converting a 2d dictionary to a numpy matrix

Question

I have a huge dictionary something like this:

d[id1][id2] = value

example:

books[\"auth1\"][\"humor\"] = 20
books[\"auth1\"         


        

Answer 1

In 2018, I think that Pandas 0.22 supports this out of the box. Specifically, please check the from_dict class method of DataFrame.

books = {}
books["auth1"] = {}
books["auth2"] = {}
books["auth1"]["humor"] = 20
books["auth1"]["action"] = 30
books["auth2"]["comedy"] = 20

pd.DataFrame.from_dict(books, orient='columns', dtype=None)

Answer 2

Use a list comprehension to turn a dict into a list of lists and/or a numpy array:

np.array([[books[author][genre] for genre in sorted(books[author])] for author in sorted(books)])

EDIT

Apparently you have an irregular number of keys in each sub-dictionary. Make a list of all the genres:

genres = ['humor', 'action', 'comedy']

And then iterate over the dictionaries in the normal manner:

list_of_lists = []
for author_name, author in sorted(books.items()):
    titles = []
    for genre in genres:
        try:
            titles.append(author[genre])
        except KeyError:
            titles.append(0)
    list_of_lists.append(titles)

books_array = numpy.array(list_of_lists)

Basically I'm attempting to append a value from each key in genres to a list. If the key is not there, it throws an error. I catch the error, and append a 0 to the list instead.

Answer 3

pandas do this very well:

books = {}
books["auth1"] = {}
books["auth2"] = {}
books["auth1"]["humor"] = 20
books["auth1"]["action"] = 30
books["auth2"]["comedy"] = 20

from pandas import *

df = DataFrame(books).T.fillna(0)

The output is:

       action  comedy  humor
auth1      30       0     20
auth2       0      20      0