/7.1/guide-tags/python/Recent content in Python on Kinetica DocsHugo -- gohugo.ioen/7.1/guides/solve_graph_seattle_backhaul/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/solve_graph_seattle_backhaul/The following is a complete example, using the Python API, of solving a graph created with Seattle road network data for a backhaul routing problem via the /solve/graph endpoint. For more information on Network Graphs & Solvers, see Network Graphs & Solvers Concepts. Prerequisites Python API Installation Data File Script Detail Constants Graph Creation Backhaul Routing Download & Run Prerequisites The prerequisites for running the backhaul routing solve graph example are listed below:/7.1/guides/jupyterlab/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/jupyterlab/Introduction Prerequisites Docker Install Setting Permissions Entering the License Key Pulling the Image Managing the Container Starting Using Stopping Exploring the Environment JupyterLab Contents Mounted Volumes Example Notebooks SVD Recommender and Visualization Example KJIO Utility Library Conclusion References Introduction JupyterLab is an integrated environment that can streamline the development of Python code and Machine Learning (ML) models in Kinetica. Jupyter notebooks integrate code execution, debugging, documentation, and visualization in a single document for consumption by multiple audiences./7.1/guides/match_graph_seattle_markov/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/match_graph_seattle_markov/The following is a complete example, using the Python API, of matching GPS sample data to road network data via the /match/graph endpoint. For more information on Network Graphs & Solvers, see Network Graphs & Solvers Concepts. Prerequisites Python API Installation Data Files Script Detail Constants Graph Creation Matching the Graph without Fold-over Filtering Matching the Graph with Fold-over Filtering Download & Run Prerequisites The prerequisites for running the match graph example are listed below:/7.1/guides/solve_graph_nyctaxi_multi_route/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/solve_graph_nyctaxi_multi_route/The following is a complete example, using the Python API, of solving a graph created with NYC Taxi data for a multiple routing problem via the /solve/graph endpoint. For more information on Network Graphs & Solvers, see Network Graphs & Solvers Concepts. Prerequisites Python API Installation Data File Script Detail Constants Graph Creation Multiple Routing Download & Run Prerequisites The prerequisites for running the multiple routing solve graph example are listed below:/7.1/guides/solve_graph_seattle_multi_route/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/solve_graph_seattle_multi_route/The following is a complete example, using the Python API, of solving a graph created with Seattle road network data for a multiple routing problem via the /solve/graph endpoint. For more information on Network Graphs & Solvers, see Network Graphs & Solvers Concepts. Prerequisites Python API Installation Data File Script Detail Constants Graph Creation Multiple Routing Download & Run Prerequisites The prerequisites for running the multiple routing solve graph example are listed below:/7.1/guides/match_graph_dc_multi_supply_demand/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/match_graph_dc_multi_supply_demand/The following is a complete example, using the Python API, of matching stores (demands) to depots/trucks (suppliers) via the /match/graph endpoint using an underlying graph created from a modified OpenStreetMap (OSM) dataset. For more information on Network Graphs & Solvers, see Network Graphs & Solvers Concepts. Prerequisites Python API Installation Data File Script Detail Constants Table Setup Graph Creation Multiple Supply Demand with Priority Solution Analysis Multiple Supply Demand with Priority and Max Trip Cost Download & Run Prerequisites The prerequisites for running the match graph example are listed below:/7.1/guides/solve_graph_nyctaxi_page_rank/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/solve_graph_nyctaxi_page_rank/The following is a complete example, using the Python API, of solving a graph created with NYC Taxi data for a page rank problem via the /solve/graph endpoint. For more information on Network Graphs & Solvers, see Network Graphs & Solvers Concepts. Prerequisites Python API Installation Data File Script Detail Constants Graph Creation Page Rank Download & Run Prerequisites The prerequisites for running the page rank solve graph example are listed below:/7.1/guides/python_guide/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/python_guide/The following guide provides step-by-step instructions to get started writing Python applications using Kinetica. This guide demonstrates only a small set of the available API. A detailed description of the complete interface is available under Python API Reference. Prerequisites Python API Installation PyPI Git Data File Connecting to the Database Creating a Schema Creating a Type Creating a Table Inserting Data Key/Value Record In-Line Record CSV Retrieving Data Updating Records Deleting Records Alter Table Indexes Dictionary Encoding Filters Aggregates Joins Projections Union, Intersect, & Except Download & Run Prerequisites The prerequisites for running the match graph example are listed below:/7.1/guides/udf_python_guide/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/udf_python_guide/The following guide provides step-by-step instructions to get started writing and running UDFs in Python. This example is a simple distributed UDF that copies data from one table to another using a CSV configuration file to determine on which processing node(s) data will be copied. Standard (non-replicated) tables have their data distributed across all processing nodes, while replicated tables have all of their data on every processing node. In this example, we'll use a standard table and copy only the portions of its data that reside on the nodes named in the CSV file./7.1/guides/solve_graph_seattle_shortest_path/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/solve_graph_seattle_shortest_path/The following is a complete example, using the Python API, of solving a graph created with Seattle road network data for a shortest path problem via the /solve/graph endpoint. For more information on Network Graphs & Solvers, see Network Graphs & Solvers Concepts. Prerequisites Python API Installation PyPI Git Data File Script Detail Constants Graph Creation Shortest Path Single Source to Single Destination Single Source to Many Destinations Many Sources to Many Destinations Download & Run Prerequisites The prerequisites for running the shortest path solve graph example are listed below:/7.1/guides/solve_graph_dc_shortest_path_turn/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/solve_graph_dc_shortest_path_turn/The following is a complete example, using the Python API, of solving a graph created with a Washington, D.C. HERE dataset for a shortest path problem with turn penalties via the /solve/graph endpoint. For more information on Network Graphs & Solvers, see Network Graphs & Solvers Concepts. For more information on turn penalties and restrictions, see Using Turn-based Weights & Restrictions. Prerequisites Python API Installation Data File Script Detail Constants Table Setup Graph Creation Shortest Path Download & Run Prerequisites The prerequisites for running this solve graph example are listed below:/7.1/guides/table_monitor/Mon, 01 Jan 0001 00:00:00 +0000/7.1/guides/table_monitor/The following is an excerpted tutorial, extracted from a complete example using the Python Table Monitor API, demonstrating the basic use of the three table monitor types: table insert monitor - watches a table for newly inserted records and can make use of those records elsewhere table update monitor - watches a table for updates and reports the number of records updated in each update request issued table delete monitor - watches a table for deletes and reports the number of records deleted in each delete request issued The table monitors will be managed via instances of the GPUdbTableMonitor.