Pandas convert Dataframe to Nested Json

Question

My question is essentially the opposite of this one:

Create a Pandas DataFrame from deeply nested JSON

I'm wondering if it's possible to do the reverse. Given a table like:

     Library  Level           School Major  2013 Total
200  MS_AVERY  UGRAD  GENERAL STUDIES  GEST        5079
201  MS_AVERY  UGRAD  GENERAL STUDIES  HIST           5
202  MS_AVERY  UGRAD  GENERAL STUDIES  MELC           2
203  MS_AVERY  UGRAD  GENERAL STUDIES  PHIL          10
204  MS_AVERY  UGRAD  GENERAL STUDIES  PHYS           1
205  MS_AVERY  UGRAD  GENERAL STUDIES  POLS          53

Is it possible to generate a nested dict (or JSON) like:

dict:

{'MS_AVERY': 
    { 'UGRAD' :
        {'GENERAL STUDIES' : {'GEST' : 5}
                             {'MELC' : 2}

 ...

Possible duplicate of [Creating complex nested dictionaries from Pandas DataFrame](http://stackoverflow.com/questions/41283161/creating-complex-nested-dictionaries-from-pandas-dataframe) — Celebrian, Dec 23 '16 at 09:31

score 9 · Accepted Answer · answered May 10 '14 at 06:17

It seems not hard to create a function will build the recursive dictionary given your DataFrame object:

def fdrec(df):
    drec = dict()
    ncols = df.values.shape[1]
    for line in df.values:
        d = drec
        for j, col in enumerate(line[:-1]):
            if not col in d.keys():
                if j != ncols-2:
                    d[col] = {}
                    d = d[col]
                else:
                    d[col] = line[-1]
            else:
                if j!= ncols-2:
                    d = d[col]
    return drec

which will produce:

{'MS_AVERY':
    {'UGRAD':
        {'GENERAL STUDIES': {'PHYS': 1L, 
                             'POLS': 53L,
                             'PHIL': 10L,
                             'HIST': 5L,
                             'MELC': 2L,
                             'GEST': 5079L}}}}

thankyou for the response saullo. I was wondering if there was a built-in function that would do this, but this works great! — Alex Spangher, May 10 '14 at 18:13
This is a lovely function, but for JSON there must be double quotes around all values. — charles ross, Mar 04 '18 at 03:48

score 1 · Answer 2 · edited May 23 '17 at 12:32

Here's a solution I came up while working on this question:

def rollup_to_dict_core(x, values, columns, d_columns=None):
    if d_columns is None:
        d_columns = []

    if len(columns) == 1:
        if len(values) == 1:
            return x.set_index(columns)[values[0]].to_dict()
        else:
            return x.set_index(columns)[values].to_dict(orient='index')
    else:
        res = x.groupby([columns[0]] + d_columns).apply(lambda y: rollup_to_dict_core(y, values, columns[1:]))
        if len(d_columns) == 0:
            return res.to_dict()
        else:
            res.name = columns[1]
            res = res.reset_index(level=range(1, len(d_columns) + 1))
            return res.to_dict(orient='index')

def rollup_to_dict(x, values, d_columns=None):
    if d_columns is None:
        d_columns = []

    columns = [c for c in x.columns if c not in values and c not in d_columns]
    return rollup_to_dict_core(x, values, columns, d_columns)

>>> pprint(rollup_to_dict(df, ['2013 Total']))
{'MS_AVERY': {'UGRAD': {'GENERAL STUDIES': {'GEST': 5079,
                                            'HIST': 5,
                                            'MELC': 2,
                                            'PHIL': 10,
                                            'PHYS': 1,
                                            'POLS': 53}}}}

score 0 · Answer 3 · answered Nov 23 '20 at 03:41

key = ['Library', 'Level', 'School']
series = (df.groupby(key, sort=False)[df.columns.difference(key)]
            .apply(lambda x: x[['Major', '2013 Total']].to_dict('records'))
         )

# build: {Major: Total}
major = {}
values = series.values[0]
for i in range(len(values)):
    major.update({values[i]['Major']: values[i]['2013 Total']})

# build the recursive dictionary
index = series.index[0]
d = {}
for i in reversed(range(len(index))):
    if not bool(d):
        d = {index[i]: major}
    else:
        d = {index[i]: d}
print(json.dumps(d, indent=2))

It will produce:

{
  "MS_AVERY": {
    "UGRAD": {
      "GENERAL STUDIES": {
        "GEST": 5079,
        "HIST": 5,
        "MELC": 2,
        "PHIL": 10,
        "PHYS": 1,
        "POLS": 53
      }
    }
  }
}

score 0 · Answer 4 · answered Apr 30 '21 at 22:58

Here is a generic way to generate this format, might be what someone else is looking for. Desired format:

{ "data": 
   [
        {
            "NAME": [1, 2, 3]
        },
        {
            "NAME": [1, 2, 3]
        },
    ]
}

To get that:

import json
jsonstr = '{"data":['
for (columnName, columnData) in df.iteritems():
    jsonstr+='{"'
    jsonstr+=columnName
    jsonstr+='":'
    jsonstr+=json.dumps(list(columnData.values))
    jsonstr+='},'
jsonstr = jsonstr[:-1]
jsonstr+=']}'
jsonobject = json.loads(jsonstr)
jsonobject

Pandas convert Dataframe to Nested Json

4 Answers4

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