semvec/pypi-packages huggingface dataset:
jetblack-open-meteo 1.0.0a0
Client implementation for open-meteo
Requires Python: >=3.8

# jetblack-open-meteo

A client API for [open-meteo](https://open-meteo.com).

## Status

This is work in progress.

## Usage

Rather than provide a client, this library provides the url and params.

This means you can use the http library of you choice.

For example with requests:

```python
import pandas as pd
import requests

from jetblack_open_meteo import prepare_weather_forecast_request


url, params = prepare_weather_forecast_request(
    (51.50242846391432, -0.1423227420691731),
    hourly=['temperature_2m'],
)

# Use requests to get the data.
response = requests.get(url, params=params)
data = response.json()

df = pd.DataFrame(data['hourly'])
print(df)
```
PIUnit 1.0.5
Library for convenient code testing with print and input functions
Requires Python: >=3.7
Keywords: python test unittest print input

# PIUnit

Добавляет возможность тестировать код с функциями print и input
<br>

Установите библиотеку:
```
pip install piunit
```

<br>

Пример использования:
```
from main import print_hello_world, log_user_state, get_data
from piunit.test import BasePIUnitTest


class TestOne(BasePIUnitTest):
    def test_one(self):
        self.assertPrint(print_hello_world, ['Hello, World!'])
        self.assertInput(log_user_state, ['Смена пароля', 'Пароль успешно изменён'], True, 'new_pass')
        self.assertPrintInput(get_data, [10, 3, 2], ['Результат выполнения кода: 15'])
```

Описание методов класса BasePIUnitTest:
<pre>
+ assertPrint(test_func: Callable, prints: SupportsIndex,
                   all_: bool = False)
  Тестирует код с функцией print.
    Проверяет, напечаталось ли в print то, что ожидалось.
    Args:
      test_func: Callable (тестируемый объект)
      prints: SupportsIndex (значения для print)
      all_: bool (при True - вернёт ошибку если остались неиспользованные
        значения)
      *args, **kwargs: аргументы для тестируемой функции


+ assertInput(test_func: Callable, inputs: SupportsIndex,
                   all_: bool = False)

  Тестирует код с функцией input.
  Возвращает указанные данные при вызове input.

  Args:
    test_func: Callable (тестируемый объект)
    inputs: SupportsIndex (значения для input)
    all_: bool (при True, вернёт ошибку если остались неиспользованные
    значения)
    *args, **kwargs: аргументы для тестируемой функции


+ assertPrintInput(self, test_func: Callable,
                         inputs: SupportsIndex,
                         prints: SupportsIndex,
                         all_: bool = False
                         ):

  Тестирует код с функцией print и input.

  Подставляет по порядку данные из prints и inputs.

  Args:
    test_func: Callable (тестируемый объект)
    inputs: SupportsIndex (значения для input)
    prints: SupportsIndex (значения для print)
    all_: bool (при True, вернёт ошибку если остались неиспользованные
    значения)
    *args, **kwargs: аргументы для тестируемой функции
</pre>
better-sqlite 0.1.0
A simple and intuitive SQLite wrapper library that provides an easy-to-use interface for interacting with SQLite databases, enabling basic CRUD operations without writing raw SQL.
Requires Python: <4.0,>=3.12

<p align="center">
  <img src="./assets/better_sqlite_logo.png" alt="Better SQLite Logo"/>
</p>
<hr>
<h4 align="center">✨ Simplify your SQLite experience without writing SQL</h4>

<p align="center">
  <a href="#🗒️ Overview">Overview</a> •
  <a href="#✨ Features">Features</a> •
  <a href="#🚀 Installation">Installation</a> •
  <a href="#📚 Usage">Usage</a> •
  <a href="#🧪 Testing">Testing</a> •
  <a href="#📝 License">License</a> •
  <a href="# 🤝 Contributing">Contributing</a>
</p>

## 🗒️ Overview

A simple and intuitive SQLite wrapper library for Python that provides an easy-to-use interface for interacting with SQLite Databases. This library allows you to perform basic CRUD ( Create, Read, Update, Delete) operations without having to write raw SQL queries.

## ✨ Features

- **Context Manager Support**: Automatically handles database connection and closing.
- **CRUD Operations**: Easy methods for creating tables, inserting, updating, retrieving, and deleting data.
- **No Raw SQL Required**: Simplified API for common database operations.

## 🚀 Installation

This library can be installed via pip( recommended ) or git by cloning the repository and installing it locally.

To install `Better SQLite` via git:

```bash
git clone https://github.com/ayrun3412/Better-SQlite.git
cd Better-SQLite
pip install .
```

Via pip:

```bash
# pip3 for MacOS & Linux
pip install better-sqlite
```

## 📚 Usage

### Creating a Table

```python
from better_sqlite import Database

with Database('example.db') as db:
    db.create_table("users", {"name": "TEXT", "age": "INTEGER"})
```

### Inserting Data

```python
with Database('example.db') as db:
    db.insert("users", {"name": "Alice", "age": 30})
```

### Retrieving Data

```python
with Database('example.db') as db:
    user = db.get("users", {"name": "Alice"})
    print(user)  # Output: {'name': 'Alice', 'age': 30}
```

### Updating Data

```python
with Database('example.db') as db:
    db.update("users", {"age": 31}, {"name": "Alice"})
```

### Deleting Data

```python
with Database('example.db') as db:
    db.delete("users", {"name": "Alice"})
```

## 🧪 Testing

 To ensure the functionality of the library, you can run the tests using `pytest`.

First, Install `pytest` if you haven't already:

```bash
pip install pytest
```

Then run, the tests:

```bash
pytest
```

## 📝 License

This project is licensed under the MIT License - see the [LICENSE](./LICENSE) for more details

## 🤝 Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue for any bugs or feature requests.
lqlpath 0.2.0
A package for searching log query language path
Requires Python: >=2.7

#lqlpath
lqlpath is a Python package designed to retrieve field paths in Google BigQuery logs based on either keys or values, making it particularly useful for working with BigQuery Log Query Language (LQL). Whether you're searching for specific keys or values in your JSON-formatted BigQuery logs, lqlpath helps you quickly locate the relevant fields.

#Features
Search by Key: Retrieve a list of field paths in a JSON file where a specific key is found, particularly for BigQuery logs.
Search by Value: Retrieve a list of field paths in a JSON file where a specific value is found.
BigQuery-Specific Searches: Specialized functions to search within BigQuery log structures, helping you extract key information from logs such as project, dataset, and table identifiers.

#Installation

To install the package, simply run:

pip install lqlpath

#Usage
The following examples show how to search for key paths or value paths in BigQuery logs.

Example: Search Field Paths by Key

from lqlpath import get_byKey

# Search for the key 'reservation' in the BigQuery JSON log file 'bq.json'

print(get_byKey('bq.json', 'reservation'))

Output:

['protoPayload.serviceData.jobInsertResponse.resource.jobStatistics.reservation']

Example: Search Field Paths by Value

from lqlpath import get_byValue

# Search for the value 'us-central1' in the BigQuery JSON log file 'bq.json'

print(get_byValue('bq.json', 'us-central1'))


Output:

['protoPayload.serviceData.jobInsertResponse.resource.jobName.location']

#Specialized BigQuery Log Search by Key

The bq_get_byKey function allows you to search specifically within the structure of BigQuery logs. You can specify the type of BigQuery resource (e.g., table, dataset) and the key you're looking for.

Supported BigQuery resource types:

-BIGQUERY_RESOURCE
-BIGQUERY_PROJECT
-BIGQUERY_DATASET
-BIGQUERY_TABLE

#Example
from lqlpath import bq_get_byKey

# Search for the key 'table_id' within BigQuery table resources

print(bq_get_byKey('BIGQUERY_TABLE', 'table_id'))

Output:

['resource.labels.table_id']

#Example Use Cases
Log Query Language (LQL) Exploration: Use lqlpath to extract paths for keys or values from complex BigQuery logs, making it easier to write queries.
BigQuery Log Analysis: Quickly find the relevant fields in your BigQuery log files when troubleshooting or monitoring BigQuery operations.

#License
This project is licensed under the MIT License. See the LICENSE file for more details.