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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?

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()

matplotlib.pyplot.hist, Plot a histogram. Compute and draw the histogram of x. The return value is a tuple (n, bins, patches) or ([n0, n1, .. Matplotlib - Histogram - A histogram is an accurate representation of the distribution of numerical data. It is an estimate of the probability distribution of a continuous variable.

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()

Demo of the histogram (hist) function with a few features, The Astropy docs have a great section on how to select these parameters. import matplotlib import numpy as np import matplotlib.pyplot  Matplotlib can be used to create histograms. A histogram shows the frequency on the vertical axis and the horizontal axis is another dimension. Usually it has bins, where every bin has a minimum and maximum value.

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.

Matplotlib Histogram - Python Tutorial, Matplotlib can be used to create histograms. A histogram shows the frequency on the vertical axis and the horizontal axis is another dimension. Plotting Histogram in Python using Matplotlib A histogram is basically used to represent data provided in a form of some groups.It is accurate method for the graphical representation of numerical data distribution.It is a type of bar plot where X-axis represents the bin ranges while Y-axis gives information about frequency.

If you're willing to use pandas:

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

How to plot a histogram using Matplotlib in Python with a list of data , If you want a histogram, you don't need to attach any 'names' to x-values, as on x-​axis you would have data bins: import matplotlib.pyplot as plt  Plotting Histogram using only Matplotlib. Plotting histogram using matplotlib is a piece of cake. All you have to do is use plt.hist() function of matplotlib and pass in the data along with the number of bins and a few optional parameters. In plt.hist(), passing bins='auto' gives you the “ideal” number of bins. The idea is to select a bin

I think this might be useful for someone.

Numpy's histogram function, to my annoyance (although, I appreciate there is a good reason for it), returns back the edges of each bin, rather than the value of the bin. While, this makes sense for floating-point numbers, which can lie within an interval (i.e. the center value is not super meaningful), this is not the desired output when dealing with discrete values or integers (0, 1, 2, etc). In particular, the length of bins returned from np.histogram is not equal to the length of the counts / density.

To get around this, I used np.digitize to quantize the input, and return a discrete number of bins, along with fraction of counts for each bin. You could easily edit to get the integer number of counts.

def compute_PMF(data)
    import numpy as np
    from collections import Counter
    _, bins = np.histogram(data, bins='auto', range=(data.min(), data.max()), density=False)
    h = Counter(np.digitize(data,bins) - 1)
    weights = np.asarray(list(h.values())) 
    weights = weights / weights.sum()
    values = np.asarray(list(h.keys()))
    return weights, values
####

Refs:

[1] https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html

[2] https://docs.scipy.org/doc/numpy/reference/generated/numpy.digitize.html

How to Plot a Histogram in Python using Matplotlib, You may apply the following template to plot a histogram in Python using Matplotlib: import matplotlib.pyplot as plt x = [value1, value2, value3,.] plt.hist(x, bins  Matplotlib histogram is used to visualize the frequency distribution of numeric array by splitting it to small equal-sized bins. In this article, we explore practical techniques that are extremely useful in your initial data analysis and plotting.

How to choose bins in matplotlib histogram, shows the frequency on the vertical axis and the horizontal axis is another dimension. Usually it has bins, where every bin has a minimum and maximum value. Each bin also has a frequency between x and infinite. A histogram is a chart that uses bars represent frequencies which helps visualize distributions of data. In this post, you’ll learn how to create histograms with Python, including Matplotlib and Pandas.

Histograms in Matplotlib, Next, let's plot the histogram using matplotlib's plt.bar function where your x-axis and y-axis will be bin_edges and hist , respectively. The type of histogram to draw. 'bar' is a traditional bar-type histogram. If multiple data are given the bars are arranged side by side. 'barstacked' is a bar-type histogram where multiple data are stacked on top of each other. 'step' generates a lineplot that is by default unfilled. 'stepfilled' generates a lineplot that is by default filled.

Python Histogram Plotting: NumPy, Matplotlib, Pandas & Seaborn , Histograms in Pure Python; Building Up From the Base: Histogram Calculations in NumPy; Visualizing Histograms with Matplotlib and Pandas; Plotting a Kernel  In this case, you can plot your two data sets on different axes. To do so, you can get your histogram data using matplotlib, clear the axis, and then re-plot it on two separate axes (shifting the bin edges so that they don't overlap):

Comments
  • Is there a way to pass the bin edges to the x-axis of the bar graph?
  • @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]).
  • 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.
  • @CMCDragonkai: You could use ax.set_xticks to set the xlabels. I've added an example above to show what I mean.
  • You can also use ax.step.
  • "Here is a solution, which doesn't require numpy" -- first line of code imports numpy :)
  • @Martin R. That's only to generate the data to be plotted. See lines 4-6. No use of numpy.
  • 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.