p2 = np.array([1.0, 1.0])
data = np.array([[0.3, 0.6], [0.25, 0.5], [1.0, 0.75]])
```
-# Building a Library of Code you Trust
-Suppose we’re going to be dealing a lot with these animal count files,
-and doing many different kinds of analysis with them. In the
-introduction to Python lesson we wrote a function that reads these files
-but it’s stuck off in an IPython notebook. We could copy and paste it
-into a new notebook every time we want to use it but that gets tedious
-and makes it difficult to add features to the function. The ideal
-solution would be to keep the function in one spot and use it over and
-over again from many different places. Python modules to the rescue!
-
-We’re going to move beyond the IPython notebook. Most Python code is
-stored in \`.py\` files and then used in other \`.py\` files where it
-has been pulled in using an \`import\` statement. Today we’ll show you
-how to do that.
-
-## Exercises
-
-### Exercise 1
-
-Make a new text file called \`animals.py\`. Copy the file reading
-function from yesterday’s IPython notebook into the file and modify it
-so that it returns the columns of the file as lists (instead of printing
-certain lines).
-
-### Exercise 2
-
-We’re going to make a function to calculate the mean of all the values
-in a list, but we’re going to write the tests for it first. Make a new
-text file called \`test\_animals.py\`. Make a function called
-\`test\_mean\` that runs your theoretical mean function through several
-tests.
-
-### Exercise 3
-
-Write the mean function in \`animals.py\` and verify that it passes your
-tests.
-
-### Exercise 4
-
-Write tests for a function that will take a file name and animal name as
-arguments, and return the average number of animals per sighting.
-
-### Exercise 5
-
-Write a function that takes a file name and animal name and returns the
-average number of animals per sighting. Make sure it passes your tests.