CODEWITHSUNDEEP
pythonjsonpandasdataframepandas-merge
M1kep
M1kep

Reputation: 33

How merge or join data in a Pandas nested DataFrame

I'm trying to figure out how to perform a Merge or Join on a nested field in a DataFrame. Below is some example data:

df_all_groups = pd.read_json("""
[
    {
        "object": "group",
        "id": "group-one",
        "collections": [
            {
                "id": "111-111-111",
                "readOnly": false
            },
            {
                "id": "222-222-222",
                "readOnly": false
            }
        ]
    },
    {
        "object": "group",
        "id": "group-two",
        "collections": [
            {
                "id": "111-111-111",
                "readOnly": false
            },
            {
                "id": "333-333-333",
                "readOnly": false
            }
        ]
    }
]
""")

df_collections_with_names = pd.read_json("""
[
    {
        "object": "collection",
        "id": "111-111-111",
        "externalId": null,
        "name": "Cats"
      },
      {
        "object": "collection",
        "id": "222-222-222",
        "externalId": null,
        "name": "Dogs"
      },
      {
        "object": "collection",
        "id": "333-333-333",
        "externalId": null,
        "name": "Fish"
      }
]
""")

I'm trying to add the name field from df_collections_with_names to each df_all_groups['collections'][<index>] by joining on df_all_groups['collections'][<index>].id The output I'm trying to get to is:

[
    {
        "object": "group",
        "id": "group-one",
        "collections": [
            {
                "id": "111-111-111",
                "readOnly": false,
                "name": "Cats" // See Collection name was added
            },
            {
                "id": "222-222-222",
                "readOnly": false,
                "name": "Dogs" // See Collection name was added
            }
        ]
    },
    {
        "object": "group",
        "id": "group-two",
        "collections": [
            {
                "id": "111-111-111",
                "readOnly": false,
                "name": "Cats" // See Collection name was added
            },
            {
                "id": "333-333-333",
                "readOnly": false,
                "name": "Fish" // See Collection name was added
            }
        ]
    }
]

I've tried to use the merge method, but can't seem to get it to run on the collections nested field as I believe it's a series at that point.

Upvotes: 0

Views: 742

Answers (1)

user7864386
user7864386

Reputation:

Here's one approach:

First convert the json string used to construct df_all_groups (I named it all_groups here) to a dictionary using json.loads. Then use json_normalize to contruct a DataFrame with it.

Then merge the DataFrame constructed above with df_collections_with_names; we have "names" column now.

The rest is constructing the desired dictionary from the result obtained above; groupby + apply(to_dict) + reset_index + to_dict will fetch the desired outcome:

import json
out = (pd.json_normalize(json.loads(all_groups), ['collections'], ['object', 'id'], meta_prefix='_')
       .merge(df_collections_with_names, on='id', suffixes=('','_'))
       .drop(columns=['object','externalId']))
out = (out.groupby(['_object','_id']).apply(lambda x: x[['id','readOnly','name']].to_dict('records'))
       .reset_index(name='collections'))
out.rename(columns={c: c.strip('_') for c in out.columns}).to_dict('records')

Output:

[{'object': 'group',
  'id': 'group-one',
  'collections': [{'id': '111-111-111', 'readOnly': False, 'name': 'Cats'},
   {'id': '222-222-222', 'readOnly': False, 'name': 'Dogs'}]},
 {'object': 'group',
  'id': 'group-two',
  'collections': [{'id': '111-111-111', 'readOnly': False, 'name': 'Cats'},
   {'id': '333-333-333', 'readOnly': False, 'name': 'Fish'}]}]

Upvotes: 1

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