Stacking to find trends
In the farmers market dataset, you are interested in the number of months that a market stays open in relation to its geography, more specifically its longitude. You're curious to see if there are any regions of the country that behave noticeably different from the others.
To do this, you create a basic map with a scatter plot of the latitude and longitude of each market, coloring each market by the number of months it's open. Further digging into the latitude relationship, you draw a regression plot of the latitude to the number of months open with a flexible fit line to determine if any trends appear. You want to view these simultaneously to get the clearest picture of the trends.
This exercise is part of the course
Improving Your Data Visualizations in Python
Exercise instructions
- Set up
plt.subplots()to have two vertically stacked plots. - Assign the first (top) plot to the
lon,latscatter plot. - Assign the second (bottom) plot to the
lontomonths_openregression plot.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Setup two stacked plots
_, (ax1, ax2) = plt.subplots(____, ____)
# Draw location scatter plot on first plot
sns.scatterplot("lon", "lat", 'months_open',
palette = sns.light_palette("orangered",n_colors = 12),
legend = False, data = markets,
ax = ____);
# Plot a regression plot on second plot
sns.regplot('lon', 'months_open',
scatter_kws = {'alpha': 0.2, 'color': 'gray', 'marker': '|'},
lowess = True,
marker = '|', data = markets,
ax = ____)
plt.show()