Bin size in Matplotlib (Histogram)

Actually, it's quite easy: instead of the number of bins you can give a list with the bin boundaries. They can be unequally distributed, too:

plt.hist(data, bins=[0, 10, 20, 30, 40, 50, 100])

If you just want them equally distributed, you can simply use range:

plt.hist(data, bins=range(min(data), max(data) + binwidth, binwidth))

Added to original answer

The above line works for data filled with integers only. As macrocosme points out, for floats you can use:

import numpy as np
plt.hist(data, bins=np.arange(min(data), max(data) + binwidth, binwidth))

For N bins, the bin edges are specified by list of N+1 values where the first N give the lower bin edges and the +1 gives the upper edge of the last bin.

Code:

from numpy import np; from pylab import *

bin_size = 0.1; min_edge = 0; max_edge = 2.5
N = (max_edge-min_edge)/bin_size; Nplus1 = N + 1
bin_list = np.linspace(min_edge, max_edge, Nplus1)

Note that linspace produces array from min_edge to max_edge broken into N+1 values or N bins

I use quantiles to do bins uniform and fitted to sample:

bins=df['Generosity'].quantile([0,.05,0.1,0.15,0.20,0.25,0.3,0.35,0.40,0.45,0.5,0.55,0.6,0.65,0.70,0.75,0.80,0.85,0.90,0.95,1]).to_list()

plt.hist(df['Generosity'], bins=bins, normed=True, alpha=0.5, histtype='stepfilled', color='steelblue', edgecolor='none')

I guess the easy way would be to calculate the minimum and maximum of the data you have, then calculate L = max - min. Then you divide L by the desired bin width (I'm assuming this is what you mean by bin size) and use the ceiling of this value as the number of bins.

I like things to happen automatically and for bins to fall on "nice" values. The following seems to work quite well.

import numpy as np
import numpy.random as random
import matplotlib.pyplot as plt
def compute_histogram_bins(data, desired_bin_size):
    min_val = np.min(data)
    max_val = np.max(data)
    min_boundary = -1.0 * (min_val % desired_bin_size - min_val)
    max_boundary = max_val - max_val % desired_bin_size + desired_bin_size
    n_bins = int((max_boundary - min_boundary) / desired_bin_size) + 1
    bins = np.linspace(min_boundary, max_boundary, n_bins)
    return bins

if __name__ == '__main__':
    data = np.random.random_sample(100) * 123.34 - 67.23
    bins = compute_histogram_bins(data, 10.0)
    print(bins)
    plt.hist(data, bins=bins)
    plt.xlabel('Value')
    plt.ylabel('Counts')
    plt.title('Compute Bins Example')
    plt.grid(True)
    plt.show()

The result has bins on nice intervals of bin size.

[-70. -60. -50. -40. -30. -20. -10.   0.  10.  20.  30.  40.  50.  60.]