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Applied Plotting, Charting & Data Representation in Python

University of Michigan

4.5 (2,867 ratings) | 70K Students Enrolled

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

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.

Skills You'll Learn

Python Programming, Data Virtualization, Data Visualization (DataViz), Matplotlib

Reviews

4.5 (2,867 ratings)

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A

Mar 06, 2018

Very helpful to understand what it takes to make a scientific and sensible visual. Recommended for someone who is interested in learning data visualization and does not have a background.

KS

Dec 11, 2017

Great course, great instructor! I think for me the more difficult was lack of simple practice tasks during course - it will much improve to understand material. Regards, and thanks!

From the lesson

Module 3: Charting Fundamentals

In this module you will explore charting fundamentals. For this week’s assignment you will work to implement a new visualization technique based on academic research. This assignment is flexible and you can address it using a variety of difficulties - from an easy static image to an interactive chart where users can set ranges of values to be used.

Interactivity5:27

Taught By

Christopher Brooks

A box plot.

Sometimes called a box-and-whisker plot is a method of showing

aggregate statistics of various samples in a concise matter.

The box plot simultaneously shows, for each sample, the median of each value,

the minimum and maximum of the samples, and the interquartile range.

Let's create three different samplings from NumPy.

One for the normal distribution.

One for random.

And one from a gamma distribution.

Next, let's put those in a pandas DataFrame.

After all, much of the data we would actually process would come from pandas or

another data manipulation toolkit and not from NumPy distribution functions.

Now we can use the pandas described function to see some summary statistics

about our data frame.

Each row has 10,000 entries.

The mean values and standard deviation vary heavily.

The minimal and

maximum values are showing, and there's three different percentage values.

These percentage values make up what's called the interquartile range.

There are four different quarters of the data.

The first is between the minimal value and the first 25% of the data.

And this value of 25% is called the first quartile.

The second quarter of data is between the 25% mark and the 50% of the data.

The third between 50 and 75% of the data.

And 75% mark is called the third quartile.

And the final piece of data is between the 75% and the maximum of the data.

Like standard deviation, the interquartile range is a measure of variability of data.

And it's common to plot this using a box plot.

In a box plot, the mean, or the median, of the data is plotted as a straight line.

Two boxes are formed, one above, which represents the 50% to 75% data group,

and one below, which represents the 25% to 50% data group.

Thin lines which are capped are then drawn out to the minimum and maximum values.

Here's an example.

We'll create a new figure.

Then we call plt.boxplot, and we pass in the column that we want to visualize.

In matplotlib, data needs to conform to NumPy arrays.

But pandas is built on top of NumPy data points, so

this is as easy as projecting the column that we're interested in.

Finally, we set the whis parameter to be the range.

This tells the box plot to set the whisker values all the way out to the minimum and

maximum values.

Also, I am going to assign the output of the box plot function to a variable,

which is just an underscore.

Now this is either horrendous or beautiful, depending on who you are.

You see, underscore is actually a legal name for a variable on Python.

But it's also completely uninformative.

It's common practice by some to use an underscore when unpacking values which you

don't care about and won't use later.

I am using it here because if we don't assign the return value of plotting

function to a variable,

the Jupiter Notebook will assume that we wanted to print that output.

Since plotting functions return a list of all of the artists plotted,

this would really muddy up our display.

It's up to you whether you want to use this underscore pattern or

not in your own code.

Great, this gives us a basic box plot.

Now let's add the other two samples to it.

Unfortunately we can't just pass a whole pandas data frame to matplotlib.

Instead we need to pull out each column and send them in as a list of values.

All right, that gives us our three distributions.

Now, we didn't normalize the scale, so that's a little wonky.

But if we look at the gamma distribution, for instance,

we see the tail of it is very, very long.

So the maximum values are very far out.

Let's take a look at this by itself in a histogram.

Interesting, we see it starts at a moderate level, spikes up,

then drops off much more gradually and does indeed have a very long tail.

Let's add this to our box plot.

Curiosity and to demonstrate something called inset axes.

Recall that we have one figure with one subplot.

Since we didn't do anything fancy with subplots,

that means we only have one axes object.

We can actually overlay an axes on top of another within a figure.

Now, this functionality isn't in the basic matplotlib space, but

it's in the toolkits, which tend to ship with matplotlib.

There are several different toolkits available, and

while they tend to be packaged, they aren't considered core.

The toolkit that we're going to use is called the axes grid,

and we import it from the mpl_toolkits.axes_grid1.inset_locator.

We create a new figure and we put up our box plot.

Then we just call the inset locator and pass it the current axes object we want

composition on top of, followed by the size of our new axis.

And we can specify this as both a width and

a height in percentage from the parent axes.

Then we give it a number from the place in which we wanted to drop the new axes.

This isn't quite as flexible as with grid spec where we're essentially trying to

connect the plot to one of ten different locations.

I'll put in position two here,

which will place the axes in the upper left of the figure.

Then we just push a histogram to that axes and it behaves as we would expect.

We've a little clash on the yaxis but

that's easy enough to flip on the inset plot.

We just access the yaxis of the plot and call tick_right.

And there we are.

An inset plot showing a distribution as a histogram layered on top of a box plot.

Now, the box plot has a few more functions which are worth investigating before we

finish this lecture off.

First, if you don't supply the whis argument,

the whiskers actually only go out to halfway between the interquartile range.

You can figure that out through the top of the box minus the bottom of the box and

times that value by 1.5.

This is one method of detecting outliers.

And the points which are plotted beyond the whiskers are called fliers.

You can see how this method of outlier detection differs with respect to our

three distributions.

You can also plot the confidence interval in a couple of different ways on the data.

The most common is to add notches to the box plot representing the 95% confidence

interval of the data and there are lots of other ways to customize the box plot.

The box plot is one of the more common plots that you might use as

a data scientist.

And matplotlib has significant support for different kinds of box plots.

Here the matplotlib documentation is key.

You can find links in the course resources to the API,

which describes the box plot functionality.

I've got one more plot to show you.

A two dimensional histogram which is better known as a heat map.

Then we'll look at a couple more advance features of matplotlib.

And I'll see you in the next lecture.

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