Too big (?) histogram values when using normed histo options in SciPy and matplotlib

I have been trying to create a normed histogram using either SciPy or matplotlib (or anything for Python). When I create my histogram with 'normed' option disabled, it looks like below (this example is for 10 bins, but the same happens for a larger number of bins): (The first number represents the start of a bin, the second the bin's height)

-2.83785600931e-17   1182
5.6688145554e-15   1137
1.13660076709e-14   1031
1.70632007864e-14   950
2.27603939019e-14   912
2.84575870174e-14   802
3.41547801329e-14   853
3.98519732484e-14   948
4.55491663639e-14   1315
5.12463594794e-14   870


Which is absolutely fine, and what I was expecting. However, I later need to fit this histogram to another histogram, and for that I prefer to have a normed version of this histogram so that fitting the height of those histograms is easier.

Strangely, when I use the option density=True (for scipy.histogram version) or normed=True (for matplotlib.pyplot.plt version) my histogram bin heights get very large values, like below:

-1.44880082614e-17   2.00318764844e+13
5.71138595513e-15   1.98921598219e+13
1.14372599185e-14   1.8040914044e+13
1.71631338819e-14   1.52465807942e+13
2.28890078453e-14   1.56133370332e+13
2.86148818087e-14   1.4617855813e+13
3.43407557721e-14   1.50020766348e+13
4.00666297355e-14   1.74296536456e+13
4.57925036989e-14   2.3769297206e+13
5.15183776622e-14   1.50020766348e+13


I hardly know anything about statistics, but I expected "normed" to mean "sums up to one". Am I incorrect in my thinking, or is the output normalized histogram wrong after all?

• Ok I got the answer from elsewhere. In this context normed means the integral over the histogram is equal to 1. – Natalia Zoń May 3 '14 at 22:24