Data-Science-For-Beginners/translations/en/3-Data-Visualization/12-visualization-relationships

Visualizing Relationships: All About Honey 🍯

Visualizing Relationships - Sketchnote by @nitya

Continuing with the nature focus of our research, let's explore fascinating ways to visualize 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, it includes data on the number of colonies, yield per colony, total production, stocks, price per pound, and the value of honey produced in each 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 examine the relationship between honey yield per colony across states. This time frame also includes the emergence of the devastating 'CCD' or 'Colony Collapse Disorder' first identified in 2006 (http://npic.orst.edu/envir/ccd.html), making this dataset particularly significant to study. 🐝

Pre-lecture quiz

In this lesson, you can use Seaborn, a library you've worked with before, to effectively visualize relationships between variables. One particularly useful function in Seaborn is relplot, which enables scatter plots and line plots to quickly illustrate 'statistical relationships', helping data scientists better understand how variables interact.

Scatterplots

Use a scatterplot to visualize how the price of honey has changed year over year in each state. Seaborn's relplot conveniently organizes state data and displays data points for both categorical and numeric data.

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 includes several interesting columns, such as 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 U.S. 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 illustrate how the price changes over the years. Add a 'hue' parameter to highlight year-over-year variations:

✅ 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. If you examine a specific state, such as Arizona, you can observe 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 represent price increases with larger dot sizes:

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

You can observe the dots growing larger over time.

Is this simply a case of supply and demand? Could factors like climate change and colony collapse be reducing honey availability year over year, thereby driving up prices?

To explore correlations between variables in this dataset, let's examine some line charts.

Line charts

Question: Is there a clear upward trend in honey prices per pound year over year? The easiest way to determine this is by creating a single line chart:

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 by plotting the mean and a 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 appears to have increased in 2003, even though overall honey production has been declining during these years.

Question: In that case, what might have caused the spike in honey prices around 2003?

To investigate further, you can use a facet grid.

Facet grids

Facet grids allow you to focus on one aspect of your dataset (e.g., 'year') and create a plot for each facet using your chosen x and y coordinates. This makes comparisons easier. Does 2003 stand out in this type of visualization?

Create a facet grid using relplot, as recommended by 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, side by side, with a column wrap set to 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 a chart to share the x-axis while displaying two y-axes. Superimpose yield per colony and number of colonies:

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 learned more about scatterplots and line grids, including facet grids. Challenge yourself to create a facet grid using a different dataset, perhaps one you've used in previous lessons. Note how long it takes to generate and consider how many grids are practical to draw using these techniques.

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 methods described in the documentation.

Assignment

Dive into the beehive

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