Histograms are great for exploring data, but numpy and matplotlib are heavy and overkill for quick analysis. They also can't be easily used on remote servers over ssh. Don't even get me started on installing them.
Bit.ly's data_hacks histogram.py is great but difficult to use from python code directly (it requires an optparse.OptionParser to pass histogram options). This is histogram.py repackaged for convenient script use.
>>> from text_histogram import histogram
>>> import random
>>> histogram([random.gauss(50, 20) for _ in xrange(100)])
# NumSamples = 100; Min = 1.42; Max = 87.36
# Mean = 51.848095; Variance = 332.055832; SD = 18.222399; Median 53.239251
# each ∎ represents a count of 1
1.4221 - 10.0159 [ 3]: ∎∎∎
10.0159 - 18.6098 [ 3]: ∎∎∎
18.6098 - 27.2036 [ 6]: ∎∎∎∎∎∎
27.2036 - 35.7974 [ 4]: ∎∎∎∎
35.7974 - 44.3913 [ 17]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
44.3913 - 52.9851 [ 16]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
52.9851 - 61.5789 [ 17]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
61.5789 - 70.1728 [ 20]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
70.1728 - 78.7666 [ 8]: ∎∎∎∎∎∎∎∎
78.7666 - 87.3604 [ 6]: ∎∎∎∎∎∎$ pip install text_histogram