Dejunkifying a Plot

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From the course by University of Michigan

Applied Plotting, Charting & Data Representation in Python

1707 ratings

University of Michigan

Applied Plotting, Charting & Data Representation in Python

1707 ratings

Course 2 of 5 in the Specialization Applied Data Science with Python

This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data. This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.

From the lesson

Module 2: Basic Charting

In this module, you will delve into basic charting. For this week’s assignment, you will work with real world CSV weather data. You will manipulate the data to display the minimum and maximum temperature for a range of dates and demonstrate that you know how to create a line graph using matplotlib. Additionally, you will demonstrate the procedure of composite charts, by overlaying a scatter plot of record breaking data for a given year.

Introduction1:47

Matplotlib Architecture6:55

Basic Plotting with Matplotlib7:32

Scatterplots8:43

Dejunkifying a Plot3:57

Meet the Instructors

Christopher Brooks

In this lecture, I want to walk you through the process of taking a regular

Matplotlib plot and applying Tufte's principles of data-ink ratio and

char junk to make it just a little bit better.

I'm going to walk through all of the steps using the Jupiter notebooks and

you're welcome to follow along.

But if you want a bit more of a challenge, I'll be including in video

quizzes along the way which prompts you to solve the problem before I address.

This is good practice.

The video will pause and you can do a quick Google to try and

find an appropriate API for solving a specific issue.

You can try it out in the system, then you can compare it to my solution right after.

Let's get started.

We use a plot of data on the popularity of

programming languages taken from stack overflow.

Here there are five different languages, Python, SQL, Java, C++, and JavaScript.

We'll find their positions as a rank using NumPy as a range function.

And here are the popularity values from stack overflow.

We'll create a bar chart based on rank and popularity, then add x and y ticks and

set a title.

Then when we call plot.show, the figure will render.

Okay, here's the first challenge.

The tics along both axis are annoying and sort of clutter the document up a bit.

And the labels on the y axis really should be direct.

So let's remove all of these.

I use the tic param's method of plot to turn them all off except for

the bottom label.

It's nice to have a single function which can handle all of this.

Now our plot still has this frame around it, but

it's not really necessary and it seems a little heavyweight.

Let's remove that ink.

This is a bit more involved, but we can get the current axis,

then iterate through all the spine, setting their visibility to false.

Already, this chart looks much more lightweight.

Now, the blue is okay,

but it doesn't really help us differentiate between the bars at all.

How about we soften all of the hard blacks to gray,

then we change the bar colors to gray as well.

Also, let's keep the Python bar the same color of blue that

it was originally to make it stand out.

Okay, so there are several different ways that we could do this.

The way I chose was to add an alpha parameter to everything,

which adds a bit of transparency and softens the colors up a bit.

I also set the bars themselves to a neutral gray color then chose

a nice blue from the Python website to accentuate the first bar.

Of course, we've actually made the chart a little bit less accurate right

now since we don't have any y labels.

Let's fix this and remove the y axis label and

just directly label the individual bars.

We don't really need the y axis label, since the title tells

us everything we need to know about the units in this chart.

Removing the label is easy, but changing the bars is a little bit of a pain.

For this we want to iterate over each of the bars and grab its height.

Then we want to create a new text object with the data information.

Unfortunately, this means doing a little bit of playing with padding.

Here I'll set up the x location to the bar x plus the width divided by two and

the y location to be the bar height minus five.

It might seem weird to get the middle of the bar in the x dimension, but

that's because I'm setting the label to center itself, horizontally.

And that's all there is to it.

A simple series of steps to make your bar charts little bit more usable.

You could find a full notebook for this example and

the course [INAUDIBLE] system directory.

When you were looking at this, did you find a different way to do things?

Or do you think there's other places that we could go to make this more readable and

increase the improvements?

Feel free to go into the discussion forums and share them with me and

your classmates.

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