PostgreSQL

This tutorial will show you how to get a PostgreSQL instance up and running locally to test JupySQL. You can run this in a Jupyter notebook.

Pre-requisites

To run this tutorial, you need to install following Python packages:

%pip install jupysql pandas pyarrow --quiet

Note: you may need to restart the kernel to use updated packages.

You also need a PostgreSQL connector. Here’s a list of supported connectors. We recommend using psycopg2. The easiest way to install it is via:

%pip install psycopg2-binary --quiet

Note: you may need to restart the kernel to use updated packages.

Tip

If you have issues, check out our installation guide or message us on Slack.

You also need Docker installed and running to start the PostgreSQL instance.

Start PostgreSQL instance

We fetch the official image, create a new database, and user (this will take 1-2 minutes):

%%bash
docker run --name postgres -e POSTGRES_DB=db \
  -e POSTGRES_USER=user \
  -e POSTGRES_PASSWORD=password \
  -p 5432:5432 -d postgres

94fa1f440c4c8e632b59fc630dd513c4d653c95c964fd4deddf3430db1223c1b

Our database is running, let’s load some data!

Load sample data

Now, let’s fetch some sample data. We’ll be using the NYC taxi dataset:

import pandas as pd

df = pd.read_parquet(
    "https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2021-01.parquet"
)
df.shape

(1369769, 19)

As you can see, this chunk of data contains ~1.4M rows, loading the data will take about a minute:

from sqlalchemy import create_engine

engine = create_engine("postgresql://user:password@localhost/db")
df.to_sql(name="taxi", con=engine, chunksize=100_000)
engine.dispose()

Query

Now, let’s start JuppySQL, authenticate and start querying the data!

%load_ext sql

%sql postgresql://user:password@localhost/db

Important

If the cell above fails, you might have some missing packages. Message us on Slack and we’ll help you!

List the tables in the database:

%sqlcmd tables

Name
taxi

List columns in the taxi table:

%sqlcmd columns --table taxi

name	type	nullable	default	autoincrement	comment
index	BIGINT	True	None	False	None
VendorID	BIGINT	True	None	False	None
tpep_pickup_datetime	TIMESTAMP	True	None	False	None
tpep_dropoff_datetime	TIMESTAMP	True	None	False	None
passenger_count	DOUBLE_PRECISION	True	None	False	None
trip_distance	DOUBLE_PRECISION	True	None	False	None
RatecodeID	DOUBLE_PRECISION	True	None	False	None
store_and_fwd_flag	TEXT	True	None	False	None
PULocationID	BIGINT	True	None	False	None
DOLocationID	BIGINT	True	None	False	None
payment_type	BIGINT	True	None	False	None
fare_amount	DOUBLE_PRECISION	True	None	False	None
extra	DOUBLE_PRECISION	True	None	False	None
mta_tax	DOUBLE_PRECISION	True	None	False	None
tip_amount	DOUBLE_PRECISION	True	None	False	None
tolls_amount	DOUBLE_PRECISION	True	None	False	None
improvement_surcharge	DOUBLE_PRECISION	True	None	False	None
total_amount	DOUBLE_PRECISION	True	None	False	None
congestion_surcharge	DOUBLE_PRECISION	True	None	False	None
airport_fee	DOUBLE_PRECISION	True	None	False	None

Query our data:

%%sql
SELECT COUNT(*) FROM taxi

*  postgresql://user:***@localhost/db
1 rows affected.

count
1369769

Parametrize queries

threshold = 10

%%sql
SELECT COUNT(*) FROM taxi
WHERE trip_distance < {{threshold}}

*  postgresql://user:***@localhost/db
1 rows affected.

count
1297415

threshold = 0.5

%%sql
SELECT COUNT(*) FROM taxi
WHERE trip_distance < {{threshold}}

*  postgresql://user:***@localhost/db
1 rows affected.

count
73849

CTEs

%%sql --save many_passengers --no-execute
SELECT *
FROM taxi
WHERE passenger_count > 3
-- remove top 1% outliers for better visualization
AND trip_distance < 18.93

*  postgresql://user:***@localhost/db
Skipping execution...

%%sql --save trip_stats --with many_passengers
SELECT MIN(trip_distance), AVG(trip_distance), MAX(trip_distance)
FROM many_passengers

*  postgresql://user:***@localhost/db
1 rows affected.

min	avg	max
0.0	2.5010889812889756	18.92

This is what JupySQL executes:

query = %sqlrender trip_stats
print(query)

WITH "many_passengers" AS (
SELECT *
FROM taxi
WHERE passenger_count > 3
-- remove top 1% outliers for better visualization
AND trip_distance < 18.93)
SELECT MIN(trip_distance), AVG(trip_distance), MAX(trip_distance)
FROM many_passengers

Plotting

%sqlplot histogram --table taxi --column trip_distance

<Axes: title={'center': "'trip_distance' from 'taxi'"}, xlabel='trip_distance', ylabel='Count'>

%sqlplot boxplot --table taxi --column trip_distance

<Axes: title={'center': "'trip_distance' from 'taxi'"}, ylabel='trip_distance'>

Executing Meta-Commands

JupySQL supports psql-style “backslash” meta-commands (\d, \dt, etc.). To run these, PGSpecial must be installed— information on how to do so can be found here. Example:

%sql \dt

*  postgresql://user:***@localhost/db
1 rows affected.

Schema	Name	Type	Owner
public	taxi	table	user

Clean up

To stop and remove the container:

! docker container ls

CONTAINER ID   IMAGE            COMMAND                  CREATED          STATUS          PORTS                    NAMES
4a6478b19d45   mariadb:latest   "docker-entrypoint.s…"   21 minutes ago   Up 21 minutes   0.0.0.0:3306->3306/tcp   mariadb

%%capture out
! docker container ls --filter ancestor=postgres --quiet

container_id = out.stdout.strip()
print(f"Container id: {container_id}")

Container id: 

! docker container stop {container_id}

"docker container stop" requires at least 1 argument.
See 'docker container stop --help'.

Usage:  docker container stop [OPTIONS] CONTAINER [CONTAINER...]

Stop one or more running containers

! docker container rm {container_id}

"docker container rm" requires at least 1 argument.
See 'docker container rm --help'.

Usage:  docker container rm [OPTIONS] CONTAINER [CONTAINER...]

Remove one or more containers

! docker container ls

CONTAINER ID   IMAGE            COMMAND                  CREATED          STATUS          PORTS                    NAMES
4a6478b19d45   mariadb:latest   "docker-entrypoint.s…"   21 minutes ago   Up 21 minutes   0.0.0.0:3306->3306/tcp   mariadb