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()

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

You can, for example, use NumPy's arange for a fixed bin size (or Python's standard range object), and NumPy's linspace for evenly spaced bins. Here are 2 simple examples from my matplotlib gallery

Fixed bin size

import numpy as np
import random
from matplotlib import pyplot as plt

data = np.random.normal(0, 20, 1000) 

# fixed bin size
bins = np.arange(-100, 100, 5) # fixed bin size

plt.xlim([min(data)-5, max(data)+5])

plt.hist(data, bins=bins, alpha=0.5)
plt.title('Random Gaussian data (fixed bin size)')
plt.xlabel('variable X (bin size = 5)')
plt.ylabel('count')

plt.show()

Fixed number of bins

import numpy as np
import math
from matplotlib import pyplot as plt

data = np.random.normal(0, 20, 1000) 

bins = np.linspace(math.ceil(min(data)), 
                   math.floor(max(data)),
                   20) # fixed number of bins

plt.xlim([min(data)-5, max(data)+5])

plt.hist(data, bins=bins, alpha=0.5)
plt.title('Random Gaussian data (fixed number of bins)')
plt.xlabel('variable X (20 evenly spaced bins)')
plt.ylabel('count')

plt.show()