pandas: create a long/tidy DataFrame from dictionary when values are sets or lists of variable length

Question

Simple dictionary:

d = {'a': set([1,2,3]), 'b': set([3, 4])}

(the sets may be turned into lists if it matters)

How do I convert it into a long/tidy DataFrame in which each column is a variable and every observation is a row, i.e.:

  letter  value
0      a      1
1      a      2
2      a      3
3      b      3
4      b      4

The following works, but it's a bit cumbersome:

id = 0
tidy_d = {}
for l, vs in d.items():
    for v in vs:
        tidy_d[id] = {'letter': l, 'value': v}
        id += 1
pd.DataFrame.from_dict(tidy_d, orient = 'index')

Is there any pandas magic to do this? Something like:

pd.DataFrame([d]).T.reset_index(level=0).unnest()

where unnest obviously doesn't exist and comes from R.

Answer 1

You can use a comprehension with itertools.chain and zip:

from itertools import chain

keys, values = map(chain.from_iterable, zip(*((k*len(v), v) for k, v in d.items())))

df = pd.DataFrame({'letter': list(keys), 'value': list(values)})

print(df)

  letter  value
0      a      1
1      a      2
2      a      3
3      b      3
4      b      4

This can be rewritten in a more readable fashion:

zipper = zip(*((k*len(v), v) for k, v in d.items()))
values = map(list, map(chain.from_iterable, zipper))

df = pd.DataFrame(list(values), columns=['letter', 'value'])

Answer 2

Use numpy.repeat with chain.from_iterable:

from itertools import chain

df = pd.DataFrame({
    'letter' : np.repeat(list(d.keys()), [len(v) for k, v in d.items()]),
    'value' : list(chain.from_iterable(d.values())), 
})
print (df)
  letter  value
0      a      1
1      a      2
2      a      3
3      b      3
4      b      4

Answer 3

A tad more "pandaic", inspired by this post:

pd.DataFrame.from_dict(d, orient = 'index') \
  .rename_axis('letter').reset_index() \
  .melt(id_vars = ['letter'], value_name = 'value') \
  .drop('variable', axis = 1) \
  .dropna()