semvec/pypi-packages huggingface dataset:
postman2runner 0.0.2
Convert POSTMAN data to JSON testcases for HttpRunner.
License: MIT
Keywords: postman converter json

# postman2runner


Convert postman data to JSON testcases for HttpRunner.

## usage

To see ``postman2runner`` version:

```shell
$ python main.py -V
```

To see available options, run

```shell
$ python main.py -h
usage: main.py [-h] [-V] [--log-level LOG_LEVEL]
               [postman_testset_file] [-- output_dir] [-- output_file_type]

Convert postman testcases to JSON testcases for HttpRunner.

positional arguments:
  postman_testset_file  Specify postman testset file.
  -- output_dir   Optional. Specify converted JSON testset folder.
  -- output_file_type  Optional. Generate file format , default json.

optional arguments:
  -h, --help            show this help message and exit
  -V, --version         show version
  --log-level LOG_LEVEL Specify logging level, default is INFO.
```

## examples

In most cases, you can run ``postman2runner`` like this:

```shell
$ python3 main.py tests/data/test.json --output_dir tests/postman2case --output_file_type yaml
INFO:root:Generate JSON testset successfully: output.json
```

As you see, the first parameter is postman source file path, and the second is converted JSON file path.

The output testset file type is detemined by the suffix of your specified file.

If you only specify postman source file path, the output testset is in JSON format by default and located in the same folder with source file.

```shell
$ python3 main.py tests/data/test.json
INFO:root:Generate JSON testset successfully: test/test.output.json
```

## generated testset

generated JSON testset ``output.json`` shows like this:

```json
[
    {
        "test": {
            "name": "/api/v1/Account/Login",
            "request": {
                "method": "POST",
                "url": "https://httprunner.top/api/v1/Account/Login",
                "headers": {
                    "Content-Type": "application/json"
                },
                "json": {
                    "UserName": "test001",
                    "Pwd": "123",
                    "VerCode": ""
                }
            },
            "validate": []
        }
    }
]
```

## 参考来源
【[HttpRunner](https://github.com/HttpRunner/postman2case)】
pycola 0.1.60
python package to parse the job bulletins and generate a structured file
License: MIT
Keywords: text parsing,entity extraction,bulletin parser

## PyCoLA : Python Module to parse the job bulletins and generate a structure file 

### Kaggle : Data Science for Good Challenge 

https://www.kaggle.com/shivamb/1-bulletin-structuring-engine-cola

## Installation 

```python
    pip install pycola 
```

## Usage 

Extractor class is used to generate the structured csv file. It accepts one user input config:

"input_path" : path of the bulletin text files    
"output_filename" : name of the output file   

```python
from pycola.bulletin_parser import Extractor

## define the input path
config = {
	"input_path" : "Bulletins/",
	"output_filename" : "structured_file.csv"
}

## create the Extractor Class object
extr = Extractor(config)

## call the extraction function
extr.extraction()
```

## Documentation 

http://www.shivambansal.com/blog/network/cola/BulletinStructuringEngine.html    

By Shivam Bansal