semvec/pypi-packages huggingface dataset:
colassigner 0.4.2
Helper for assigning and accessing pandas columns
Requires Python: >=3.10

# colassigner

[![Documentation Status](https://readthedocs.org/projects/colassigner/badge/?version=latest)](https://colassigner.readthedocs.io/en/latest)
[![codeclimate](https://img.shields.io/codeclimate/maintainability/endremborza/colassigner.svg)](https://codeclimate.com/github/endremborza/colassigner)
[![codecov](https://img.shields.io/codecov/c/github/endremborza/colassigner)](https://codecov.io/gh/endremborza/colassigner)
[![pypi](https://img.shields.io/pypi/v/colassigner.svg)](https://pypi.org/project/colassigner/)


fitting somewhat complex, nested data structures into tables, and removing the need to remember the name and construction logic of any column, if you can rely on static analysis

things to think about:
- draw a reliance dag based on calls
- pivot table: data content based columns
  - enum type support
- changing record entity type
- partial inheritance / composite types
simple-fstring 0.1
a simple basic f-string like behavior in Python 2

# simple-fstring

A simple basic f-string like behavior in Python 2

### Installation
```
$ pip install simple-fstring
```

### Usage
```
from fstring import f
a = 1
b = 2
c = u"哈哈"
d = "哈哈"
print f("${a + b}")  # 3
print f(u"我是c${c}")  # 我是c哈哈
print f("我是d${d}")  # 我是d哈哈
print f("我是d${c}c")  # 鎴戞槸d哈哈c
print f(u"我是c${d}d")  # 我是c鍝堝搱d
print f("$${}")  # ${}
print f("${{'a':'dict test'\}.get('a')}")  # dict test
print f("${{'a':'dict test'}.get('a')}")  # {'a':'dict test'.get('a')}
# specify expr namespace
print f("${a + b}", globals_ns={"a": 10, "b": 20}, locals_ns={"a": 1})  # 21
```


### License

[MIT](https://github.com/hefvcjm/simple-fstring/blob/master/LICENSE)
dataqa-extractor 0.1.3
No-code data extractor
Requires Python: >=3.9, !=2.7.*, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*, !=3.7.*, !=3.8.*

# DataQA extractor
Extract structured fields into a spreadsheet from unstructured text

&nbsp;

<div align="center">
    <img src="https://github.com/dataqa/extractor/blob/main/extractor.gif?raw=True" width="600" align="center"/>
</div>

&nbsp;

## Usage

This package uses the OpenAI API, so you need a valid API key before you start.

Install the package with pip: `pip install dataqa-extractor`

Run it from the terminal: `dataqa-extract`