Pure Python Histograms

When you’re preparing to plot a histogram, it’s simplest to not think in terms of bins but rather to report how many times each value appears (a frequency table). A Python dictionary is well-suited for this task:

>>> # Need not be sorted, necessarily
>>> a = (0, 1, 1, 1, 2, 3, 7, 7, 23)

>>> def count_elements(seq) -> dict:
...     """Tally elements from `seq`."""
...     hist = {}
...     for i in seq:
...         hist[i] = hist.get(i, 0) + 1
...     return hist

>>> counted = count_elements(a)
>>> counted
{0: 1, 1: 3, 2: 1, 3: 1, 7: 2, 23: 1}

count_elements() returns a dictionary with unique elements from the sequence as keys and their frequencies (counts) as values. Within the loop over seq, hist[i] = hist.get(i, 0) + 1 says, “For each element of the sequence, increment its corresponding value in hist by 1.”

In fact, this is precisely what is done by the collections.Counter class from Python’s standard library, which subclasses a Python dictionary and overrides its .update() method:

>>> from collections import Counter

>>> recounted = Counter(a)
>>> recounted
Counter({0: 1, 1: 3, 3: 1, 2: 1, 7: 2, 23: 1})

You can confirm that your handmade function does virtually the same thing as collections.Counter by testing for equality between the two:

>>> recounted.items() == counted.items()
True