How to have logarithmic bins in a Python histogram

Question

As far as I know the option Log=True in the histogram function only refers to the y-axis.

P.hist(d,bins=50,log=True,alpha=0.5,color='b',histtype='step')

I need the bins to be equally spaced in log10. Is there something that can do this?

Answer 1

use logspace() to create a geometric sequence, and pass it to bins parameter. And set the scale of xaxis to log scale.

import pylab as pl
import numpy as np

data = np.random.normal(size=10000)
pl.hist(data, bins=np.logspace(np.log10(0.1),np.log10(1.0), 50))
pl.gca().set_xscale("log")
pl.show()

Answer 2

The most direct way is to just compute the log10 of the limits, compute linearly spaced bins, and then convert back by raising to the power of 10, as below:

import pylab as pl
import numpy as np

data = np.random.normal(size=10000)

MIN, MAX = .01, 10.0

pl.figure()
pl.hist(data, bins = 10 ** np.linspace(np.log10(MIN), np.log10(MAX), 50))
pl.gca().set_xscale("log")
pl.show()

Answer 3

The following code indicates how you can use bins='auto' with the log scale.

import numpy as np
import matplotlib.pyplot as plt

data = 10**np.random.normal(size=500)

_, bins = np.histogram(np.log10(data + 1), bins='auto')
plt.hist(data, bins=10**bins);
plt.gca().set_xscale("log")

Answer 4

In addition to what was stated, performing this on pandas dataframes works as well:

some_column_hist = dataframe['some_column'].plot(bins=np.logspace(-2, np.log10(max_value), 100), kind='hist', loglog=True, xlim=(0,max_value))

I would caution, that there may be an issue with normalizing the bins. Each bin is larger than the previous one, and therefore must be divided by it's size to normalize the frequencies before plotting, and it seems that neither my solution, nor HYRY's solution accounts for this.

Source: https://arxiv.org/pdf/cond-mat/0412004.pdf

Answer 5

A variation of the proposed answers for the case where we need to include zero or even negative values:

import numpy as np
import matplotlib.pyplot as plt

data = np.random.normal(size=10000)
cutoff = 0.01
bins = np.logspace(np.log10(cutoff),np.log10(1.0), 50)
bins = np.concatenate((-bins[::-1], bins))
plt.figure()
plt.hist(data, bins=bins)
plt.xscale("symlog", linthresh=cutoff)
plt.show()