recommend becoming a NumPy contributor. Tell us something about yourself. Hi, I am Mukulika.
A quick tour of QMC with SciPy
At the end of this article, my goal is to convince you that: if you need to use random numbers, you should consider using scipy.stats.qmc instead of np.random. In the following, we assume that SciPy, NumPy and Matplotlib are installed and imported: import numpy as np from scipy.stats import qmc import matplotlib.pyplot as plt Note that no seeding is used in these examples. This will be the topic of another article: seeding should only be used for testing purposes.
Scientific Python GSoD 2022 Proposal
Create educational content for the Scientific Python Blog About your organization With an extensive and high-quality ecosystem of libraries, scientific Python has emerged as the leading platform for data analysis. This ecosystem is sustained largely by volunteers working on independent projects with separate mailing lists, websites, roadmaps, documentation, engineering and packaging solutions, and governance structures. The Scientific Python project aims to better coordinate the ecosystem and prepare the software projects in this ecosystem for the next decade of data science.
How to create custom tables
Introduction This tutorial will teach you how to create custom tables in Matplotlib, which are extremely flexible in terms of the design and layout. You’ll hopefully see that the code is very straightforward! In fact, the main methods we will be using are ax.text() and ax.plot(). I want to give a lot of credit to Todd Whitehead who has created these types of tables for various Basketball teams and players. His approach to tables is nothing short of fantastic due to the simplicity in design and how he manages to effectively communicate data to his audience.
Art from UNC BIOL222
As part of the University of North Carolina BIOL222 class, Dr. Catherine Kehl asked her students to “use matplotlib.pyplot to make art.” BIOL222 is Introduction to Programming, aimed at students with no programming background. The emphasis is on practical, hands-on active learning. The students completed the assignment with festive enthusiasm around Halloween. Here are some great examples: Harris Davis showed an affinity for pumpkins, opting to go 3D! # get library for 3d plotting from mpl_toolkits.
Newly released open access book
It’s my great pleasure to announce that I’ve finished my book on matplotlib and it is now freely available at www.labri.fr/perso/nrougier/scientific-visualization.html while sources for the book are hosted at github.com/rougier/scientific-visualization-book. Abstract The Python scientific visualisation landscape is huge. It is composed of a myriad of tools, ranging from the most versatile and widely used down to the more specialised and confidential. Some of these tools are community based while others are developed by companies.
Battery Charts - Visualise usage rates & more
Introduction I have been creating common visualisations like scatter plots, bar charts, beeswarms etc. for a while and thought about doing something different. Since I’m an avid football fan, I thought of ideas to represent players’ usage or involvement over a period (a season, a couple of seasons). I have seen some cool visualisations like donuts which depict usage and I wanted to make something different and simple to understand. I thought about representing batteries as a form of player usage and it made a lot of sense.
GSoC'21: Final Report
Matplotlib: Revisiting Text/Font Handling To kick things off for the final report, here’s a meme to nudge about the previous blogs. About Matplotlib Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations, which has become a de-facto Python plotting library. Much of the implementation behind its font manager is inspired by W3C compliant algorithms, allowing users to interact with font properties like font-size, font-weight, font-family, etc. However, the way Matplotlib handled fonts and general text layout was not ideal, which is what Summer 2021 was all about.
GSoC'21: Quarter Progress
“Matplotlib, I want 多个汉字 in between my text.” Let’s say you asked Matplotlib to render a plot with some label containing 多个汉字 (multiple Chinese characters) in between your English text. Or conversely, let’s say you use a Chinese font with Matplotlib, but you had English text in between (which is quite common). Assumption: the Chinese font doesn’t have those English glyphs, and vice versa With this short writeup, I’ll talk about how does a migration from a font-first to a text-first approach in Matplotlib looks like, which ideally solves the above problem.
The Python Graph Gallery: hundreds of python charts with reproducible code.
Data visualization is a key step in a data science pipeline. Python offers great possibilities when it comes to representing some data graphically, but it can be hard and time-consuming to create the appropriate chart. The Python Graph Gallery is here to help. It displays many examples, always providing the reproducible code. It allows to build the desired chart in minutes. About 400 charts in 40 sections The gallery currently provides more than 400 chart examples.