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

Visualizing Relationships: All About Honey 🍯

Visualizing Relationships - Sketchnote by @nitya

As we dey continue wit di nature focus for our research, make we check beta ways to show di relationship wey dey between different kain honey, based on one dataset wey come from di United States Department of Agriculture.

Dis dataset get about 600 items wey dey show honey production for plenty U.S. states. For example, you fit look di number of colonies, yield per colony, total production, stocks, price per pound, and di value of di honey wey dem produce for one state from 1998-2012, wit one row per year for each state.

E go dey interesting to show di relationship wey dey between di production for one state per year and di price of honey for dat state. Or you fit show di relationship wey dey between di honey yield per colony for different states. Dis year range cover di time wey 'CCD' or 'Colony Collapse Disorder' first show for 2006 (http://npic.orst.edu/envir/ccd.html), so na one important dataset to study. 🐝

Pre-lecture quiz

For dis lesson, you fit use Seaborn, wey you don use before, as one good library to show di relationship wey dey between variables. Di relplot function for Seaborn dey very useful to create scatter plots and line plots wey go help you quickly show 'statistical relationships', wey go help data scientist understand how di variables dey relate to each other.

Scatterplots

Use scatterplot to show how di price of honey don change year by year for each state. Seaborn, wit relplot, dey group di state data well and e dey show data points for both categorical and numeric data.

Make we start by importing di 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 go notice say di honey data get plenty interesting columns, like year and price per pound. Make we check dis 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 one basic scatterplot to show di relationship wey dey between di price per pound of honey and di U.S. state wey e come from. Make di y axis tall enough to show all di states:

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

Now, show di same data wit honey color scheme to show how di price don change over di years. You fit do dis by adding 'hue' parameter to show di change year by year:

✅ Learn more about di color palettes wey you fit use for Seaborn - try one fine rainbow color scheme!

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

Wit dis color scheme change, you fit see say di price of honey don dey increase over di years. If you check one sample set for di data to confirm (like Arizona for example), you go see say di price dey increase year by year, wit small 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 show dis progression na to use size instead of color. For people wey no fit see color well, dis one go better. Edit your visualization to show di increase of price by di increase in dot size:

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

You go see say di size of di dots dey increase small small.

E be like say na supply and demand dey cause dis? Abi na climate change and colony collapse dey make honey dey reduce year by year, wey make di price dey go up?

To find di correlation wey dey between some of di variables for dis dataset, make we check some line charts.

Line charts

Question: Di price of honey per pound dey rise year by year? You fit check dis easily by creating one single line chart:

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

Answer: Yes, wit small exceptions around di year 2003:

✅ Because Seaborn dey group di data around one line, e dey show "di multiple measurements for each x value by plotting di mean and di 95% confidence interval around di mean". Source. You fit stop dis behavior by adding ci=None.

Question: For 2003, we fit see say di honey supply increase? Wetin go happen if you check total production year by year?

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

Answer: E no really show. If you check total production, e be like say e increase for dat year, even though generally di amount of honey wey dem dey produce dey reduce for dis years.

Question: So wetin fit cause di spike for di price of honey around 2003?

To find dis, you fit use facet grid.

Facet grids

Facet grids dey take one part of your dataset (for dis case, you fit choose 'year' so e no go too plenty). Seaborn fit make one plot for each of di facets wey you choose for x and y coordinates so e go dey easy to compare. 2003 go stand out for dis kain comparison?

Create one facet grid by using relplot as Seaborn recommend for their documentation.

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

For dis visualization, you fit compare di yield per colony and number of colonies year by year, side by side wit wrap set at 3 for di columns:

For dis dataset, nothing special dey show for di number of colonies and their yield year by year and state by state. We fit look di correlation between dis two variables in another way?

Dual-line Plots

Try multiline plot by putting two lineplots on top of each other, use Seaborn's 'despine' to remove di top and right spines, and use ax.twinx from Matplotlib. Twinx dey allow chart to share di x axis and show two y axes. So, show di 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();

Even though nothing dey obvious for di year 2003, e dey show say di number of colonies dey stabilize even though their yield per colony dey reduce.

Go, bees, go!

🐝❤️

🚀 Challenge

For dis lesson, you don learn more about how to use scatterplots and line grids, including facet grids. Try create one facet grid wit another dataset, maybe one wey you don use before for dis lessons. Note how long e dey take to create and how you need to dey careful about di number of grids wey you dey draw wit dis techniques.

Post-lecture quiz

Review & Self Study

Line plots fit dey simple or dey complex. Read more for di Seaborn documentation about di different ways wey you fit build dem. Try improve di line charts wey you don build for dis lesson wit other methods wey dey for di docs.

Assignment

Dive into the beehive

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Disclaimer:
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