Getting nested data from MongoDB into a Pandas data frame

Question

I'm collecting Twitter data (tweets + meta data) into a MongoDB server. Now I want to do some statistical analysis. To get the data from MongoDB into a Pandas data frame I used the following code:

cursor = collection.find({},{'id': 1, 'text': 1})

tweet_fields = ['id', 'text']

result = pd.DataFrame(list(cursor), columns = tweet_fields)

This way i successfully loaded the data into Pandas, which is great. Now I wanted to do some analysis on the users that created the tweets which was also data I collected. This data is located in a nested part of the JSON (I'm not 100% sure if this is true JSON), for instance user.id which is the id of the Twitter user account.

I can just add that to the cursor using dot notation:

cursor = collection.find({},{'id': 1, 'text': 1, 'user.id': 1})

But this results in a NaN for that column. I found that the problem lies with the way the data is structured:

bit of the cursor without user.id:

[{'_id': ObjectId('561547ae5371c0637f57769e'),
  'id': 651795711403683840,
  'text': 'Video: Zuuuu gut! Caro Korneli besucht für extra 3 Pegida Via KFMW http://t.co/BJX5GKrp7s'},
 {'_id': ObjectId('561547bf5371c0637f5776ac'),
  'id': 651795781557583872,
  'text': 'Iets voor werkloze xenofobe PVV-ers, (en dat zijn waarschijnlijk wel de meeste).........Ze zoeken bij Frontex een paar honderd grenswachten.'},
 {'_id': ObjectId('561547ab5371c0637f57769c'),
  'id': 651795699881889792,
  'text': 'RT @ansichtssache47: Geht gefälligst arbeiten, die #Flüchtlinge haben Hunger! http://t.co/QxUYfFjZB5 #grenzendicht #rente #ZivilerUngehorsa…'}]

bit of the cursor with user.id:

[{'_id': ObjectId('561547ae5371c0637f57769e'),
  'id': 651795711403683840,
  'text': 'Video: Zuuuu gut! Caro Korneli besucht für extra 3 Pegida Via KFMW http://t.co/BJX5GKrp7s',
  'user': {'id': 223528499}},
 {'_id': ObjectId('561547bf5371c0637f5776ac'),
  'id': 651795781557583872,
  'text': 'Iets voor werkloze xenofobe PVV-ers, (en dat zijn waarschijnlijk wel de meeste).........Ze zoeken bij Frontex een paar honderd grenswachten.',
  'user': {'id': 3544739837}}]

So in short I don't understand how I get the nested part of my collected data in a separate column of my Pandas data frame.

Answer 1

I use a function like this to get nested JSON lines into a dataframe. It uses the handy pandas json.normalize function:

import pandas as pd
from bson import json_util, ObjectId
from pandas.io.json import json_normalize
import json

def mongo_to_dataframe(mongo_data):

        sanitized = json.loads(json_util.dumps(mongo_data))
        normalized = json_normalize(sanitized)
        df = pd.DataFrame(normalized)

        return df

Just pass your mongo data by calling the function with it as an argument.

sanitized = json.loads(json_util.dumps(mongo_data)) loads the JSON lines as regular JSON

normalized = json_normalize(sanitized) un-nests the data

df = pd.DataFrame(normalized) simply turns it into a dataframe