Matplotlib

Very nice site!
Very nice site!
Very nice site!
Project steps
1. Create a basic plot
-
Run
python basic_plot.py
. This will pop up a window with a dot plot of some data. -
Open
basic_plot.py
. Read through the code in this file. The meat of the file is in one line:pyplot.plot([0, 2, 4, 8, 16, 32], "o")
In this example, the first argument to
pyplot.plot
is the list of y values, and the second argument describes how to plot the data. If two lists had been supplied,pyplot.plot
would consider the first list to be the x values and the second list to be the y values. - Change the plot to display lines between the data points by changing
pyplot.plot([0, 2, 4, 8, 16, 32], "o")
to
pyplot.plot([0, 2, 4, 8, 16, 32], "o-")
and re-run the script. What changed?
-
Add x-values to the data by changing
pyplot.plot([0, 2, 4, 8, 16, 32], "o-")
to
x_values = [0, 4, 7, 20, 22, 25] y_values = [0, 2, 4, 8, 16, 32] pyplot.plot(x_values, y_values, "o-")
and re-run the script. What changed?
Note how matplotlib automatically resizes the graph to fit all of the points in the figure for you.
-
Read about how to generate random integers on http://docs.python.org/library/random.html#random.randint.
Then, instead of hard-coding y values in
basic_plot.py
, generate a list of random y values and plot them. An example plot using random y values might look like this:
Read these short documents:
- Pyplot tutorial (just this one section; stop before the next section "Controlling line properties"): http://matplotlib.sourceforge.net/users/pyplot_tutorial.html#pyplot-tutorial
- List of line options, including line style and marker shapes and colors: http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.plot
Check your understanding:
- What does matplotlib pick as the x values if you don't supply them yourself?
- What options would you pass to
pyplot.plot
to generate a plot with red triangles and dotted lines?
2. Plotting the world population over time
-
Run
python world_population.py
. This will pop up a window with a dot plot of the world population over the last 10,000 years. -
Open
world_population.py
. Read through the code in this file. In this example, we read our data from a file. Open the data fileworld_population.txt
and examine the format of the file. - Find the documentation on http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.plot for customizing the linewidth of plots. Then change the world population plot to use a magenta, down-triangle marker and a linewidth of 2.
World population resources:
- File input and output: http://docs.python.org/tutorial/inputoutput.html#reading-and-writing-files.
- Splitting sprints into parts based on a delimiter: http://www.hacksparrow.com/python-split-string-method-and-examples.html
Check your understanding:
- In
world_population.py
, what doesfile("world_population.txt", "r").readlines()
return? - In
world_population.py
, what doespoint.split()
return?
3. Plotting life expectancy over time
In a new file, write code to plot the data in life_expectancies_usa.txt
. The format in this file is <year>,<male life expectancy>,<female life expectancy>.
You can call pyplot.plot
multiple times to draw multiple lines on the same figure. For example:
pyplot.plot(my_data_1, "mo-", label="my data 1") pyplot.plot(my_data_2, "bo-", label="my data 2")
will plot my_data_1
in magenta and my_data_2
in blue on the same figure.
Supply labels for your plots, like above. Then use pyplot.legend
to give your graph a legend. Just plain pyplot.legend()
will work, but providing more options may give a better effect.
Your graph should look something like this:
To save your graph to a file instead of or in addition to displaying it, call pyplot.savefig
.
Life expectancy resources:
- File input and output: http://docs.python.org/tutorial/inputoutput.html#reading-and-writing-files.
- Splitting sprints into parts based on a delimiter: http://www.hacksparrow.com/python-split-string-method-and-examples.html
- Examples of legends: http://matplotlib.sourceforge.net/examples/pylab_examples/legend_auto.html
- Ways to configure your legend: http://matplotlib.sourceforge.net/api/legend_api.html
- Saving your graph to a file: http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.savefig
Bonus exercises
1. Letter frequency analysis of the US Constitution
- Run
python constitution.py
. It will generate a bar chart showing the frequency of each letter in the alphabet in the US Constitution. - Open and read through
constitution.py
. The code for gathering and displaying the frequencies is a bit more complicated than the previous scripts in this projects, but try to trace the general strategy for plotting the data. Be sure to read the comments! - Try to answer the following questions:
- On line 11, what is
string.ascii_lowercase
? - On line 18, what is the purpose of
char = char.lower()
? - What are the contents of
labels
after thefor
loop on line 30 completes? - On line 41, what are the two arguments passed to
pyplot.xticks
- On line 44, we use
pyplot.bar
instead of our usualpyplot.plot
. What are the 3 arguments passed topyplot.bar
?
- On line 11, what is
- We've included a mystery text file
mystery.txt
: an excerpt from an actual novel. Alterconstitution.py
to process the data inmystery.txt
instead ofconstitution.txt
, and re-run the script. What do you notice that is odd about this file? You can read more about this odd novel here.
2. Tour the matplotlib gallery
You can truly make any kind of graph with matplotlib. You can even create animated graphs. Check out some of the amazing possibilities, including their source code, at the matplotlib gallery: http://matplotlib.sourceforge.net/gallery.html.
Congratulations!
You've read, modified, and created scripts that plot and analyze data using matplotlib. Keep practicing!