Graph To DataFrame

The graphToDataFrame sub-module provides results from various built-in endpoints in a Pandas DataFrame. For this module to work, you will have to install the Pandas module, by running pip install pandas. Take a look here for some demos displaying some of the functionality.

Getting Started

First, you will need to create a TigerGraphConnection:

import pyTigerGraph as tg 

conn = tg.TigerGraphConnection(host="https://20bd42e3162a40db9ca0a2f0a4352948.i.tgcloud.io", graphname="CrunchBasePre_2013", apiToken=token)

getVertexDataframe

getVertexDataframe(vertexType, select="", where="", limit="", sort="", timeout=0) Returns the verticies of a given vertex type that conform to the various arguments.

Arguments:

• vertexType: Type of vertex desired

• select: Comma separated list of vertex attributes to be retrieved or omitted. See https://docs.tigergraph.com/dev/restpp-api/built-in-endpoints#select

• where: Comma separated list of conditions that are all applied on each vertex' attributes. The conditions are in logical conjunction (i.e. they are "AND'ed" together). See https://docs.tigergraph.com/dev/restpp-api/built-in-endpoints#filter

• limit: Maximum number of vertex instances to be returned (after sorting). See https://docs.tigergraph.com/dev/restpp-api/built-in-endpoints#limit

• sort Comma separated list of attributes the results should be sorted by. See https://docs.tigergraph.com/dev/restpp-api/built-in-endpoints#sort Example:

df = conn.getVertexDataframe("company", limit=100)

getVertexDataframeByID

getVertexDataframeByID(vertexType, vertexIds) Retrieves vertices of the given vertex type, identified by their ID.

Arguments:

• vertexType: Type of vertex desired

• vertexIds: A list of vertex IDs.

Example:

df = conn.getVertexDataframeByID("company", ["c:1", "c:2"])

upsertVertexDataframe

upsertVertexDataframe(df, vertexType, v_id=None, attributes=None) Upserts vertices from a Pandas data frame.

Arguments: - df: The data frame to upsert.

• vertexType: The type of vertex to upsert data to.

• v_id: The field name where the vertex primary id is given. If omitted the dataframe index will be used instead.

• attributes: A dictionary in the form of {target: source} where source is the column name in the dataframe and target is the attribute name in the graph vertex. When omitted all columns would be upserted with their current names. In this case column names must match the vertex's attribute names.

conn.upsertVertexDataframe(df=person, vertexType='person', v_id='name')

getEdgesDataframe

getEdgesDataframe(sourceVertexType, sourceVerticies, edgeType=None, targetVertexType=None, targetVertexId=None, select="", where="", limit="", sort="", timeout=0)

Retrieves edges of the given edge type originating from the list of source verticies.

Only sourceVertexType and sourceVerticies are required. If targetVertexId is specified, then targetVertexType must also be specified. If targetVertexType is specified, then edgeType must also be specified.

Arguments:

• select: Comma separated list of edge attributes to be retrieved or omitted. See https://docs.tigergraph.com/dev/restpp-api/built-in-endpoints#select

• where: Comma separated list of conditions that are all applied on each edge's attributes. The conditions are in logical conjunction (i.e. they are "AND'ed" together). See https://docs.tigergraph.com/dev/restpp-api/built-in-endpoints#filter

• limit: Maximum number of edge instances to be returned (after sorting). See https://docs.tigergraph.com/dev/restpp-api/built-in-endpoints#limit

• sort Comma separated list of attributes the results should be sorted by. See https://docs.tigergraph.com/dev/restpp-api/built-in-endpoints#sort

Example:

edgeDf = conn.getEdgesDataframe("company", ["c:1", "c:2"])

upsertEdgesDataframe

upsertEdgesDataframe(df, sourceVertexType, edgeType, targetVertexType, from_id=None, to_id=None, attributes=None)

Upserts edges from a Pandas dataframe.

Arguments:

• df: The dataframe to upsert.

• sourceVertexType: The type of source vertex for the edge.

• edgeType: The type of edge to upsert data to.

• targetVertexType: The type of target vertex for the edge.

• from_id: The field name where the source vertex primary id is given. If omitted the dataframe index would be used instead.

• to_id: The field name where the target vertex primary id is given. If omitted the dataframe index would be used instead.

• attributes: A dictionary in the form of {target: source} where source is the column name in the dataframe and target is the attribute name in the graph vertex. When omitted all columns would be upserted with their current names. In this case column names must match the vertex's attribute names.

getInstalledQueriesDataframe

getInstalledQueriesDataframe() Returns dataframe of all installed queries, does not take any arguments. Example:

queries = conn.getInstalledQueriesDataframe()