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-2.0.4a202604250054.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-2.0.4a202604250054-py3-none-any.whl (16.3 kB view details)

Uploaded Python 3

File details

Details for the file dx-2.0.4a202604250054.tar.gz.

File metadata

  • Download URL: dx-2.0.4a202604250054.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-2.0.4a202604250054.tar.gz
Algorithm Hash digest
SHA256 d848e615dfb31a4aeee06cf5611659cdc2faba0fbd4573748ac26950f2a727de
MD5 a4c7ac743cb313b62ba4c9eafeb39fbf
BLAKE2b-256 b89f9aea4e31b2c205567dc181f1740ff763733115b67d6fab7a212a0d1d2e20

See more details on using hashes here.

File details

Details for the file dx-2.0.4a202604250054-py3-none-any.whl.

File metadata

  • Download URL: dx-2.0.4a202604250054-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-2.0.4a202604250054-py3-none-any.whl
Algorithm Hash digest
SHA256 24f82b0d63268bb604e218e8451c7e573cd14148288265853b5b2457d5a09509
MD5 e416dc9f1ab5e5ec4b4418b3bd9dcd7e
BLAKE2b-256 44ec5e79a54fcb8a8d017a456bfefa8b30cf4085ebada14d1d794579a6305ecf

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