#Scatterplot matrix code

Which is very time-consuming: with my 1.3GHz MBA, it is not even completing and taking very long time to plot or not at all. Small demo (taking too long time to compute) in R library(gridExtra) Looking like a R solution 2 created with the R base commands somehow from Wikipedia. plot(iris) with the same problem of wasting space, why do we have the diagonal? Matrix scatterplot wasting a lot of space, could we enrich this?īasic R solution 1. I want to have more enriched data, no duplicates: for example, diagonal values could have distributions. The trick is that I want to get a bit more generalised version of the matrix scatterplots. The grid plot I am trying to get is a bit like the plot in Weka: a multiplot where each row is a field of a dataframe and each column is a field as well.

#Scatterplot matrix code

I have used it many times! Oh, and I re-arranged the main() part of the code so that it can be a formal example code or not get called if it is being imported into another piece of code.I want to create a grid plot with the size of N times N where N is the number of fields. # correct axes limits, so we pull them from other axes # FIX #2: if numvars is odd, the bottom right corner plot doesn't have the # FIX #1: this needed to be changed from. Each row of 'data' is plottedįig.subplots_adjust(hspace=0.0, wspace=0.0) import itertoolsĭef scatterplot_matrix(data, names=, **kwargs): Conditioning by using small multiples for different levels of additional variables. Some techniques that work in three dimensions but can also be used in higher dimensions: Grouping by encoding additional variables in color or shape channels. Not a fix, but I made it optional to explicitly input names, so that it puts a default xi for variable i in the diagonal positions.īelow you'll find an updated version of your code that addresses these two points, otherwise preserving the beauty of your code. Most of us have no intuition for four and more dimensions. It just leaves it as the default 0.1 ticks. Creating a matrix of scatter plots between a set of variables is a good way to visualize the relationship between each pair of variables. If you have an odd number of variables you are plotting with, the bottom right corner axes doesn't pull the correct xtics or ytics. The axis tics weren't lining up like I would expect (i.e., in your example above, you should be able to draw a vertical and horizontal line through any point across all plots and the lines should cross through the corresponding point in the other plots, but as it sits now this doesn't occur. As I was working with it, I noticed a few little things that didn't look quite right. Thanks for sharing your code! You figured out all the hard stuff for us. # Set up ticks only on one side for the 'edge' subplots.įor i, j in zip(*np.triu_indices_from(axes, k=1)):Īxes.plot(data, data, **kwargs)Īxes.annotate(label, (0.5, 0.5), xycoords='axes fraction',įor i, j in zip(range(numvars), itertools.cycle((-1, 0))): Returns the matplotlib figureįig, axes = plt.subplots(nrows=numvars, ncols=numvars, figsize=(8,8))įig.subplots_adjust(hspace=0.05, wspace=0.05) Passed on to matplotlib's 'plot' command. Finding meaningful groups can help you describe. Even if you didnt include a grouping variable in your graph, you may be able to identify meaningful groups. Look for differences in x-y relationships between groups of observations. Each row of 'data' is plottedĪgainst other rows, resulting in a nrows by nrows grid of subplots with theĭiagonal subplots labeled with 'names'. If your matrix plot has groups, you can look for group-related patterns.

'''Plots a scatterplot matrix of subplots. Each term will give a separate variable in the pairs plot, so terms should be numeric vectors.

Logical and factor columns are converted to numeric in the same way that data.matrix does. Linestyle='none', marker='o', color='black', mfc='none')įig.suptitle('Simple Scatterplot Matrix')ĭef scatterplot_matrix(data, names, **kwargs): the coordinates of points given as numeric columns of a matrix or data frame. There's always a name associated with each data series, so you can omit having to specify names.)ĭata = 10 * np.random.random((numvars, numdata))įig = scatterplot_matrix(data, , If you're always going to be working with structured or rec arrays, then you can simplify this a touch. I'm not quite sure what your data looks like, but it's quite simple to just build a function to do this from scratch. The expectation is that you'd write a simple function to string things together however you'd like. Generally speaking, matplotlib doesn't usually contain plotting functions that operate on more than one axes object (subplot, in this case).