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    <pre><code>import matplotlib.pyplot as plt import numpy as np data = np.array([1,1,-1,-1,1]) cmap = np.array([(1,0,0), (0,1,0)]) uniqdata, idx = np.unique(data, return_inverse=True) N = len(data) fig, ax = plt.subplots() plt.scatter(np.zeros(N), np.arange(1, N+1), s=100, c=cmap[idx]) plt.grid() plt.show() </code></pre> <p>yields</p> <p><img src="https://i.stack.imgur.com/JntQk.png" alt="enter image description here"></p> <hr> <p><strong>Explanation:</strong></p> <p>If you print out <code>np.unique(data, return_inverse=True)</code>, you'll see it returns a tuple of arrays:</p> <pre><code>In [71]: np.unique(data, return_inverse=True) Out[71]: (array([-1, 1]), array([1, 1, 0, 0, 1])) </code></pre> <p>The first array says the unique values in <code>data</code> is -1 and 1. The second array assigns the value 0 wherever <code>data</code> is -1 and the value 1 wherever <code>data</code> is 1. Essentially, <code>np.unique</code> allows us to transform <code>[1,1,-1,-1,1]</code> to <code>[1, 1, 0, 0, 1]</code>. Now <code>cmap[idx]</code> is an array of RGB values:</p> <pre><code>In [74]: cmap[idx] Out[74]: array([[0, 1, 0], [0, 1, 0], [1, 0, 0], [1, 0, 0], [0, 1, 0]]) </code></pre> <p>This is an application of so-called <a href="http://docs.scipy.org/numpy/docs/numpy-docs/reference/arrays.indexing.rst/#arrays-indexing" rel="nofollow noreferrer">"fancy indexing" on NumPy arrays</a>. <code>cmap[0]</code> is the first row of <code>cmap</code>. <code>cmap[1]</code> is the second row of <code>cmap</code>. <code>cmap[idx]</code> is an array such that the ith element in <code>cmap[idx]</code> is <code>cmap[idx[i]]</code>. So, you end up with <code>cmap[idx]</code> being a 2D-array where the ith row is <code>cmap[idx[i]]</code>. Thus <code>cmap[idx]</code> can be thought of as a sequence of RGB color values.</p> <hr> <p>If you have more than one set of dots and you wish to plot them in columns, the simplest way I can think of is to call <code>ax.scatter</code> once for each list of <code>data</code>:</p> <pre><code>import matplotlib.pyplot as plt import numpy as np def plot_data(ax, data, xval): N = len(data) uniqdata, idx = np.unique(data, return_inverse=True) ax.scatter(np.ones(N)*xval, np.arange(1, N+1), s=100, c=cmap[idx]) cmap = np.array([(1,0,0), (0,1,0)]) fig, ax = plt.subplots() data = np.array([1,1,-1,-1,1]) data2 = np.array([1,-1,1,1,-1]) plot_data(ax, data, 0) plot_data(ax, data2, 1) plt.grid() plt.show() </code></pre> <p><img src="https://i.stack.imgur.com/OdKZx.png" alt="enter image description here"></p> <p>The nice thing about this is that it is relatively easy to understand. The bad thing about this is that it calls <code>ax.scatter</code> more than once. If you have lots of data sets it is more efficient to collate your data and <em>call <code>ax.scatter</code> once</em>. This is faster for Matplotlib, but its a little more complicated to code:</p> <pre><code>import matplotlib.pyplot as plt import numpy as np import itertools as IT def plot_dots(ax, datasets): N = sum(len(data) for data in datasets) x = np.fromiter( (i for i, data in enumerate(datasets) for j in np.arange(len(data))), dtype='float', count=N) y = np.fromiter( (j for data in datasets for j in np.arange(1, len(data)+1)), dtype='float', count=N) c = np.fromiter( (val for data in datasets for rgb in cmap[np.unique(data, return_inverse=True)[-1]] for val in rgb), dtype='float', count=3*N).reshape(-1,3) ax.scatter(x, y, s=100, c=c) cmap = np.array([(1,0,0), (0,1,0)]) fig, ax = plt.subplots() N = 100 datasets = [np.random.randint(2, size=5) for i in range(N)] plot_dots(ax, datasets) plt.grid() plt.show() </code></pre> <p><img src="https://i.stack.imgur.com/vl6To.png" alt="enter image description here"></p> <hr> <p>References:</p> <ul> <li><a href="http://docs.scipy.org/numpy/docs/numpy-docs/reference/arrays.indexing.rst/#arrays-indexing" rel="nofollow noreferrer">fancy indexing (for NumPy arrays)</a></li> <li><a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.unique.html" rel="nofollow noreferrer">numpy.unique</a></li> <li><a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.scatter" rel="nofollow noreferrer">ax.scatter</a></li> <li><a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.fromiter.html" rel="nofollow noreferrer">numpy.fromiter</a></li> <li><a href="http://docs.python.org/reference/expressions.html#generator-expressions" rel="nofollow noreferrer">generator expressions</a></li> </ul>
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    1. COThanks so much!! I was trying to do something like this in Pylab, but I kept getting something else
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    2. COOne more quick question for my own reference. So cmap is for the color, right? Also, what is numpy.unique() telling python to do?
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    3. COFinally, if i wanted to graph another list the same way so that those dots are a bit farther away from the first set of dots, how do I do that? It keeps graphing the first set of dots two times
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