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Quick Success Data Science One plotting ring to rule them all! One ring to Plot them all (by Dall-E2)

Have you started using the new Seaborn Objects System for plotting with Python? You definitely should; its a wonderful thing.

Introduced in late 2022, the new system is based on the Grammar of Graphics paradigm that powers Tableau and Rs ggplot2. This makes it more flexible, modular, and intuitive. Plotting with Python has never been better.

In this Quick Success Data Science project, youll get a quick start tutorial on the basics of the new system. Youll also get several useful cheat sheets compiled from the Seaborn Objects official docs.

Well use the following open-source libraries for this project: pandas, Matplotlib, and seaborn. You can find installation instructions in each of the previous hyperlinks. I recommend installing these in a virtual environment or, if youre an Anaconda user, in a conda environment dedicated to this project.

The goal of Seaborn has always been to make Matplotlib Pythons primary plotting library both easier to use and nicer to look at. As part of this, Seaborn has relied on declarative plotting, where much of the plotting code is abstracted away.

The new system is designed to be even more intuitive and to rely less on difficult Matplotlib syntax. Plots are built incrementally, using interchangeable marker types. This reduces the number of things you need to remember while allowing for a logical, repeatable workflow.

The use of a modular approach means you dont need to remember a dozen or more method names like barplot() or scatterplot() to build plots. Every plot is now initiated with a single Plot() class.

The Plot() class sets up the blank canvas for your graphic. Enter the following code to see an example (shown using JupyterLab):

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Introducing Seaborn Objects: One Ring to Rule Them All! - Towards Data Science

image:

An infographic explaining the creation of a new map of around 1.3 million quasars from across the visible universe.

Credit: ESA/Gaia/DPAC; Lucy Reading-Ikkanda/Simons Foundation; K. Storey-Fisher et al. 2024

Astronomers have charted the largest-ever volume of the universe with a new map of active supermassive black holes living at the centers of galaxies. Called quasars, the gas-gobbling black holes are, ironically, some of the universes brightest objects.

The new map logs the location of about 1.3 million quasars in space and time, the furthest of which shone bright when the universe was only 1.5 billion years old. (For comparison, the universe is now 13.7 billion years old.)

This quasar catalog is different from all previous catalogs in that it gives us a three-dimensional map of the largest-ever volume of the universe, says map co-creator David Hogg, a senior research scientist at the Flatiron Institutes Center for Computational Astrophysics in New York City and a professor of physics and data science at New York University. It isnt the catalog withthe most quasars, and it isnt the catalog with the best-quality measurements of quasars, but it is the catalog with the largest total volume of the universe mapped.

Hogg and his colleagues present the map in a paper published March 18 in The Astrophysical Journal. The papers lead author, Kate Storey-Fisher, is a postdoctoral researcher at the Donostia International Physics Center in Spain.

The scientists built the new map using data from the European Space Agencys Gaia space telescope. While Gaias main objective is to map the stars in our galaxy, it also inadvertently spots objects outside the Milky Way, such as quasars and other galaxies, as it scans the sky.

We were able to make measurements of how matter clusters together in the early universe that are as precise as some of those from major international survey projects which is quite remarkable given that we got our data as a bonus from the Milky Wayfocused Gaia project, Storey-Fisher says.

Quasars are powered by supermassive black holes at the centers of galaxies and can be hundreds of times as bright as an entire galaxy. As the black holes gravitational pull spins up nearby gas, the process generates an extremely bright disk and sometimes jets of light that telescopes can observe.

The galaxies that quasars inhabit are surrounded by massive halos of invisible material called dark matter. By studying quasars, astronomers can learn more about dark matter, such as how much it clumps together.

Astronomers can also use the locations of distant quasars and their host galaxies to better understand how the cosmos expanded over time. For example, scientists have already compared the new quasar map with the oldest light in our cosmos, the cosmic microwave background. As this light travels to us, it is bent by the intervening web of dark matter the same web mapped out by the quasars. By comparing the two, scientists can measure how strongly matter clumps together.

It has been very exciting to see this catalog spurring so much new science, Storey-Fisher says. Researchers around the world are using the quasar map to measure everything from the initial density fluctuations that seeded the cosmic web to the distribution of cosmic voids to the motion of our solar system through the universe.

The team used data from Gaias third data release, which contained 6.6 million quasar candidates, and data from NASAs Wide-Field Infrared Survey Explorer and the Sloan Digital Sky Survey. By combining the datasets, the team removed contaminants such as stars and galaxies from Gaias original dataset and more precisely pinpointed the distances to the quasars. The team also created a map showing where dust, stars and other nuisances are expected to block our view of certain quasars, which is critical for interpreting the quasar map.

This quasar catalog is a great example of how productive astronomical projects are, says Hogg. Gaia was designed to measure stars in our own galaxy, but it also found millions of quasars at the same time, which give us a map of the entire universe.

ABOUT THE FLATIRON INSTITUTE

The Flatiron Institute is the research division of the Simons Foundation. The institute's mission is to advance scientific research through computational methods, including data analysis, theory, modeling and simulation. The institute's Center for Computational Astrophysics creates new computational frameworks that allow scientists to analyze big astronomical datasets and to understand complex, multi-scale physics in a cosmological context.

The Astrophysical Journal

Observational study

Not applicable

18-Mar-2024

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Largest-ever map of universes active superma - EurekAlert

How to draw a fancy chord chart with links using PyCirclize

In my previous article, I have introduced the Python library called PyCirclize. It can help us to generate very nice Circos Charts (or Chord Charts if you like) with very little effort. If you want to know how it can make the Data Visualisation well- Rounded, please don't miss out.

However, dont worry if you are only interested in the Chord Charts with Links. This article will make sure you understand how to draw this type of chart.

In this article, Ill introduce another type of Chord Chart that PyCirclize can do. That is a Chord Chart with links that will visualize proportional relationships between many-to-many entities very well, and so far is the best one among all the known typical diagram types.

Before we start, just make sure to use pip for installing the library as follows. Then, we are all good to go. Lets explore this fancy chart together!

As usual, lets start with something abstract but easy to follow. The purpose is to show you what the chart looks like and whats the basic way of plotting it. Let me put the full code and the diagram at the beginning.

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Probably the Best Data Visualisation for Showing Many-to-Many Proportion In Python - Towards Data Science

Career advice for aspiring data practitioners 14 min read

If you are reading this you were probably considering a career change lately. I am assuming that you want to learn somewhat close to software engineering and database design. It doesnt matter what your background is marketing, analytics or finance, you can do this! This story is to help you find the fastest way to enter the data space. Many years ago I did the same and never regretted since then. Technology space and especially data is full of wonders and perks. Not to mention remote working and massive benefit packages from the leading IT companies, it makes you capable of doing magic with files and numbers. In this story, Ill try to summarise a set of skills and possible projects which could be accomplished within two to three months timeframe. Imagine, just a few months of active learning and you are ready for your first job interview.

Any sufficiently advanced technology is indistinguishable from magic.

Indeed, why not Data Analytics or Data Science? I think the answer resides in the nature of this role as it combines the most difficult parts of these worlds. To become a data engineer you would need to learn Software engineering and database design, Machine Learning (ML) models, and understand data modelling and Business Intelligence (BI) development.

Data engineering is the fastest growing job according to DICE. They conducted research to demonstrate that there is a gap so be quick.

While Data Scientists have been considered to be the sexiest job in the market for a long time now it seems there is a certain lack of Data Engineers. I can see a massive demand in this area. This includes not only experienced and highly qualified engineers but also entry-level roles. Data engineering has been one of the fastest-growing careers in the UK over the last five years, ranking 13 on LinkedIns list of the most in-demand jobs in 2023 [1]. On

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What Does it Take to Get into Data Engineering in 2024? - Towards Data Science