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@ -11,6 +11,7 @@ It will be interesting to visualize the relationship between a given state's pro
[Pre-lecture quiz]() [Pre-lecture quiz]()
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 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.
## Scatterplots ## Scatterplots
Use a scatterplot to show how the price of honey has evolved, year over year, per state. Seaborn, using `relplot`, conveniently groups the state data and displays data points for both categorical and numeric data. Use a scatterplot to show how the price of honey has evolved, year over year, per state. Seaborn, using `relplot`, conveniently groups the state data and displays data points for both categorical and numeric data.
@ -85,6 +86,8 @@ Is this a simple case of supply and demand? Due to factors such as climate chang
To discover a correlation between some of the variables in this dataset, let's explore some line charts. To discover a correlation between some of the variables in this dataset, let's explore some line charts.
## Line charts
Question: Is there a clear rise in price of honey per pound year over year? You can most easily discover that by creating a single line chart: Question: Is there a clear rise in price of honey per pound year over year? You can most easily discover that by creating a single line chart:
```python ```python
@ -110,7 +113,7 @@ Question: In that case, what could have caused that spike in the price of honey
To discover this, you can explore a facet grid. To discover this, you can explore a facet grid.
## Facet Grids ## Facet grids
Facet grids take one facet of your dataset (in our case, you can choose 'year' to avoid having too many facets produced). Seaborn can then make a plot for each of those facets of your chosen x and y coordinates for more easy visual comparison. Does 2003 stand out in this type of comparison? Facet grids take one facet of your dataset (in our case, you can choose 'year' to avoid having too many facets produced). Seaborn can then make a plot for each of those facets of your chosen x and y coordinates for more easy visual comparison. Does 2003 stand out in this type of comparison?

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