> ## Documentation Index > Fetch the complete documentation index at: https://docs.kinetica.com/llms.txt > Use this file to discover all available pages before exploring further. # Shortest Path with Python > A Shortest Path graph solver example with the Python API 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](/content/api/rest/solve_graph_rest) endpoint. For more information on Graphs & Solvers, see [Graphs & Solvers Concepts](/content/graph_solver/network_graph_solver). ## Prerequisites The prerequisites for running the shortest path solve graph example are listed below: * Graph server enabled * Python API * [Solve graph script](https://raw.githubusercontent.com/kineticadb/kinetica-docs/master/content/examples/python/graph/solve_graph_seattle_shortest_path.py) * [Seattle road network CSV file](https://raw.githubusercontent.com/kineticadb/kinetica-docs/master/content/examples/data/road_weights.csv) ### Python API Installation Depending on the target operating system, a Python virtual environment may need to be installed first: * [Python Virtual Environment](#python-virtual-environment) The native *Kinetica Python API* is accessible through the following means: * [PyPI](#pypi) * [Git](#git) #### Python Virtual Environment A Python virtual environment is necessary to install in an operating environment where Python is externally managed. 1. Install a Python virtual environment: ```bash theme={null} python3 -m venv .venv ``` 2. Activate the Python virtual environment: ```bash theme={null} source .venv/bin/activate ``` #### PyPI 1. Install the API: ```bash theme={null} pip3 install gpudb ``` 2. Test the installation: ```python theme={null} python3 -c "import gpudb;print('Import Successful')" ``` If *Import Successful* is displayed, the API has been installed as is ready for use. #### Git 1. In the desired directory, run the following, but be sure to replace `` with the name of the installed Kinetica version, e.g., `v7.2`: ```bash theme={null} git clone -b release/ --single-branch https://github.com/kineticadb/kinetica-api-python.git ``` 2. Change directory into the newly downloaded repository: ```bash theme={null} cd kinetica-api-python ``` 3. In the root directory of the unzipped repository, install the Kinetica API: ```bash theme={null} sudo pip3 install . ``` 4. Test the installation (*Python3* is necessary for running the API example): ```bash theme={null} python3 examples/example.py ``` ### Data File The example script references the road\_weights.csv data file, mentioned in the [Prerequisites](#prerequisites), in the current local directory, by default. This directory can specified as a parameter when running the example script. ## Script Detail This example is going to demonstrate solving (both individual and batch solve) for the shortest path between source points and several destination points located within a road network in Seattle. ### Constants Several constants are defined at the beginning of the script: * `SCHEMA` -- the name of the schema in which the tables supporting the graph creation and match operations will be created The schema is created during the table setup portion of the script because the schema must exist prior to creating the tables that will later support the graph creation and match operations. * `TABLE_SRN` -- the name of the table into which the Seattle road network dataset is loaded * `GRAPH_S` -- the Seattle road network graph * `TABLE_GRAPH_S_SPSOLVED` / `TABLE_GRAPH_S_SPSOLVED2` / `TABLE_GRAPH_S_SPSOLVED3` -- the Seattle road network graph shortest path solution tables ```python Constant Definitions theme={null} SCHEMA = "graph_s_seattle_shortest_path" TABLE_SRN = SCHEMA + ".seattle_road_network" GRAPH_S = SCHEMA + ".seattle_road_network_graph" TABLE_GRAPH_S_SPSOLVED = GRAPH_S + "_shortest_path_solved" TABLE_GRAPH_S_SPSOLVED2 = GRAPH_S + "_shortest_path_solved2" TABLE_GRAPH_S_SPSOLVED3 = GRAPH_S + "_shortest_path_solved3" ``` ### Graph Creation One graph is used for the shortest path solve graph example utilized in the script: `seattle_road_network_graph`, a graph based on the `road_weights` dataset (the CSV file mentioned in [Prerequisites](#prerequisites)). The `GRAPH_S` graph is created with the following characteristics: * It is [directed](/content/graph_solver/network_graph_solver#directed-graphs) because the roads in the graph have directionality (one-way and two-way roads) * It has no explicitly defined *nodes* because the example relies on implicit *nodes* attached to the defined *edges* * The *edges* are identified by `WKTLINE`, using WKT LINESTRINGs from the `WKTLINE` column of the `seattle_road_network` table. The road segments' directionality (`DIRECTION`) is derived from the `TwoWay` column of the `seattle_road_network` table. * The *weights* (`VALUESPECIFIED`) are represented using the time taken to travel the segment found in the `time` column of the `seattle_road_network` table. The *weights* are matched to the *edges* using the same `WKTLINE` column as *edges* (`EDGE_WKTLINE`) and the same `TwoWay` column as the *edge* direction (`EDGE_DIRECTION`). * It has no inherent *restrictions* for any of the *nodes* or *edges* in the graph * It will be replaced with this instance of the graph if a graph of the same name exists (`recreate`) ```python Create Seattle Road Network Graph theme={null} create_s_graph_response = kinetica.create_graph( graph_name = GRAPH_S, directed_graph = True, nodes = [], edges = [ TABLE_SRN + ".WKTLINE AS WKTLINE", TABLE_SRN + ".TwoWay AS DIRECTION" ], weights = [ TABLE_SRN + ".WKTLINE AS EDGE_WKTLINE", TABLE_SRN + ".TwoWay AS EDGE_DIRECTION", TABLE_SRN + ".time AS VALUESPECIFIED" ], restrictions = [], options = { "recreate": "true" } ) ``` ### Shortest Path #### Single Source to Single Destination The first example illustrates a simple shortest path solve from a single source node to a single destination node. First, the source node and destination node are defined. ```python Define Beginning & Ending Points theme={null} source_nodes = ["POINT(-122.1792501 47.2113606)"] destination_nodes = ["POINT(-122.2221 47.5707)"] ``` Next, the `seattle_road_network_graph` graph is solved with the solve results being exported to the response: ```python Solve Graph for Shortest Path theme={null} kinetica.solve_graph( graph_name = GRAPH_S, solver_type = "SHORTEST_PATH", source_nodes = source_nodes, destination_nodes = destination_nodes, solution_table = TABLE_GRAPH_S_SPSOLVED ) ``` The cost for the source node to visit the destination node is represented as time in minutes: ```text Solve Graph for Shortest Path Solution theme={null} +-------------------------------------------+---------------------+ | Destination Node | Cost (in minutes) | |-------------------------------------------+---------------------| | POINT (-122.22286015749 47.5709295272827) | 40.6023 | +-------------------------------------------+---------------------+ ``` The solution output to WMS: seattle_sp_solved.png #### Single Source to Many Destinations The second example illustrates a shortest path solve from a single source node to many destination nodes. First, the source node and destination nodes are defined. When one source node and many destination nodes are provided, the graph solver will calculate a shortest path solve for each destination node. ```python Define Beginning & Multiple Ending Points theme={null} source_nodes = ["POINT(-122.1792501 47.2113606)"] destination_nodes = [ "POINT(-122.222100 47.570700)", "POINT(-122.541017 47.809121)", "POINT(-122.520440 47.624725)", "POINT(-122.467915 47.427280)" ] ``` Next, the `seattle_road_network_graph` graph is solved with the solve results being exported to the response ```python Solve Graph for Shortest Paths with Multiple Destinations theme={null} kinetica.solve_graph( graph_name = GRAPH_S, solver_type = "SHORTEST_PATH", source_nodes = source_nodes, destination_nodes = destination_nodes, solution_table = TABLE_GRAPH_S_SPSOLVED2 ) ``` The cost for the source node to visit each of the destination nodes separately is represented as time in minutes: ```text Solve Graph for Shortest Paths with Multiple Destinations Solution theme={null} +--------------------------------------------+---------------------+ | Destination Node | Cost (in minutes) | |--------------------------------------------+---------------------| | POINT (-122.22286015749 47.5709295272827) | 40.6023 | | POINT (-122.471138834953 47.4291801452637) | 69.5329 | | POINT (-122.519598305225 47.6248499751091) | 79.7307 | | POINT (-122.541549503803 47.8095200657845) | 90.872 | +--------------------------------------------+---------------------+ ``` The solutions output to WMS: seattle_sp_solved2.png #### Many Sources to Many Destinations The third example illustrates a shortest path solve from a many source nodes to many destination nodes. First, the source node and destination nodes are defined. If many source node and many destination nodes are provided, the graph solver will pair the source and destination node by list index and calculate a shortest path solve for each pair. For this example, there are two starting points (`POINT(-122.1792501 47.2113606)` and `POINT(-122.375180125237 47.8122103214264)`) and paths will be calculated from the first source to two different destinations and from the second source to two other destinations. Calculations from multiple unique sources are faster and more efficient than calculations with one unique source, but results may differ slightly between multiple unique source calculations and single unique source calculations (less than \~1% variance). ```python Define Multiple Beginning & Multiple Ending Points theme={null} source_nodes = [ "POINT(-122.1792501 47.2113606)", "POINT(-122.1792501 47.2113606)", "POINT(-122.375180125237 47.8122103214264)", "POINT(-122.375180125237 47.8122103214264)" ] destination_nodes = [ "POINT(-122.222100 47.570700)", "POINT(-122.541017 47.809121)", "POINT(-122.520440 47.624725)", "POINT(-122.467915 47.427280)" ] ``` Next, the `seattle_road_network_graph` graph is solved with the solve results being exported to the response: ```python Solve Graph for Shortest Paths with Multiple Sources & Multiple Destinations theme={null} kinetica.solve_graph( graph_name = GRAPH_S, solver_type = "SHORTEST_PATH", source_nodes = source_nodes, destination_nodes = destination_nodes, solution_table = TABLE_GRAPH_S_SPSOLVED3 ) ``` The cost for the source node to visit each of the destination nodes separately is represented as time in minutes: ```text Solve Graph for Shortest Paths with Multiple Sources & Multiple Destinations Solution theme={null} +--------------------------------------------+--------------------------------------------+---------------------+ | Source Node | Destination Node | Cost (in minutes) | |--------------------------------------------+--------------------------------------------+---------------------| | POINT (-122.179250121117 47.2113606333733) | POINT (-122.221578061581 47.5709509849548) | 40.6791 | | POINT (-122.375180125237 47.8122103214264) | POINT (-122.520979642868 47.6248607039452) | 51.1339 | | POINT (-122.375180125237 47.8122103214264) | POINT (-122.471138834953 47.4291801452637) | 60.8013 | | POINT (-122.179250121117 47.2113606333733) | POINT (-122.541549503803 47.8095200657845) | 90.2997 | +--------------------------------------------+--------------------------------------------+---------------------+ ``` The solutions output to WMS: seattle_sp_solved3.png ## Download & Run Included below is a complete example containing all the above requests, the data files, and output. * [Shortest path solve graph script](https://raw.githubusercontent.com/kineticadb/kinetica-docs/master/content/examples/python/graph/solve_graph_seattle_shortest_path.py) * [Seattle road network data file](https://raw.githubusercontent.com/kineticadb/kinetica-docs/master/content/examples/data/road_weights.csv) * [Python output](https://raw.githubusercontent.com/kineticadb/kinetica-docs/master/content/examples/python/graph/solve_graph_seattle_shortest_path.out) To run the complete sample, ensure that: * the solve\_graph\_seattle\_shortest\_path.py script is in the current directory * the road\_weights.csv file is in the current directory or use the `data_dir` parameter to specify the local directory containing it Then, run the following: ```bash title="Run Example" theme={null} python solve_graph_seattle_shortest_path.py [--url ] --username --password [--data_dir ] ```