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-2.0.2a202604231336.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-2.0.2a202604231336-py3-none-any.whl (16.3 kB view details)

Uploaded Python 3

File details

Details for the file dx-2.0.2a202604231336.tar.gz.

File metadata

  • Download URL: dx-2.0.2a202604231336.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-2.0.2a202604231336.tar.gz
Algorithm Hash digest
SHA256 7cff30460ef852f39781833ff786697960ce6477fb519cc7761f9d297110447e
MD5 12ddf7177301683c458c406bdc7d463d
BLAKE2b-256 c9dfb4ffb1c80508421c99a020fc165e81cd7bffe24340838eaa4ae99602155d

See more details on using hashes here.

File details

Details for the file dx-2.0.2a202604231336-py3-none-any.whl.

File metadata

  • Download URL: dx-2.0.2a202604231336-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-2.0.2a202604231336-py3-none-any.whl
Algorithm Hash digest
SHA256 e8a8a4736eb5e91a7363cac360606895451350babf9e2241ea57f8efa4c01246
MD5 54e80ecb5e0f63538e045075229182d6
BLAKE2b-256 a74830b1a6d3063766c548356e630da04d19ab6790cbb3376bab76a8d49ff1ba

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