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.
Line Plots
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来自 University of Michigan 的课程
Applied Plotting, Charting & Data Representation in Python
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从本节课中
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.
与讲师见面
Christopher Brooks
We've already seen line plots a couple times, but
they looked a lot like scatter plots.
A line plot is created with the plot function.
And plots a number of different series of data points.
Connecting each series in a point with a line.
Let's jump right in.
We can use numpy to use a linear series of data points one through eight.
We'll also create a number of quadratically increasing values as
the second series.
Remember that you can use broadcasting in numpy to do this efficiently.
Here we can plot this, and this time I'll use the mini formatting language,
task map plot lib, to draw lines between data points.
We'll see the result as a two data series, the linear one at the bottom and
the quadratic one at the top.
Both are using dots because we use the -o flag.
So there are a couple of things which are new about this versus the scatter plots.
First, we only gave y-axes values to our plot call, no x axes values.
Instead, the plot function was smart enough to figure out that what we
wanted was to use the index of the series as the x value.
Which is pretty handy when you want to make quick plots.
Second we see that the plot identifies this as two series of data and that
the colors of the data from the series are different including the data points and
the lines between the data points.
This is different from the scatter plot which required us to label
the lines directly.
Like all good Python libraries matplotlib invents a string based mini language for
commonly used formatting.
For instance, we could use an s inside of the formatting string which would plot
another point using a square marker.
Or we could use a series of dashes and
dots to identify that a line should be dashed instead of solid.
Here, let's plot another series.
We can use the regular axes functions creating labels for the axes and for
the figure as a whole.
And we can create a legend too.
But note that since we didn't label the data points as we did with the scatter
plot, we need to create legend entries when we add the legend itself.
This is a good time to introduce the fill between function of matplotlib.
Now this function isn't specific to line plots, but you can see it commonly used
with these plots, so I'll introduce it in this context.
Let's imagine that we want to highlight the difference between the green and
the blue curves.
We could tell the access to paint a color between these series
using the fill between function.
First we get the current axes object,
then we indicate the range of x values we want to be filled.
We didn't specify any x values in our call to plot, so
we'll just use the same range of data points it's already using.
Then we'll put in our lower bounds and our upper bounds along with the color we want
painted and for fun I'll include a transparency value.
And that's all there is to it.
This is a great way to show, for instance, the standard deviation of results or
error bars.
More likely you'll have a series of x values and
y values which you're looking to plot.
Often with line plots this comes in the form of date time on the x axes.
Let's change our x axis to a series of eight date time instances in
one day intervals.
Let's first create a new image.
Then the NumPy arrange function which you saw previously is really helpful at
sampling some dates for us.
We can ask for
an arrangement of dates at daily intervals just by providing the start and end dates.
We can now replot our linear and
exponential data against this set of dates.
That didn't seem to get us the effect that we were interested in.
This is unfortunately one of the great pain points in data science in Python,
date time handling.
The standard library does it in two different ways, while NumPy,
which is used for scientific computing, does it a third way.
In fact, there's probably a dozen replacement libraries for
date times in Python.
The solution I'm going to use here comes from a helper library in Pandas called
to datetime.
This specifically converts NumPy dates into standard library dates
which is what matplotlib is expecting.
Let's first import Pandas then create a new figure.
And let's arrange our set of dates as per the previous code block.
Now I could iterate through the list, converting them.
But instead I'll use the map function of the standard library.
Which will apply the Pandas to datetime against each of the elements in
observation_dates, and return the result.
Now let's
plot that
result.
That resulted in error.
It's a pretty confusing one at that.
You see, the problem is the map function returns an iterator.
Matplotlib can't handle the iterator, so we need to convert it to a list first.
This is really easy to do, but I wanted to demonstrate some of the real-world
problems you can run into when you're looking to build plots in Python.
And why it's important to understand, not only the toolkit, but
how the language works.
Here's a quick fix.
We can convert the map result to a list, but
keep in mind this isn't a very memory-efficient way of handling the data.
This produces a pretty decent looking figure with the data we want.
But the dates overlap pretty heavily.
Now, there's a couple of things that we could do here.
For instance, everything is from 2017.
So we could iterate through and change the labels by stripping the year.
And add an x axis label which clarifies that it's all for the year of 2017.
This is pretty reasonable, but
I wanted to show you a couple of features of how matplotlib uses text.
First we can get a single axis using the x-axis or y-axis properties of
the axes object which we can get, remember, with GCA or get current axis.
There are lots of interesting properties of the axes object and
you use some them in the assignment.
For instance; you can get the grid lines, the tick locations for
both major and minor ticks and so on.
Just like all artists, an axes has a bunch of children which are themselves artists.
In fact, if you're are following along in the Jupiter notebook with this lecture
why don't you just pause the video.
And run the wreck_gc function we wrote earlier to explore what kind of
artists the x axes object actually contains.
What I want to show you though,
is that you can access the text of the tics using the get tick labels function.
Each of the tick labels are a text object which itself is an artist.
This means that you can use a number of different artist functions.
One specific to text is the set rotation function
which changes the rotation based on degrees.
Let's iterate through the axis labels and change that.
The text is a bit hard to read because it just runs off the image.
So you can adjust the subplot.
And there we go, wonderfully readable dates in matplotlib.
And when we're talking of text let me share one more insight.
Matplotlib has a fairly strong connection to LaTeX a type setting language
used by mathematicians and scientists.
This means that you can use a subset of LaTeX directly in your labels then
matplotlib will render them as equations Here, for instance,
we can set the title of the axes so that there's an x squared directly in it.
We do this by escaping to LaTeX math mode with dollar signs.
Note that this works regardless of whether you have LaTeX installed.
But with a full LaTeX installation,
you have significant control over text formatting.
Okay, that wraps up line plots.
In the next lecture, we'll dive in to bar charts and I'll see you there.
