Conversation
github-actions
bot
added
the
documentation
Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #290 +/- ##
=========================================
Coverage 100.00% 100.00%
=========================================
Files 54 54
Lines 3125 3127 +2
=========================================
+ Hits 3125 3127 +2 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
|
There was a problem hiding this comment.
Pull request overview
Adds documentation aimed at helping users integrate dataframely with AI coding agents by introducing a new “coding agents” guide and a reusable SKILL.md skill file.
Changes:
- Add a new docs guide page describing how to use
dataframelywith coding agents. - Introduce
SKILL.mdintended to be consumed by agent skill frameworks. - Minor docs/build metadata adjustments (docs index, docs conf whitespace, pixi lock metadata).
Reviewed changes
Copilot reviewed 5 out of 6 changed files in this pull request and generated 4 comments.
Show a summary per file
| File | Description |
|---|---|
| pixi.toml | No functional change (whitespace-only). |
| pixi.lock | Adds pypi-prerelease-mode options metadata across environments. |
| docs/guides/index.md | Adds coding-agents to the guides toctree. |
| docs/guides/coding-agents.md | New guide on using dataframely with coding agents and referencing SKILL.md. |
| docs/conf.py | Removes stray whitespace line. |
| SKILL.md | New agent skill document with examples and conventions for using dataframely. |
You can also share your feedback on Copilot code review. Take the survey.
|
|
||
| result = my_code(df) | ||
|
|
||
| assert assert_frame_equal(expected, result) |
There was a problem hiding this comment.
polars.testing.assert_frame_equal raises on mismatch and returns None on success. Using assert assert_frame_equal(...) will always fail when frames are equal. Call assert_frame_equal(...) directly (without assert) and consider keeping argument order consistent with the rest of the docs/examples.
| assert assert_frame_equal(expected, result) | |
| assert_frame_equal(result, expected) |
| ## Verifying results | ||
|
|
||
| `dataframely` supports you and your coding agent in writing unit tests for individual pieces of logic. One significant | ||
| bottle neck is the generation of appropriate test data. Check |
There was a problem hiding this comment.
Spelling: “bottle neck” should be “bottleneck”.
| bottle neck is the generation of appropriate test data. Check | |
| bottleneck is the generation of appropriate test data. Check |
| class HouseSchema(dy.Schema): | ||
| """A schema for a dataframe describing houses.""" | ||
|
|
||
| street: dy.String(primary_key=True) | ||
| number: dy.UInt16(primary_key=True) | ||
| # Number of rooms | ||
| rooms: dy.UInt8() | ||
| # Area in square meters | ||
| area: dy.UInt16() |
There was a problem hiding this comment.
The dy.Schema example uses variable annotations with constructor calls (e.g., street: dy.String(...)), which is not valid Python and also contradicts Dataframely’s Schema API (columns must be defined via simple assignment). Update the example to use assignment (e.g., street = dy.String(...)).
| class MyStreetSchema(dy.Schema): | ||
| """A schema for a dataframe describing streets.""" | ||
|
|
||
| # Shared primary key component with MyHouseSchema | ||
| street: dy.String(primary_key=True) | ||
| city: dy.String() | ||
|
|
||
|
|
||
| class MyCollection(dy.Collection): | ||
| """A collection of related dataframes.""" | ||
|
|
||
| houses: MyHouseSchema | ||
| streets: MyStreetSchema | ||
| ``` |
There was a problem hiding this comment.
The dy.Collection example references MyHouseSchema but only defines HouseSchema, and it annotates members as bare schemas (houses: MyHouseSchema) whereas Dataframely collections expect dy.LazyFrame[SomeSchema] (or similar) annotations. Please make the example consistent and use the correct member annotation type.
Motivation
We want to provide instructions for using
dataframelywith AI coding agents.Changes