How to Use Python with MongoDB
Connecting Python and MongoDB Atlas
There are three ways to connect MongoDB to Python:
- PyMongo - The native driver for connecting MongoDB and Python. PyMongo has all the libraries to perform database operations from Python code. Since PyMongo is a low-level driver, it is fast and intuitive and provides more control.
- MongoEngine - MongoEngine is a Document Object Mapper. We can define a schema that maps application objects and document data.
- Djongo - We use Djongo for python web applications using the Django framework. It converts existing SQL queries to mongodb query documents.
Python works well with different databases. The choice depends on your project requirements. MongoDB, because of its flexible schema and how it maps closely to Python native objects, is a great choice for Python applications. This makes Python and MongoDB, a great choice for doing web development work.
For more information, read NoSQL vs. SQL Databases. There are some native python databases as well but they aren't popular and have very limited capabilities.
MongoDB stores data in flexible and schema-less JSON-like documents. Python has rich libraries that directly process JSON and BSON data formats. Python integrates well with MongoDB through drivers like PyMongo, MongoEngine etc.
This makes MongoDB good for Python by eliminating rigidity in the database schema.
Python stores data in MongoDB through libraries like PyMongo and MongoEngine. For web applications using the Django framework, we can use Djongo.
- PyMongo: PyMongo is the native python driver for MongoDB database. Since it’s a low-level driver, it’s faster and also a preferred way of connecting Python and MongoDB.
- MongoEngine: With MongoEngine, we can create a schema (yes, for a schema-less database). MongoEngine follows the ODM approach to map application classes and database documents.
- Djongo: Djongo is a SQL transpiler. You can migrate existing SQL projects to MongoDB without many changes to the code.
We can connect MongoDB with Python using PyMongo. Pymongo is the native Python driver for MongoDB. It has a syntax similar to MongoShell, so that we can easily correlate and use the right method. For example, insertMany() on MongoShell corresponds to insert_many() in PyMongo. We can also connect Python and MongoDB using MongoEngine and Djongo. But, the preferred approach is to use PyMongo because it’s a low-level driver that is faster and provides more control. To learn more about PyMongo, check our documentation on PyMongo.
The most efficient and easy method to connect to MongoDB in Python is to use PyMongo. PyMongo is the native Python driver for MongoDB. To connect, we use the command pymongo.MongoClient() with the connection_string as argument. Then, we can use the find() method to get the required documents. Example:
~~~~
import pymongo
# connect to mongodb from python using pymongo
client = pymongo.MongoClient(CONNECTION_STRING)
# open the database
dbname = client['user_shopping_list']
# get the collection
collection_name = dbname["item_details"]
# get the data from the collection
item_details = collection_name.find()import pymongo
# connect to mongodb from python using pymongo
client = pymongo.MongoClient(CONNECTION_STRING)
# open the database
dbname = client['user_shopping_list']
# get the collection
collection_name = dbname["item_details"]
# get the data from the collection
item_details = collection_name.find()
~~~~
To insert data, connect MongoDB and Python using PyMongo. PyMongo is the native Python driver for MongoDB. Once we connect, we can use PyMongo’s methods like `insert_one()` and `insert_many()`. Example:
~~~~
# Get the mongoclient
client = pymongo.MongoClient(CONNECTION_STRING)
# Get/Create database
dbname = client['user_shopping_list']
# Get/create collection
collection_name = dbname["item_details"]
# Create the document
item_1 = {"item_name": "Bread",...,"category" : "food",
"quantity" : 2}
# Insert one row
collection_name.insert_one(item_1)# Get the mongoclient
client = pymongo.MongoClient(CONNECTION_STRING)
# Get/Create database
dbname = client['user_shopping_list']
# Get/create collection
collection_name = dbname["item_details"]
# Create the document
item_1 = {"item_name": "Bread",...,"category" : "food",
"quantity" : 2}
# Insert one row
collection_name.insert_one(item_1)
~~~~
We use PyMongo driver to create a MongoDB database using Python code. Example:
~~~~
import pymongo
# Get the mongoclient
client = pymongo.MongoClient(CONNECTION_STRING)
# Get/Create database
dbname = client['user_shopping_list']import pymongo
# Get the mongoclient
client = pymongo.MongoClient(CONNECTION_STRING)
# Get/Create database
dbname = client['user_shopping_list']
~~~~
SQL databases are also called relational databases and NoSQL (“non SQL” or “not only SQL”) databases are also called non-relational databases. Relational databases are termed relational because it is based on the "relational data model" in mathematics.
SQL databases store data in the form of tables with fixed rows and columns. NoSQL databases comes in many types, for example:
- Document type: JSON documents
- Key-value: Key-value pairs
- Wide-column: Wide-column data store has tables with rows and dynamic columns
Example of SQL based databases are MySQL, Microsoft SQL Server, PostgreSQL, and SQLite. NoSQL database examples are: MongoDB, CouchDB, Redis, DynamoDB etc.
For More detailed difference, please refer SQL vs NoSQL.