A dependency-free library to quickly make ascii histograms from data.
Project description
text_histogram
Histograms are great. This is Bit.ly’s data_hacks 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]: ∎∎∎∎∎∎
Installation
$ pip install data_hacks
Why?
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 or over ssh. Don’t even get me started on installing them.
data_hacks is pretty great, but difficult to use from python code directly because it requires an optparse.OptionParser to pass histogram options.
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