(bright gentle music)
- [Instructor] Now,
it's your turn to dive into creating some Python
and Excel visualizations
with the help of generative AI and Copilot.
Start by opening the exercise file named copilot-challenge,
which contains the familiar penguins dataset.
Let's explore how Copilot can accelerate
your ability to visualize this data.
Begin by loading the data into advanced analysis.
First up,
see if you can create a kernel density estimate
or KDE plot for the flipper lengths differentiated
by penguin species.
Next,
try your head at crafting a scatterplot
that overlies a regression line showing the relationship
between body mass and flipper length,
also grouped by species.
Following that,
challenge yourself further
by constructing small multiples of these two variables.
This time, organize the plots by island,
and layer each with a scatterplot
combine with a regression line by species.
Finally,
cap it off by creating a classic pairplot
for all four numeric variables in the dataset,
distinctly colored by species.
Remember, this exercise is very open-ended.
There are countless directions
you could take with these plots,
and data visualizations, and advanced analysis more broadly,
so don't hesitate to adapt or iterate.
That said,
I'd suggest being very detailed in your prompts
to maintain control over the outcomes
and to achieve more consistency, good luck.
If Copilot doesn't deliver exactly what you need,
don't be shy about trying again,
or even starting from scratch,
perhaps requesting a specific package if necessary.
Take your time, enjoy the process.
And remember, that the more you practice with Copilot,
the more time you'll save
when building Python data visualizations in the future.