11.4 Quick Start: Using the Python Client as a Module

This section describes how to use the Python client as a module in Python applications.

Remote Server

For this mode, all you need is the Python client to be installed. In your Python program, you must authenticate with the remote server before you can create a session as illustrated in the following example. Note that you must replace the values for base_url, jdbc_url, username, and password with values to match your environment details.

import pypgx
import opg4py
import opg4py.graph_server as graph_server
pgql_conn = opg4py.pgql.get_connection("<username>","<password>", "<jdbc_url>")
pgql_statement = pgql_conn.create_statement()
pgql = """
        CREATE PROPERTY GRAPH bank_graph
        VERTEX TABLES (
          bank_accounts
            LABEL ACCOUNTS
            PROPERTIES (ID, NAME)
        )
        EDGE TABLES (
          bank_txns
            SOURCE KEY (from_acct_id) REFERENCES bank_accounts (ID)
            DESTINATION KEY (to_acct_id) REFERENCES bank_accounts (ID)
            LABEL TRANSFERS
            PROPERTIES (FROM_ACCT_ID, TO_ACCT_ID, AMOUNT, DESCRIPTION)
        ) OPTIONS(PG_PGQL)
"""
pgql_statement.execute(pgql)
instance = graph_server.get_instance("<base_url>", "<username>", "<password>")
session = instance.create_session("my_session")
graph = session.read_graph_by_name('BANK_GRAPH', 'pg_view')
analyst = session.create_analyst()
analyst.pagerank(graph)
rs = graph.query_pgql("SELECT id(x), x.pagerank FROM MATCH (x) LIMIT 5")
rs.print()

To execute, save the above program into a file named program.py and run the following command:

python3 program.py

You will see the following output:

+-------------------------------------------+
| id(x)             | pagerank              |
+-------------------------------------------+
| BANK_ACCOUNTS(2)  | 9.749447313256548E-4  |
| BANK_ACCOUNTS(4)  | 0.004584001759076056  |
| BANK_ACCOUNTS(6)  | 5.358461393401424E-4  |
| BANK_ACCOUNTS(8)  | 0.0013051552434930175 |
| BANK_ACCOUNTS(10) | 0.0015040122009364232 |
+-------------------------------------------+

Converting PGQL result set into pandas dataframe

Additionally, you can also convert the PGQL result set to a pandas.DataFrame object using the to_pandas() method. This makes it easier to perform various data filtering operations on the result set and it can also be used in Lambda functions. For example,

example_query = (
    "SELECT n.name AS name, n.age AS age "
    "WHERE (n)"
)
result_set = sample_graph.query_pgql(example_query)
result_df = result_set.to_pandas()

result_df['age_bin'] = result_df['age'].apply(lambda x: int(x)/20) # create age bins based on age ranges

Note:

To view the complete set of available Python APIs, see OPG4PY Python API Reference.

Embedded Server

For this mode, the Python client and the Graph Server RPM package must be installed on the same machine.

import os
os.environ["PGX_CLASSPATH"] = "/opt/oracle/graph/lib/*"
instance = graph_server.get_embedded_instance()
session = instance.create_session("python_pgx_client")
print(session)

To execute, save the above program into a file named program.py and run the following command.

python3 program.py

After successful login, you must see a similar message indicating a PGX session was created:

PgxSession(id: 32fc7037-18f1-4381-ba94-107e5f63aec2, name: python_pgx_client)

Note:

To view the complete set of available Python APIs, see OPG4PY Python API Reference.