Skip to main content

Amphi is a no-code ETL extension for Jupyterlab.

Project description

Amphi for Jupyterlab

jupyter-lab-amph-screenshot

Amphi for JupyterLab is a Micro ETL designed for ingesting, cleansing, and processing data from files and databases. Amphi addresses use cases such as data extraction from structured and unstructured data, data preparation and enrichment, and data processing for LLMs-based systems. Use Amphi within the Jupyterlab environment to design your data pipelines with a graphical user-interface and generate native Python code you can deploy anywhere.

📣 Beta release

Amphi for Jupyterlab is currently in beta. To start with Amphi, see below for install instructions.

As Amphi is in beta version, we welcome feedback and suggestions. Join the Slack community or reach out directly at hello@amphi.ai.

Requirements

  • JupyterLab >= 4.0

Install Jupyterlab + Amphi

If you want to launch Jupyterlab + Amphi, you can run the following commands. It is recommended to a virtual environment (venv or conda for example).

To install perform the following steps, with pip:

pip install --upgrade jupyterlab jupyterlab-amphi

For more installation instructions, visit the docs.

Install Amphi on an existing Jupyterlab instance

If you already have a Jupyterlab instance, just install the amphi package:

pip install --upgrade jupyterlab-amphi

Alternatively, you can search in the Extension Manager for Amphi.

For more information, see docs.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

jupyterlab_amphi-0.7.5.tar.gz (2.7 MB view details)

Uploaded Source

Built Distribution

jupyterlab_amphi-0.7.5-py3-none-any.whl (2.7 MB view details)

Uploaded Python 3

File details

Details for the file jupyterlab_amphi-0.7.5.tar.gz.

File metadata

  • Download URL: jupyterlab_amphi-0.7.5.tar.gz
  • Upload date:
  • Size: 2.7 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.6

File hashes

Hashes for jupyterlab_amphi-0.7.5.tar.gz
Algorithm Hash digest
SHA256 787cdb92106fc22cc2cfb6fbe9cd3255217a5b4e2f4ea7e8106d26859b5c494d
MD5 49945ec669c3376539bd4719dc44946c
BLAKE2b-256 00375073be4f35d875dbf581758ddc7a57299f182649e500d983d49ed189c12d

See more details on using hashes here.

File details

Details for the file jupyterlab_amphi-0.7.5-py3-none-any.whl.

File metadata

File hashes

Hashes for jupyterlab_amphi-0.7.5-py3-none-any.whl
Algorithm Hash digest
SHA256 aac9c69900316f5a04649fe116010613b69793c2a5e5f750c04b371088949ac7
MD5 b106c9f8f15aefd0b7e672dd3d2546c7
BLAKE2b-256 c284e1862d646806a3b7aa33e822b72479b79b1dfa1fd1b96c197668c6ac1d10

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page