How to make a histogram from a list of data

Question

Well I think matplotlib got downloaded but with my new script I get this error:

/usr/lib64/python2.6/site-packages/matplotlib/backends/backend_gtk.py:621:     DeprecationWarning: Use the new widget gtk.Tooltip
  self.tooltips = gtk.Tooltips()
Traceback (most recent call last):
  File "vector_final", line 42, in <module>
plt.hist(data, num_bins)
  File "/usr/lib64/python2.6/site-packages/matplotlib/pyplot.py", line 2008, in hist
ret = ax.hist(x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, **kwargs)
  File "/usr/lib64/python2.6/site-packages/matplotlib/axes.py", line 7098, in hist
w = [None]*len(x)
TypeError: len() of unsized object

And my code is: #!/usr/bin/python

l=[]
with open("testdata") as f:
    line = f.next()
    f.next()# skip headers
    nat = int(line.split()[0])
    print nat

    for line in f:
        if line.strip():
          if line.strip():
            l.append(map(float,line.split()[1:]))  


    b = 0
    a = 1
for b in range(53):
    for a in range(b+1,54):
        import operator
        import matplotlib.pyplot as plt
        import numpy as np

        vector1 = (l[b][0],l[b][1],l[b][2])
        vector2 = (l[a][0],l[a][1],l[a][2])

            x = vector1
            y = vector2
            vector3 = list(np.array(x) - np.array(y))
            dotProduct = reduce( operator.add, map( operator.mul, vector3, vector3))


        dp = dotProduct**.5
        print dp

        data = dp
        num_bins = 200 # <- number of bins for the histogram
        plt.hist(data, num_bins)
        plt.show()

But the code thats getting me the error is the new addition that I added which is the last part, reproduced below:

                data = dp
                num_bins = 200 # <- number of bins for the histogram
                plt.hist(data, num_bins)
                plt.show()

Answer 1

Automatic bins

how to make 200 evenly spaced out bins, and have your program store the data in the appropriate bins?

The accepted answer manually creates 200 bins with np.arange and np.linspace, but matplotlib already does this automatically:

1. plt.hist itself returns counts and bins

   counts, bins, _ = plt.hist(data, bins=200)
   

Or if you need the bins before plotting:

2. np.histogram with plt.stairs

   counts, bins = np.histogram(data, bins=200)
   plt.stairs(counts, bins, fill=True)
   

   Note that stair plots require matplotlib 3.4.0+.

3. pd.cut with plt.hist

   _, bins = pd.cut(data, bins=200, retbins=True)
   plt.hist(data, bins)
   

   

Answer 2

there's a couple of ways to do this.

if you can not guarantee your items all to be the same type and numeric then --> use the builtin standard library collections

import collections
hist = dict(collections.Counter(your_list))

otherwise if your data is guaranteed to be all the same type and numeric --> then use the python module numpy

import numpy as np
# for one dimensional data
(hist, bin_edges) = np.histogram(your_list)
# for two dimensional data
(hist, xedges, yedges) = np.histogram2d(your_list)
# for N dimensional data
(hist, edges) = np.histogramdd(your_list)

The numpy histogram functionality is really the Cadillac option because np.histogram can do things like try to figure out how many bins you need and it can do weighting and it has all the algorithms it uses documented with lots of great documentation and example code.