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.
Subplots
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Del curso dictado por University of Michigan
Applied Plotting, Charting & Data Representation in Python
1617 calificaciones
University of Michigan
1617 calificaciones
Curso 2 de 5 en SpecializationApplied Data Science with Python
De la lección
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.
Conoce a los instructores
Christopher Brooks
In module one, we talked about principles, which underly good visualizations,
we pulled from work by Cairo and Tufty.
In Module 2, we opened up the Matplotlib Python Library.
Starting with the architecture and
working our way through some the most common kinds of plots.
Scatter plots, line graphs and bar charts.
In this module, we're going to do a bit deeper and
talk about multiple plots with the same figure, interaction, animation and
a few more kinds of plots which you might find useful in your data science journey.
Now, if you've been doing these assignments,
you've undoubtedly been visiting our course discussion forms,
going to stack overflow, and reading the map plot live API documentation.
I want to point you to the Matplotlib mailing list as well
is another great resource.
There would be resource posted below on how to browse that list.
It's pretty common with open source projects to have two mailing lists.
One for developers and the other for users.
The users place is where most of the question answering happens.
But I encourage to you look at the developer archives, too.
To get an idea as to what these kind of projects look like behind the scenes.
Let's start this module with a deeper look at subplots.
Thus far we have been using a signal axis object to plot a single graph or figure.
Sometimes it is useful to show two complex plot side by side for
the viewer to compare.
Matplotlib handles this with a single figure.
Let's first set our rendering backend to the NPA backend,
then import our pyplot module and NumPy, as we'll need then both.
If we look at the subplot documentation,
we see that the first argument is the number of rows,
the second the number of columns, and the third is the plot number.
In matplotlib, a conceptual grid is overlayed on the figure.
And a subplot command allows you to create axis to different portions of this grid.
For instance, if we want to to create two plots side by side,
we would call subplot with the parameters 1, 2, and 1.
This would allow us to use 1 row, with 2 columns, and
set the first axis to be the current axis.
This. Let's do this now.
We'll create a new figure and then a new subplot with one row and two columns.
And we'll ask for the left hand side axis.
The greatest number alternatively from left to right and top to bottom.
So subsequent calls to plot would create the left hand side plot.
Okay, great we've got a skinny plot on the left hand side.
We can then call subplot again with the last parameter as a two to plot new data
to a plot on the right hand side.
And that's nice.
Now we have two plots, each with their own axis objects.
Now the norm with matplotlib is that you store the axis
object that you get back from the subplot.
But you can call subplot again.
At any time with the parameters that interest you
in order to get back a given axis.
Here, let's put exponential on the linear graph 2
This demonstrates a common problem,
it looked like linear had roughly the same area under the line on the chart until we
asked matplotlib to put them into one graph.
Then the y axis was refreshed.
There would be a big problem with misleading the reader if we didn't find
a way to lock axis between two plots.
When you create a new subplot you are able to share the x, y, or
both axis using the share x and share y parameters.
Here's how we could clean this up, we create a new figure.
Then created a subplot on the left-hand side store it in AX1.
After plotting that data we create the axis on the right-hand side,
and we indicate explicitly we want to share the y-axis.
Now we don't have to store the subplot to a variable like we did with AX1.
Remember that when you use the scripting interface, high plot is going to get
the current axis object with GCA or get current axis underneath.
So a call to pipeplot.plot will transparently work with
the last axis objects that we're using.
There we go.
Two plots side by side and we've locked the y axis.
Now, those of you who have been playing close attention will note that I used
the add subplot function in the last module.
But I didn't pass in three parameters, just one.
The maplotlib developers allow you to specify the row, columns, and number
of the plot that you want with either three parameters or a single parameter.
Where the hundreds values the first argument, the tens the second argument,
and the ones the third argument.
So we will call subplot with three parameters, one, two, and one.
And the axis that return should be equal to 1,
which is created with the 1 parameter, 121.
I'm frankly not a big fan of this second syntax.
It feels pretty hacky and it really only saves typing two commas and yet
limits us to single digit radius.
Now computer science folks might feel a little twitch inside
like something's wrong with this notation.
And I'll say that it certainly bugged me the first few times I saw it.
An important fact to remember is that the plot location in the matrix of items
is index starting at one and
not at zero, as would be the convention if you were using something like NumPy.
So if you're iterating through a matrix or list, create subplots.
Remember to start at position plus one.
Now, there's a nice function called subplots, note the plural,
which allows you to get many axis objects at once, and I think this is great.
So, if we wanted to get a three by three grid with all of the axis x and
y ranges locked, we can do so like this.
The syntax looks a little goofy maybe since we're unpacking
the results of the subplots function directly.
Wherein it's an effective way to build a grid where everything shares an axis.
And of course, you could just return the tuple of plots and
iterate it over if you wanted to.
The results however look really nice.
Note that this method turns off the y and x labels except for
those plots which are on the left hand side or the bottom of the figure.
If you want to turn the labels back on,
you need to iterate through the axis objects and do it yourself.
You could do this directly through the axis objects you have or
iterate through all the axis objects in the figure and change the x and
y tick labels to be visible.
Unfortunately, on the notebook backend on my installation,
this doesn't redraw the figure.
But you can force the figure to redraw by calling the canvas draw function.
Remember you can get the current figure at any time using pyplots gcf or
get current figure or function.
Okay, so those are the basics of subplots and matplotlib.
You can see that you can leverage everything you learned to the last
module with this new knowledge to create figures which have rich subplots.
In the next module, I want to exploit this while introducing you to pretty
fundamental data science chart, the histogram.
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