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Very nice site!
Very nice site!
== Project steps ==
=== 1. Create a basic plot ===
<ol>
<li>
Run <code>python basic_plot.py</code>. This will pop up a window with a dot plot of some data.
</li>
<li>
Open <code>basic_plot.py</code>. Read through the code in this file. The meat of the file is in one line:
<pre>pyplot.plot([0, 2, 4, 8, 16, 32], "o")</pre>
In this example, the first argument to <code>pyplot.plot</code> is the list of y values, and the second argument describes how to plot the data. If two lists had been supplied, <code>pyplot.plot</code> would consider the first list to be the x values and the second list to be the y values.
</li>
<li>Change the plot to display lines between the data points by changing
<pre>pyplot.plot([0, 2, 4, 8, 16, 32], "o")</pre>
to
<pre>pyplot.plot([0, 2, 4, 8, 16, 32], "o-")</pre>
and re-run the script. What changed?
</li>
<li>
Add x-values to the data by changing
<pre>pyplot.plot([0, 2, 4, 8, 16, 32], "o-")</pre>
to
<pre>x_values = [0, 4, 7, 20, 22, 25]
y_values = [0, 2, 4, 8, 16, 32]
pyplot.plot(x_values, y_values, "o-")</pre>
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.
</li>
<li>
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 <code>basic_plot.py</code>, generate a list of random y values and plot them.
An example plot using random y values might look like this:
<br />
[[File:Basic_plot.png|300px]]
</li>
</ol>
<b>Read these short documents</b>:
* 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
<b>Check your understanding</b>:
* What does matplotlib pick as the x values if you don't supply them yourself?
* What options would you pass to <code>pyplot.plot</code> to generate a plot with red triangles and dotted lines?
=== 2. Plotting the world population over time ===
<ol>
<li>
Run <code>python world_population.py</code>. This will pop up a window with a dot plot of the world population over the last 10,000 years.
</li>
<li>
Open <code>world_population.py</code>. Read through the code in this file.
In this example, we read our data from a file. Open the data file <code>world_population.txt</code> and examine the format of the file.
</li>
<li>
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.
</li>
</ol>
<b>World population resources</b>:
<ul>
<li>
File input and output: http://docs.python.org/tutorial/inputoutput.html#reading-and-writing-files.
</li>
<li>
Splitting sprints into parts based on a delimiter: http://www.hacksparrow.com/python-split-string-method-and-examples.html
</li>
</ul>
<b>Check your understanding</b>:
* In <code>world_population.py</code>, what does <code>file("world_population.txt", "r").readlines()</code> return?
* In <code>world_population.py</code>, what does <code>point.split()</code> return?
=== 3. Plotting life expectancy over time ===
In a new file, write code to plot the data in <code>life_expectancies_usa.txt</code>. The format in this file is <year>,<male life expectancy>,<female life expectancy>.
You can call <code>pyplot.plot</code> multiple times to draw multiple lines on the same figure. For example:
<pre>pyplot.plot(my_data_1, "mo-", label="my data 1")
pyplot.plot(my_data_2, "bo-", label="my data 2")</pre>
will plot <code>my_data_1</code> in magenta and <code>my_data_2</code> in blue on the same figure.
Supply labels for your plots, like above. Then use <code>pyplot.legend</code> to give your graph a legend. Just plain <code>pyplot.legend()</code> will work, but providing more options may give a better effect.
Your graph should look something like this:
[[File:Life_expectancies.png|300px]]
To save your graph to a file instead of or in addition to displaying it, call <code>pyplot.savefig</code>.
<b>Life expectancy resources</b>:
<ul>
<li>
File input and output: http://docs.python.org/tutorial/inputoutput.html#reading-and-writing-files.
</li>
<li>
Splitting sprints into parts based on a delimiter: http://www.hacksparrow.com/python-split-string-method-and-examples.html
</li>
<li>
Examples of legends: http://matplotlib.sourceforge.net/examples/pylab_examples/legend_auto.html
</li>
<li>
Ways to configure your legend: http://matplotlib.sourceforge.net/api/legend_api.html
</li>
<li>
Saving your graph to a file: http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.savefig
</li>
</ul>
==Bonus exercises==
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