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Do curso por University of Michigan

Applied Plotting, Charting & Data Representation in Python

2268 classificações

University of Michigan

Applied Plotting, Charting & Data Representation in Python

2268 classificações

Curso 2 de 5 em 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.

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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

Conheça os instrutores

Christopher Brooks

So far we focused on static images, but matplotlib does have some support for

both animation and interactivity.

We call this the backend that renders the plot to the stream.

Animation and interactivity heavily depend on support from this backend layer.

And using a backend like the image png1 doesn't provide this.

However, the NBN backend or the matplotlib notebook magic function does provide for

some interactivity, so we can leverage that here.

The Maplotlib.animation module contains important helpers for building animations.

For our discussion, the important object here is to call FuncAnimation.

And it builds an animation by iteratively calling a function which you define.

Essentially, your function will either clear the axis object and

redraw the next frame, which you want users to see or

will return a list of objects which need to be redrawn.

Let's see an example.

First, let's import the animation module.

Next, let's define a cut-off for our animation.

I'd like to show you how the histogram is built from one sample through 100 samples.

So let's set our cut off to 100 then randomly pick 100 numbers and

put them into a variable.

Okay, next we want to actually create a function which will do the plotting.

We'll call this function update.

Now the matplotlib FuncAnimation object is going to call this every few milliseconds

and pass in the frame number we are on starting with frame zero.

So we can use this is as the index into our array values, which we called x.

The very first thing we want to do is see if the current frame

is at the end of our list.

If so, we need to tell the animation to stop.

We do this by calling the stop object on the event source object attached

to the FuncAnimation object.

We're going to call our animation a.

So, I'll just use that here.

I didn't know we can just drop plot as per normal.

So lets first clear the current axis with cla,

then create a histogram using a set of value in the x up to the current value.

Slicing is great for this.

Now we also need to consider the bins.

Previously we just passed a single number in for the bins eg 10 or 100.

But we can also pass in the spacing in between bins.

Since we want all of our bins set and evenly spaced, because we're redrawing

the animation in each clock tick, we can use the NumPy arange function.

This will ensure that the bins don't change.

We use the balance of minus 4 to plus 4, in half-step increments.

We also need to set the axis values since otherwise,

the histogram will continually autoscale between frames which could be annoying.

So I'll just hard code some values here, often the bin sizes and use 30 as an x and

a couple of labels and titles to make the chart look a little better.

Finally, let me show you another text function called annotate.

This places text at a certain position in the chart and

we'll use it to show how many samples are currently being rendered to the screen.

Now let's just generate a new figure, then call the FuncAnimation constructor and

we'll assign this to variable a.

The first parameter is the figure that we're working with.

This isn't so important here,

since we're using the pipe plot scripting interface to manage the figure.

Then the name of our function and then the amount of time we want between updates.

Let's set this to 100 milliseconds.

Also, remember that we have to set this to variable a.

Otherwise, our function isn't going to know how to stop the animation.

There we go.

A nice example of how sampling from a distribution an be used with an animation.

FuncAnimation like most of the other classes in the animation package is

a subclass of the animation object.

The animation object has a handy safe function,

which allows you to write the animation to a file.

This requires additional third party library such as

FFM page which can take a bit to install and set up.

But the result is that you can fairly easily export your animations directly

from the Jupiter Web programming environment.

A figure animation with four sub-plots, one for each kind of distribution we might

be interested in understanding, could be pretty neat.

We could plot the samples for the normal distribution in one, for a gamma

distribution in another, and then maybe a couple of paramaterized distributions

like the normal distribution with different levels of standard deviation.

This would be a great way to practice the skills that you've learned in this module,

as it would require that you manage multiple sub-plots within an animation

using histograms.

In the next lecture, we'll dig into this event framework a bit more and

look at the interactivity.

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