7 Secrets To Help You Master Python Data Science

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#Python #datascience #dataanalytics 1. Focus on Foundations

How to Make Small Multiple Charts in Python, with Plotly

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sharpsightlabs.com/blog/plotly-sm…

#datascience #datavisualization #Python #pythonlearning Remember: small multiple charts break out a visualization by a categorical variable.

We take a simple chart and break it out into panels.

#Datavisualization #datascience

How to Create Small Multiple Charts in Python, with Plotly

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sharpsightlabs.com/blog/plotly-sm…

#python #datascience #pythoncode #datavisualization [2/]

Before I get into the mechanics of how to create a small multiple charts in Python, let me quickly explain why they are so important.

How to Do a Data Analysis

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#datascience #DataAnalytics #Python #rstats [2/42]

When you do data analysis, you first need to start by clarifying objectives.

Why are you doing the analysis?
What’s the end goal? (e.g., the thing you’re trying to improve, understand, etc)

#datascience #DataAnalytics #data

Why You're Very Likely to Become A Millionaire in Data Science or Machine Learning

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#datascience #jobs #money #machinelearning

https://twitter.com/iamRobONeill/status/1480552051146952712

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The reasons that you're likely to become a millionaire in data science:

1. salaries are already high in 2021
2. competition for high salaries is weaker than you think
3. salaries are likely to increase in the 2020s

Let's look at each of these.

#datascience #money #jobs

Merging two or more datasets is extremely important in data science.

Here's a quick thread that covers the basics of data merges in Python.

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#Python #datascience #DataAnalytics [2/19]

In Python ...

You can combine two Pandas dataframes using the "merge" function.

You can also use the "join" function (which defaults to joining on the index)

#Python #datascience #DataAnalytics

In Python ...

You can combine Numpy arrays vertically or horizontally using np.concatenate

#Python #pythoncode #datascience The first argument to the function is a list (or collection) of arrays that you want to combine.

You can actually combine many arrays ...just put them inside the list.

In Matplotlib ...

You can get the RGBA representation of a color with the to_rgba() function.

#Python #pythoncode #datavisualization You'll notice that the output of to_rgba is a tuple with four floats: (%red, %green, %blue, alpha)

#Python

In Python, you can visualize images with the Plotly IMshow function.

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sharpsightlabs.com/blog/plotly-im…

#Python #pythonlearning #datascience #datavisualization [2/8]

You can use Plotly IMshow for a few uses.

You can use it to plot heatmaps ...

But you can also use it to plot images.

In Python ...

You can use the Pandas dropna method to drop rows with missing values.

#Python #pythoncode #datascience As seen above, you can limit dropna to specific columns with the 'subset=' parameter.

With 'subset=', you can specify the columns in which dropna will look for missing values

#Python #pythonlearning #datascience

In Python ...

You can use Numpy all to test conditions about the properties of a Numpy array.

#Python #pythoncode #datascience ☝️

So for instance, in the example above, I test if all of the values are greater than 2, by column.

#Python

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

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https://twitter.com/Josh_Ebner/status/1432827174453661698

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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.

https://twitter.com/Josh_Ebner/status/1432483276791353351?s=20

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

Here are a few quick tips:

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#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

@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 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.

If you want to master data science in Python, you need to learn Pandas method chaining.

https://twitter.com/Josh_Ebner/status/1430989142977097735?s=20

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#data #datascience #Python [2/14]

Pandas method chains enable you to combine together several individual Pandas techniques in complex ways.

How to Add New Variables to a Python Dataframe

sharpsightlabs.com/blog/pandas-as…

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#data #datascience #Python [2/9]

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

But my preferred way is the Pandas "assign" method.

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)

https://twitter.com/jaltucher/status/1256712532468924422

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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