Python: Creating Heatmaps for Data Visualization

Name: Python: Creating Heatmaps for Data Visualization
Author: Brian Higgins

Create heatmaps for data visualization.

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Course: On-Demand

Hello, my name is Brian Higgins and in these lessons I will show you how to create heat maps for data visualization. In this lesson I will show you how to create a two-dimensional heat map. We will then add styling to the heat map and then we will create an interactive heatmap using HVPlot. Let's create our first heatmap. A 2D or two-dimensional heatmap is a data visualization that shows the magnitude of the data using colors. First, we're gonna create a 2D heat map using Matplotlib's pyplot imshow function. The imshow function takes the data and a color map name as input parameters. Color map values can be found on this web page shown here so you can reference that for all the possible values that you can use for different colors in your heatmap. Now let's look at the code in the following cell. So we're going to import the NumPy library as np alias and we will use pyplot with the PLT alias. So what we're going to do is plot a 2D heatmap using the PiPlot IamShow method. First, before we do that, we're going to create a 10 by 10 array of random float values. So a float value is anything with a decimal in it, and the values will range from zero up to .999. So we're going to use NumPy's random function, and we'll pass in 10 rows and 10 columns as the dimensions of the data array that we want to create. And we'll place that into the array called data. Next we will call pyplot's imshow function which will create our heatmap for us. And we will pass in the 10 by 10 array called data which we just created along with our color map in this case the color map will be named hot and hot will signify that we're using colors that are reds and oranges and yellows. Then we'll just add a title to our heat map to indicate that it's a 2D heat map using the I am show function. So let's show what this plot looks like. OK, notice we've created a 2D heat map with imshow, and it has hot colors, and they're yellows, reds, oranges, and browns, essentially. Moving on, we'll create another 2D heatmap using Matplotlib's pyplot pColorMesh function. So this function's a little bit different. The pColorMesh function takes data and a color map value as input parameters, same as the IM Show, but the difference between IM Show and pColorMesh is that, first of all, pColorMesh is much faster. Indexing for IM Show starts at the top left of the graph. As opposed to pColorMesh, indexing starts at the bottom left. So if you'll notice going back to our IAM show heat map, the index 0, 0 is in the top left. We'll see something different in our p-color mesh. So again, our code will be very much the same as what we did previously using NumPy and PyPlot, a random array of 10 by 10 floats ranging from 0 to 0.99 and then we're going to call pColorMesh. Now this is the difference from the last code cell and again we're going to pass in data and our color map. In this case, we'll use the color map called blues. And so it'll have a different range of blue colors in our heat map. And we'll just give a different title. Again, it'll be a 2D heat map being called from Color Mesh. So let's show this heat map. Okay, so notice here we've created a 2D heat map using pColorMesh function, and we have different blue colors, anything from white up to a very dark blue, showing in the different cells of our heat map. And the other thing is in the bottom left, our indexing starts with the 0, 0 there. Thanks for watching. Stay tuned for our next lesson where we will add styling to our heatmap.

Intermediate Provider : Brian Higgins 3 Lessons · 12m in Arabic, German, English, Spanish, French, Portuguese, Chinese

Course Description

In these lessons, you will learn to create two-dimensional heatmap. Initially you will create heatmaps using the Python Matplotlib library Pyplot sub library. We will simulate heatmap data that is generated using the Numpy random function for a two-dimensional array of values. We’ll demonstrate how to style heatmaps with labels, a title, coloring, and a color bar, and you’ll also learn the difference between the Pyplot imshow() function and pcolormesh() function for creating heatmaps.

We’ll also show you how to use the Python Seaborn library to generate a heatmap. The benefit of the Seaborn heatmap will be illustrated showing the annotate feature for heatmap cells. You will also learn to create an interactive heatmap using HVPlot, adding an interactive toolbar widget that will enable you to drill into the heatmap graph and gain insights into that data.

What You'll Learn

Create two-dimensional heatmaps using the Python Matplotlib Pyplot sub-library
Simulate heatmap data using the Numpy random function for a two-dimensional array of values
Style a heatmap with labels, a title, coloring, and a color bar
Distinguish between the Pyplot imshow() and pcolormesh() functions for creating heatmaps
Generate an annotated heatmap using the Python Seaborn library
Build an interactive heatmap with HVPlot, adding a toolbar widget to drill into the data

Key Takeaways

Heatmaps can be created in Matplotlib using the Pyplot sub-library, with simulated data generated by the Numpy random function.
Heatmaps can be styled with labels, a title, coloring, and a color bar.
The Pyplot imshow() and pcolormesh() functions offer different approaches to creating heatmaps.
The Seaborn library can generate heatmaps and supports an annotate feature for heatmap cells.
An interactive heatmap built with HVPlot includes a toolbar widget that lets you drill into the graph for deeper insights.

Frequently Asked Questions

What will I learn in this course?

You will learn to create two-dimensional heatmaps using Matplotlib's Pyplot, style them with labels, titles, coloring, and color bars, generate annotated heatmaps with Seaborn, and build an interactive heatmap using HVPlot.

Which Python libraries does this course cover?

The course covers the Matplotlib Pyplot sub-library, Numpy (for simulating heatmap data), Seaborn, and HVPlot.

What is the difference between imshow() and pcolormesh()?

Both are Pyplot functions for creating heatmaps; the course demonstrates the difference between the imshow() and pcolormesh() functions.

Does this course cover interactive heatmaps?

Yes. You will learn to create an interactive heatmap using HVPlot, adding an interactive toolbar widget that lets you drill into the heatmap graph and gain insights into the data.

What skills does this course help build?

It builds skills in data visualization, datamaps, heat maps, information visualization, and scientific visualization.