Using PyMongo with Python

Introduction

PyMongo is the official Python driver for MongoDB, allowing developers to interact with MongoDB databases using Python. It provides an easy-to-use API for performing CRUD operations (Create, Read, Update, Delete) and more.

Installation

To install PyMongo, you can use pip, the Python package manager. Open your terminal and run the following command:

pip install pymongo

Basic Operations

This section covers the fundamental operations you can perform using PyMongo.

1. Connecting to MongoDB

To connect to a MongoDB server, you need the connection string and the MongoClient class.

from pymongo import MongoClient

client = MongoClient("mongodb://localhost:27017/")
db = client["mydatabase"]

2. Creating a Collection

You can create a collection (similar to a table in SQL) by accessing it through the database object.

collection = db["mycollection"]

3. Inserting Documents

You can insert documents into a collection using the insert_one or insert_many method:

document = {"name": "Alice", "age": 25}
result = collection.insert_one(document)
print("Inserted document ID:", result.inserted_id)

4. Querying Documents

To retrieve documents, you can use the find method:

for doc in collection.find({"age": 25}):
    print(doc)

5. Updating Documents

To update existing documents, use the update_one or update_many methods:

collection.update_one({"name": "Alice"}, {"$set": {"age": 26}})

6. Deleting Documents

You can delete documents with the delete_one or delete_many methods:

collection.delete_one({"name": "Alice"})

Advanced Usage

Beyond basic operations, PyMongo supports advanced features such as aggregation, indexing, and transactions. Here are some examples:

Aggregation

Aggregation allows you to process data and return computed results. Here’s a simple aggregation example:

pipeline = [
    {"$match": {"age": {"$gte": 20}}},
    {"$group": {"_id": "$age", "count": {"$sum": 1}}}
]
result = collection.aggregate(pipeline)
for doc in result:
    print(doc)

Indexing

Creating indexes can significantly improve query performance:

collection.create_index([("name", 1)])  # 1 for ascending order

Best Practices

To ensure efficient and effective use of PyMongo, consider the following best practices:

• Always close connections to MongoDB when done.
• Use indexes to optimize queries.
• Handle exceptions to avoid crashes during database operations.
• Regularly back up your data.

FAQ