Skip to main content

nteract/dx — efficient display and blob-store uploads from Python kernels

Project description

dx

Smart DataFrame display for Jupyter, built for nteract.

dx upgrades how pandas and polars DataFrames render in a notebook. Instead of serializing megabytes of HTML into your output cells, dx hands the data to nteract's content-addressed blob store and renders it through a fast Arrow/parquet grid. Your .ipynb stays tiny, the cell stays snappy, and AI agents reading the notebook get a compact per-column summary — dtypes, ranges, distinct/top values, null counts — instead of raw bytes.

Install

# pandas
pip install "dx[pandas]"

# polars
pip install "dx[polars]"

# both
pip install "dx[pandas,polars]"

Python 3.10+.

Use

import dx
dx.install()

import pandas as pd
df = pd.read_parquet("large-dataset.parquet")
df  # rendered via nteract's sift grid — no base64 in your .ipynb

That's it. dx.install() is idempotent and automatically called by nteract's kernel bootstrap, so most nteract users never touch it directly. Calling it yourself is fine when you want the behavior in an environment nteract didn't configure for you (a standalone kernel, a test harness, etc.).

What you get

  • Fast rendering. Large DataFrames stream through the blob store; the .ipynb payload stays small.
  • AI-friendly summaries. Every DataFrame ships a text/llm+plain column summary — dtypes, numeric ranges, string distinct/top values, null counts — so agents reason about the shape without materializing the whole table.
  • Visualization integration. Altair and Plotly are automatically switched to their nteract renderers for interactive output that works inside nteract's isolated iframe sandbox.
  • Narwhals-aware. narwhals-wrapped DataFrames are unwrapped via .to_native() and dispatched through the pandas/polars path.
  • Safe outside nteract. When no nteract runtime is reachable, dx.install() is a no-op. import dx is safe from plain Python, vanilla Jupyter, scripts, CI.

Links

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

dx-2.0.1a202604210931.tar.gz (23.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

dx-2.0.1a202604210931-py3-none-any.whl (16.3 kB view details)

Uploaded Python 3

File details

Details for the file dx-2.0.1a202604210931.tar.gz.

File metadata

  • Download URL: dx-2.0.1a202604210931.tar.gz
  • Upload date:
  • Size: 23.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: uv/0.11.7 {"installer":{"name":"uv","version":"0.11.7","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}

File hashes

Hashes for dx-2.0.1a202604210931.tar.gz
Algorithm Hash digest
SHA256 91bbf31df5f87255f39e17c37a801a8deb1ae5e5c8c9d796754f8c93a7c8c95f
MD5 c0a1ee4ce9304d79cc5b3b905a451473
BLAKE2b-256 03f183106a58c589b69efff2b6347c05dfe4a78d9802d17c585ec654a04c3877

See more details on using hashes here.

File details

Details for the file dx-2.0.1a202604210931-py3-none-any.whl.

File metadata

  • Download URL: dx-2.0.1a202604210931-py3-none-any.whl
  • Upload date:
  • Size: 16.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: uv/0.11.7 {"installer":{"name":"uv","version":"0.11.7","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}

File hashes

Hashes for dx-2.0.1a202604210931-py3-none-any.whl
Algorithm Hash digest
SHA256 c733b9cdda909b94adc8125cb6774935eeb4b8dd11e2326036e30516012c0927
MD5 858d2da8df73b778319ebdd99246a797
BLAKE2b-256 87e1fb1a0b6faa8150d9e7445934c82de4e2ff762e1bb7bfdaef0fa931ec7d84

See more details on using hashes here.

Supported by

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