Histogram Matplotlib

Question

So I have a little problem. I have a data set in scipy that is already in the histogram format, so I have the center of the bins and the number of events per bin. How can I now plot is as a histogram. I tried just doing

bins, n=hist()

but it didn't like that. Any recommendations?

Solution 1

import matplotlib.pyplot as plt
import numpy as np

mu, sigma = 100, 15
x = mu + sigma * np.random.randn(10000)
hist, bins = np.histogram(x, bins=50)
width = 0.7 * (bins[1] - bins[0])
center = (bins[:-1] + bins[1:]) / 2
plt.bar(center, hist, align='center', width=width)
plt.show()

The object-oriented interface is also straightforward:

fig, ax = plt.subplots()
ax.bar(center, hist, align='center', width=width)
fig.savefig("1.png")

---

If you are using custom (non-constant) bins, you can pass compute the widths using np.diff, pass the widths to ax.bar and use ax.set_xticks to label the bin edges:

import matplotlib.pyplot as plt
import numpy as np

mu, sigma = 100, 15
x = mu + sigma * np.random.randn(10000)
bins = [0, 40, 60, 75, 90, 110, 125, 140, 160, 200]
hist, bins = np.histogram(x, bins=bins)
width = np.diff(bins)
center = (bins[:-1] + bins[1:]) / 2

fig, ax = plt.subplots(figsize=(8,3))
ax.bar(center, hist, align='center', width=width)
ax.set_xticks(bins)
fig.savefig("/tmp/out.png")

plt.show()

Solution 2

If you don't want bars you can plot it like this:

import numpy as np
import matplotlib.pyplot as plt

mu, sigma = 100, 15
x = mu + sigma * np.random.randn(10000)

bins, edges = np.histogram(x, 50, normed=1)
left,right = edges[:-1],edges[1:]
X = np.array([left,right]).T.flatten()
Y = np.array([bins,bins]).T.flatten()

plt.plot(X,Y)
plt.show()

Solution 3

I know this does not answer your question, but I always end up on this page, when I search for the matplotlib solution to histograms, because the simple histogram_demo was removed from the matplotlib example gallery page.

Here is a solution, which doesn't require numpy to be imported. I only import numpy to generate the data x to be plotted. It relies on the function hist instead of the function bar as in the answer by @unutbu.

import numpy as np
mu, sigma = 100, 15
x = mu + sigma * np.random.randn(10000)

import matplotlib.pyplot as plt
plt.hist(x, bins=50)
plt.savefig('hist.png')

Also check out the matplotlib gallery and the matplotlib examples.

Solution 4

I just realized that the hist documentation is explicit about what to do when you already have an np.histogram

counts, bins = np.histogram(data)
plt.hist(bins[:-1], bins, weights=counts)

The important part here is that your counts are simply the weights. If you do it like that, you don't need the bar function anymore

Solution 5

If you're willing to use pandas:

pandas.DataFrame({'x':hist[1][1:],'y':hist[0]}).plot(x='x',kind='bar')

View more solutions

Comments

[tacaswell]

If you are going to suggest using pandas you should probably include a link to their site and a more through example that explains what is going on.

[tacaswell]

You can also use ax.step.

[CMCDragonkai]

Is there a way to pass the bin edges to the x-axis of the bar graph?

[unutbu]

@CMCDragonkai: plt.bar's width parameter can accept an array-like object (instead of a scalar). So you could use width = np.diff(bins) instead of width = 0.7 * (bins[1] - bins[0]).

[CMCDragonkai]

But the width setting by itself only sets the width of the bar right? I'm talking about the x-axis labels (that is I want to see the actual bin edges being labels on the x-axis). It should be similar to how plt.hist works.

[unutbu]

@CMCDragonkai: You could use ax.set_xticks to set the xlabels. I've added an example above to show what I mean.

[Martin R.]

"Here is a solution, which doesn't require numpy" -- first line of code imports numpy :)

[tommy.carstensen]

@Martin R. That's only to generate the data to be plotted. See lines 4-6. No use of numpy.