From JSON file to Numpy Array

Assuming you've successfully loaded that JSON into Python, here's one way to create the Numpy array you want. My code has a minimal definition of ObjectId so that it won't raise a NameError on ObjectId entries.

sorted(d["B"].items())]

produces a list of (key, value) tuples from the contents of a "B" dictionary, sorted by key. We then extract just the values from those tuples into a list, and append that list to a list containing the value from the "A" item.

import numpy as npclass ObjectId(object):    def __init__(self, objectid):        self.objectid = objectid    def __repr__(self):        return 'ObjectId("{}")'.format(self.objectid)data = [    {        "_id" : ObjectId("57065024c3d1132426c4dd53"),        "B" : {            "BA" : 14,            "BB" : 23,            "BC" : 32,            "BD" : 41        },        "A" : 50    },    {        "_id" : ObjectId("57065024c3d1132426c4dd53"),        "A" : 1,        "B" : {            "BA" : 1,            "BB" : 2,            "BC" : 3,            "BD" : 4        }    }]array2 = np.array([[d["A"]] + [v for _, v in sorted(d["B"].items())] for d in data])print(array2)

output

[[50 14 23 32 41] [ 1  1  2  3  4]]

The flatdict module can sometimes be useful when working with mongodb data structures. It will handle flattening the nested dictionary structure for you:

columns = []for d in data:    flat = flatdict.FlatDict(d)    del flat['_id']    columns.append([item[1] for item in sorted(flat.items(), key=lambda item: item[0])])np.vstack(columns)

Of course this can be solved without flatdict too.