Thread by @gusthema

Did you know that you can apply styles to your Pandas visualization?

Let's take a brief look at it 👀

[1 min]
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Now that you have loaded the data, it's very important to understand it.

To help with that it's good to be able to read it properly and formatting the data definitely help!

Let's come back to the New York Taxi fare

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The fare amount is money.

To format a financial value in Python, we would use the string format "${15,.2f}"

Pandas has a style object and a very similar format method:

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But how about that date column? We don't need all those zeros.

Let's just show a simple iso format. To do that we can use: strftime('%Y-%m-%d')

You can use a lambda function to apply that to the column (in a previous thread we already casted it to datetime)!

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How big are the values compared to the average of the column? To know that, we can add some bars per cell!

This can give a good insight on the size of numerical values.

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How about adding some more color.

The higher the value we will paint with a darker color
In our case green but you can use any of the Matplotlib colormap options

This can also be applied per row or per column

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You usually will not see all your rows unless you have a very basic dataset.

Ideally you'd use this styling in one of the 2 use cases:

• A sample or projection of the data
• The descriptive statistics view (describe method)

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To understand data you need all the help you can get, giving some visual clues to your eyes is very welcome! 👁️🔍

Don't forget to share this thread with your friends and let's make some colorful reports!

And if you're not following me yet, you're missing out!😉

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