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Visualizing Relationships: All About Honey 🍯


Visualizing Relationships - Sketchnote by @nitya

Continuing with the nature focus of our research, let's explore some fascinating visualizations to illustrate the relationships between different types of honey, based on a dataset from the United States Department of Agriculture.

This dataset, containing around 600 entries, showcases honey production across various U.S. states. For instance, you can examine the number of colonies, yield per colony, total production, stocks, price per pound, and the value of honey produced in a specific state from 1998 to 2012, with one row per year for each state.

It would be intriguing to visualize the relationship between a state's annual production and, for example, the price of honey in that state. Alternatively, you could explore the relationship between states' honey yield per colony. This time period includes the emergence of the devastating 'CCD' or 'Colony Collapse Disorder,' first observed in 2006 (http://npic.orst.edu/envir/ccd.html), making this dataset particularly significant. 🐝

Pre-lecture quiz

In this lesson, you’ll use Seaborn, a library you’ve worked with before, to visualize relationships between variables. A particularly useful feature is Seaborn's relplot function, which enables scatter plots and line plots to quickly visualize 'statistical relationships.' This helps data scientists better understand how variables interact.

Scatterplots

Use a scatterplot to illustrate how the price of honey has changed year over year in each state. Seaborn's relplot makes it easy to group state data and display both categorical and numerical data points.

Let’s begin by importing the data and Seaborn:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
honey = pd.read_csv('../../data/honey.csv')
honey.head()

You’ll notice that the honey dataset contains several interesting columns, including year and price per pound. Let’s explore this data, grouped by U.S. state:

state	numcol	yieldpercol	totalprod	stocks	priceperlb	prodvalue	year
AL	16000	71	1136000	159000	0.72	818000	1998
AZ	55000	60	3300000	1485000	0.64	2112000	1998
AR	53000	65	3445000	1688000	0.59	2033000	1998
CA	450000	83	37350000	12326000	0.62	23157000	1998
CO	27000	72	1944000	1594000	0.7	1361000	1998

Create a basic scatterplot to show the relationship between the price per pound of honey and its state of origin. Adjust the y axis to ensure all states are visible:

sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5);

Next, use a honey-inspired color scheme to show how the price evolves over the years. Add a 'hue' parameter to highlight year-over-year changes:

✅ Learn more about the color palettes you can use in Seaborn - try a beautiful rainbow color scheme!

sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5);

With this color scheme, you can clearly see a strong upward trend in honey prices over the years. For example, if you examine Arizona's data, you’ll notice a consistent pattern of price increases year over year, with only a few exceptions:

state	numcol	yieldpercol	totalprod	stocks	priceperlb	prodvalue	year
AZ	55000	60	3300000	1485000	0.64	2112000	1998
AZ	52000	62	3224000	1548000	0.62	1999000	1999
AZ	40000	59	2360000	1322000	0.73	1723000	2000
AZ	43000	59	2537000	1142000	0.72	1827000	2001
AZ	38000	63	2394000	1197000	1.08	2586000	2002
AZ	35000	72	2520000	983000	1.34	3377000	2003
AZ	32000	55	1760000	774000	1.11	1954000	2004
AZ	36000	50	1800000	720000	1.04	1872000	2005
AZ	30000	65	1950000	839000	0.91	1775000	2006
AZ	30000	64	1920000	902000	1.26	2419000	2007
AZ	25000	64	1600000	336000	1.26	2016000	2008
AZ	20000	52	1040000	562000	1.45	1508000	2009
AZ	24000	77	1848000	665000	1.52	2809000	2010
AZ	23000	53	1219000	427000	1.55	1889000	2011
AZ	22000	46	1012000	253000	1.79	1811000	2012

Another way to visualize this trend is by using size instead of color. For colorblind users, this might be a better option. Modify your visualization to show price increases through larger dot sizes:

sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspect=.5);

You’ll notice the dots gradually increasing in size.

Could this simply be a case of supply and demand? Are factors like climate change and colony collapse reducing the honey supply year over year, leading to higher prices?

To investigate correlations between variables in this dataset, let’s explore some line charts.

Line charts

Question: Is there a clear upward trend in honey prices per pound year over year? A single line chart can help answer this:

sns.relplot(x="year", y="priceperlb", kind="line", data=honey);

Answer: Yes, although there are some exceptions around 2003:

✅ Seaborn aggregates data into one line, displaying "multiple measurements at each x value by plotting the mean and the 95% confidence interval around the mean." Source. You can disable this behavior by adding ci=None.

Question: In 2003, was there also a spike in honey supply? What happens if you examine total production year over year?

sns.relplot(x="year", y="totalprod", kind="line", data=honey);

Answer: Not really. Total production actually seems to have increased in 2003, even though honey production generally declined during these years.

Question: If not supply, what could have caused the price spike in 2003?

To investigate, let’s use a facet grid.

Facet grids

Facet grids allow you to break down your dataset into smaller subsets (facets). For example, you can use 'year' as a facet to avoid creating too many grids. Seaborn can then plot each facet side by side for easier comparison. Does 2003 stand out in this comparison?

Create a facet grid using relplot as recommended in Seaborn's documentation.

sns.relplot(
    data=honey, 
    x="yieldpercol", y="numcol",
    col="year", 
    col_wrap=3,
    kind="line"

In this visualization, you can compare yield per colony and number of colonies year over year, with columns wrapped at 3:

For this dataset, nothing particularly stands out regarding the number of colonies and their yield year over year or state by state. Is there another way to explore correlations between these variables?

Dual-line Plots

Try a multiline plot by overlaying two line plots, using Seaborn's 'despine' to remove the top and right spines, and ax.twinx from Matplotlib. Twinx allows you to share the x-axis while displaying two y-axes. Plot yield per colony and number of colonies together:

fig, ax = plt.subplots(figsize=(12,6))
lineplot = sns.lineplot(x=honey['year'], y=honey['numcol'], data=honey, 
                        label = 'Number of bee colonies', legend=False)
sns.despine()
plt.ylabel('# colonies')
plt.title('Honey Production Year over Year');

ax2 = ax.twinx()
lineplot2 = sns.lineplot(x=honey['year'], y=honey['yieldpercol'], ax=ax2, color="r", 
                         label ='Yield per colony', legend=False) 
sns.despine(right=False)
plt.ylabel('colony yield')
ax.figure.legend();

While nothing particularly stands out around 2003, this visualization ends the lesson on a slightly positive note: although the number of colonies is declining overall, it seems to be stabilizing, even if their yield per colony is decreasing.

Go, bees, go!

🐝❤️

🚀 Challenge

In this lesson, you explored scatterplots, line grids, and facet grids. Challenge yourself to create a facet grid using a different dataset, perhaps one you’ve used in previous lessons. Pay attention to how long it takes to create and how to manage the number of grids effectively.

Post-lecture quiz

Review & Self Study

Line plots can range from simple to complex. Spend some time reading the Seaborn documentation to learn about the various ways to build them. Try enhancing the line charts you created in this lesson using other methods from the documentation.

Assignment

Dive into the beehive

Disclaimer:
This document has been translated using the AI translation service Co-op Translator. While we strive for accuracy, please note that automated translations may contain errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is recommended. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.

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