Matplotlib for diagrams This is my first post for the Matplotlib blog so I wanted to lead with an example of what I most love about it: How much control Matplotlib gives you. I like to use it as a programmable drawing tool that happens to be good at plotting data.
The default layout for Matplotlib works great for a lot of things, but sometimes you want to exert more control.
Introduction This post will outline how we can leverage gridspec to create ridgeplots in Matplotlib. While this is a relatively straightforward tutorial, some experience working with sklearn would be beneficial. Naturally it being a vast undertaking, this will not be an sklearn tutorial, those interested can read through the docs here. However, I will use its KernelDensity module from sklearn.neighbors.
Packages import pandas as pd import numpy as np from sklearn.
My name is Ted Petrou, founder of Dunder Data, and in this tutorial you will learn how to create the new Tesla Cybertruck using Matplotlib. I was inspired by the image below which was originally created by Lynn Fisher (without Matplotlib).
Before going into detail, let’s jump to the results. Here is the completed recreation of the Tesla Cybertruck that drives off the screen.
Tutorial A tutorial now follows containing all the steps that creates a Tesla Cybertruck that drives.
Preliminaries import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl A Top-Down runnable Jupyter Notebook with the exact contents of this blog can be found here
An interactive version of this guide can be accessed on Google Colab
A word before we get started… Although a beginner can follow along with this guide, it is primarily meant for people who have at least a basic knowledge of how Matplotlib’s plotting functionality works.
Matplotlib has a really nice 3D interface with many capabilities (and some limitations) that is quite popular among users. Yet, 3D is still considered to be some kind of black magic for some users (or maybe for the majority of users). I would thus like to explain in this post that 3D rendering is really easy once you’ve understood a few concepts. To demonstrate that, we’ll render the bunny above with 60 lines of Python and one Matplotlib call.
Search Engine Optimization (SEO) is a process that aims to increase quantity and quality of website traffic by ensuring a website can be found in search engines for phrases that are relevant to what the site is offering. Google is the most popular search engine in the world and presence in top search results is invaluable for any online business since click rates drop exponentially with ranking position. Since the beginning, specialized entities have been decoding signals that influence position in search engine result page (SERP) focusing on e.
Earth’s temperatures are rising and nothing shows this in a simpler, more approachable graphic than the “Warming Stripes”. Introduced by Prof. Ed Hawkins they show the temperatures either for the global average or for your region as colored bars from blue to red for the last 170 years, available at #ShowYourStripes.
The stripes have since become the logo of the Scientists for Future. Here is how you can recreate this yourself using Matplotlib.
Postdocs are the workers of academia. They are the main players beyond the majority of scientific papers published in journals and conferences. Yet, their effort is often not recognized in terms of salary and benefits.
A few years ago, the NIH has established stipend levels for undergraduate, predoctoral and postdoctoral trainees and fellows, the so-called NIH guidelines. Many universities and research institutes currently adopt these guidelines for deciding how much to pay postdocs.