If you are looking for a more general way to unfold multiple hierarchies from a json you can use recursion and list comprehension to reshape your data. One alternative is presented below:
def flatten_json(nested_json, exclude=['']):
"""Flatten json object with nested keys into a single level.
Args:
nested_json: A nested json object.
exclude: Keys to exclude from output.
Returns:
The flattened json object if successful, None otherwise.
"""
out = {}
def flatten(x, name="", exclude=exclude):
if type(x) is dict:
for a in x:
if a not in exclude: flatten(x[a], name + a + '_')
elif type(x) is list:
i = 0
for a in x:
flatten(a, name + str(i) + '_')
i += 1
else:
out[name[:-1]] = x
flatten(nested_json)
return out
Then you can apply to your data, independent of nested levels:
New sample data
this_dict = {'events': [
{'id': 142896214,
'playerId': 37831,
'teamId': 3157,
'matchId': 2214569,
'matchPeriod': '1H',
'eventSec': 0.8935539999999946,
'eventId': 8,
'eventName': 'Pass',
'subEventId': 85,
'subEventName': 'Simple pass',
'positions': [{'x': 51, 'y': 49}, {'x': 40, 'y': 53}],
'tags': [{'id': 1801, 'tag': {'label': 'accurate'}}]},
{'id': 142896214,
'playerId': 37831,
'teamId': 3157,
'matchId': 2214569,
'matchPeriod': '1H',
'eventSec': 0.8935539999999946,
'eventId': 8,
'eventName': 'Pass',
'subEventId': 85,
'subEventName': 'Simple pass',
'positions': [{'x': 51, 'y': 49}, {'x': 40, 'y': 53},{'x': 51, 'y': 49}],
'tags': [{'id': 1801, 'tag': {'label': 'accurate'}}]}
]}
Usage
pd.DataFrame([flatten_json(x) for x in this_dict['events']])
Out[1]:
id playerId teamId matchId matchPeriod eventSec eventId \
0 142896214 37831 3157 2214569 1H 0.893554 8
1 142896214 37831 3157 2214569 1H 0.893554 8
eventName subEventId subEventName positions_0_x positions_0_y \
0 Pass 85 Simple pass 51 49
1 Pass 85 Simple pass 51 49
positions_1_x positions_1_y tags_0_id tags_0_tag_label positions_2_x \
0 40 53 1801 accurate NaN
1 40 53 1801 accurate 51.0
positions_2_y
0 NaN
1 49.0
Note that this flatten_json code is not mine, I have seen it here and here without much certainty of the original source.