# Share a ydata-profiling Report — Send the Profile, Not the 40 MB File

Canonical: https://commareports.com/share-ydata-profiling-report
Published: 2026-08-23

> ydata-profiling (pandas-profiling) writes one huge self-contained HTML file that email rejects and Slack truncates. Publish it to Comma for a URL, anchored comments on individual columns, and a revision per refresh.

# Share a ydata-profiling report

`ProfileReport(df).to_file("profile.html")` produces the single most useful
artifact in exploratory data analysis and the single most annoying one to
send. It is one file, which is good, and it is often 15–40 MB, which is why
it dies in an email filter, gets zipped, gets renamed `profile_final_v2.html`,
and ends up on one laptop.

Worse, the interesting part of a profile is never the profile. It is the
argument about it: whether a 31% null rate in `customer_tier` is a bug or a
business rule, whether that bimodal distribution is two products stapled
together. That conversation needs to happen on the numbers, not next to them.

## Publish it

```python
from ydata_profiling import ProfileReport
import requests, os

ProfileReport(df, title="Orders — profile", minimal=True).to_file("profile.html")

requests.post(
    "https://commareports.com/api/v1/reports",
    headers={"Authorization": f"Bearer {os.environ['COMMA_API_TOKEN']}"},
    json={"title": "Orders — profile", "html": open("profile.html").read()},
).raise_for_status()
```

`minimal=True` is doing real work here. The default config computes pairwise
interactions and every correlation matrix, which is most of the file size and
most of the runtime — and for a first-pass review, nobody reads them. Turn it
off, get a file that fits comfortably under the 5 MB HTML cap, and turn it
back on for the one column pair that turns out to matter.

## What the URL changes

- **Comments land on columns.** Highlight the `customer_tier` warning and pin
  the reason. See [commenting on HTML](/comment-on-html).
- **Profiles become a series.** PATCH the same report id after each run and
  every refresh appends a revision — so schema drift and null-rate creep are
  visible as a diff instead of a vibe.
- **Analysts without your environment can read it.** No pandas, no kernel, no
  "can you re-run it and screenshot the summary".
- **It can refresh on a schedule.** A [routine](/docs/routines) profiles the
  table weekly and posts the result to the same URL.

## Limits

- **HTML body: 5 MB.** `minimal=True`, or profile a subset of columns.
- **Scripts run, sandboxed:** `allow-scripts`, no `allow-same-origin`.
- **60 requests/minute per token.** Use a
  [scoped token](/docs/api-tokens) with `reports:write` only.

## Try it

Comma is free — unlimited reports, unlimited commenters, unlimited revision
history.

**[Publish your first profile →](https://commareports.com/)**

### Related

- [Share an EDA report](/share-eda-report) — the broader analysis pattern
- [Great Expectations data docs](/share-great-expectations-data-docs) · [Evidently reports](/share-evidently-report)
- [dbt docs](/share-dbt-docs) · [Jupyter workflow](/with/jupyter)