qpdb 0.0.5

Pandas API reference

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

qp is available from PyPI, and the code is on GitHub

Usage

The package can be used on the command line by calling qp

usage: qp [-h] [-v VERSION] [-d | --debug | --no-debug]
          [-f | --fetch | --no-fetch]
          [package_name]

positional arguments:
  package_name          (default: pandas)

options:
  -h, --help            show this help message and exit
  -v VERSION, --version VERSION
                        (default: )
  -d, --debug, --no-debug
                        (default: False)
  -f, --fetch, --no-fetch
                        (default: True)

To print the inventory of names and their corresponding URLs, run qp --no-fetch. (The --fetch flag is assumed by default)

To breakpoint and take a look at what info is available, run either qp --debug or qp --debug --no-fetch

To silence the STDERR output, 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 --no-fetch

and for example to pull out the torch.Tensor class methods, run:

echo "$(qp torch -v stable -q --no-fetch | grep -E '^torch.Tensor\.')" | cut -d\. -f 3-

This gives:

H https://pytorch.org/docs/1.12/tensors.html#torch.Tensor.H
T https://pytorch.org/docs/1.12/tensors.html#torch.Tensor.T
abs https://pytorch.org/docs/1.12/generated/torch.Tensor.abs.html#torch.Tensor.abs
abs_ https://pytorch.org/docs/1.12/generated/torch.Tensor.abs_.html#torch.Tensor.abs_
absolute https://pytorch.org/docs/1.12/generated/torch.Tensor.absolute.html#torch.Tensor.absolute
absolute_ https://pytorch.org/docs/1.12/generated/torch.Tensor.absolute_.html#torch.Tensor.absolute_
...

For example, to create a list of markdown format links, pipe that on to:

... | sed 's/ /]: /g' | sed 's/^/[/g'

⇣

[H]: https://pytorch.org/docs/stable/tensors.html#torch.Tensor.H
[T]: https://pytorch.org/docs/stable/tensors.html#torch.Tensor.T
[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_
[acosh]: https://pytorch.org/docs/stable/generated/torch.Tensor.acosh.html#torch.Tensor.acosh
[acosh_]: https://pytorch.org/docs/stable/generated/torch.Tensor.acosh_.html#torch.Tensor.acosh_
...

but perhaps you only want methods of the class, not attributes: