Pandas json_normalize produces confusing `KeyError` message?

Question

I'm trying to convert a nested JSON to a Pandas dataframe. I've been using json_normalize with success until I came across a certain JSON. I've made a smaller version of it to recreate the problem.

from pandas.io.json import json_normalize

json=[{"events": [{"schedule": {"date": "2015-08-27",
     "location": {"building": "BDC", "floor": 5},
     "ID": 815},
    "group": "A"},
   {"schedule": {"date": "2015-08-27",
     "location": {"building": "BDC", "floor": 5},
 "ID": 816},
"group": "A"}]}]

I then run:

json_normalize(json[0],'events',[['schedule','date'],['schedule','location','building'],['schedule','location','floor']])

Expecting to see something like this:

ID      group   schedule.date   schedule.location.building schedule.location.floor  
'815'   'A'     '2015-08-27'            'BDC'                       5
'816'   'A'     '2015-08-27'            'BDC'                       5

But instead I get this error:

In [2]: json_normalize(json[0],'events',[['schedule','date'],['schedule','location','building'],['schedule','location','floor']])
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-2-b588a9e3ef1d> in <module>()
----> 1 json_normalize(json[0],'events',[['schedule','date'],['schedule','location','building'],['schedule','location','floor']])

/Users/logan/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/io/json.pyc in json_normalize(data, record_path, meta, meta_prefix, record_prefix)
    739                 records.extend(recs)
    740
--> 741     _recursive_extract(data, record_path, {}, level=0)
    742
    743     result = DataFrame(records)

/Users/logan/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/io/json.pyc in _recursive_extract(data, path, seen_meta, level)
    734                         meta_val = seen_meta[key]
    735                     else:
--> 736                         meta_val = _pull_field(obj, val[level:])
    737                     meta_vals[key].append(meta_val)
    738

/Users/logan/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/io/json.pyc in _pull_field(js, spec)
    674         if isinstance(spec, list):
    675             for field in spec:
--> 676                 result = result[field]
    677         else:
    678             result = result[spec]

KeyError: 'schedule'

Answer 1

In this case, I think you'd just use this:

In [57]: json_normalize(data[0]['events'])
Out[57]: 
  group  schedule.ID schedule.date schedule.location.building  \
0     A          815    2015-08-27                        BDC   
1     A          816    2015-08-27                        BDC   

   schedule.location.floor  
0                        5  
1                        5  

The meta paths ([['schedule','date']...]) are for specifying data at the same level of nesting as your records, i.e. at the same level as 'events'. It doesn't look like json_normalize handles dicts with nested lists particularly well, so you may need to do some manual reshaping if your actual data is much more complicated.

Answer 2

I got the KeyError when the structue of the json was not consistent. Meaning, when one of the nested strucutes were missing from the json, I got KeyError.

https://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.json.json_normalize.html

From the examples mentioned on the pandas documentation site, if you make the nested tag (counties) missing on one of the records, you will get a KeyError. To circumvent this, you might have to make sure ignore the missing tag or consider only the records which have nested column/tag populated with data.

Answer 3

I had this same problem! This thread helped, especially parachute py's answer.

I found a solution using:

df.dropna(subset = *column(s) with nested data*)

then saving the resultant df as a new json. Load the new json and now you'll be able to flatten the nested columns.

There's probably a more efficient way to get around this, but my solution works.

edit: forgot to mention, I tried using the *errors = 'ignore'* arg in json.normalize() and it didn't help.