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-0.8.7.tar.gz (2.7 MB view details)

Uploaded Source

Built Distribution

jupyterlab_amphi-0.8.7-py3-none-any.whl (2.7 MB view details)

Uploaded Python 3

File details

Details for the file jupyterlab_amphi-0.8.7.tar.gz.

File metadata

  • Download URL: jupyterlab_amphi-0.8.7.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-0.8.7.tar.gz
Algorithm Hash digest
SHA256 663631a7961a99030a3aacb05a34991f45fef4e712b91109f3f88497e91e4a9c
MD5 99ceea8e746ce0c369a909b6bed81071
BLAKE2b-256 3cd077358dd6dbf9b541bf96e712fd14e26a9638e7e2715b3dbb95442f7ce031

See more details on using hashes here.

File details

Details for the file jupyterlab_amphi-0.8.7-py3-none-any.whl.

File metadata

File hashes

Hashes for jupyterlab_amphi-0.8.7-py3-none-any.whl
Algorithm Hash digest
SHA256 b89a4b16f0e295a72f9722560e68b4b614d2caab267cb5368e0e5b88556fb07a
MD5 9bf95ccbebc4887d9e53f85b638e2b96
BLAKE2b-256 5b4793648bf315ed91f5d936316eafae338c420b1bd68244dc511429042218f9

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