qu ⠶ pd
Pandas API reference
Motivation
To collect a database of the pandas API to enable gamified study, or simple reference usage.
Outline
- Either download docs as ZIP of HTML, or mine the package repo (parse RST with docutils to give doctrees). The latter would be preferable (but perhaps not useful since autosummary is used).
- Make sqlite3 database with fields: name (e.g. "DataFrame"), qualname prefix (e.g. "pandas"), type (e.g. "class"), and so on. This would amount to a 'walk' of the library's entity tree.
- Expose these entities in a structured way (as an entity tree).
Possible applications
- 🐼 PQ Test: pandas API recall score, like an IQ test
- 🐼 PPM: typing test, for completing tasks in pandas
Requires
- Python 3.10+
Installation
pip install qpdb
Usage
The package can be used on the command line by calling qp
usage: qp [-h] [-v VERSION] [--domain DOMAIN] [-r ROLE] [-n NAMES]
[--debug | --no-debug] [-c | --crawl | --no-crawl]
[-q | --quiet | --no-quiet]
[package]
positional arguments:
package (default: pandas)
options:
-h, --help show this help message and exit
-v VERSION, --version VERSION
(default: )
--domain DOMAIN (default: py)
-r ROLE, --role ROLE (default: )
-n NAMES, --names NAMES
(default: )
--debug, --no-debug (default: False)
-c, --crawl, --no-crawl
(default: False)
-q, --quiet, --no-quiet
(default: False)
To print the inventory of names and their corresponding URLs, run qp.
To breakpoint and take a look at what info is available, run either
qp --debug or qp --debug --no-crawl
To crawl each page of the docs, use --crawl (experimental)
To silence the STDERR header lines, add -q or --quiet
To get a list of all the entities in PyTorch (stable version) and their URLs, run:
qp torch -v stable -q | wc -l
⇣
3366
To pull out just the torch.Tensor class methods, run:
qp torch -v stable --role method --names torch.Tensor -q | wc -l
⇣
514
This has many uses, for example to create a list of markdown format links, pipe it as:
echo "$(qp torch -v stable -r method -n torch.Tensor -q)" | \
sed -e 's/ /]: /g' -e 's/^torch\.Tensor\./[/g'
⇣
[abs]: https://pytorch.org/docs/stable/generated/torch.Tensor.abs.html#torch.Tensor.abs
[abs_]: https://pytorch.org/docs/stable/generated/torch.Tensor.abs_.html#torch.Tensor.abs_
[absolute]: https://pytorch.org/docs/stable/generated/torch.Tensor.absolute.html#torch.Tensor.absolute
[absolute_]: https://pytorch.org/docs/stable/generated/torch.Tensor.absolute_.html#torch.Tensor.absolute_
[acos]: https://pytorch.org/docs/stable/generated/torch.Tensor.acos.html#torch.Tensor.acos
[acos_]: https://pytorch.org/docs/stable/generated/torch.Tensor.acos_.html#torch.Tensor.acos_
...
Metadata
Release files for qpdb 0.2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| qpdb-0.2.0.tar.gz | 12.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| qpdb-0.2.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 19.6 kB
Release files / qpdb-0.2.0.tar.gz
| Download URL | qpdb-0.2.0.tar.gz |
|---|---|
| Size | 12.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/4.0.1 CPython/3.9.12
|
Release files / qpdb-0.2.0-py3-none-any.whl
| Download URL | qpdb-0.2.0-py3-none-any.whl |
|---|---|
| Size | 7.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.9.12
|