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Topics

Python Basics

Introduction to PythonInstalling PythonPython SyntaxPython CommentsPython VariablesPython Data TypesPython Type ConversionPython OperatorsPython Input/OutputPython Indentation

Control Structures

Python If...ElsePython While LoopsPython For LoopsPython Break and ContinuePython Pass StatementPython Match-Case Statements

Python Data Structures

Python ListsPython TuplesPython SetsPython DictionariesPython ArraysPython List Comprehension

Python Functions

Defining Python FunctionsPython Function ArgumentsPython Function Return ValuesPython Lambda FunctionsPython Recursive FunctionsPython Global and Local Variables

Python OOP

Python Classes and ObjectsPython InheritancePython EncapsulationPython PolymorphismPython Method OverridingPython Abstract Classes

Python Modules and Packages

Creating Python ModulesImporting Python ModulesPython Built-in ModulesPython Package Management (pip)Creating Python Packages

Python File Handling

Python File Open/ClosePython File ReadPython File WritePython File ManagementPython Directory Handling

Python Exception Handling

Python Try...ExceptPython User-defined ExceptionsPython Raise KeywordPython Finally Clause

Python Advanced Topics

Python IteratorsPython GeneratorsPython DecoratorsPython Context ManagersPython Regular ExpressionsPython MultithreadingPython Multiprocessing

Python Standard Library

Python Math ModulePython Random ModulePython DateTime ModulePython JSON ModulePython OS ModulePython Sys Module

Python Data Manipulation

Python String ManipulationPython List OperationsPython Dictionary OperationsPython Set Operations

Python Testing

Python Unit TestingPython DoctestPython PytestPython Test-Driven Development

Python Performance

Python Code OptimizationPython ProfilingPython Memory ManagementPython Caching Techniques

Python and Databases

Python SQLitePython MySQL ConnectorPython PostgreSQLPython MongoDB

Python Web Scraping

Python Requests LibraryPython BeautifulSoupPython Scrapy Basics

Python and APIs

Python REST API BasicsPython API AuthenticationPython API Data Handling

Python MongoDB Integration

MongoDB is a popular NoSQL database that pairs excellently with Python. This guide will walk you through the basics of using MongoDB in your Python applications.

Getting Started

To begin working with MongoDB in Python, you'll need to install the PyMongo driver. Use pip to install it:

pip install pymongo

Connecting to MongoDB

Establishing a connection to MongoDB is straightforward:


from pymongo import MongoClient

# Connect to MongoDB
client = MongoClient('mongodb://localhost:27017/')

# Access a database
db = client['mydatabase']

# Access a collection
collection = db['mycollection']

CRUD Operations

Create (Insert)

To insert a document into a collection:


# Insert one document
result = collection.insert_one({"name": "John", "age": 30})
print(f"Inserted ID: {result.inserted_id}")

# Insert multiple documents
documents = [
    {"name": "Alice", "age": 25},
    {"name": "Bob", "age": 35}
]
result = collection.insert_many(documents)
print(f"Inserted IDs: {result.inserted_ids}")

Read (Query)

Retrieving documents from MongoDB is simple:


# Find one document
doc = collection.find_one({"name": "John"})
print(doc)

# Find multiple documents
for doc in collection.find({"age": {"$gt": 25}}):
    print(doc)

Update

Updating documents can be done using various methods:


# Update one document
result = collection.update_one(
    {"name": "John"},
    {"$set": {"age": 31}}
)
print(f"Modified count: {result.modified_count}")

# Update multiple documents
result = collection.update_many(
    {"age": {"$lt": 30}},
    {"$inc": {"age": 1}}
)
print(f"Modified count: {result.modified_count}")

Delete

Removing documents from a collection is straightforward:


# Delete one document
result = collection.delete_one({"name": "John"})
print(f"Deleted count: {result.deleted_count}")

# Delete multiple documents
result = collection.delete_many({"age": {"$gt": 35}})
print(f"Deleted count: {result.deleted_count}")

Best Practices

  • Use connection pooling for efficient database access.
  • Implement proper error handling and connection management.
  • Utilize indexes for improved query performance.
  • Be mindful of data consistency in a distributed environment.
  • Consider using Python Context Managers for managing database connections.

Advanced Topics

As you become more comfortable with Python MongoDB integration, explore these advanced concepts:

  • Aggregation pipelines for complex data processing
  • Transactions for maintaining data integrity
  • Geospatial queries for location-based applications
  • Text search capabilities for full-text search functionality

MongoDB's flexibility and Python's simplicity make for a powerful combination in building scalable applications. As you progress, you may want to explore how MongoDB integrates with other Python web frameworks or compare it with SQL databases using Python SQLite or Python MySQL Connector.

Conclusion

Python MongoDB integration offers a robust solution for handling unstructured data in your applications. By mastering these basics, you'll be well-equipped to build sophisticated, data-driven Python applications using MongoDB as your database backend.