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.
Scatterplots
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Del curso dictado por University of Michigan
Applied Plotting, Charting & Data Representation in Python
1724 calificaciones
University of Michigan
1724 calificaciones
Curso 2 de 5 en SpecializationApplied Data Science with Python
De la lección
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.
Conoce a los instructores
Christopher Brooks
In the previous lecture, I introduced the plot function,
which generates a series of lines which get rendered against an axis object.
Matplotlib actually has a number of useful plotting methods in the scripting layer
which correspond to different kinds of plots.
We are not going to go through all of them here but
we'll touch on a few of the major ones.
Regardless, there are a few things to keep in mind which we learned in
the previous lecture.
One, pyplot is going to retrieve the current figure with the function gcf and
then get the current axis with the function gca.
Pyplot is keeping track of the axis objects for you.
But don't forget that they're there and we can get them when we want to get them.
Two, also pyplot just mirrors the API of the axis objects.
So you can call the plot function against the pyplot module.
But this is calling the axis plot functions underneath, so be aware.
And three, finally, remember that the function declaration from most
of the functions in matplotlib end with an open set of keyword arguments.
There are a lot of different properties you can control through these
keyword arguments.
And I'll start peppering in some of them this week to demostrate that
the matplotlib documentation isreally key in
gaining a full understanding of the options available.
With that in mind let's move on to talk first about scatterplots.
A scatterplot is a two dimensional plot similar to the line plots I've shown.
The scatter function takes an x-axis value as a first argument and
y-axis value as the second.
If the two arguments are the same, we get a nice diagonal alignment of points.
Let's do this now.
I'll bring in numpy as we're going to use it later and
matplotlib is really based on numpy arrays.
I'll create a short array for x and make y the same.
Then create a new figure and scatterplot.
You can see in this plot a nice diagonal line and
matplotlib has sized our axises accordingly.
Now, scatter doesn't represent items as a series.
Instead, we can pass it a list of colors which correspond to given points.
Let's use some list arithmetic to create a new list just short of the number of data
points we need and set all of the values to green.
Then we'll add a final value of red.
Then we'll plot this, and
we'll set the size of the data points to 100 to make it a little easier to see.
The separation of data points into lists,
which describes them as components is a pretty common pattern in matplotlib.
You have some set of items, but
they're actually described as the same slice across many different data sources.
It's in one object-based language, but you might expect that each data point is
represented with its own instance, which encapsulates all of its properties.
For instance, it's called an x value and a y value and a color and a size.
That's not the case here.
This is where it's useful to have some knowledge of list comprehensions and
lambdas as we talked about in the first course.
Also important is the zip function and list unpacking.
And because this is so common in the Python data science world,
I'm going to take a brief aside and talk about that here.
Recall that the zip method takes a number of iterables and
creates tuples out of them, matching elements based on index.
So if we have two lists of numbers, zip will take the first from each and
create a tuple, then the second from each and create a tuple, and so on.
Also remember that zip has lazy evaluation because it's actually a generator
in Python 3 which means we need to use the list function
if we want to see the results of iterating over zip.
Let's demonstrate.
Here we'll create a new zip generator with a zip function
passing in two lists of numbers.
When we convert this generator to a list, we see there's a list of pairwise tuples.
Storing point data as tuples is common and
you should be familiar with being able to convert data to and from this form.
If we want to turn the data back into two lists, one with the x component and
one with the y component, we can use parameter unpacking with zip.
When you pass a list or an interval more generally to a function and
prepend it with an asterisk, each item is taken out of the iterable and
passed as a separate argument.
So we can do that here, passing into a new zip function five parameters,
each of length two.
Zip will then create a new two value list which is identical to our original x and
y values.
If we want to get even fancier,
we can unpack this result into two variables directly, x and y.
So that's a brief aside on a variable tool.
When it comes to manipulating and cleaning data in the wild.
Now, let's get back to plotting.
Let's take these two lists and plot them to a new figure using scatter.
Instead of plotting them as one data series, we'll slice the lists and
plot them as two data series.
You'll note that we can color each series with a single value if we want to.
We can also do things like change the color or
transparency of an entire series, or individual data points as well.
Finally, we have the ability to label the data series, and I'll do that now.
This will come in handy later when building a legend.
Now before we leave the scatter plot,
let's talk a bit more about the properties of axis.
The axis generally have labels to them to explain what they represent or
the units that they describe.
Charts tend to have titles as well, so let's put this in place now.
Since pyplot mirrors much of the axis API, we can make calls directly on pyplot.
Let's do this now, adding in an xlabel, a ylabel, and then a title.
And let's add a legend as well.
You'll see that matplotlib places this in the upper left hand corner and
puts a little visual about our data series there.
In the docs for the legend, you'll see that there are a number of different
parameters and one of them is called lock.
And a value of 4, we'll put it in the lower right hand corner of the axis.
We could also get rid of the frame and add a title, and
do any number of different transformations.
The legend itself is an artist, which means it can contain children.
Let's take advantage of this and
write a little routine to recursively go through the list of children in an artist.
First, we'll import the artist class from matplotlib.
Then we'll make a recursive function which takes in an artist and
some depth parameter.
Then checks if the object is an artist and if so, prints out its string name.
Then recurses and increases the depth for pretty printing.
Finally, we can call this on the legend object itself.
So you can see the legend artist is just made up of a number of different
offsetboxes for drawing, as well as TextAreas and PathCollections.
What I want you to take away from this is that there is nothing magical about what
matplotlib is doing.
Calls to the scripting interface, just create figures, subplots, and axis.
Then load those axis up with various artists,
which the back-end renders to the screen or some other medium like a file.
While you'll spend 95% of your time at the scripting layer, happily creating graphs
and charts, it's important to understand how the library works underneath for
the other 5% of the time.
And the time that you will use this is when you really want to have control over
and to create your own charting functions, and
you'll have a chance to do that by the end of this course.
In the next lecture, we'll go back into line plots and
create a few more then onto bar charts.
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