Inserting new fields(columns) to mongoDB with pandas - pandas

Question

I have an existing data in MongoDB where Primary Key is set on 'date' with a few fields in it.
And I want to insert a new pandas dataframe with new fields(columns) to the existing data in MongoDB, joining on the 'date' field which exists on the both dataframe.
For example, lets say the this is dataframe A I have in my MongoDB ( I set the index with 'date' field when calling the data from MongoDB)
And this is the new dataframe B I want to insert to MongoDB
And this is the final dataframe C with new fields( 'std_50_3000window', 'std_50_300window', 'std_50_500window' added on 'date' index), which I want it to have on my MongoDB.
Is there any way to do this?? (Maybe with insert_many method?)

Answer 1

The method you need is update_one() with upsert=True in a loop; you can't use insert_many() for two reasons; firstly your not always inserting; sometime you are updating; secondly update_many() (and insert_many()) only work on a single filter; in your case each filter is different as each update relates to a different time.
This is generic solution that will combine dataframes (df_a, df_b in this case - you can have as many as you like) in the manner that you need. It uses iterrows to get each row of the dataframe, filters on the date, and sets the values to those in the dataframe. the $set operator will override values if they are there already and set them if not set. upsert=True will perform an insert if there's no match on the date.
for df in [df_a, df_b]:
for _, row in df.iterrows():
db.mycollection.update_one({'date': row.get('date')}, {'$set': row.to_dict()}, upsert=True)
Full worked example:
from pymongo import MongoClient
from pprint import pprint
import datetime
import pandas as pd
# Sample data setup
db = MongoClient()['mydatabase']
data_a = [[datetime.datetime(2017, 5, 19, 21, 20), 96, 8, 98],
[datetime.datetime(2017, 5, 19, 21, 21), 95, 8, 97],
[datetime.datetime(2017, 5, 19, 21, 22), 95, 8, 97]]
df_a = pd.DataFrame(data_a, columns=['date', 'std_500_1000window', 'std_50_100window', 'std_50_2000window'])
data_b = [[datetime.datetime(2017, 5, 19, 21, 20), 98, 9, 10],
[datetime.datetime(2017, 5, 19, 21, 21), 98, 9, 10],
[datetime.datetime(2017, 5, 19, 21, 22), 98, 9, 10]]
df_b = pd.DataFrame(data_b, columns=['date', 'std_50_3000window', 'std_50_300window', 'std_50_500window'])
# Perform the upserts
for df in [df_a, df_b]:
for _, row in df.iterrows():
db.mycollection.update_one({'date': row.get('date')}, {'$set': row.to_dict()}, upsert=True)
# Print the results
for record in db.mycollection.find():
pprint(record)
Result:
{'_id': ObjectId('5f0ae909df5531ac655ce528'),
'date': datetime.datetime(2017, 5, 19, 21, 20),
'std_500_1000window': 96,
'std_50_100window': 8,
'std_50_2000window': 98,
'std_50_3000window': 98,
'std_50_300window': 9,
'std_50_500window': 10}
{'_id': ObjectId('5f0ae909df5531ac655ce52a'),
'date': datetime.datetime(2017, 5, 19, 21, 21),
'std_500_1000window': 95,
'std_50_100window': 8,
'std_50_2000window': 97,
'std_50_3000window': 98,
'std_50_300window': 9,
'std_50_500window': 10}
{'_id': ObjectId('5f0ae909df5531ac655ce52c'),
'date': datetime.datetime(2017, 5, 19, 21, 22),
'std_500_1000window': 95,
'std_50_100window': 8,
'std_50_2000window': 97,
'std_50_3000window': 98,
'std_50_300window': 9,
'std_50_500window': 10}