databridge 0.2.1

Bridge to import shared datasets across diverse projects.

databridge

The databridge package streamlines the management of common datasets across diverse projects. It facilitates the setup of paths, file names, and Python commands to access centralized files efficiently.

Why Use databridge?

Managing common datasets across multiple projects often involves repetitive tasks such as saving files, configuring paths, and importing libraries. The databridge package aims to simplify this process by offering a centralized data bucket.

Instead of manually saving files and configuring paths for each project, databridge allows you to create a config.json file. This file guides Python on where to locate common datasets locally, reducing redundancy and enhancing efficiency.

Example 1 - Without databridge

# Import geopandas package
import geopandas as gpd

# Import data
texas_boundaries = gpd.read_file("myproject_data_folder/Texas_boundaries.shp")

This method is straightforward but can become repetitive when dealing with multiple projects.

Example 2 - with databrigde

# Import databridge package
from databridge.databridge import DataBridge

# Initiate databridge class
db = DataBridge()

# Load dataset
texas_boundaries = db.load_dataset("local", "texas_boundaries")

Although Example 2 has one extra line of code compared to Example 1, it significantly reduces redundancy. When dealing with multiple projects, you no longer need to save the file multiple times or manually set different paths.

Explore the power of databridge to enhance your workflow and centralize your datasets effortlessly.