jupysql
datapane
| jupysql | datapane | |
|---|---|---|
| 8 | 30 | |
| 605 | 1,349 | |
| 4.6% | 0.5% | |
| 9.1 | 7.3 | |
| 21 days ago | 7 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
jupysql
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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
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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.
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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
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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.
datapane
- Datapane: Build and share data reports in 100% Python
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Polars: Company Formation Announcement
If you're looking for an easy way to build an HTML report using Python, you might find Datapane (https://github.com/datapane/datapane) helpful. I'm one of the people building it! We don't support polars (yet, on the roadmap) but we do support pandas so you can convert to a pandas DataFrame and include your data and any plots, etc.
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JupyterLab 4.0
If you're interested in an easier way to create reports using Python and Plotly/Pandas, you should check out our open-source library, Datapane: https://github.com/datapane/datapane - you can create a standalone, redistributable HTML file in a few lines of Python.
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Evidence – Business Intelligence as Code
You might be interested in what we're hacking on at Datapane (I'm one of the founders): https://github.com/datapane/datapane.
You can create standalone HTML data reports from Python/Jupyter in ~3 lines of code: https://docs.datapane.com/reports/overview/
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Ask HN: Fastest way to turn a Jupyter notebook into a website these days?
You can build web apps from Jupyter using Datapane [0]. I'm one of the founders, so let me know if I can help at all.
You can either export a static site [1] (and host on GH pages or S3), or, if you need backend logic, you can add Python functions [2] and serve on your favourite host (we use Fly).
We have specific Jupyter integration to automatically convert your notebook into an app [3].
[0] https://github.com/datapane/datapane
[1] https://docs.datapane.com/reference/reports/#datapane.proces...
[2] https://docs.datapane.com/apps/overview/
[3] https://docs.datapane.com/reports/jupyter-integration/#conve...
- Datapane – Build full-stack data apps in 100% Python
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Datapane - Build full-stack data apps in 100% Python
Our GitHub is https://github.com/datapane/datapane and you can get started here: https://docs.datapane.com/quickstart/
- Datapane: Build internal analytics products in minutes using Python
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Datapane - Build internal data products in 100% Python
Thanks a lot! Yes, absolutely, a few people have brought this up and working working on removing the header right now. If I can help at all, feel free to reach us on GH Discussions: https://github.com/datapane/datapane/discussions
- Datapane/datapane: Build full-stack data analytics apps in Python
What are some alternatives?
grai-core
streamlit - Streamlit — A faster way to build and share data apps.
tpch
dash - Data Apps & Dashboards for Python. No JavaScript Required.
nba-monte-carlo - Monte Carlo simulation of the NBA season, leveraging dbt, duckdb and evidence.dev
jupyter-dash - OBSOLETE - Dash v2.11+ has Jupyter support built in!
chdb-server-bak - API Server for chDB, an in-process SQL OLAP Engine powered by ClickHouse
perspective - A data visualization and analytics component, especially well-suited for large and/or streaming datasets.
pytest-mock-resources - Pytest Fixtures that let you actually test against external resource (Postgres, Mongo, Redshift...) dependent code.
superset - Apache Superset is a Data Visualization and Data Exploration Platform
prism - Prism is the easiest way to develop, orchestrate, and execute data pipelines in Python.
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!