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I'm currently using Matplotlib to create a histogram:

enter image description here

import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as pyplot ... fig = pyplot.figure() ax = fig.add_subplot(1,1,1,) n, bins, patches = ax.hist(measurements, bins=50, range=(graph_minimum, graph_maximum), histtype='bar') #ax.set_xticklabels([n], rotation='vertical') for patch in patches: patch.set_facecolor('r') pyplot.title('Spam and Ham') pyplot.xlabel('Time (in seconds)') pyplot.ylabel('Bits of Ham') pyplot.savefig(output_filename)

I'd like to make the x-axis labels a bit more meaningful.

Firstly, the x-axis ticks here seem to be limited to five ticks. No matter what I do, I can't seem to change this - even if I add more xticklabels, it only uses the first five. I'm not sure how Matplotlib calculates this, but I assume it's auto-calculated from the range/data?

Is there some way I can increase the resolution of x-tick labels - even to the point of one for each bar/bin?

(Ideally, I'd also like the seconds to be reformatted in micro-seconds/milli-seconds, but that's a question for another day).

Secondly, I'd like each individual bar labeled - with the actual number in that bin, as well as the percentage of the total of all bins.

The final output might look something like this:

enter image description here

Is something like that possible with Matplotlib?

Cheers, Victor

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3 Answers 3

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Sure! To set the ticks, just, well... Set the ticks (see matplotlib.pyplot.xticks or ax.set_xticks). (Also, you don't need to manually set the facecolor of the patches. You can just pass in a keyword argument.)

For the rest, you'll need to do some slightly more fancy things with the labeling, but matplotlib makes it fairly easy.

As an example:

import matplotlib.pyplot as plt import numpy as np from matplotlib.ticker import FormatStrFormatter data = np.random.randn(82) fig, ax = plt.subplots() counts, bins, patches = ax.hist(data, facecolor='yellow', edgecolor='gray') # Set the ticks to be at the edges of the bins. ax.set_xticks(bins) # Set the xaxis's tick labels to be formatted with 1 decimal place... ax.xaxis.set_major_formatter(FormatStrFormatter('%0.1f')) # Change the colors of bars at the edges... twentyfifth, seventyfifth = np.percentile(data, [25, 75]) for patch, rightside, leftside in zip(patches, bins[1:], bins[:-1]): if rightside < twentyfifth: patch.set_facecolor('green') elif leftside > seventyfifth: patch.set_facecolor('red') # Label the raw counts and the percentages below the x-axis... bin_centers = 0.5 * np.diff(bins) + bins[:-1] for count, x in zip(counts, bin_centers): # Label the raw counts ax.annotate(str(count), xy=(x, 0), xycoords=('data', 'axes fraction'), xytext=(0, -18), textcoords='offset points', va='top', ha='center') # Label the percentages percent = '%0.0f%%' % (100 * float(count) / counts.sum()) ax.annotate(percent, xy=(x, 0), xycoords=('data', 'axes fraction'), xytext=(0, -32), textcoords='offset points', va='top', ha='center') # Give ourselves some more room at the bottom of the plot plt.subplots_adjust(bottom=0.15) plt.show()

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6 Comments

Aha, great =). One other note - originally, I was using "fig = pyplot.figure(figsize=(32,24),)" and "ax = fig.add_subplot(1,1,1,)" to set the size of the figure. However, if I swap out the second lien for yours "fig, ax = pyplot.subplots()", it seems to ignore my figsize now? Any idea why?
@victorhooi - It should work if you just specify the figsize as a kwarg to subplots. E.g. fig, ax = plt.subplots(figsize=(32, 34)) If it's not, perhaps it's a bug? subplots was only added in 1.0 as a convenience function.
Kingston: Aha, great, yup, that line works =). You are awesome, dude. There's one final bug/niggle I don't understand - the xlabel text directly underneath the annotation text - not sure how to offset it. I tried "ax.xaxis.LABELPAD=30", but it seemed to ignore that.
@victorhooi - There are several different ways to set the tick padding, but the easiest is ax.tick_params(axis='x', pad=30) (which is a bit counterintuitive.) Hope that helps!
@ Joe Kingston: Hmm, tried that, but it moves both the x-axis label and the tick marks. Dang this. Lol. Anyhow, I think this deserve another question, so I've reposted it here: stackoverflow.com/questions/6406368/…
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One thing I wanted to add to the plots in the histogram with "density = True" was the relative frequency values for each bin, search but I couldn't find a function that would do that. A solution I made follows as image:

SAMPLE PLOT IMAGE

The function:

def label_densityHist(ax, n, bins, x=4, y=0.01, r=2, **kwargs): """ Add labels,relative value of bin, to each bin in a density histogram . :param ax: Object axe of matplotlib The axis to plot. :param n: list, array of int, float The values of the histogram bins. :param bins: list, array of int, float The edges of the bins. :param x: int, float Related the x position of the bin labels. The higher, the lower the value on the x-axis. Default: 4 :param y: int, float Related the y position of the bin labels. The higher, the greater the value on the y-axis. Default: 0.01 :param r: int Number of decimal places. Default: 2 :param **kwargs: Text properties in matplotlib :return: None Example import matplotlib.pyplot as plt import numpy as np dados = np.random.randn(100) axe = plt.gca() n, bins, _ = axe.hist(x=dados, edgecolor='black') label_densityHist(axe,n, bins) plt.show() Example: import matplotlib.pyplot as plt import numpy as np dados = np.random.randn(100) axe = plt.gca() n, bins, _ = axe.hist(x=dados, edgecolor='black') label_densityHist(axe,n, bins, x=6, fontsize='large') plt.show() Reference: [1]https://matplotlib.org/3.1.1/api/text_api.html#matplotlib.text.Text """ k = [] # calculate the relative frequency of each bin for i in range(0,len(n)): k.append((bins[i+1]-bins[i])*n[i]) # rounded k = around(k,r); #print(k) # plot the label/text to each bin for i in range(0, len(n)): x_pos = (bins[i + 1] - bins[i]) / x + bins[i] y_pos = n[i] + (n[i] * y) label = str(k[i]) # relative frequency of each bin ax.text(x_pos, y_pos, label, kwargs)
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To add SI prefixes to your axis labels you want to use QuantiPhy. In fact, in its documentation it has an example that shows how to do this exact thing: MatPlotLib Example.

I think you would add something like this to your code:

from matplotlib.ticker import FuncFormatter from quantiphy import Quantity time_fmtr = FuncFormatter(lambda v, p: Quantity(v, 's').render(prec=2)) ax.xaxis.set_major_formatter(time_fmtr)
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