can either pass string of the json, or a filepath to a file with valid json

In [99]: pd.read_json('[{"A": 1, "B": 2}, {"A": 3, "B": 4}]')
   A  B
0  1  2
1  3  4

Alternatively to conserve memory:

with open('test.json') as f:
    data = pd.DataFrame(json.loads(line) for line in f)

Dataframe into nested JSON as in flare.js files used in D3.js

def to_flare_json(df, filename):
    """Convert dataframe into nested JSON as in flare files used for D3.js"""
    flare = dict()
    d = {"name":"flare", "children": []}
    for index, row in df.iterrows():
        parent = row[0]
        child = row[1]
        child_size = row[2]

        # Make a list of keys
        key_list = []
        for item in d['children']:

        #if 'parent' is NOT a key in flare.JSON, append it
        if not parent in key_list:
            d['children'].append({"name": parent, "children":[{"value": child_size, "name": child}]})
        # if parent IS a key in flare.json, add a new child to it
            d['children'][key_list.index(parent)]['children'].append({"value": child_size, "name": child})
    flare = d
    # export the final result to a json file
    with open(filename +'.json', 'w') as outfile:
        json.dump(flare, outfile, indent=4)
    return ("Done")

Read JSON from file

Content of file.json (one JSON object per line):

{"A": 1, "B": 2}
{"A": 3, "B": 4}

How to read directly from a local file:

pd.read_json('file.json', lines=True)
# Output:
#    A  B
# 0  1  2
# 1  3  4