python dictionary count of unique values

I have a problem with counting distinct values for each key in Python.

I have a dictionary d like

[{"abc":"movies"}, {"abc": "sports"}, {"abc": "music"}, {"xyz": "music"}, {"pqr":"music"}, {"pqr":"movies"},{"pqr":"sports"}, {"pqr":"news"}, {"pqr":"sports"}]

I need to print number of distinct values per each key individually.

That means I would want to print

abc 3
xyz 1
pqr 4

Please help.

Thank you

Over 6 years after answering, someone pointed out to me I misread the question. While my original answer (below) counts unique keys in the input sequence, you actually have a different count-distinct problem; you want to count values per key.

To count unique values per key, exactly, you'd have to collect those values into sets first:

values_per_key = {}
for d in iterable_of_dicts:
    for k, v in d.items():
        values_per_key.setdefault(k, set()).add(v)
counts = {k: len(v) for k, v in values_per_key.items()}

which for your input, produces:

>>> values_per_key = {}
>>> for d in iterable_of_dicts:
...     for k, v in d.items():
...         values_per_key.setdefault(k, set()).add(v)
...
>>> counts = {k: len(v) for k, v in values_per_key.items()}
>>> counts
{'abc': 3, 'xyz': 1, 'pqr': 4}

We can still wrap that object in a Counter() instance if you want to make use of the additional functionality this class offers, see below:

>>> from collections import Counter
>>> Counter(counts)
Counter({'pqr': 4, 'abc': 3, 'xyz': 1})

The downside is that if your input iterable is very large the above approach can require a lot of memory. In case you don't need exact counts, e.g. when orders of magnitude suffice, there are other approaches, such as a hyperloglog structure or other algorithms that 'sketch out' a count for the stream.

This approach requires you install a 3rd-party library. As an example, the datasketch project offers both HyperLogLog and MinHash. Here's a HLL example (using the HyperLogLogPlusPlus class, which is a recent improvement to the HLL approach):

from collections import defaultdict
from datasketch import HyperLogLogPlusPlus

counts = defaultdict(HyperLogLogPlusPlus)

for d in iterable_of_dicts:
    for k, v in d.items():
        counts[k].update(v.encode('utf8'))

In a distributed setup, you could use Redis to manage the HLL counts.

---

My original answer:

Use a collections.Counter() instance, together with some chaining:

from collections import Counter
from itertools import chain

counts = Counter(chain.from_iterable(e.keys() for e in d))

This ensures that dictionaries with more than one key in your input list are counted correctly.

Demo:

>>> from collections import Counter
>>> from itertools import chain
>>> d = [{"abc":"movies"}, {"abc": "sports"}, {"abc": "music"}, {"xyz": "music"}, {"pqr":"music"}, {"pqr":"movies"},{"pqr":"sports"}, {"pqr":"news"}, {"pqr":"sports"}]
>>> Counter(chain.from_iterable(e.keys() for e in d))
Counter({'pqr': 5, 'abc': 3, 'xyz': 1})

or with multiple keys in the input dictionaries:

>>> d = [{"abc":"movies", 'xyz': 'music', 'pqr': 'music'}, {"abc": "sports", 'pqr': 'movies'}, {"abc": "music", 'pqr': 'sports'}, {"pqr":"news"}, {"pqr":"sports"}]
>>> Counter(chain.from_iterable(e.keys() for e in d))
Counter({'pqr': 5, 'abc': 3, 'xyz': 1})

A Counter() has additional, helpful functionality, such as the .most_common() method that lists elements and their counts in reverse sorted order:

for key, count in counts.most_common():
    print '{}: {}'.format(key, count)

# prints
# 5: pqr
# 3: abc
# 1: xyz