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The official Python library for the Log-hub API

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log-hub/log-hub-python

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Header Image

Log-hub Python API library

PyPI version

Table of Contents

Introduction

Thepyloghub package provides convinient access to various Log-hub API services for Supply Chain Visualization, Network Design Optimization, and Transport Optimization as well as access to the Log-hub platform data.

Prerequisites

  • Python 3.10 or later recommended
  • Pip (Python package manager)
  • Log-hub API key
  • Supply Chain APPS PRO subscription

Installation

Setting Up Python Environment

Recommended Python Version

Python 3.10 or later is recommended for optimal performance and compatibility.

Optional: Setting Up a Virtual Environment

A virtual environment allows you to manage Python packages for different projects separately.

  1. Create a Virtual Environment:

    • Windows:
      python -m venv loghub_env
    • macOS/Linux:
      python3 -m venv loghub_env

  2. Activate the Virtual Environment:

    • Windows:
      .\loghub_env\Scripts\activate
    • macOS/Linux:
      source loghub_env/bin/activate

    Deactivate withdeactivate when done.

Installingpyloghub Package

Within the environment, install the package using:

pip install pyloghub

Configuration

Obtaining an API Key

  1. Sign up or log in atLog-hub Account Integration.
  2. Obtain your API key.

Setting Up Your Environment

Securely store your API key for use in your Python scripts or as an environment variable.

api_key="YOUR API KEY"

Usage

Sample Code: Reverse Distance Calculation

This example demonstrates using the Reverse Distance Calculation feature:

  1. Import Functions:

    frompyloghub.distance_calculationimportreverse_distance_calculation,reverse_distance_calculation_sample_data

  2. Load Sample Data:

    sample_data=reverse_distance_calculation_sample_data()geocode_data_df=sample_data['geocode_data']parameters=sample_data['parameters']

  3. Perform Calculation:

    reverse_distance_result_df=reverse_distance_calculation(geocode_data_df,parameters,'YOUR_API_KEY')

    Replace'YOUR_API_KEY' with your actual Log-hub API key.

  4. View Results:

    print(reverse_distance_result_df)

Available Functionalities

Overview

pyloghub offers a suite of functionalities to enhance your supply chain management processes. Below is a quick guide to the available features and sample usage for each.

Geocoding

Forward Geocoding

Convert addresses to geographic coordinates.

frompyloghub.geocodingimportforward_geocoding,forward_geocoding_sample_datasample_data=forward_geocoding_sample_data()addresses_df=sample_data['addresses']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['scenarioName']="YOUR SCENARIO NAME"save_scenario['workspaceId']="YOUR WORKSPACE ID"geocoded_df=forward_geocoding(addresses_df,api_key,save_scenario,show_buttons=True)geocoded_df.head()

Reverse Geocoding

Convert geographic coordinates to addresses.

frompyloghub.geocodingimportreverse_geocoding,reverse_geocoding_sample_datasample_data=reverse_geocoding_sample_data()geocodes_df=sample_data['geocodes']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['scenarioName']="YOUR SCENARIO NAME"save_scenario['workspaceId']="YOUR WORKSPACE ID"reverse_geocoding_result_df=reverse_geocoding(geocodes_df,api_key,save_scenario,show_buttons=True)reverse_geocoding_result_df.head()

Distance Calculation

Forward Distance Calculation

Calculate distances based on address data.

frompyloghub.distance_calculationimportforward_distance_calculation,forward_distance_calculation_sample_datasample_data=forward_distance_calculation_sample_data()address_data_df=sample_data['address_data']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['scenarioName']="YOUR SCENARIO NAME"save_scenario['workspaceId']="YOUR WORKSPACE ID"forward_distance_calculation_result_df=forward_distance_calculation(address_data_df,parameters,api_key,save_scenario,show_buttons=True)forward_distance_calculation_result_df.head()

Reverse Distance Calculation

Calculate distances based on geocode data.

frompyloghub.distance_calculationimportreverse_distance_calculation,reverse_distance_calculation_sample_datasample_data=reverse_distance_calculation_sample_data()geocode_data_df=sample_data['geocode_data']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['scenarioName']="YOUR SCENARIO NAME"save_scenario['workspaceId']="YOUR WORKSPACE ID"reverse_distance_calculation_df=reverse_distance_calculation(geocode_data_df,parameters,api_key,save_scenario,show_buttons=True)reverse_distance_calculation_df.head()

Forward Distance Calculation With Extra Details

Calculate distances based on addresses and getting additional details about the road between start and end point.

frompyloghub.distance_calculation_with_extra_detailsimportforward_distance_calculation_with_extra_details,forward_distance_calculation_with_extra_details_sample_datafromIPython.displayimportdisplaysample_data=forward_distance_calculation_with_extra_details_sample_data()address_data_df=sample_data['address_data']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['scenarioName']="YOUR SCENARIO NAME"save_scenario['workspaceId']="YOUR WORKSPACE ID"forward_distance_calculation_result_df,additional_details_df=forward_distance_calculation_with_extra_details(address_data_df,parameters,api_key,save_scenario,show_buttons=True)display(forward_distance_calculation_result_df.head())display(additional_details_df.head())

Reverse Distance Calculation With Extra Details

Calculate distances based on coordinates and getting additional details about the road between start and end point.

frompyloghub.distance_calculation_with_extra_detailsimportreverse_distance_calculation_with_extra_details,reverse_distance_calculation_with_extra_details_sample_datafromIPython.displayimportdisplaysample_data=reverse_distance_calculation_with_extra_details_sample_data()address_data_df=sample_data['geocode_data']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['scenarioName']="YOUR SCENARIO NAME"save_scenario['workspaceId']="YOUR WORKSPACE ID"reverse_distance_calculation_result_df,additional_details_df=reverse_distance_calculation_with_extra_details(address_data_df,parameters,api_key,save_scenario,show_buttons=True)display(reverse_distance_calculation_result_df.head())display(additional_details_df.head())

Isochrone

Forward Isochrone

Determine the areas that can be rached within a certain amount of time or distance from the starting location with the given address.

frompyloghub.isochroneimportforward_isochrone,forward_isochrone_sample_datasample_data=forward_isochrone_sample_data()addresses_df=sample_data['addresses']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['scenarioName']="YOUR SCENARIO NAME"save_scenario['workspaceId']="YOUR WORKSPACE ID"reachable_areas_df=forward_isochrone(addresses_df,parameters,api_key,save_scenario,show_buttons=True)reachable_areas_df.head()

Reverse Isochrone

Determine the areas that can be rached within a certain amount of time or distance from the starting location with the given coordinates.

frompyloghub.isochroneimportreverse_isochrone,reverse_isochrone_sample_datasample_data=reverse_isochrone_sample_data()coordinates_df=sample_data['coordinates']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['scenarioName']="YOUR SCENARIO NAME"save_scenario['workspaceId']="YOUR WORKSPACE ID"reachable_areas_df=reverse_isochrone(addresses_df,parameters,api_key,save_scenario,show_buttons=True)reachable_areas_df.head()

Isochrone Plus

Forward Isochrone Plus

Determine the areas that can be rached within a certain amount of time or distance from the starting location with the given address, using the additional parameters for calculating the isochrones.

frompyloghub.isochrone_plusimportforward_isochrone_plus,forward_isochrone_plus_sample_datafromIPython.displayimportdisplaysample_data=forward_isochrone_plus_sample_data()addresses_df=sample_data['addresses']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['scenarioName']="YOUR SCENARIO NAME"save_scenario['workspaceId']="YOUR WORKSPACE ID"geocoded_data_df,reachable_areas_df=forward_isochrone_plus(addresses_df,parameters,api_key,save_scenario,show_buttons=True)display(geocoded_data_df.head())display(reachable_areas_df.head())

Reverse Isochrone Plus

Determine the areas that can be rached within a certain amount of time or distance from the starting location with the given coordinates, using the additional parameters for calculating the isochrones.

frompyloghub.isochrone_plusimportreverse_isochrone_plus,reverse_isochrone_plus_sample_datafromIPython.displayimportdisplaysample_data=reverse_isochrone_plus_sample_data()coordinates_df=sample_data['coordinates']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['scenarioName']="YOUR SCENARIO NAME"save_scenario['workspaceId']="YOUR WORKSPACE ID"geocoded_data_df,reachable_areas_df=reverse_isochrone_plus(coordinates_df,parameters,api_key,save_scenario,show_buttons=True)display(geocoded_data_df.head())display(reachable_areas_df.head())

Supply Chain Map

Creating a Supply Chain Map

In order to create a Supply Chain Map, a Workspace Id is required. Please, go to the platform and click on the "three dots" next to the workspace in which you want to save the map. Click on "Copy Workspace Id" and paste the corresponding workspace id instead of "YOUR WORKSPACE ID". If there are no workspaces, please create one using the GUI.

Forward Supply Chain Map Locations

Creating a map of locations based on their addresses.

frompyloghub.supply_chain_map_locationsimportforward_supply_chain_map_locations_sample_data,forward_supply_chain_map_locationssample_data=forward_supply_chain_map_locations_sample_data()address_data_df=sample_data['addresses']save_scenario=sample_data['saveScenarioParameters']save_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"locations_df=forward_supply_chain_map_locations(address_data_df,api_key,save_scenario,show_buttons=True)locations_df.head()

Reverse Supply Chain Map Locations

Creating a map of locations based on their geocodes.

frompyloghub.supply_chain_map_locationsimportreverse_supply_chain_map_locations_sample_data,reverse_supply_chain_map_locationssample_data=reverse_supply_chain_map_locations_sample_data()coordinates_df=sample_data['coordinates']save_scenario=sample_data['saveScenarioParameters']save_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"locations_df=reverse_supply_chain_map_locations(coordinates_df,api_key,save_scenario,show_buttons=True)locations_df.head()

Forward Supply Chain Map Relations

Creating a map of relations based on the addresses.

frompyloghub.supply_chain_map_relationsimportforward_supply_chain_map_relations_sample_data,forward_supply_chain_map_relationssample_data=forward_supply_chain_map_relations_sample_data()address_data_df=sample_data['addresses']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"relations_df=forward_supply_chain_map_relations(address_data_df,parameters,api_key,save_scenario,show_buttons=True)relations_df.head()

Reverse Supply Chain Map Relations

Creating a map of relations based on the coordinates.

frompyloghub.supply_chain_map_relationsimportreverse_supply_chain_map_relations_sample_data,reverse_supply_chain_map_relationssample_data=reverse_supply_chain_map_relations_sample_data()coordinates_df=sample_data['coordinates']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"relations_df=reverse_supply_chain_map_relations(coordinates_df,parameters,api_key,save_scenario,show_buttons=True)relations_df.head()

Forward Supply Chain Map Areas

Creating a map based on the given areas information.

frompyloghub.supply_chain_map_areasimportforward_supply_chain_map_areas_sample_data,forward_supply_chain_map_areassample_data=forward_supply_chain_map_areas_sample_data()areas_df=sample_data['areas']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"areas_output_df=forward_supply_chain_map_areas(areas_df,parameters,api_key,save_scenario,show_buttons=True)areas_output_df.head()

Reverse Supply Chain Map Polyline

Visualizing polylines on a map based on the given coordinates.

frompyloghub.supply_chain_map_polylineimportreverse_supply_chain_map_polyline_sample_data,reverse_supply_chain_map_polylinesample_data=reverse_supply_chain_map_polyline_sample_data()polyline_df=sample_data['polyline']save_scenario=sample_data['saveScenarioParameters']save_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"polyline_output_df=reverse_supply_chain_map_polyline(polyline_df,api_key,save_scenario,show_buttons=True)polyline_output_df.head()

Forward Supply Chain Map Routes

Creating a route map based on the addresses.

frompyloghub.supply_chain_map_routesimportforward_supply_chain_map_routes_sample_data,forward_supply_chain_map_routessample_data=forward_supply_chain_map_routes_sample_data()addresses_df=sample_data['addresses']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"routes_df=forward_supply_chain_map_routes(addresses_df,parameters,api_key,save_scenario,show_buttons=True)routes_df.head()

Reverse Supply Chain Map Routes

Creating a route map based on the coordinates.

frompyloghub.supply_chain_map_routesimportreverse_supply_chain_map_routes_sample_data,reverse_supply_chain_map_routessample_data=reverse_supply_chain_map_routes_sample_data()coordinates_df=sample_data['coordinates']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"routes_df=reverse_supply_chain_map_routes(coordinates_df,parameters,api_key,save_scenario,show_buttons=True)routes_df.head()

Forward Supply Chain Map Sea Routes

Creates a map of sea routes based on the given UN/LOCODEs.

frompyloghub.supply_chain_map_sea_routesimportforward_supply_chain_map_sea_routes_sample_data,forward_supply_chain_map_sea_routessample_data=forward_supply_chain_map_sea_routes_sample_data()sea_routes_df=sample_data['addresses']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"sea_routes_output_df=forward_supply_chain_map_sea_routes(sea_routes_df,parameters,api_key,save_scenario,show_buttons=True)sea_routes_output_df.head()

Reverse Supply Chain Map Sea Routes

Creating a sea routes map based on the given coordinates.

frompyloghub.supply_chain_map_sea_routesimportreverse_supply_chain_map_sea_routes_sample_data,reverse_supply_chain_map_sea_routessample_data=reverse_supply_chain_map_sea_routes_sample_data()sea_routes_df=sample_data['coordinates']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"sea_routes_output_df=reverse_supply_chain_map_sea_routes(sea_routes_df,parameters,api_key,save_scenario,show_buttons=True)sea_routes_output_df.head()

Center of Gravity

Forward Center of Gravity

Determine optimal facility locations based on addresses.

fromIPython.displayimportdisplayfrompyloghub.center_of_gravityimportforward_center_of_gravity,forward_center_of_gravity_sample_datasample_data=forward_center_of_gravity_sample_data()addresses_df=sample_data['addresses']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"assigned_addresses_df,centers_df=forward_center_of_gravity(addresses_df,parameters,api_key,save_scenario,show_buttons=True)display(assigned_addresses_df.head())display(centers_df.head())

Reverse Center of Gravity

Determine optimal facility locations based on coordinates.

fromIPython.displayimportdisplayfrompyloghub.center_of_gravityimportreverse_center_of_gravity,reverse_center_of_gravity_sample_datasample_data=reverse_center_of_gravity_sample_data()coordinates_df=sample_data['coordinates']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"assigned_geocodes_df,centers_df=reverse_center_of_gravity(coordinates_df,parameters,api_key,save_scenario,show_buttons=True)display(assigned_geocodes_df.head())display(centers_df.head())

Fixed Center of Gravity

Forward Fixed Center of Gravity

Calculating the optimal locations for new warehouses based on the address location of customers, their respective weights and existing warehouses.

fromIPython.displayimportdisplayfrompyloghub.fixed_center_of_gravityimportforward_fixed_center_of_gravity_sample_data,forward_fixed_center_of_gravitysample_data=forward_fixed_center_of_gravity_sample_data()customers_df=sample_data['customers']fixed_centers_df=sample_data['fixedCenters']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"assigned_geocodes_df,centers_df=forward_fixed_center_of_gravity(customers_df,fixed_centers_df,parameters,api_key,save_scenario,show_buttons=True)display(assigned_geocodes_df.head())display(centers_df.head())

Reverse Fixed Center of Gravity

Calculating the optimal location for new warehouses based on the geocode of customers, their respective weights and existing warehouses.

fromIPython.displayimportdisplayfrompyloghub.fixed_center_of_gravityimportreverse_fixed_center_of_gravity_sample_data,reverse_fixed_center_of_gravitysample_data=reverse_fixed_center_of_gravity_sample_data()customers_df=sample_data['customers']fixed_centers_df=sample_data['fixedCenters']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"assigned_geocodes_df,centers_df=reverse_fixed_center_of_gravity(customers_df,fixed_centers_df,parameters,api_key,save_scenario,show_buttons=True)display(assigned_geocodes_df.head())display(centers_df.head())

Center of Gravity Plus

Forward Center of Gravity Plus

Calculating the optimal location for new warehouses given the address location of customers and their respective weights, volumes and revenues.

fromIPython.displayimportdisplayfrompyloghub.center_of_gravity_plusimportforward_center_of_gravity_plus_sample_data,forward_center_of_gravity_plussample_data=forward_center_of_gravity_plus_sample_data()addresses_df=sample_data['addresses']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"assigned_addresses_df,centers_df=forward_center_of_gravity_plus(addresses_df,parameters,api_key,save_scenario,show_buttons=True)display(assigned_addresses_df.head())display(centers_df.head())

Reverse Center of Gravity Plus

Calculating the optimal location for new warehouses based on the coordinates of customers and their respective weights, volumes and revenues.

fromIPython.displayimportdisplayfrompyloghub.center_of_gravity_plusimportreverse_center_of_gravity_plus_sample_data,reverse_center_of_gravity_plussample_data=reverse_center_of_gravity_plus_sample_data()coordinates_df=sample_data['coordinates']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"assigned_geocodes_df,centers_df=reverse_center_of_gravity_plus(coordinates_df,parameters,api_key,save_scenario,show_buttons=True)display(assigned_geocodes_df.head())display(centers_df.head())

Advanced Center of Gravity

Forward Advanced Center of Gravity

Calculating the optimal location for new warehouses given the address location of customers, their respective weights and product groups they require, as well as sources of the product groups.

fromIPython.displayimportdisplayfrompyloghub.advanced_center_of_gravityimportforward_advanced_center_of_gravity_sample_data,forward_advanced_center_of_gravitysample_data=forward_advanced_center_of_gravity_sample_data()customers_df=sample_data['customers']sources_df=sample_data['sources']fixed_centers_df=sample_data['fixedCenters']product_groups_df=sample_data['productGroups']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"assigned_centers_df,inbound_df,outbound_df=forward_advanced_center_of_gravity(customers_df,sources_df,fixed_centers_df,product_groups_df,parameters,api_key,save_scenario,show_buttons=True)display(assigned_centers_df.head())display(inbound_df.head())display(outbound_df.head())

Reverse Advanced Center of Gravity

Calculating the optimal location for new warehouses given the coordinates of customers, their respective weights and product groups they require, as well as sources of the product groups.

fromIPython.displayimportdisplayfrompyloghub.advanced_center_of_gravityimportreverse_advanced_center_of_gravity_sample_data,reverse_advanced_center_of_gravitysample_data=reverse_advanced_center_of_gravity_sample_data()customers_df=sample_data['customers']sources_df=sample_data['sources']fixed_centers_df=sample_data['fixedCenters']product_groups_df=sample_data['productGroups']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"assigned_centers_df,inbound_df,outbound_df=reverse_advanced_center_of_gravity(customers_df,sources_df,fixed_centers_df,product_groups_df,parameters,api_key,save_scenario,show_buttons=True)display(assigned_centers_df.head())display(inbound_df.head())display(outbound_df.head())

Nearest Warehouses

Forward Nearest Warehouses

Calculating a given number of the nearest warehouses from a customer address.

fromIPython.displayimportdisplayfrompyloghub.nearest_warehousesimportforward_nearest_warehouses_sample_data,forward_nearest_warehousessample_data=forward_nearest_warehouses_sample_data()warehouses_df=sample_data['warehouses']customers_df=sample_data['customers']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"nearest_warehouses_df,unassigned_df=forward_nearest_warehouses(warehouses_df,customers_df,parameters,api_key,save_scenario,show_buttons=True)display(nearest_warehouses_df.head())display(unassigned_df.head())

Reverse Nearest Warehouses

Calculating a given number of the nearest warehouses from a customer coordinates.

fromIPython.displayimportdisplayfrompyloghub.nearest_warehousesimportreverse_nearest_warehouses_sample_data,reverse_nearest_warehousessample_data=reverse_nearest_warehouses_sample_data()warehouses_df=sample_data['warehouses']customers_df=sample_data['customers']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"nearest_warehouses_df,unassigned_df=reverse_nearest_warehouses(warehouses_df,customers_df,parameters,api_key,save_scenario,show_buttons=True)display(nearest_warehouses_df.head())display(unassigned_df.head())

Network Design Plus

Forward Network Design Plus

Finds the optimal number and locations of warehouses based on transport, handling and fixed warehouse costs.

fromIPython.displayimportdisplayfrompyloghub.network_design_plusimportforward_network_design_plus_sample_data,forward_network_design_plussample_data=forward_network_design_plus_sample_data()factories_df=sample_data['factories']warehouses_df=sample_data['warehouses']customers_df=sample_data['customers']product_segments_df=sample_data['productSegments']transport_costs_df=sample_data['transportCosts']transport_costs_rules_df=sample_data['transportCostsRules']stepwise_function_weight_df=sample_data['stepwiseCostFunctionWeight']stepwise_function_volume_df=sample_data['stepwiseCostFunctionVolume']distance_limits_df=sample_data['distanceLimits']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"open_warehouses,factory_assignement,customer_assignement,solution_kpis=forward_network_design_plus(factories_df,warehouses_df,customers_df,product_segments_df,transport_costs_df,transport_costs_rules_df,stepwise_function_weight_df,stepwise_function_volume_df,distance_limits_df,parameters,api_key,save_scenario,show_buttons=True)display(open_warehouses.head())display(factory_assignement.head())display(customer_assignement.head())display(solution_kpis.head())

Reverse Network Design Plus

Finding the optimal number and locations of warehouses based on transport, handling and fixed warehouse costs.

fromIPython.displayimportdisplayfrompyloghub.network_design_plusimportreverse_network_design_plus_sample_data,reverse_network_design_plussample_data=reverse_network_design_plus_sample_data()factories_df=sample_data['factories']warehouses_df=sample_data['warehouses']customers_df=sample_data['customers']product_segments_df=sample_data['productSegments']transport_costs_df=sample_data['transportCosts']transport_costs_rules_df=sample_data['transportCostsRules']stepwise_function_weight_df=sample_data['stepwiseCostFunctionWeight']stepwise_function_volume_df=sample_data['stepwiseCostFunctionVolume']distance_limits_df=sample_data['distanceLimits']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"open_warehouses,factory_assignement,customer_assignement,solution_kpis=reverse_network_design_plus(factories_df,warehouses_df,customers_df,product_segments_df,transport_costs_df,transport_costs_rules_df,stepwise_function_weight_df,stepwise_function_volume_df,distance_limits_df,parameters,api_key,save_scenario,show_buttons=True)display(open_warehouses.head())display(factory_assignement.head())display(customer_assignement.head())display(solution_kpis.head())

Location Planning

Forward Location Planning

Optimizing flows from the warehouses to the customers.

fromIPython.displayimportdisplayfrompyloghub.location_planningimportforward_location_planning_sample_data,forward_location_planningsample_data=forward_location_planning_sample_data()warehouses_df=sample_data['warehouses']customers_df=sample_data['customers']cost_adjustments_df=sample_data['costsAdjustments']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"open_warehouses,customer_assignement,solution_kpis=forward_location_planning(customers_df,warehouses_df,cost_adjustments_df,parameters,api_key,save_scenario,show_buttons=True)display(open_warehouses.head())display(customer_assignement.head())display(solution_kpis.head())

Reverse Location Planning

Optimizing flows from the warehouses to the customers.

fromIPython.displayimportdisplayfrompyloghub.location_planningimportreverse_location_planning_sample_data,reverse_location_planningsample_data=reverse_location_planning_sample_data()warehouses_df=sample_data['warehouses']customers_df=sample_data['customers']cost_adjustments_df=sample_data['costsAdjustments']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"open_warehouses,customer_assignement,solution_kpis=reverse_location_planning(customers_df,warehouses_df,cost_adjustments_df,parameters,api_key,save_scenario,show_buttons=True)display(open_warehouses.head())display(customer_assignement.head())display(solution_kpis.head())

Milkrun Optimization

Forward Milkrun Optimization

Calculate cost-optimal routes for inbound and outbound orders described with their addresses.

frompyloghub.milkrun_optimizationimportforward_milkrun_optimization_sample_data,forward_milkrun_optimizationfromIPython.displayimportdisplaysample_data=forward_milkrun_optimization_sample_data()depots_df=sample_data['depots']vehicle_types_df=sample_data['vehicleTypes']pickup_and_delivery_df=sample_data['pickupAndDelivery']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"route_overview_df,route_details_df,external_orders_df,input_map_routes_df,input_map_routes_geocodes_df=forward_milkrun_optimization(depots_df,vehicle_types_df,pickup_and_delivery_df,parameters,api_key,save_scenario,show_buttons=True)display(route_overview_df.head())display(route_details_df.head())display(external_orders_df.head())display(input_map_routes_df.head())display(input_map_routes_geocodes_df.head())

Reverse Milkrun Optimization

Calculate cost-optimal routes for inbound and outbound orders described with their coordinates.

frompyloghub.milkrun_optimizationimportreverse_milkrun_optimization_sample_data,reverse_milkrun_optimizationfromIPython.displayimportdisplaysample_data=reverse_milkrun_optimization_sample_data()depots_df=sample_data['depots']vehicle_types_df=sample_data['vehicleTypes']pickup_and_delivery_df=sample_data['pickupAndDelivery']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"route_overview_df,route_details_df,external_orders_df,input_map_routes_geocodes_df=reverse_milkrun_optimization(depots_df,vehicle_types_df,pickup_and_delivery_df,parameters,api_key,save_scenario,show_buttons=True)display(route_overview_df.head())display(route_details_df.head())display(external_orders_df.head())display(input_map_routes_geocodes_df.head())

Milkrun Optimization Plus

Forward Milkrun Optimization Plus

Calculating optimal routes for multiple days shipments by taking into consideration constraints such as customer time windows, detailed vehicle, and capacity profiles.

fromIPython.displayimportdisplayfrompyloghub.milkrun_optimization_plusimportforward_milkrun_optimization_plus_sample_data,forward_milkrun_optimization_plussample_data=forward_milkrun_optimization_plus_sample_data()depots_df=sample_data['depots']vehicles_df=sample_data['vehicles']jobs_df=sample_data['jobs']time_window_profiles_df=sample_data['timeWindowProfiles']breaks_df=sample_data['breaks']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"route_overview_df,route_details_df,external_orders_df=forward_milkrun_optimization_plus(depots_df,vehicles_df,jobs_df,time_window_profiles_df,breaks_df,parameters,api_key,save_scenario,show_buttons=True)display(route_overview_df.head())display(route_details_df.head())display(external_orders_df.head())

Reverse Milkrun Optimization Plus

Calculating optimal routes for multiple days shipments by taking into consideration constraints such as customer time windows, detailed vehicle, and capacity profiles.

fromIPython.displayimportdisplayfrompyloghub.milkrun_optimization_plusimportreverse_milkrun_optimization_plus_sample_data,reverse_milkrun_optimization_plussample_data=reverse_milkrun_optimization_plus_sample_data()depots_df=sample_data['depots']vehicles_df=sample_data['vehicles']jobs_df=sample_data['jobs']time_window_profiles_df=sample_data['timeWindowProfiles']breaks_df=sample_data['breaks']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"route_overview_df,route_details_df,external_orders_df=reverse_milkrun_optimization_plus(depots_df,vehicles_df,jobs_df,time_window_profiles_df,breaks_df,parameters,api_key,save_scenario,show_buttons=True)display(route_overview_df.head())display(route_details_df.head())display(external_orders_df.head())

Transport Optimization

Forward Transport Optimization

Assign shipments with corresponding addresses to available vehicles in an optimal way.

fromIPython.displayimportdisplayfrompyloghub.transport_optimizationimportforward_transport_optimization,forward_transport_optimization_sample_datasample_data=forward_transport_optimization_sample_data()locations_df=sample_data['locations']vehicle_types_df=sample_data['vehicleTypes']shipments_df=sample_data['shipments']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"route_overview_df,route_details_df,external_orders_df,input_map_routes_df,input_map_routes_geocodes_df=forward_transport_optimization(locations_df,vehicle_types_df,shipments_df,parameters,api_key,save_scenario,show_buttons=True)display(route_overview_df.head())display(route_details_df.head())display(external_orders_df.head())display(input_map_routes_df.head())display(input_map_routes_geocodes_df.head())

Reverse Transport Optimization

Assign shipments with corresponding coordinates to available vehicles in an optimal way.

fromIPython.displayimportdisplayfrompyloghub.transport_optimizationimportreverse_transport_optimization,reverse_transport_optimization_sample_datasample_data=reverse_transport_optimization_sample_data()locations_df=sample_data['locations']vehicle_types_df=sample_data['vehicleTypes']shipments_df=sample_data['shipments']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"route_overview_df,route_details_df,external_orders_df,input_map_routes_geocodes_df=reverse_transport_optimization(locations_df,vehicle_types_df,shipments_df,parameters,api_key,save_scenario,show_buttons=True)display(route_overview_df.head())display(route_details_df.head())display(external_orders_df.head())display(input_map_routes_geocodes_df.head())

Transport Optimization Plus

Forward Transport Optimization Plus

Calculating optimal routes for multiple days shipments by taking into consideration constraints such as customer time windows, detailed vehicle, and capacity profiles.

fromIPython.displayimportdisplayfrompyloghub.transport_optimization_plusimportforward_transport_optimization_plus,forward_transport_optimization_plus_sample_datasample_data=forward_transport_optimization_plus_sample_data()vehicles_df=sample_data['vehicles']shipments_df=sample_data['shipments']timeWindowProfiles_df=sample_data['timeWindowProfiles']breaks_df=sample_data['breaks']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"route_overview_df,route_details_df,external_orders_df=forward_transport_optimization_plus(vehicles_df,shipments_df,timeWindowProfiles_df,breaks_df,parameters,api_key,save_scenario,show_buttons=True)display(route_overview_df.head())display(route_details_df.head())display(external_orders_df.head())

Reverse Transport Optimization Plus

Calculating optimal routes for multiple days shipments by taking into consideration constraints such as customer time windows, detailed vehicle, and capacity profiles.

fromIPython.displayimportdisplayfrompyloghub.transport_optimization_plusimportreverse_transport_optimization_plus,reverse_transport_optimization_plus_sample_datasample_data=reverse_transport_optimization_plus_sample_data()vehicles_df=sample_data['vehicles']shipments_df=sample_data['shipments']timeWindowProfiles_df=sample_data['timeWindowProfiles']breaks_df=sample_data['breaks']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"route_overview_df,route_details_df,external_orders_df=reverse_transport_optimization_plus(vehicles_df,shipments_df,timeWindowProfiles_df,breaks_df,parameters,api_key,save_scenario,show_buttons=True)display(route_overview_df.head())display(route_details_df.head())display(external_orders_df.head())

Shipment Analyzer

Forward Shipment Analyzer

Analyzing and optimizing shipment costs and operations.

fromIPython.displayimportdisplayfrompyloghub.shipment_analyzerimportforward_shipment_analyzer,forward_shipment_analyzer_sample_datasample_data=forward_shipment_analyzer_sample_data()shipments_df=sample_data['shipments']transport_costs_adjustments_df=sample_data['transportCostAdjustments']consolidation_df=sample_data['consolidation']surcharges_df=sample_data['surcharges']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"shipments_analysis_df,transports_analysis_df=forward_shipment_analyzer(shipments_df,transport_costs_adjustments_df,consolidation_df,surcharges_df,parameters,api_key,save_scenario,show_buttons=True)display(shipments_analysis_df.head())display(transports_analysis_df.head())

Reverse Shipment Analyzer

Analyzing and optimizing shipment costs and operations.

fromIPython.displayimportdisplayfrompyloghub.shipment_analyzerimportreverse_shipment_analyzer,reverse_shipment_analyzer_sample_datasample_data=reverse_shipment_analyzer_sample_data()shipments_df=sample_data['shipments']transport_costs_adjustments_df=sample_data['transportCostAdjustments']consolidation_df=sample_data['consolidation']surcharges_df=sample_data['surcharges']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"shipments_analysis_df,transports_analysis_df=reverse_shipment_analyzer(shipments_df,transport_costs_adjustments_df,consolidation_df,surcharges_df,parameters,api_key,save_scenario,show_buttons=True)display(shipments_analysis_df.head())display(transports_analysis_df.head())

Freight Matrix Plus

Forward Freight Matrix Plus

Evaluate shipments with costs based on your own freight cost matrices. The following matrix types are supported:

  • Absolute weight distance matrix
  • Relative weight distance matrix
  • Absolute weight zone matrix
  • Relative weight zone matrix
  • Zone zone matrix
  • Absolute weight zone distance matrix
  • Relative weight zone distance matrix
frompyloghub.freight_matrix_plusimportforward_freight_matrix_plus,forward_freight_matrix_plus_sample_datasample_data=forward_freight_matrix_plus_sample_data()shipments_df=sample_data['shipments']matrix_id="YOUR FREIGHT MATRIX ID"save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"evaluated_shipments_df=forward_freight_matrix_plus(shipments_df,matrix_id,api_key,save_scenario,show_buttons=True)evaluated_shipments_df.head()

Reverse Freight Matrix

Evaluating shipments with costs based on your own freight cost matrices. Supported matrix types are the same as in the forward version.

sample_data = reverse_freight_matrix_plus_sample_data()shipments_df = sample_data['shipments']matrix_id = "YOUR FREIGHT MATRIX ID"save_scenario = sample_data['saveScenarioParameters']save_scenario['saveScenario'] = Truesave_scenario['workspaceId'] = "YOUR WORKSPACE ID"save_scenario['scenarioName'] = "YOUR SCENARIO NAME"evaluated_shipments_df = reverse_freight_matrix_plus(shipments_df, matrix_id, api_key, save_scenario, show_buttons=True)evaluated_shipments_df.head()

You can create a freight matrix on the Log-hub Platform. Therefore, please create a workspace and click within the workspace on "Create Freight Matrix". There you can provide the matrix a name, select the matrix type and define all other parameters.To get the matrix id, please click on the "gear" icon. There you can copy & paste the matrix id that is needed in your API request.

CO2 Emissions

Forward CO2 Emissions Road

Calculating a CO2 footprint based on your shipments transported by road.

fromIPython.displayimportdisplayfrompyloghub.freight_shipment_emissions_roadimportforward_freight_shipment_emissions_road_sample_data,forward_freight_shipment_emissions_roadsample_data=forward_freight_shipment_emissions_road_sample_data()addresses_df=sample_data['addresses']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"freight_emissions_df,not_evaluated_df=forward_freight_shipment_emissions_road(addresses_df,parameters,api_key,save_scenario,show_buttons=True)display(freight_emissions_df.head())display(not_evaluated_df.head())

Reverse CO2 Emissions Road

Calculating a CO2 footprint based on your shipments transported by road.

fromIPython.displayimportdisplayfrompyloghub.freight_shipment_emissions_roadimportreverse_freight_shipment_emissions_road_sample_data,reverse_freight_shipment_emissions_roadsample_data=reverse_freight_shipment_emissions_road_sample_data()coordinates_df=sample_data['coordinates']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"freight_emissions_df,not_evaluated_df=reverse_freight_shipment_emissions_road(coordinates_df,parameters,api_key,save_scenario,show_buttons=True)display(freight_emissions_df.head())display(not_evaluated_df.head())

Forward CO2 Emissions Rail

Calculating a CO2 footprint based on your shipments transported by train.

frompyloghub.freight_shipment_emissions_railimportforward_freight_shipment_emissions_rail_sample_data,forward_freight_shipment_emissions_railsample_data=forward_freight_shipment_emissions_rail_sample_data()addresses_df=sample_data['addresses']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"freight_emissions_df=forward_freight_shipment_emissions_rail(addresses_df,parameters,api_key,save_scenario,show_buttons=True)freight_emissions_df.head()

Reverse CO2 Emissions Rail

Calculating a CO2 footprint based on your shipments transported by train.

frompyloghub.freight_shipment_emissions_railimportreverse_freight_shipment_emissions_rail_sample_data,reverse_freight_shipment_emissions_railsample_data=reverse_freight_shipment_emissions_rail_sample_data()coordinates_df=sample_data['coordinates']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"freight_emissions_df=reverse_freight_shipment_emissions_rail(coordinates_df,parameters,api_key,save_scenario,show_buttons=True)freight_emissions_df.head()

CO2 Emissions Air

Calculating a CO2 footprint based on your shipments transported by air.

frompyloghub.freight_shipment_emissions_airimportforward_freight_shipment_emissions_air_sample_data,forward_freight_shipment_emissions_airsample_data=forward_freight_shipment_emissions_air_sample_data()iata_codes_df=sample_data['iataCodes']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"freight_emissions_df=forward_freight_shipment_emissions_air(iata_codes_df,parameters,api_key,save_scenario,show_buttons=True)freight_emissions_df.head()

CO2 Emissions Sea

Calculating a CO2 footprint based on your shipments transported by sea.

frompyloghub.freight_shipment_emissions_seaimportforward_freight_shipment_emissions_sea_sample_data,forward_freight_shipment_emissions_seasample_data=forward_freight_shipment_emissions_sea_sample_data()un_locodes_df=sample_data['unLocodes']parameters=sample_data['parameters']save_scenario=sample_data['saveScenarioParameters']save_scenario['saveScenario']=Truesave_scenario['workspaceId']="YOUR WORKSPACE ID"save_scenario['scenarioName']="YOUR SCENARIO NAME"freight_emissions_df=forward_freight_shipment_emissions_sea(un_locodes_df,parameters,api_key,save_scenario,show_buttons=True)freight_emissions_df.head()

Demand Forecasting

Demand Forecasting

Predicting future demand for your products based on the past demand data.

frompyloghub.demand_forecastingimportdemand_forecasting_sample_data,demand_forecastingsample_data=demand_forecasting_sample_data()past_demand_data_df=sample_data['pastDemandData']future_impact_factors_df=sample_data['futureImpactFactors']sku_parameters_df=sample_data['skuParameters']prediction_df=demand_forecasting(past_demand_data_df,future_impact_factors_df,sku_parameters_df,api_key)prediction_df.head()

Working with Log-hub Tables

Working with Log-hub Tables

To read or update a table, you need a table link from a table in the Log-hub platform. Therefore, please navigate in a workspace with an existing dataset and go to the table you would like to read or update. Click on the "three dots" and click on "Table Link". Then copy the corresponding table link. If no table exists create a dataset and a new table via the GUI.

Reading Data from a Table

The read_table function simplifies the process of fetching and formatting data from a specific table on the Log-hub platform into a pandas DataFrame. This function ensures that the data types in the DataFrame match those in the Log-hub table, leveraging metadata from the table for precise formatting.

frompyloghub.datasetimportread_tableimportpandasaspd# Replace with actual table link, email, and API keytable_link="https://production.supply-chain-apps.log-hub.com/api/v1/datasets/.../tables/.../rows"email="your_email@example.com"api_key="your_api_key"# Read data from tabledataframe=read_table(table_link,email,api_key)# Check the DataFrameifdataframeisnotNone:print(dataframe.head())else:print("Failed to read data from the table.")

Updating Data in a Table

The update_table function is designed for uploading data from a local pandas DataFrame to a specific table on the Log-hub platform. It requires the table link, the DataFrame, metadata describing the table structure (optional). If no metadata are provided, the datatypes are automatically extracted from the pandas dataframe.

frompyloghub.datasetimportupdate_tableimportpandasaspd# Replace with actual credentials and linktable_link="https://production.supply-chain-apps.log-hub.com/api/v1/datasets/.../tables/.../rows"email="your_email@example.com"api_key="your_api_key"# Example DataFramedataframe=pd.DataFrame({'ColumnA': ['Value1','Value2'],'ColumnB': [123,456]})# Metadata for the tablemetadata= {'table_name':'YourTableName',# Optional, defaults to 'Table 01' if not provided'columns': [        {'name':'ColumnA','propertyName':'ColumnA','dataType':'string','format':'General'        },        {'name':'ColumnB','propertyName':'ColumnB','dataType':'number','format':'General'        }# More columns as needed    ]}# Update tableresponse=update_table(table_link,dataframe,metadata,email,api_key)ifresponseisNone:print("Table updated successfully.")else:print("Failed to update the table.")

Contact Us

For any inquiries, assistance, or additional information, feel free to reach out to us at our office or via email.

Address:
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Email:
support@log-hub.com

Alternatively, for more information and resources, visitLog-hub API Documentation.

License

Distributed under theMIT License.

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