And now, finally, we have something amazing.

We can put all of the code we have written so far together.

By layering the functions we have written, we can write coordinating functions,

or little robots.

Once we've written [SOUND] functions with instructions on how to do things,

then we can create functions that execute [SOUND] those instructions.

Thanks for watching.

I hope you enjoyed this introduction to data science.

Analytical thinking combined with hard data can help you convey your messages

to your audience about what you're demonstrating.

Labeling any figures to help guide your audience can avoid confusion.

Python gives you wings, but remember to stay grounded.

Try to break down your tasks by writing functions, and

following the PEP 8's style guidelines, and being precise in your definitions.

You'll thank yourself later.

For example, say you wanna filter by brand name for one report, but in the next,

you need to filter by material.

By having one function that filters the string type fields,

it allows you to pass in different arguments depending on your search

criteria, which can mean that you avoid repeating yourself with the tasks, and

just pass in the arguments for different strings.

Everything we've covered in this course will be easier to code with the pandas

library as it contains built-in functions.

Now that you understand the concepts, you're ready to try out pandas and

more advanced features of matplotlib.

Now that you've finished this course, here's some projects you can try.

You can download more recent data from ShopSense.

You can create more reports with the updated data and

you can compare the reports from two different time periods

to see if the tie prices have changed.

Go out, have fun, and enjoy the fruits of your labor.