pymongo-automation 0.0.2

A python package for connecting with database.

PyMongo Automation

Overview

The PyMongo Automation is a Python package designed to facilitate MongoDB database operations using the PyMongo library. This package provides a set of utility functions to interact with MongoDB collections, including inserting, updating, deleting, and querying records, as well as exporting collections to various formats.

Features

• Insert records into MongoDB collections.
• Bulk insert data from CSV or Excel files.
• Export collections to Pandas DataFrames or save them as CSV/Excel files.
• Run aggregate pipelines and retrieve results as DataFrames.
• Backup and restore collections to/from JSON files.
• Count documents in a collection based on a query.
• Check if a collection exists and list all collections.
• Fetch a random sample of documents from a collection.

Installation

To install the package, use pip:

pip install pymongo-automation

Usage

1. Initialization

from pymongo_automation.mongo_crud import mongo_operation

# Initialize the MongoDB operation helper
mongo = mongo_operation(client_url='your_mongo_db_url', database_name='your_database_name')

2. Inserting Records

Insert a Single Record

record = {"name": "Product A", "price": 25.99}
mongo.insert_record(record, collection_name="products")

Insert Multiple Records

records = [
    {"name": "Product B", "price": 19.99},
    {"name": "Product C", "price": 29.99}
]
mongo.insert_record(records, collection_name="products")

3. Bulk Insert from CSV or Excel

Insert from a CSV File

mongo.bulk_insert(datafile="path/to/file.csv", collection_name="products")

Insert from an Excel File

mongo.bulk_insert(datafile="path/to/file.xlsx", collection_name="products")

4. Export Collection to DataFrame or File

Export Collection to a Pandas DataFrame

df = mongo.export_collection_to_df(collection_name="products", query={})
print(df)

Export Collection to a CSV File

mongo.export_collection_to_file(collection_name="products", file_path="products.csv", query={}, file_format='csv')

5. Update Records

query = {"name": "Product A"}
update_values = {"price": 30.99}
mongo.update_records(collection_name="products", query=query, update_values=update_values)

6. Delete Records

query = {"name": "Product A"}
mongo.delete_records(collection_name="products", query=query)

7. Count Documents in a Collection

query = {"price": {"$gt": 20}}
count = mongo.count_documents(collection_name="products", query=query)
print(f"Number of documents: {count}")

8. Run an Aggregate Pipeline

Example 1: Grouping and Counting by Name

pipeline = [
    {'$group': {'_id': '$name', 'count': {'$sum': 1}}}
]

df = mongo.run_aggregate_pipeline('products', pipeline)
print(df)

Example 2: Summing Total Stock

pipeline = [
    {'$group': {'_id': None, 'total_stock': {'$sum': '$stock'}}}
]

df = mongo.run_aggregate_pipeline('products', pipeline)
print(f"Total Stock: {df['total_stock'][0]}")

9. Backup and Restore Collection

Backup Collection to a JSON File

mongo.backup_collection(collection_name="products", backup_file="products_backup.json")

Restore Collection from a JSON File

mongo.restore_collection(collection_name="products_restored", backup_file="products_backup.json")

10. Check Collection Existence and List Collections

Check if a Collection Exists

exists = mongo.collection_exists(collection_name="products")
print(f"Collection exists: {exists}")

List All Collections

collections = mongo.list_collections()
print(f"Collections: {collections}")

11. Fetch a Random Sample of Documents

df_sample = mongo.fetch_random_sample(collection_name="products", sample_size=5)
print(df_sample)

Contributing

Contributions are welcome! If you have any suggestions, bug reports, or pull requests, please feel free to reach out or open an issue on the GitHub repository.

License

This project is licensed under the MIT License - see the LICENSE file for details.