# Matplotlib

## Project

Learn how to plot data with the matplotlib plotting library. Ditch Excel forever!

## Goals

• practice reading data from a file
• practice using the matplotlib Python plotting library to analyze data and generate graphs

## Project setup

### 1. Install the project dependencies

The dependencies vary across operating systems. http://matplotlib.sourceforge.net/users/installing.html#build-requirements summarizes what you'll need for your operating system. We also give specific recommendations for each platform below.

Installing matplotlib and its dependencies is somewhat involved; please ask for help if you get stuck or don't know where to start!

#### Linux users only

1. Install the `python-numpy` package through your package manager
2. Install the `python-matplotlib` package through your package manager

Un-archiving will produce a `Matplotlib` folder containing several Python and text files.

Run the `basic_plot.py` script in your `Matplotlib` directory. A window with a graph should pop up.

## Project steps

### 1. Create a basic plot

1. Run `python basic_plot.py`. This will pop up a window with a dot plot of some data.
2. 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.

3. 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-")`
4. 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-")```

Note how matplotlib automatically resizes the graph to fit all of the points in the figure for you.

5. 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:

• 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

1. 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.
2. Open `world_population.py`. Read through the code in this file. In this example, we read our data from a file. Open the data file `world_population.txt` and examine the format of the file.
3. 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:

• In `world_population.py`, what does `file("world_population.txt", "r").readlines()` return?
• In `world_population.py`, what does `point.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:

## Bonus exercises

### 1. Letter frequency analysis of the US Constitution

1. Run `python constitution.py`. It will generate a bar chart showing the frequency of each letter in the alphabet in the US Constitution.
2. 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!
3. Try to answer the following questions:
1. On line 11, what is `string.ascii_lowercase`?
2. On line 18, what is the purpose of `char = char.lower()`?
3. What are the contents of `labels` after the `for` loop on line 30 completes?
4. On line 41, what are the two arguments passed to `pyplot.xticks`
5. On line 44, we use `pyplot.bar` instead of our usual `pyplot.plot`. What are the 3 arguments passed to `pyplot.bar`?
4. We've included a mystery text file `mystery.txt`: an excerpt from an actual novel. Alter `constitution.py` to process the data in `mystery.txt` instead of `constitution.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!