PyMongo Cheatsheet: MongoDB Python Tutorial

This cheatsheet provides a comprehensive guide to using PyMongo, the official Python driver for MongoDB. Learn how to interact with your MongoDB databases efficiently using Python.

MongoDB Server Setup

Run a MongoDB server with Docker for easy setup and management:

docker run --rm -itd --name mongodb \
  -e MONGO_INITDB_ROOT_USERNAME=root \
  -e MONGO_INITDB_ROOT_PASSWORD=password \
  -p 27017:27017 \
  mongo:4.4

Python Package Installation

Install the PyMongo package to enable Python interaction with MongoDB:

pip install pymongo

Establishing a MongoDB Connection

Connect to your MongoDB instance. Here are examples for connections with and without authentication:

Connection without Authentication:

>>> from pymongo import MongoClient
>>> uri = 'mongodb://localhost:27017/'
>>> client = MongoClient(uri)
>>> client.list_database_names()
['admin', 'config', 'local']

Connection with Authentication:

>>> from pymongo import MongoClient
>>> uri = 'mongodb://root:password@localhost:27017/admin?authSource=admin&authMechanism=SCRAM-SHA-1'
>>> client = MongoClient(uri)
>>> client.database_names()
['admin', 'config', 'local']

Managing Databases and Collections

Listing Databases:

>>> client.database_names()
['admin', 'config', 'local']

Listing Collections:

>>> db = client.config
>>> db.list_collection_names()
['system.sessions']

Document Operations

Writing a Single Document:

Create a database store_db, use the transactions collection, and write a document:

>>> db = client.store_db
>>> transactions = db.transactions
>>> doc_data = {
    'store_name': 'sportsmans',
    'branch_name': 'tygervalley',
    'account_id': 'sns_03821023',
    'total_costs': 109.20,
    'products_purchased': ['cricket bat', 'cricket ball', 'sports hat'],
    'purchase_method': 'credit card'
}
>>> response = transactions.insert_one(doc_data)
>>> response.inserted_id
ObjectId('5cad16a5a5f3826f6f046d74')

Verify the collection's presence:

>>> db.list_collection_names()
['transactions']

Writing Multiple Documents:

Batch write multiple documents for efficiency:

>>> transaction_1 = {
    'store_name': 'sportsmans', 'branch_name': 'tygervalley',
    'account_id': 'sns_09121024', 'total_costs': 129.84,
    'products_purchased': ['sportsdrink', 'sunglasses', 'sports illustrated'],
    'purchase_method': 'credit card'
}
>>> transaction_2 = {
    'store_name': 'burger king', 'branch_name':
    'somerset west', 'account_id': 'bk_29151823',
    'total_costs': 89.99, 'products_purchased': ['cheese burger', 'pepsi'],
    'purchase_method': 'cash'
}
>>> transaction_3 = {
    'store_name': 'game', 'branch_name': 'bellvile', 'account_id': 'gm_49121229',
    'total_costs': 499.99, 'products_purchased': ['ps4 remote'],
    'purchase_method': 'cash'
}
>>> response = transactions.insert_many([transaction_1, transaction_2, transaction_3])
>>> response.inserted_ids
[ObjectId('5cad18d4a5f3826f6f046d75'), ObjectId('5cad18d4a5f3826f6f046d76'), ObjectId('5cad18d4a5f3826f6f046d77')]

Finding One Document:

>>> transactions.find_one({'account_id': 'gm_49121229'})
{u'account_id': u'gm_49121229', u'store_name': u'game', u'purchase_method': u'cash', u'branch_name': u'bellvile', u'products_purchased': [u'ps4 remote'], u'_id': ObjectId('5cad18d4a5f3826f6f046d77'), u'total_costs': 499.99}

Finding Multiple Documents:

>>> response = transactions.find({'purchase_method': 'cash'})
>>> [doc for doc in response]
[{u'account_id': u'bk_29151823', u'store_name': u'burger king', u'purchase_method': u'cash', u'branch_name': u'somerset west', u'products_purchased': [u'cheese burger', u'pepsi'], u'_id': ObjectId('5cad18d4a5f3826f6f046d76'), u'total_costs': 89.99}, {u'account_id': u'gm_49121229', u'store_name': u'game', u'purchase_method': u'cash', u'branch_name': u'bellvile', u'products_purchased': [u'ps4 remote'], u'_id': ObjectId('5cad18d4a5f3826f6f046d77'), u'total_costs': 499.99}]

Filter results to show only account IDs:

>>> response = transactions.find({'purchase_method': 'cash'})
>>> [doc['account_id'] for doc in response]
[u'bk_29151823', u'gm_49121229']

Count documents matching a query:

>>> transactions.count_documents({'purchase_method': 'cash'})
2

Querying with Logical Operators (AND, OR):

Query with the AND condition (SQL equivalent: where branch_name = 'tygervalley' AND account_id = 'sns_03821023'):

>>> response = transactions.find({ '$and': [{'branch_name': 'tygervalley'},{'account_id': 'sns_03821023'}]})
>>> [v for v in response]
[{u'account_id': u'sns_03821023', u'store_name': u'sportsmans', u'purchase_method': u'credit card', u'branch_name': u'tygervalley', u'products_purchased': [u'cricket bat', u'cricket ball', u'sports hat'], u'_id': ObjectId('5cb18881df585e003c976d5d'), u'total_costs': 109.2}]

Query with the OR condition (SQL equivalent: where branch_name = 'tygervalley' OR account_id = 'sns_03821023'):

>>> response = transactions.find({ '$or': [{'branch_name': 'tygervalley'},{'account_id': 'sns_03821023'}]})
>>> [v for v in response]
[{u'account_id': u'sns_03821023', u'store_name': u'sportsmans', u'purchase_method': u'credit card', u'branch_name': u'tygervalley', u'products_purchased': [u'cricket bat', u'cricket ball', u'sports hat'], u'_id': ObjectId('5cb18881df585e003c976d5d'), u'total_costs': 109.2}, {u'account_id': u'sns_09121024', u'store_name': u'sportsmans', u'purchase_method': u'credit card', u'branch_name': u'tygervalley', u'products_purchased': [u'sportsdrink', u'sunglasses', u'sports illustrated'], u'_id': ObjectId('5cb188a3df585e003c976d5e'), u'total_costs': 129.84}]

Combining conditions:

>>> response = transactions.find({ 'total_costs': {'$gt': 120}, '$or': [{'branch_name': 'tygervalley'},{'account_id': 'sns_03821023'}]})
>>> [v for v in response]
[{u'account_id': u'sns_09121024', u'store_name': u'sportsmans', u'purchase_method': u'credit card', u'branch_name': u'tygervalley', u'products_purchased': [u'sportsdrink', u'sunglasses', u'sports illustrated'], u'_id': ObjectId('5cb188a3df585e003c976d5e'), u'total_costs': 129.84}]

Other comparison operators include:

lt  - Less Than
lte - Less Than Equals
gt  - Greater Than
gte - Greater Than Equals
ne  - Not Equals

Range Queries:

Perform queries based on date ranges or numerical ranges:

>>> import datetime
>>> new_posts = [{"author": "Mike","text": "Another post!","tags": ["bulk", "insert"],"date": datetime.datetime(2009, 11, 12, 11, 14)},{"author": "Eliot","title": "MongoDB is fun","text": "and pretty easy too!","date": datetime.datetime(2009, 11, 10, 10, 45)}]
>>> db.posts.insert(new_posts)
[ObjectId('5cb439c0f90e2e002a164d15'), ObjectId('5cb439c0f90e2e002a164d16')]

>>> for post in db.posts.find():
...     post
...
{u'date': datetime.datetime(2009, 11, 12, 11, 14), u'text': u'Another post!', u'_id': ObjectId('5cb439c0f90e2e002a164d15'), u'author': u'Mike', u'tags': [u'bulk', u'insert']}
{u'date': datetime.datetime(2009, 11, 10, 10, 45), u'text': u'and pretty easy too!', u'_id': ObjectId('5cb439c0f90e2e002a164d16'), u'author': u'Eliot', u'title': u'MongoDB is fun'}

>>> d = datetime.datetime(2009, 11, 11, 12)
>>> for post in db.posts.find({"date": {"$lt": d}}).sort("author"):
...     post
...
{u'date': datetime.datetime(2009, 11, 10, 10, 45), u'text': u'and pretty easy too!', u'_id': ObjectId('5cb439c0f90e2e002a164d16'), u'author': u'Eliot', u'title': u'MongoDB is fun'}

Updating Documents

Modify existing documents in your collection. For example, changing a transaction's payment method:

>>> transactions.find_one({'account_id':'sns_03821023'})
{u'account_id': u'sns_03821023', u'store_name': u'sportsmans', u'purchase_method': u'credit card', u'branch_name': u'tygervalley', u'products_purchased': [u'cricket bat', u'cricket ball', u'sports hat'], u'_id': ObjectId('5cb18881df585e003c976d5d'), u'total_costs': 109.2}

Update the purchase_method to 'account':

>>> transactions.update( {'account_id': 'sns_03821023'}, {'$set': {'purchase_method': 'account'}})
{'updatedExisting': True, u'nModified': 1, u'ok': 1.0, u'n': 1}

Verify the update:

>>> transactions.find_one({'account_id':'sns_03821023'})
{u'account_id': u'sns_03821023', u'store_name': u'sportsmans', u'purchase_method': u'account', u'branch_name': u'tygervalley', u'products_purchased': [u'cricket bat', u'cricket ball', u'sports hat'], u'_id': ObjectId('5cb18881df585e003c976d5d'), u'total_costs': 109.2}

To update multiple documents, use the multi option:

transactions.update( {'k': 'v'}, {'$set': {'k': 'new_v'}},{multi:true})

Filtering Documents

Retrieve documents based on specific criteria, such as purchase price greater than 120:

>>> response = transactions.find({'total_costs': {'$gt': 120}})
>>> [doc for doc in response]
[{u'account_id': u'sns_09121024', u'store_name': u'sportsmans', u'purchase_method': u'credit card', u'branch_name': u'tygervalley', u'products_purchased': [u'sportsdrink', u'sunglasses', u'sports illustrated'], u'_id': ObjectId('5cad18d4a5f3826f6f046d75'), u'total_costs': 129.84}, {u'account_id': u'gm_49121229', u'store_name': u'game', u'purchase_method': u'cash', u'branch_name': u'bellvile', u'products_purchased': [u'ps4 remote'], u'_id': ObjectId('5cad18d4a5f3826f6f046d77'), u'total_costs': 499.99}]

Projections

Select specific fields to be returned in your query results:

>>> response = transactions.find({}, {'branch_name': 'tygervalley', 'purchase_method': 'credit card'})
>>> [doc for doc in response]
[{u'purchase_method': u'credit card', u'branch_name': u'tygervalley', u'_id': ObjectId('5cad16a5a5f3826f6f046d74')}, {u'purchase_method': u'credit card', u'branch_name': u'tygervalley', u'_id': ObjectId('5cad18d4a5f3826f6f046d75')}]

Sorting Documents

Order your query results. Here's an example of sorting by total_costs in descending order:

>>> from pymongo import MongoClient, DESCENDING
>>> response = transactions.find().sort('total_costs', DESCENDING)
>>> ['Products: {}, Price: {}'.format(doc['products_purchased'], doc['total_costs']) for doc in response]
["Products: [u'ps4 remote'], Price: 499.99", "Products: [u'sportsdrink', u'sunglasses', u'sports illustrated'], Price: 129.84", "Products: [u'cricket bat', u'cricket ball', u'sports hat'], Price: 109.2", "Products: [u'cheese burger', u'pepsi'], Price: 89.99"]

Aggregations

Perform complex data processing and analysis using MongoDB's aggregation framework:

>>> agr = [{'$group': {'_id': 1, 'all': { '$sum': '$total_costs' }}}]
>> [a for a in transactions.aggregate(agr)]
[{u'all': 829.02, u'_id': 1}]

Or as a list:

>>> agr = [{'$group': {'_id': 1, 'all': { '$sum': '$total_costs' }}}]
>>> val = list(transactions.aggregate(agr))
>>> val
[{u'all': 829.02, u'_id': 1}]

Aggregate costs for selected stores:

>>> agr = [{ '$match': {'$or': [ { 'store_name': 'sportsmans' }, { 'store_name': 'game' }] }}, { '$group': {'_id': 1, 'sum2stores': { '$sum': '$total_costs' } }}]
>>> [a for a in transactions.aggregate(agr)]
[{u'_id': 1, u'sum2stores': 739.03}]

Limiting Data Output

Control the number of documents returned and skip documents using limit() and skip():

>>> response = transactions.find()
>>> [a['account_id'] for a in response]
[u'sns_03821023', u'sns_09121024', u'bk_29151823', u'gm_49121229']

>>> response = transactions.find().limit(2)
>>> [a['account_id'] for a in response]
[u'sns_03821023', u'sns_09121024']

>>> response = transactions.find().skip(1).limit(3)
>>> [a['account_id'] for a in response]
[u'sns_09121024', u'bk_29151823', u'gm_49121229']

>>> response = transactions.find().skip(1).limit(2)
>>> [a['account_id'] for a in response]
[u'sns_09121024', u'bk_29151823']

>>> response = transactions.find().skip(3).limit(1)
>>> [a['account_id'] for a in response]
[u'gm_49121229']

Indexes

Improve query performance by creating indexes:

>>> from pymongo import TEXT
>>> transactions.create_index([("store_name", TEXT)], name='store_index', default_language='english')
'store_index'

Deleting Documents

Remove documents from your collection:

Delete selected documents:

>>> transactions.remove({'account_id':'sns_03821029'})
{u'ok': 1.0, u'n': 2}

Delete all documents:

>>> transactions.remove()

Dropping Collections

>>> transactions.drop()
>>> db.collection_names()
[]

Dropping Databases

>>> c.drop_database('store_db')

MongoEngine - Object-Relational Mapper (ORM)

Use MongoEngine for a more Pythonic way to interact with MongoDB:

>>> from mongoengine import *
>>> connect('project1', host="mongodb://user:pass@mongodb.domain.com:27017/random_api?authSource=admin&authMechanism=SCRAM-SHA-1")
MongoClient(host=['mongodb.domain.com:27017'], document_class=dict, tz_aware=False, connect=True, read_preference=Primary(), authsource='admin', authmechanism='SCRAM-SHA-1')

Define a model:

>>> class Student(Document):
    name = StringField(required=True, max_length=200)
    city = StringField(required=True, max_length=200)
    can_code = BooleanField(required=True)

Create a student record:

>>> doc_1 = Student(name='Josh', city='Cape Town', can_code=True)
>>> doc_1.save()
<Student: Student object>
>>> doc_1.id
ObjectId('5cad27dea5f38276a40f43db')
>>> doc_1.name
u'Josh'
>>> doc_1.city
u'Cape Town'
>>> doc_1.can_code
True

Test validation (can_code is required):

>>> doc_2 = Student(name='Max', city='Cape Town')
>>> doc_2.save()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python2.7/site-packages/mongoengine/document.py", line 369, in save
    self.validate(clean=clean)
  File "/usr/local/lib/python2.7/site-packages/mongoengine/base/document.py", line 392, in validate
    raise ValidationError(message, errors=errors)
mongoengine.errors.ValidationError: ValidationError (Student:None) (Field is required: ['can_code'])

Update a user's city:

>>> doc_2 = Student(name='Max', city='Cape Town', can_code=False)
>>> doc_2.save()
<Student: Student object>
>>> doc_2.id
ObjectId('5cad2835a5f38276a40f43dc')
>>> doc_2.city
u'Cape Town'
>>> doc_2.city = 'Johannesburg'
>>> doc_2.save()
<Student: Student object>
>>> doc_2.city
'Johannesburg'

Datasets

• Movie Details Dataset

Resources

• Indexing and MongoDB Query Performance
• Introduction to MongoDB and Python
• MongoDB Charts Tutorial: Prerequisites and Import Data
• MongoDB Relationships
• PyMongo Tutorial
• PyMongo Official Documentation
• MongoDB Manual