# Exercise 7#

Note

Exercise 7 is due by 17:00 on Friday, October 28, 2022.

You can start working on your copy of Exercise 7 by accepting the GitHub Classroom assignment

You can also take a look at the template repository for Exercise 7 on GitHub (does not require logging in). Note that you should not try to make changes to this copy of the exercise, but rather only to the copy available via GitHub Classroom.

Pair programming (optional!)

Students attending the course in Helsinki, if you wish, you can continue working in pairs. See more information in Slack, and in week 2: Why are we working in pairs?. Those students who want to submit their own solutions, please contact the course assistant that is grading your assignments (see list in Slack).

## Hints for Exercise 7#

### Generating random numbers#

We can generate random numbers using using a method `random.rand()` from the NumPy package. This example generates 10 random values:

```import numpy as np
random_numbers = np.random.rand(10)
```

This produces an array object `random_numbers` that could look, for example, like this (each time you run the code you get a different set of random numbers!):

```array([0.30888937, 0.02648327, 0.62740074, 0.75795089, 0.41083545,
0.91937694, 0.90100588, 0.33312242, 0.39950947, 0.8181788 ]
```

You can insert this array into a column in a pandas DataFrame as follows, assuming that the array length and DataFrame length match:

```...

data["new_column"] = random_numbers
```

• You can control the marker size using the parameter `markersize` when plotting. The example plot in Problem 2 uses `markersize=3`.
• You can add grid lines to your plot using `plt.grid()`