In this lesson, you can use Seaborn, which you use before, as a good library to visualize relationships between variables. Particularly interesting is the use of Seaborn's `relplot` function that allows scatter plots and line plots to quickly visualize '[statistical relationships](https://seaborn.pydata.org/tutorial/relational.html?highlight=relationships)', which allow the data scientist to better understand how variables relate to each other.
In this lesson, you can use Seaborn, which you have used before, as a good library to visualize relationships between variables. Particularly interesting is the use of Seaborn's `relplot` function that allows scatter plots and line plots to quickly visualize '[statistical relationships](https://seaborn.pydata.org/tutorial/relational.html?highlight=relationships)', which allow the data scientist to better understand how variables relate to each other.
## Scatterplots
@ -156,7 +156,7 @@ ax.figure.legend();
```
![superimposed plots](images/dual-line.png)
While nothing jumps out to the eye around the year 2003, it does allow us to end this lesson on a little happier note: while there are overall a declining number of colonies, their numbers might seem to be stabilizing and their yield per colony is actually increasing, even with fewer bees.
While nothing jumps out to the eye around the year 2003, it does allow us to end this lesson on a little happier note: while there are overall a declining number of colonies, the number of colonies is stabilizing even if their yield per colony is decreasing.