The big thing that I'd change here is the color palette.

This color palette is hard to interpret and frankly, just look a little ugly.

#datascience #DataVisualization

[1/11]
[2/11]

The fix here is pretty simple.

The data are sequential in nature. There's a low and a high.

When you have sequential data, you should almost always look at sequential color palettes.

[3/11]

More specifically:

For sequential data, your go-to palettes should almost always be perceptually uniform sequential palettes like viridis or magma.

[4/11]

In this particular case, I'd probably test out viridis, magma, and plasma ...

And see which one "pops"

Sometimes, you try a few out and one just looks great.
[5/11]

Moreover, I'd probably reverse the orientation of the colors.

In the original seen above, 1790 is mapped to a bright color

And 2020 is mapped to black.
[6/11]

Effectively, the orientation of the color mapping seen above highlights the older dates.

That might be fine if you want to tell a story about the places in the USA that demographically "peaked" a long time ago.
[7/11]

But in my opinion the more interesting and timely story is about the places that are still getting demographically stronger right now.
[8/11]

To visually tell that story, you want to map your dark/desaturated color to the older dates.

And map the brighter or more saturated color to the recent dates.
[9/11]

Remember:

The color palette you choose and how you map that palette to your data will change the story you tell with your visualization.

#DataVisualization #data
[10/11]

Again ...

If you want to be great at "telling stories with data"

Then you need to understand how to use color palettes.

#DataVisualization #data

[11/11]

To learn more about data visualization and data science, follow me here: @Josh_Ebner

Almost every day, I post tutorials and threads about data science in R and Python.

#datascience #DataVisualization #Python #rstats

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More from @Josh_Ebner

30 Aug
If you want to create great data visualizations, you need to understand color palettes.

Here are a few quick tips:

[1/n]

#datascience #datavisualization #Python #rstats
[2/n]

For data that has a sequential ordering (i.e., low to high), you should use sequential color scales.

matplotlib.org/stable/tutoria…

#Python #matplotlib Image
[3/n]

Sequential color scales incrementally change saturation or lightness.

For example, this is a red-sequential color palette: Image
Read 24 tweets
28 Aug
@JoshuaSteinman My understanding based on some research last year and beyond, is that these are deep-water.

... and there's possibly more ports with shallow depth
@JoshuaSteinman For example, @PeterZeihan wrote that Texas has "thirteen world-class deepwater ports"

amzn.to/3BihKN6
@JoshuaSteinman @PeterZeihan Army Corps of Engineers puts it at "15 deep draft ports" and "13 shallow draft ports" along the TX coast.

swd.usace.army.mil/About/Texas-Po…
Read 5 tweets
28 Aug
@JoshuaSteinman Regarding: An American Shenzhen

There's a *lot* of good ports along the Texas coast, and I think much of it under-used.

Great for logistics into the American Heartland, and also into LatAm and Mexico.
@JoshuaSteinman The Texas/Mexico combo provides a unique mix of high-skill, medium-skill, and low-skill labor.

High end design and MFG in TX, lower skill MFG and assembly in MX.
@JoshuaSteinman Also great energy resources in TX (although nuclear would augment).
Read 5 tweets
27 Aug
If you want to master data science in Python, you need to learn Pandas method chaining.



[thread: 1/14]

#data #datascience #Python
[2/14]

Pandas method chains enable you to combine together several individual Pandas techniques in complex ways.
[3/14]

When most people do this, they do it with very long chains of techniques, *all on a single line*.

These are hard to read and hard to debug.

They get more challenging the longer they get.
Read 14 tweets
26 Aug
How to Add New Variables to a Python Dataframe

sharpsightlabs.com/blog/pandas-as…

[Thread: 1/9]

#data #datascience #Python Image
[2/9]

There are several ways to add a variable to a Python dataframe ...

But my preferred way is the Pandas "assign" method.
[3/9]

The Pandas assign method has fairly simple syntax.

You can use the technique to add a single new variable like this: Image
Read 9 tweets
3 May 20
If there's a large migration of talented people from SF and NYC to Austin, Austin has a shot at being the next Silicon Valley.

#SF #NewYork #SiliconValley #technology #Austin #Texas

(Thread)
2/n

Remember: Texas actually has a long tradition of innovation.

For example, the integrated circuit was invented at Texas Instruments.

en.wikipedia.org/wiki/Texas_Ins…

#technology #tech #Texas ImageImage
3/n

More recently, Amazon, Facebook, Google, and Apple have announced major campuses in Austin.

Google is building a new riverside building, and Facebook already has one.

austin.curbed.com/2019/4/2/18291… ImageImage
Read 15 tweets

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