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(This article was first published on <strong><a href="https://plotlyblog.tumblr.com/post/174657459542"> Plotly Blog</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>The scatterplot matrix, known acronymically as SPLOM, is a relatively uncommon graphical tool that uses multiple scatterplots to determine the correlation (if any) between a series of variables.</p>
<p>These scatterplots are then organized into a matrix, making it easy to look at all the potential correlations in one place.</p>
<p>SPLOMs, invented by <a rel="nofollow" href="https://statistics.yale.edu/people/john-hartigan" target="_blank">John Hartigan</a> in <a rel="nofollow" href="http://people.inf.elte.hu/fekete/algoritmusok_msc/klaszterezes/John%20A.%20Hartigan-Clustering%20Algorithms-John%20Wiley%20&amp;%20Sons%20(1975).pdf" target="_blank">1975</a>, allow data aficionados to quickly realize any interesting correlations between parameters in the data set.</p>
<p>In this post, we’ll go over how to make SPLOMs in Plotly with Python. For extra insights, check out our SPLOM tutorial in <a rel="nofollow" href="https://plot.ly/python/splom/" target="_blank">Python</a> and <a rel="nofollow" href="https://plot.ly/r/splom/" target="_blank">R</a>.<br>
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<h2>Scatterplot Matrix for Diabetes Dataset</h2>
<p>This <a rel="nofollow" href="https://www.kaggle.com/uciml/pima-indians-diabetes-database" target="_blank">dataset</a>, originally from the National Institute of Diabetes and Digestive and Kidney Diseases, can be used to attempt to predict whether or not a patient has diabetes based on diagnostic measurements.</p>
<p>The dataset includes several medical predictor variables, such as glucose, blood pressure, and BMI.</p>
<p>In this case, a SPLOM is a effective way of examining any correlations between the variables.</p>
<p>• The existence of high blood pressure and high glucose is correlated well with diabetes.<br>
<br>
• BMI above 30 especially for those over the age of 40 appears to be an indicator for diabetes.<br>
<br>
• Have high glucose while being pregnant appears to typically be associated with diabetes.</p>
<figure data-orig-width="450" data-orig-src="https://i.imgur.com/asRJnUR.png" data-orig-height="1000"><img data-orig-width="450" data-orig-src="https://i.imgur.com/asRJnUR.png" src="https://i0.wp.com/78.media.tumblr.com/6cf61ff5cd3b6412d1be0c426c290f49/tumblr_inline_p9y6fuxmKS1srua81_540.png?resize=450%2C1000&ssl=1" data-recalc-dims="1" data-orig-height="1000"></figure>
<p><a rel="nofollow" href="https://www.kaggle.com/uciml/pima-indians-diabetes-database" target="_blank">Source</a> | <a rel="nofollow" href="https://raw.githubusercontent.com/plotly/datasets/master/diabetes.csv" target="_blank">Data</a> | <a rel="nofollow" href="https://plot.ly/~Dreamshot/9242/import-plotly-plotly-version-/" target="_blank">Python code</a></p>
<h2>Scatterplot Matrix for Heart Disease Dataset</h2>
<p>This <a rel="nofollow" href="http://archive.ics.uci.edu/ml/datasets/heart+disease" target="_blank">database</a> contains 76 medical attributes where the goal is to identify the presence of heart disease in the patient.</p>
<p>Published experiments refer to a subset of the attributes, in which researchers concentrated on attempting to distinguish heart disease positive (values 1, 2, 3, 4) from heart disease negative (value 0) patients.</p>
<p>The dataset was included in the University of <a rel="nofollow" href="https://www.kaggle.com/c/heart-disease" target="_blank">Toronto Deep Health Hackathon</a> in 2017.</p>
<p>• Having a lower maximum heart rate and a higher cholesterol value seems to be a heart disease indicator.<br>
<br>
• A larger ST depression as determined by an electrocardiogram appears to be a good indicator of heart disease.<br>
<br>
• The finding of a reversible defect during a thallium stress test appears to be a reasonable indicator for heart disease, particularly in older patients and those with a higher resting blood pressure.</p>
<figure data-orig-width="450" data-orig-src="https://i.imgur.com/pFGfGzt.png" data-orig-height="1000"><img data-orig-width="450" data-orig-src="https://i.imgur.com/pFGfGzt.png" src="https://i1.wp.com/78.media.tumblr.com/33b59913694a91314bf7d8340d27f9c3/tumblr_inline_p9y6fwvT6L1srua81_540.png?resize=450%2C1000&ssl=1" data-recalc-dims="1" data-orig-height="1000"></figure>
<p><a rel="nofollow" href="http://archive.ics.uci.edu/ml/datasets/heart+disease" target="_blank">Source</a> | <a rel="nofollow" href="https://raw.githubusercontent.com/Dreamsh0t/SPLOM/master/heart_disease.csv" target="_blank">Data</a> | <a rel="nofollow" href="https://plot.ly/~Dreamshot/9243/import-plotly-plotly-version-/" target="_blank">Python code</a></p>
<h2>Scatterplot Matrix for Predicting Rain</h2>
<p>Precipitation is typically considered to be the most impactful weather parameter.</p>
<p>While sophisticated computer models are used to forecast rain (or the lack thereof) on a daily basis, a meteorologist might glean useful climatological information from a SPLOM.</p>
<p>In this plot, we examine the relationship between various weather parameters in an attempt to understand when rainfall may be more likely.</p>
<p><b>Blue</b> dots indicate days on which <b>rain</b> fell and <b>brown</b> dots indicate days on which <b>no rain</b> fell.</p>
<p>The city used in this example is Auckland, New Zealand.</p>
<p>• Lower amounts of sun hours and higher humidity are typical of days that have rain.<br>
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• When the minimum temperature is 5.0ºC (41ºF) or lower, rain is unlikely.<br>
<br>
• When the maximum temperature is 23.0ºC (73ºF) or higher, rain is unlikely.<br>
<br>
• Two particular wind directions, in conjunction with higher wind speeds, are more associated with rain than others: 220–300º on the compass (southwest to northwest) and 010–070º (north to northeast).</p>
<figure data-orig-width="450" data-orig-src="https://i.imgur.com/PelHGTa.png" data-orig-height="1000"><img data-orig-width="450" data-orig-src="https://i.imgur.com/PelHGTa.png" src="https://i2.wp.com/78.media.tumblr.com/771db110a2ee98434629b06c827f00b1/tumblr_inline_p9y6fxmj751srua81_540.png?resize=450%2C1000&ssl=1" data-recalc-dims="1" data-orig-height="1000"></figure>
<p><a rel="nofollow" href="https://cliflo.niwa.co.nz/" target="_blank">Source</a> | <a rel="nofollow" href="https://raw.githubusercontent.com/Dreamsh0t/SPLOM/master/SPLOM_weather.csv" target="_blank">Data</a> | <a rel="nofollow" href="https://plot.ly/~Dreamshot/9248/import-plotly-plotly-version-/" target="_blank">Python code</a></p>
<h2>Scatterplot Matrix for Identifying Iris Flower Species</h2>
<p>Through the analysis of flower traits, a scatterplot matrix can help to distinguish between different Iris flower species.</p>
<p>This <a rel="nofollow" href="https://raw.githubusercontent.com/plotly/datasets/master/iris-data.csv" target="_blank">dataset</a> examines sepal length, sepal width, petal length, and petal width and then classifies the species.</p>
<p>Light blue indicates Iris setosa, pink Iris versicolor, and purple Iris virginica.</p>
<p>• Iris setosa has a large sepal width but small petal width.<br>
<br>
• Iris virginica has a large sepal length and petal length.<br>
<br>
• Iris versicolor sits between setosa and virginica much of the time.</p>
<figure data-orig-width="450" data-orig-src="https://i.imgur.com/G9fpEjw.png" data-orig-height="600"><img data-orig-width="450" data-orig-src="https://i.imgur.com/G9fpEjw.png" src="https://i2.wp.com/78.media.tumblr.com/07fa39c28cf501759d2d8ea1ec88a84d/tumblr_inline_p9y6fy5Uz01srua81_540.png?resize=450%2C600&ssl=1" data-recalc-dims="1" data-orig-height="600"></figure>
<p><a rel="nofollow" href="https://archive.ics.uci.edu/ml/datasets/iris" target="_blank">Source</a> | <a rel="nofollow" href="https://raw.githubusercontent.com/plotly/datasets/master/iris-data.csv" target="_blank">Data</a> | <a rel="nofollow" href="https://plot.ly/~Dreamshot/9255/import-plotly-plotly-version-/" target="_blank">Python code</a></p>
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