Overview

Teaching: ?? min
Exercises: ?? min

Questions

• How can you visualize your data?

Objectives

• Visualizing data using matplotlib.

About Matplotlib

Matplotlib is a library that can be used to visualize data that has been loaded with a library like Pandas, Numpy, or Scipy. For this tutorial, we’ll use Pandas.

Loading Data

For a more detailed tutorial on loading data, see the lesson on beginning with Pandas

For now, we’ll just use a simple statement to load the articles data.

import pandas as pd
articles_df = pd.read_csv('articles.csv')

Matplotlib has a wide variety of plots that it can produce. First, we’ll introduce the simplest of plots: the 2 dimensional line plot.

First, we’ll import matplotlib.

import matplotlib.pyplot as plt

Matplotlib can easily plot a set of data even larger than articles.csv, but for this example, we’ll take the first 50 of the ~1000 entries that are in articles.csv. For a more detailed tutorial on slicing data, see this lesson on masking and grouping.

small_dataset = articles_df[:50]

There’s a column in articles.csv named Author_Count which would make an excellent value to plot.

plot_data = small_dataset['Author_Count']

Simple Plotting

Now, we have an array of plot data indexed by the record_id value. Let’s plot it and give it a label.

plt.plot(plot_data, label='My Data')

The data has now been plotted, to see it we can do 2 things:

1. We can interact with the plot by using plt.show() like so:

plt.show()

1. Or we can save the plot to a file using plt.savefig like so:

plt.savefig('myplot.png')

This would save the file as a rasterized PNG image. The format is deduced from the file name or can be given explicitly using format parameter, eg. format="png". For raster images, in order to enhance quality one can also request particular resolution in dots per inch using dpi parameter. This may be useful when creating quality images for printing/publication. Vectorized images are supported as well, we just need to save the file as a SVG, EPS or PDF which is as simple as:

plt.savefig('myplot.pdf')

Plot Essentials

What’s a plot without a title, axis labels, and a legend? These can be easily set like so:

plt.xlabel('Index')
plt.ylabel('Author count')
plt.title('Author counts From articles.csv')

Clearing the Plot

The plot is created using some default settings, eg. default line color. You may have noticed, the first line plotted is blue. We can change this by replotting the figure. But first, the blue line is still on the plot, so we must clear it. Like so:

plt.clf()

Managing figures

It is important to note, that subsequent plots we may have created with plt.plot are (by default) superimposed on the same figure that is created implicitly upon first plt.plot call. Figures are numbered from 1 and one can switch between them by calling plt.figure(number). When creating a new figures one can give a number of options, for example one can fine tune the size and default resolution by using figsize and dpi parameters:

plt.figure(figsize=(10, 8), dpi=200)

which will create a figure 8 inches high and 10 inches wide with resolution of 200 dots per inch.

It is also possible to control the margin between the edge of the axes box and the edge of the image:

plt.subplots_adjust(left=0.1, bottom=0.2, right=0.99, top=0.99)

Values are fractions of the image size and denote the position of the respective edge of the axes bounding box. Lower left corner of the image is at (0,0). The invocation above leaves 10% margin on the left side, 20% margin on the bottom and 1% for top and right edges.

Managing styles

Colors

To use a different color, like red, we would plot our data like so:

plt.plot(plot_data, color='r')

Note that creating more than one plot on the same figure will make them use different colors (like in gnuplot) unless specified otherwise. Here’s a list of predefined colors:

For more color flexibility, you can specify hexadecimal RGB values like so:

plt.plot(plot_data, color='#aa5599')

or use RGB coefficients of range 0-1 (which makes it easy to create multiple color-encoded plots in a loop):

plt.plot(plot_data, color=(0.1, 0.9, 0.6))

Line style

The default line style is a solid line. We can make it thinner or thicker by specifying linewidth or lw:

plt.plot(plot_data, linewidth=3)

The default linewidth is 1. A linewidth of 3 would be 3 times as thick as the default. Likewise, a linewidth of .75 would be 3/4 of the thickness of the default.

Other types of plots

Matplotlib can do many types of plots. For example, a dot plot can be constructed like so:

plt.plot(plot_data, 'o')

The o means a dot. There are a variety of markers you can use. Here’s a complete list: Matplotlib line markers

A simple dashed line:

plt.plot(plot_data, linestyle='--')

Marker style

So far we have plotted only a simple line plot which is default. It is possible to specify also the data marker style which will create scatter plot or various connect-the-dots-like plot. For example, to use square data marker:

plt.plot(plot_data, marker='s')

Configuration of plot axes

So far we have used a simple, default, uniform axis. The user has, however, a complete control over the way axes are organized.

Plot range

One can adjust the range of axes using set plt.xlim for horizontal and plt.ylim for vertical axis. For instance to set X limit to [-10; 15] one can use:

plt.xlim(-10, 15)

Plot scale

In many cases it is useful to use logarithmic scale on one or both axes. One can use dedicated plot methods:

plt.loglog(plot_data)

Two independent X or Y axes

To create a plot with two X or two Y axes having different scales, units, ranges one can use plt.twinx and plt.twiny:

plt.bar(plot_data.index, plot_data.values)
plt.twinx()
plt.plot(1/ plot_data, color='k')

This will create a plot with two independent Y axes, one for barplot and one for line plot of inverse values. Both plots will share the same X-axis.

Describing the plot

In the examples above the plot is not ready to be published. We would like to add titles, axes labels, tick markers, maybe some grid or legend.

Adding legend

All plots can be labelled upon creation:

plt.plot(..., label='some description')

and a legend can be automatically generated in the automatically chosen best location:

plt.legend(loc='best')

Modifying ticks

One can change the location and labels of the axes ticks using plt.xticks and plt.yticks methods:

plt.xticks([1,2,3,4])* # put ticks in given locations of X-axis

plt.yticks([1,2,3], ['A', 'B', 'C'])* # put ticks in given locations on Y-axis, denote them with letters

Labels can be rotated by adding parameter rotation=angle_in_degrees. To draw a grid with grid lines at the ticks use plt.grid().

Labelling

Axes can be labelled using:

plt.xlabel('X-axis label')
plt.ylabel('Y-axis label')

To set a title use

plt.title('Plot title')

Plot variations

Matplotlib supports a number of different plot variations including;

• bar plot (plt.bar),
• contour plots (plt.contour),
• pie chart (plt.pie),
• error bars (plt.errorbar) and
• polar plot (plt.polar).

To use a bar plot:

plt.bar(plot_data.index, plot_data.values)

Go exploring

There are excellent examples on Matplotlib website, especially:

• Matplotlib Gallery
• Scipy Cookbook
• Pandas Visualisation

A box and whisker plot:

plt.boxplot(plot_data.values)

A Realistic Example

You may have noticed there’s some more data beyond just the Author_Count value in articles.csv. Let’s plot the number of articles per month.

Pandas has some built-in tools that make it easy to group your data.

grouped_plot_data = articles_df.groupby('Month')

This returns our data in an iterable object. Each entry in grouped_plot_data is formatted like so:

('group_name', pandas data pertaining to the group)

We can check the size of each data group using the len() function. So we can plot our data like so:

for group_label, group_data in grouped_plot_data:
    plt.plot(group_label, len(group_data), 'o')
plt.xlim(0, 13)
plt.xlabel('Month')
plt.ylabel('Number of articles')
plt.title('Article count per month')

Making each point a different color may not be useful for this plot, lets try a bar chart instead (using Pandas plot to access Matplotlib instead of plt):

ax = grouped_plot_data.Title.count().plot(kind='bar', color='green', title='Article count per month')
ax.set_ylabel('Number of articles')

Using a boxplot we can look at the distribution of the number of authors and citations per article for each language:

ax = articles_df.boxplot(column=['Author_Count', 'Citation_Count'], by='LanguageId')

More Information

This is a basic tutorial to get you started using Python to make your graphs. For more information on Matplotlib, visit the official site: http://matplotlib.org/

Key Points

• Line graphs, scatter plots.