dbsconnector 1.3

Python package for connecting and importing data from different DataBases

Database Connector Package

Overview

dbsconnector is a Python package designed to simplify data integration from various sources, including CSV, Excel, Google Sheets, and MongoDB. The package provides a unified interface to connect, load, and process data with minimal setup, making it easier for Developers and Data Scientists to work across multiple data formats.

Current Features:

• Connect to CSV files and load them into a Pandas DataFrame
• Handle Excel files with multiple sheets
• Fetch data from Google Sheets using an API key
• Interact with MongoDB collections

Future Features (Upcoming):

• Support for more databases (SQL, NoSQL)
• Cloud storage integration (AWS S3, Google Cloud, etc.)
• API-based data sources

Installation

To install the package, use pip:

pip install dbsconnector==1.3

How to use this package?

Connecting to csv

# import the module:
from dbsconnector.databases import CSV

# load csv file:
df = CSV().load_csv(filepath="filedir/filename.csv", delimiter=",")

# convert dataframe to csv file:
CSV().to_csv(data=df, filepath="filepath.csv")

Connecting to Excel

# import the module:
from dbsconnector.databases import Excel

# load the data:
df = Excel().load_excelsheet(filepath='filedir/filename.xlsx', sheet_name='sheet_name')

# convert dataframe to excel sheet:
Excel().to_excel(data=df, filepath='filedir/filename.xlsx', sheet_name='sheet_name')

Connecting to gsheet

# import the module:
from dbsconnector.databases import GSheet

# load the data:
df = GSheet().load_gsheet(gsheet_id='17r9f4BL7sjmdLBnt92OdQP3CHK5bdT3hozg6DUJXGqU',sheet_name='sample_sheet')

Connecting to MongoDB

# import the module:
from dbsconnector.databases import MongoDB

# load data from mongodb:
df = MongoDB(host_url="mongodb://localhost:27017").load_data(database="database_name", collection_name="collection_name")

# upload data to mongodb:
MongoDB(host_url="mongodb://localhost:27017").upload_data(database="database_name", collection_name="collection_name", data=df)

# upload any kind of objects (preprocessor object or ML model object) to mongodb:
MongoDB(host_url="mongodb://localhost:27017").upload_object(database="database_name", collection_name="collection_name", object_name="preprocessor_object", object_=preprocessor)

# loading object from mongodb:
pre_obj = MongoDB(host_url="mongodb://localhost:27017").load_object(database="database_name", collection_name="collection_name", object_name="preprocessor_object")

Contributions

• Contributions are welcome! Please open an issue or submit a pull request on GitHub for adding new features, fixing bugs, or improving documentation. Open-source collaboration is highly encouraged!

License

This project is licensed under the MIT License.

Contact

For any questions or suggestions, please contact [e-mail]

Connect

Connect with me on LinkedIn