SaaSHub helps you find the best software and product alternatives Learn more →
Jupysql Alternatives
Similar projects and alternatives to jupysql
-
Pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
-
evidence
Business intelligence as code: build fast, interactive data visualizations in pure SQL and markdown
-
SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
-
chdb-server-bak
Discontinued API Server for chDB, an in-process SQL OLAP Engine powered by ClickHouse
-
pytest-mock-resources
Pytest Fixtures that let you actually test against external resource (Postgres, Mongo, Redshift...) dependent code.
-
prism
Prism is the easiest way to develop, orchestrate, and execute data pipelines in Python. (by runprism)
-
dbt-ml-preprocessing
A SQL port of python's scikit-learn preprocessing module, provided as cross-database dbt macros.
-
portable-data-stack-dagster
A portable Datamart and Business Intelligence suite built with Docker, Dagster, dbt, DuckDB, PostgreSQL and Superset
-
mais
⚙️ Código de manutenção do datalake (metadados e pacotes de acesso) | 📖 Docs: https://basedosdados.github.io/mais/
-
SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
jupysql reviews and mentions
-
Show HN: JupySQL – a SQL client for Jupyter (ipython-SQL successor)
Hey, HN community!
We're stoked to launch JupySQL today! JupySQL is an open-source library that brings a modern SQL experience to Jupyter. JupySQL is compatible with all major databases, such as Snowflake, Redshift, PostgreSQL, MySQL, MariaDB, DuckDB, SQL Server, Clickhouse, Trino, and more!
To get started, check out our tutorial: https://jupysql.ploomber.io/en/latest/quick-start.html
SQL is the defacto language for data analysis; however, analysis often requires a mix of SQL and Python. JupySQL bridges this gap, allowing users to execute SQL queries seamlessly in Jupyter and continue their analysis in Python. Add %%sql to the top of your cell and start writing SQL.
Here are some of JupySQL's main features:
- Syntax highlighting
-
JupySQL: Connecting to a SQL database from Jupyter
Please show your support with a 🌟: https://github.com/ploomber/jupysql
- GitHub - ploomber/jupysql: Better SQL in Jupyter. 📊
- SQL CTE's in Jupyter notebooks, DuckDB integration and more
- TL;DR incorporate SQL functionality within Jupyter, access to modern data processing DBs (like DuckDB), polars and data exploration through plotting easier with JupySQL.
-
Evidence – Business Intelligence as Code
If anyone is looking for something like this in Python/Jupyter, check out JupySQL: https://github.com/ploomber/jupysql
- A full-featured SQL client for Jupyter
-
Pandas v2.0 Released
How are people managing the existence of data frame APIs like pandas/polars with SQL engines like BigQuery, Snowflake, and DuckDB?
Most of my notebooks are a mix of SQL and Python: SQL for most processing, dump the results as a pandas dataframe (via https://github.com/ploomber/jupysql) and then use Python for operations that are difficult to express with SQL (or that I don't know how to do it), so I end up with 80% SQL, 20% Python.
Unsure if this is the best workflow but it's the most efficient one I've come up with.
Disclaimer: my team develops JupySQL.
-
A note from our sponsor - SaaSHub
www.saashub.com | 23 May 2024
Stats
ploomber/jupysql is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of jupysql is Python.
Sponsored