start () print ( "server connected" ) params = conn = psycopg2. Import psycopg2 from sshtunnel import SSHTunnelForwarder import pandas as pd import pandas_to_sql try : server = SSHTunnelForwarder ( ( '', 22 ), ssh_username = "username", ssh_pkey = "your/private/key", ssh_private_key_password = "****", remote_bind_address = ( 'localhost', 5432 ) ) server. In this example I retrieve 3 rows, create the dummy df just like above and wrap it with pandas_to_sql: In this way you can check your output before firing it in SQL. Then you take the generated SQL string and fire it on your remote PostgreSQL via Pandas.įor the actual operation, just use a dummy df with only a few entries, e.g. Install with pip install pandas-to-sql and import with import pandas_to_sql (be aware of the underscore/space confusion).Īll you need to do is to wrap an existing dataframe and actually perform some operation on it. The magic is called pandas_to_sql and does what it claims: it converts the Pandas logic into an SQL string. There is even an easier way to work with SQL in Pandas, almost completely neglecting the overhead of the SSH connection, the SQL string and the database connection. read_sql_query ( "select * from post_codes limit 3", conn ) df You need psycopg2 and sshtunnel installed in order to create a database connection over SSH with Python:ĭf = pd. In this way, technically speaking, there is not much of a difference anymore in handling a locally running PostgreSQL for example or a remote one as either way they are running on port 5432 now. Instead, you bind the remote database connection to your localhost on your machine. The SSH connection itself is not suited for direct database access. It’s actually not that big of a deal but one must understand the workflow first. Database connection with sshtunnel and psycopg2 The challenging part is of course not only to connect Pandas to SQL database but to do so via SSH. VS Code -> Jupyter Notebook -> Pandas -> PostgreSQL.The convenience of Pandas with the speed of PostgreSQLĪ better way to visualize the output is to simply use the great visualization tools you know in combination with PostgreSQL and its speed. Only since PostgreSQL version 9.2, psql has a useful \x auto mode formatting the output according to your shell size. However, it’s quite cumbersome to get some nicely formatted data output. PSQL is a command-line interface enabling you to fire direct SQL commands for example in PostgreSQL which comes in really handy for quick queries. If you’re working in big data chances are that you need to SSH into a remote server and perform some data operations there. Tl dr Skip to the last section for the code Querying Materialize is identical to querying a PostgreSQL database: Python executes the query, and Materialize returns the state of the view, source, or table at that point in time.īecause Materialize keeps results incrementally updated, response times are much faster than traditional database queries, and polling (repeatedly querying) a view doesn’t impact performance.Use Pandas to execute SQL statements in a remote database such as PostgreSQL via SSH execute ( "SELECT COUNT(*) FROM countries " ) print ( cur. execute ( "INSERT INTO countries (name, code) VALUES ( %s, %s )", ( 'Germany', 'DE' )) conn. execute ( "INSERT INTO countries (name, code) VALUES ( %s, %s )", ( 'Mexico', 'MX' )) cur. execute ( "INSERT INTO countries (name, code) VALUES ( %s, %s )", ( 'Canada', 'CA' )) cur. execute ( "INSERT INTO countries (name, code) VALUES ( %s, %s )", ( 'United States', 'US' )) cur. #!/usr/bin/env python3 import psycopg2 import sys dsn = "user=MATERIALIZE_USERNAME password=MATERIALIZE_PASSWORD host=MATERIALIZE_HOST port=6875 dbname=materialize sslmode=require" conn = psycopg2.
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