csv2clickhouse
jupysql
Our great sponsors
| csv2clickhouse | jupysql | |
|---|---|---|
| 1 | 8 | |
| 4 | 598 | |
| - | 7.0% | |
| 5.0 | 9.3 | |
| about 1 year ago | 18 days ago | |
| Python | Python | |
| - | Apache License 2.0 |
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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.
csv2clickhouse
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Fast way to upload several CSV files -> ClickHouse with create a database and tables
This is how this script was born, which I want to share. I hope it saves you some time, and it will give me the opportunity to receive a couple of feedbacks and ideas from you for improvement.
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.
What are some alternatives?
clickhouse-driver - ClickHouse Python Driver with native interface support
grai-core
dbt-clickhouse - The Clickhouse plugin for dbt (data build tool)
tpch
ibis - the portable Python dataframe library
datapane - Build and share data reports in 100% Python
django-clickhouse - This project's goal is to build Yandex ClickHouse database into Django project.
nba-monte-carlo - Monte Carlo simulation of the NBA season, leveraging dbt, duckdb and evidence.dev
sqlglot - Python SQL Parser and Transpiler
chdb-server-bak - 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.