The bins parameter tells you the number of bins that your data will be divided into. You can specify it as an integer or as a list of bin edges.
For example, here we ask for 20 bins:
import numpy as np
import matplotlib.pyplot as plt
x = np.random.randn(1000)
plt.hist(x, bins=20)
And here we ask for bin edges at the locations [-4, -3, -2... 3, 4].
plt.hist(x, bins=range(-4, 5))
Your question about how to choose the "best" number of bins is an interesting one, and there's actually a fairly vast literature on the subject. There are some commonly-used rules-of-thumb that have been proposed (e.g. the Freedman-Diaconis Rule, Sturges' Rule, Scott's Rule, the Square-root rule, etc.) each of which has its own strengths and weaknesses.
If you want a nice Python implementation of a variety of these auto-tuning histogram rules, you might check out the histogram functionality in the latest version of the AstroPy package, described here.
This works just like plt.hist, but lets you use syntax like, e.g. hist(x, bins='freedman') for choosing bins via the Freedman-Diaconis rule mentioned above.
My personal favorite is "Bayesian Blocks" (bins="blocks"), which solves for optimal binning with unequal bin widths. You can read a bit more on that here.
Edit, April 2017: with matplotlib version 2.0 or later and numpy version 1.11 or later, you can now specify automatically-determined bins directly in matplotlib, by specifying, e.g. bins='auto'. This uses the maximum of the Sturges and Freedman-Diaconis bin choice. You can read more about the options in the numpy.histogram docs.
The bins parameter tells you the number of bins that your data will be divided into. You can specify it as an integer or as a list of bin edges.
For example, here we ask for 20 bins:
import numpy as np
import matplotlib.pyplot as plt
x = np.random.randn(1000)
plt.hist(x, bins=20)
And here we ask for bin edges at the locations [-4, -3, -2... 3, 4].
plt.hist(x, bins=range(-4, 5))
Your question about how to choose the "best" number of bins is an interesting one, and there's actually a fairly vast literature on the subject. There are some commonly-used rules-of-thumb that have been proposed (e.g. the Freedman-Diaconis Rule, Sturges' Rule, Scott's Rule, the Square-root rule, etc.) each of which has its own strengths and weaknesses.
If you want a nice Python implementation of a variety of these auto-tuning histogram rules, you might check out the histogram functionality in the latest version of the AstroPy package, described here.
This works just like plt.hist, but lets you use syntax like, e.g. hist(x, bins='freedman') for choosing bins via the Freedman-Diaconis rule mentioned above.
My personal favorite is "Bayesian Blocks" (bins="blocks"), which solves for optimal binning with unequal bin widths. You can read a bit more on that here.
Edit, April 2017: with matplotlib version 2.0 or later and numpy version 1.11 or later, you can now specify automatically-determined bins directly in matplotlib, by specifying, e.g. bins='auto'. This uses the maximum of the Sturges and Freedman-Diaconis bin choice. You can read more about the options in the numpy.histogram docs.
To complemented jakes answer, you can use
numpy.histogram_bin_edges if you just want to calculate the optimal bin edges, without actually doing the histogram. histogram_bin_edges is a function specifically designed for the optimal calculation of bin edges. You can choose seven different algorithms for the optimisation.
Hello,
So I created a histogram using matplotlib and seaborn:
sns.set()
_ = plt.hist(df['value'], bins=[1,25,45,55,80])
_ = plt.xlabel('value')
_ = plt.ylabel('frequency')
_ = plt.title('histogram title')
plt.show()and I am given a histogram, but the x-axis marks do not reflect my histogram bin ranges. I recognize I have uneven bin ranges, but these ranges are necessary for my research question. Instead my x-axis just shows intervals of 20, which would not allow the viewer to discern what the exact bin ranges are, though the bars of the histogram are of different widths reflecting the different bin ranges.
My question is, how do I augment my python code to show:
-
my bin ranges, so that on the x-axis, or pointing to the bars themselves, I can see '1-25', '25-45', etc.
-
the actual frequency value of each bar
Thanks!
EDIT: I realized I probably just need to make a legend...