Basic Plotting with Matplotlib

Loading...

En provenance du cours de University of Michigan

Applied Plotting, Charting & Data Representation in Python

1968 notes

University of Michigan

Applied Plotting, Charting & Data Representation in Python

1968 notes

Cours 2 sur 5 dans 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.

À partir de la leçon

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

Rencontrer les enseignants

Christopher Brooks

With the Matplotlib magic having set the back end for us,

we're going to start our plotting journey by making a graph using the plot function.

A plot has two axis.

An x-axis, along the horizon, and a y-axis, which runs vertically.

First, let's import the pyplot scripting layer as plt.

All of the functions which we'll run against the pyplot module

are part of the scripting layer of the architecture.

Let's take a look at the plot function by looking at the docstring.

If you're not used to seeing it, this kind of python function declaration with two

arguments, the star args and the double star kw args, can be a bit obscure.

What's being said here by the star args is that the function supports any number of

unnamed arguments.

The DubStar Q also means that it supports any number of named arguments.

This makes the function declaration very flexible since you can pass in basically

any number of arguments, named or not.

But it makes it difficult to know what is an appropriate argument.

Reading on, we see that the arguments will be interpreted as X, Y pairs.

So let's try with just one data point at position (3, 2).

Here we see that the return value is aligned to the object.

And we see our first figure appear.

We don't see any data points though, which is a bit odd.

It turns out that the third argument should be a string,

which signifies how we want that data point to be rendered.

Let's use a period for a dot and we'll see that our data point shows up.

There we go.

You'll notice that the subsequent costs of plot have actually

updated our visualization.

This is a particular feature of this interactive back end.

Other back ends might not.

For instance, you might see some web tutorials using the older percent

matplotlib in line magic instead of the percent matplotlib notebook magic.

The inline magic is not interactive.

Subsequent cause create new plots as new cells in the notebook.

This can be handy too.

I think here's where some of the confusion

with matplotlib as a library tends to come from.

In the last lecture, I explained that there's an artist layer, and

that it is figures, with subplots and axes and

data points which are rendered as patches onto these axes.

But we haven't seen any of that here.

Instead we've just called one function on a module named plot.

So what's going on?

You see, the pie plot scripting interface is managing a lot of objects for you.

It keeps track of the latest figure, of sub plots and of the axis objects.

Moreover, it actually hides some of these behind methods of its own.

So, the pie plot module itself has a function which is called plot.

But it redirects calls to this function to the current axis object.

This makes for a significant learning curve and

you'll see many discussions in web tutorials stack overflow where

people are confused by these two different approaches to making figures show up.

So let's take a look at a comparable approach that's a bit more verbose.

And some people would call this the matplotlib object API.

But I think it's more accurate to think of it as directly interfacing

with the artist layer instead.

First I'm going to import a new backend called FigureCanvasAgg

I don't have to use the modular level use function since we're not calling this from

the scripting API.

All to import the figure object.

Then we'll go ahead and create a new figure and associate it with the backend.

We can then add a subplot directly to this.

We're going to talk more about subplots in a future lecture but this number, 111,

actually means that we just want one plot.

The return value for

the subplot is the axes object, which contains methods for plotting.

So, we can plot our image as per usual.

Now, the backend that we're using,

the one for the jupiter notebooks, isn't able to render this directly.

Since it expects the scripting layer pie plot to have created all of the objects.

So here, we'll save the figure to a png file instead.

Then we'll do a quick html cell magic and execute that, and see the rendered image.

That's a lot more work than using the scripting layer.

The scripting layer,

though, isn't magic, it's just doing some of the behind the scenes work for us.

For instance, when we make a call to pyplot's plot.plot,

the scripting layer actually looks to see if there's a figure that currently exists

and if not, it creates a new one.

It then returns the axis for this figure.

We can actually get access to the figure using the GCF function, which stands for

get current figure, of Pi Plot, and

get access to the axes as well using the GCA function, get current axes.

Okay, let's create a new figure with Pi Plot.

This means it won't update our figure at the top of this file.

Then let's make a plot, grab the axes, and set the x and y limits.

We can do this using the axis function.

This function takes four parameters: a minimum value for

x, which we'll put at zero, a maximum value for x, which we'll put it at six.

In corresponding minimum and maximum values for Y which we'll put it zero and

10.

Since we're doing this with the scripting layer

once we run the jupiter cell it render to the NBA back end as we might expect.

All right, one more demonstration on the fundamentals of putting together a plot.

You can add artists to an axes object at any time.

Pi plot i doing that for us when we call the plot function.

It's determining what shape we want from the string, the location associate with

that shape, then it's creating a patch object and adding that to the axes.

If we make subsequent calls to the plot function this will

add more data to our chart.

You can see that when this is done, the points are rendered in different colors.

As the axis recognizes them as different data series.

We can go further with the axis object to the point where we can actually get

all of the child objects that that axis contains.

We do this with the axis get children function.

Here we can see that there's actually three line 2D objects contained

in this axis.

These are our data points.

The number of spines, which are actual renderings of the borders of the frame,

including tic markers, two axis objects, and

a bunch of text, which are the labels for the chart.

There's even a rectangle, which is the background for the axis.

Okay.

There is a whirlwind tour of how to be productive with matplotlib and

make your first chart.

In the next lecture, we're going to go through some of the different built in

charting options which are available to us.

Explorer notre catalogue

Rejoignez-nous gratuitement et obtenez des recommendations, des mises à jour et des offres personnalisées.

Coursera propose un accès universel à la meilleure formation au monde, en partenariat avec des universités et des organisations du plus haut niveau, pour proposer des cours en ligne.

© 2018 Coursera Inc. Tous droits réservés.

Télécharger dans l'App Store

Disponible sur Google Play