In matplotlib.pyplot, how to plot two datasets using interleaved bar graphs?

Question

I am going through Think Stats and I would like to compare multiple data sets visually. I can see from the book examples that it is possible to generate an interleaved bar g

Answer 1

I came across this problem a while ago and created a wrapper function that takes a 2D array and automatically creates a multi-barchart from it:

The code:

import matplotlib.pyplot as plt
import matplotlib.cm as cm
import operator as o

import numpy as np

dpoints = np.array([['rosetta', '1mfq', 9.97],
           ['rosetta', '1gid', 27.31],
           ['rosetta', '1y26', 5.77],
           ['rnacomposer', '1mfq', 5.55],
           ['rnacomposer', '1gid', 37.74],
           ['rnacomposer', '1y26', 5.77],
           ['random', '1mfq', 10.32],
           ['random', '1gid', 31.46],
           ['random', '1y26', 18.16]])

fig = plt.figure()
ax = fig.add_subplot(111)

def barplot(ax, dpoints):
    '''
    Create a barchart for data across different categories with
    multiple conditions for each category.

    @param ax: The plotting axes from matplotlib.
    @param dpoints: The data set as an (n, 3) numpy array
    '''

    # Aggregate the conditions and the categories according to their
    # mean values
    conditions = [(c, np.mean(dpoints[dpoints[:,0] == c][:,2].astype(float))) 
                  for c in np.unique(dpoints[:,0])]
    categories = [(c, np.mean(dpoints[dpoints[:,1] == c][:,2].astype(float))) 
                  for c in np.unique(dpoints[:,1])]

    # sort the conditions, categories and data so that the bars in
    # the plot will be ordered by category and condition
    conditions = [c[0] for c in sorted(conditions, key=o.itemgetter(1))]
    categories = [c[0] for c in sorted(categories, key=o.itemgetter(1))]

    dpoints = np.array(sorted(dpoints, key=lambda x: categories.index(x[1])))

    # the space between each set of bars
    space = 0.3
    n = len(conditions)
    width = (1 - space) / (len(conditions))

    # Create a set of bars at each position
    for i,cond in enumerate(conditions):
        indeces = range(1, len(categories)+1)
        vals = dpoints[dpoints[:,0] == cond][:,2].astype(np.float)
        pos = [j - (1 - space) / 2. + i * width for j in indeces]
        ax.bar(pos, vals, width=width, label=cond, 
               color=cm.Accent(float(i) / n))

    # Set the x-axis tick labels to be equal to the categories
    ax.set_xticks(indeces)
    ax.set_xticklabels(categories)
    plt.setp(plt.xticks()[1], rotation=90)

    # Add the axis labels
    ax.set_ylabel("RMSD")
    ax.set_xlabel("Structure")

    # Add a legend
    handles, labels = ax.get_legend_handles_labels()
    ax.legend(handles[::-1], labels[::-1], loc='upper left')

barplot(ax, dpoints)
plt.show()

If you're interested in what this function does and the logic behind it, here's a (shamelessly self-promoting) link to the blog post describing it.