semvec/pypi-packages huggingface dataset:
ptijson 0.1.0
Interactive JSON queries, built on prompt toolkit and jmespath
License: MIT
Requires Python: >=3.8,<4.0

# Interactive JSON queries, built on prompt toolkit and jmespath
![Example](./demo.gif)

## Installation
Recommended installation is with [pipx](https://github.com/pypa/pipx):
```bash
python3 -m pip install --user pipx
python3 -m pipx ensurepath
python3 -m pipx install ptijson
```
pyshelltest 0.8.3
Generate test cases for shell scripts
License: MIT
Requires Python: >=3.7,<4.0
Keywords: generation,test,case

# PyShellTest

[![Build/Test](https://github.com/bnichs/pyshelltest/actions/workflows/python-test.yml/badge.svg)](https://github.com/bnichs/pyshelltest/actions/workflows/python-test.yml)

Generate python test cases for shell commands based on simple configuration. Allows you to seemlessly test commands that need to be run from a shell but within the python testing framework. 

We all need to write more tests and including outside commands allows for more coverage. 
For instance:
* Add linkchecker to your integ runs for a django project
* Ensure tools in `bin/` have a `--help` 


# Installation
```bash
pip install pyshelltest
```

# Integration
Add this to you test files where you see fit: 
```python 
generator = PyShellTestGenerator.from_json("sample-config.json")
generator = PyShellTestGenerator.from_toml("sample-config.toml")
test_class = generator.generate()
```

You can then run your tests like you would normally and PyShellTest will generate tests based on your conifg.json
```
python -m pytest tests/
```


#  Configuration
See `sample-config/` as well as `tests/test-config.toml`

## Toml config
Example configuration for a command:
```toml
[[command]]
 name = "the-command-name"

# The command to run
command = ["path/to/script.sh"]

# How long to wait before timing out
timeout = 30

# Print the output of the ocmmand to stdout
print_output = true

# Expect this in stdout, fail otherwise
stdout_contains = "bar" 
  
# Expect this in stderr, fail otherwise
stderr_contains = "bar"

    # Set this dict if you expect errors from the command
    [command.error] 
    # Expect an error
    expect = true
    
    # Expect an error with this class
    error_class = "FileNotFoundError"
```


## Json config
Example configuration for a command: 
```json
{
    "command": [
         {
            "_comment":  "# The command name",
            "name": "the-command-name",
            
            "_comment":  "# The command to run",
            "command": ["path/to/script.sh"], 
            
            "_comment":  "# How long to wait before timing out",
            "timeout": 30, 
            
            "_comment":  "# Print the output of the command to stdout",
            "print_output": true,
            
            "_comment":  "# Expect this in stdout, fail otherwise",
            "stdout_contains": "bar" ,
              
            "_comment":  "# Expect this in stderr, fail otherwise",
            "stderr_contains": "bar",
            
            "_comment":  "# Set this dict if you expect errors from the command",
            "error": { 
                "_comment":  "# Expect an error",
                "expect": true,
                
                "_comment":  "# Expect an error with this class",
                "error_class": "FileNotFoundError"
            }
        }
    ]
}
```


# Development 

## Testing
How to test this project


```bash
poetry run python -m pytest 
```
premierleaguedata 1.0.1
An unofficial Premier League API client.
License: MIT
Keywords: premier league api

# premier-league-data 

An **unofficial** [Premier League](https://www.premierleague.com) API client.

# How to install ⚙

Using pip

```bash
pip install premierleaguedata
```

Or you can [download](https://github.com/ghurone/premier-league-data/archive/refs/heads/main.zip) a zip file from Github
seriesbr 1.0.0
A Python library to interact with brazilian time series databases
License: MIT
Requires Python: >=3.8,<4.0

# seriesbr: Uma biblioteca em Python para consultar bancos de dados com séries temporais do BCB, IPEA e IBGE

[![pypi version](https://img.shields.io/pypi/v/seriesbr.svg)](https://pypi.org/project/seriesbr/)
[![readthedocs status](https://readthedocs.org/projects/seriesbr/badge/?version=latest)](https://seriesbr.readthedocs.io/en/latest/?badge=latest)
![codecov](https://codecov.io/gh/phelipetls/seriesbr/branch/master/graph/badge.svg)

**seriesbr** ajuda a consultar, de forma programática, séries temporais dos
bancos de dados do Banco Central do Brasil (BCB), Instituto de Pesquisa
Econômica Aplicada (IPEA) e Instituto Brasileiro de Geografia e Estatística
(IBGE).

É inspirado nos seguintes pacotes escritos em R:
[rbcb](https://github.com/wilsonfreitas/rbcb),
[ipeaData](https://github.com/ipea/ipeaData) e
[sidrar](https://github.com/cran/sidrar).

# API

A biblioteca possui três módulos, `bcb`, `ipea` e `ibge`. Cada uma possui uma
função chamada `get_series` que aceita o identificador da série e retorna um
[`DataFrame`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html).

Para mais detalhes, leia a [documentação](https://seriesbr.readthedocs.io/).

# Exemplo

```python
from seriesbr import bcb

bcb.get_series(20786, start="2015", end="2016")
```

```
            20786
Date
2015-01-01  26.54
2015-02-01  27.46
2015-03-01  27.21
2015-04-01  28.41
2015-05-01  29.09
2015-06-01  29.75
2015-07-01  30.67
2015-08-01  31.05
2015-09-01  30.89
2015-10-01  32.00
2015-11-01  32.66
2015-12-01  31.08
2016-01-01  33.62
2016-02-01  35.15
2016-03-01  36.44
2016-04-01  38.05
2016-05-01  38.50
2016-06-01  38.36
2016-07-01  39.21
2016-08-01  39.71
2016-09-01  40.22
2016-10-01  41.18
2016-11-01  40.84
2016-12-01  39.12
```

# Contribuindo

Sinta-se à vontade para abrir issues ou pull requests.

Caso queira contribuir, primeiro instale o `poetry` e depois `poetry install`.
Rode os testes localmente com `poetry run pytest`.

# Licença

[MIT](https://github.com/phelipetls/seriesbr/blob/master/LICENSE)