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.

Release files for dx 2.0.1a202604211955

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for dx 2.0.1a202604211955
File	Size	Uploaded
dx-2.0.1a202604211955.tar.gz	23.2 kB	Apr 21, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for dx 2.0.1a202604211955
File	Interpreter	ABI	Platform	Reset
dx-2.0.1a202604211955-py3-none-any.whl 16.3 kB Apr 21, 2026	Python 3	none	any	Details

Total release size: 39.4 kB

Release files / dx-2.0.1a202604211955.tar.gz

Download URL	dx-2.0.1a202604211955.tar.gz
Size	23.2 kB
Tags	Source
SHA-256 checksum How to use checksums	`bf0ea7d780c03fe3ed8890dcd620204d07258b136be534cb2c5beb699738b38c`
BLAKE2b-256 checksum How to use checksums	`b7d8d9af74889259eacf26bfac3aac3c1a72e007b5acc072202c6546093745bb`
Upload date	Apr 21, 2026
Uploaded using Trusted Publishing? What is 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}`

Release files / dx-2.0.1a202604211955-py3-none-any.whl

Download URL	dx-2.0.1a202604211955-py3-none-any.whl
Size	16.3 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`5291f097572746099dcd26a5711db35ff52ffdebcdc1d422f98f2e991030c4c2`
BLAKE2b-256 checksum How to use checksums	`c73ebd8efd832888db40fa7fca6a6f18e26acb8f1693b3a964ea29087497c3cf`
Upload date	Apr 21, 2026
Uploaded using Trusted Publishing? What is 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}`