MongoDB Collections & Data Structure - mongodb

Question

I have just started using MongoDB so I apologize if this is an obvious or simple question.
I am trying to store my information using the following data structure:
database: {
Customers: {
Date_added_1: {
{customer 1 info},
{customer 2 info},
{customer 3 info}
}
Date_added_2: {
{customer 1 info},
{customer 2 info}
}
}
Employees: {
Date_hired_1: {
{employee 1 info},
{employee 2 info}
}
Date_hired_2: {
{employee 1 info}
}
}
}
The code I have written to input the information into the database looks like this:
from pymongo import MongoClient
def addLeadsToDatabase(personCategory, personInformation, date):
client = MongoClient('localhost', port#)
db = client.database[personCategory][date]
db.insert({personInformation})
person_to_add = {'Name':'John Smith', 'Phone':'888-888-8888', 'Email':'example#email.com'}
addLeadsToDatabase('Customers', person_to_add, '06/28/2017')
However when navigating through the database it looks like each [personCategory][date] is getting saved as a separate collection. Rather than storing the data first within the personCategory collection, and then within the date sub-collection.
Therefore when I run 'show collections' in the MongoDB shell it outputs:
Customers.6/25/2017
Employees.6/25/2017
Customers.6/26/2017
Customers.6/27/2017
Employees.6/27/2017
Rather than just:
Customers
Employees
With the date category stored within each.
Is there a way to store the data the way I have described so it is not making a new collection each time I run the code and just storing data within the appropriate collection(s)?

Answer 1

Please consider:
from pymongo import MongoClient
def addLeadsToDatabase(personCategory, personInformation):
client = MongoClient('localhost', port#)
db = client.database[personCategory]
db.insert({personInformation})
person_to_add = {'Name':'John Smith', 'Phone':'888-888-8888', 'Email':'example#email.com', 'Date': '06/28/2017' } //I would suggest you to use new Date()
addLeadsToDatabase('Customers', person_to_add)
Summary:
Keep it simple. Later on you can filter by date or even aggregate your query by this date.