semvec/pypi-packages huggingface dataset:
autoclean-ds 0.1.0
Automated data cleaning library for pandas DataFrames
Requires Python: >=3.9

<!-- Day-3 -->
<!-- git add .
git commit -m "Day 3: Added remove_duplicates, drop_empty_columns, detect_constant_columns, updated pipeline"
git push -u origin day3-cleaning -->

# AutoClean 🧹

**AutoClean** is an automated data cleaning library for pandas DataFrames.  
It intelligently detects column types, handles missing values, removes outliers, 
drops useless columns, and generates a clear before/after report.

Built for **data scientists**, **ML engineers**, and **students** who want
clean data in one line.

---

## ✨ Features

- Automatic column standardization
- Intelligent data type detection (numerical, categorical, datetime)
- Missing value handling with smart strategies
- Outlier detection & removal (IQR-based)
- Constant & empty column removal
- Before/After cleaning report
- Configurable & extensible pipeline

---

## 🚀 Installation (Local / Dev)

```bash
pip install .
pytest-checkers 1.0.3
Pytest Plugin for dry-run checks LSPs, Type Checkers, Linters, and Formatters during testing
Requires Python: >=3.13

[github_release]: https://img.shields.io/github/release/the-citto/pytest-checkers.svg?logo=github&logoColor=white&color=orange
[pypi_version]: https://img.shields.io/pypi/v/pytest-checkers.svg?logo=python&logoColor=white
[python_versions]: https://img.shields.io/pypi/pyversions/pytest-checkers.svg?logo=python&logoColor=white
[github_license]: https://img.shields.io/github/license/the-citto/pytest-checkers.svg?logo=github&logoColor=white
[coverage]: https://raw.githubusercontent.com/the-citto/pytest-checkers/refs/heads/main/docs/coverage.svg
[tox_tests]: https://github.com/the-citto/pytest-checkers/actions/workflows/tox-tests.yml/badge.svg
<!-- [github_action_tests]: https://github.com/the-citto/culting/actions/workflows/tests.yml/badge.svg -->

[![GitHub Release][github_release]](https://github.com/the-citto/pytest-checkers/releases/)
[![PyPI Version][pypi_version]](https://pypi.org/project/pytest-checkers/)
[![Python Versions][python_versions]](https://pypi.org/project/pytest-checkers/)
[![License][github_license]](https://github.com/the-citto/pytest-checkers/blob/master/LICENSE)
[![Coverage][coverage]](https://github.com/the-citto/pytest-checkers/)
[![Tox tests][tox_tests]](https://github.com/the-citto/pytest-checkers/actions/workflows/tox-tests.yml)
![PyPI - Downloads](https://img.shields.io/pypi/dm/pytest-checkers)
<br>


<figure style="display: inline-block;">
  <img src="https://github.com/the-citto/pytest-checkers/raw/main/docs/the-power-of-exponential-growth-a-story-about-knowledge.jpg" width="200" style="vertical-align: top;">
</figure>

# pytest-checkers

Use desired LSPs, type checkers, linters, and formatters (diff only).

Available optional dependencies:

`pytest_checkers[black,isort,flake8,mypy,pyright,ruff,ty]`

either of the above, or

`pytest_checkers[all]`

Simple flags: `--ruff` `--mypy` etc. or just `--checkers` for all the dependencies installed. 

Use `pyproject.toml` (and `.flake8` until they finally decide to move)
for your preferred settings for every tool.


## Note

pyright installs `pyright[nodejs]`

isort install `isort[colors]`

## Kudos

All of them are somewhat different from what I wanted and made here:

[pytest-black](https://github.com/coherent-oss/pytest-black)

[pytest-isort](https://github.com/stephrdev/pytest-isort)

[pytest-flake8](https://github.com/coherent-oss/pytest-flake8)

[pytest-mypy](https://github.com/realpython/pytest-mypy)

[pytest-pyright](https://github.com/RobertCraigie/pytest-pyright)

[pytest-ruff](https://github.com/businho/pytest-ruff)

[pytest-ty](https://github.com/boidolr/pytest-ty)
test-helper-by-delica 0.1.7
A Python package that simplifies the creation and execution of unittest test cases.
Requires Python: >=3.9

# test-helper-by-delica
## Purpose
This Python package simplifies the creation and execution of unittest test cases,   
in order to help programmers efficiently develop and run tests for their Python software.
## Installation
This package is available through the Python Package Index (PyPI).     
One can easily download the package with the following pip install statement:   
`pip install test_helper_by_delica`
## Getting Started
After installation, all we need to start using the package is a function that we want to test.   
For a simple example, we can define a boolean function that always returns True.  
````
def always_true():    
    return True   
````
We then use the test helper package to create a unittest TestCase object.
````
import test_helper_by_delica as test_lib 
test_obj = test_lib.create_test_obj()
````
Now we can call the boolean testing function to check whether always_true returns the expected result.
````
test_lib.test_bool_func(test_obj, always_True, true_inputs=())
````
## Current Features
## Citation
langchain-salesforce 0.2.2
An integration package connecting Salesforce and LangChain
Requires Python: <4.0,>=3.9
Keywords: ai, crm, langchain, llm, salesforce

# langchain-salesforce

[![PyPI version](https://badge.fury.io/py/langchain-salesforce.svg)](https://badge.fury.io/py/langchain-salesforce)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python Versions](https://img.shields.io/pypi/pyversions/langchain-salesforce.svg)](https://pypi.org/project/langchain-salesforce/)
[![CI](https://github.com/colesmcintosh/langchain-salesforce/actions/workflows/ci.yml/badge.svg)](https://github.com/colesmcintosh/langchain-salesforce/actions/workflows/ci.yml)

LangChain integration for Salesforce CRM. Query data with SOQL, inspect schemas, and manage records (CRUD) directly from your LangChain applications.

## Installation

```bash
pip install -U langchain-salesforce
```

## Configuration

Set these environment variables:

| Variable | Description |
|----------|-------------|
| `SALESFORCE_USERNAME` | Your Salesforce username |
| `SALESFORCE_PASSWORD` | Your Salesforce password |
| `SALESFORCE_SECURITY_TOKEN` | Your Salesforce security token |
| `SALESFORCE_DOMAIN` | `login` (production) or `test` (sandbox). Default: `login` |

## Quick Start

```python
from langchain_salesforce import SalesforceTool

tool = SalesforceTool()

# Query contacts
result = tool.run({
    "operation": "query",
    "query": "SELECT Id, Name, Email FROM Contact LIMIT 5"
})
```

## Operations

| Operation | Description | Required Parameters |
|-----------|-------------|---------------------|
| `query` | Execute SOQL queries | `query` |
| `describe` | Get object schema | `object_name` |
| `list_objects` | List all SObjects | — |
| `create` | Create a record | `object_name`, `record_data` |
| `update` | Update a record | `object_name`, `record_id`, `record_data` |
| `delete` | Delete a record | `object_name`, `record_id` |
| `get_field_metadata` | Get field details | `object_name`, `field_name` |

### Examples

```python
# Describe an object
tool.run({"operation": "describe", "object_name": "Account"})

# Create a record
tool.run({
    "operation": "create",
    "object_name": "Contact",
    "record_data": {"LastName": "Doe", "Email": "doe@example.com"}
})

# Update a record
tool.run({
    "operation": "update",
    "object_name": "Contact",
    "record_id": "003XXXXXXXXXXXXXXX",
    "record_data": {"Email": "updated@example.com"}
})

# Delete a record
tool.run({"operation": "delete", "object_name": "Contact", "record_id": "003XXXXXXXXXXXXXXX"})
```

## Development

```bash
git clone https://github.com/colesmcintosh/langchain-salesforce.git
cd langchain-salesforce
uv sync --all-groups

make format  # Format code
make lint    # Run linters
make test    # Run tests
```

See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

## License

MIT License. See [LICENSE](LICENSE).
num2ltr 1.0
A simple Python package for converting numbers to words (letters).
Requires Python: >=3.9

# num2ltr

[![es](https://img.shields.io/badge/lang-es-yellow.svg)](https://github.com/DanielMolina33/num2ltr/blob/main/README.es.md)

num2ltr is a simple Python package for converting numbers to words (letters).

It uses Spanish standard nomenclature, which means that most Spanish grammar rules are applied.

This package is still under **testing**, so you may experience inconsistencies.

This is also a personal project to improve my programming skills.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install num2ltr.

```bash
pip install num2ltr
```

Or you can use the test package

```bash
pip install -i https://test.pypi.org/simple/ num2ltr
```

## Usage

```python
import num2ltr

# returns 'treinta y tres'
num2ltr.number_to_letters('33') # input must be a numeric string
```

Or you can use

```python
from num2ltr import number_to_letters

# returns 'treinta y tres'
number_to_letters('33') # input must be a numeric string
```

## Features
- Converts numeric strings to Spanish words
- Supports numbers up to 15 digits (for now)

## Limitations
- No decimal support yet
- No negative numbers
- No scientific notation

## Roadmap
- Decimal number support
- Negative numbers
- Support for larger numbers (up to 50 digits)

## Contributing

Pull requests are welcome. For major changes, please open an issue first
to discuss what you would like to add or change.

Please make sure to update tests as appropriate.

## License

[MIT](https://choosealicense.com/licenses/mit/)
toon-format-parser 1.1.0
Token-Oriented Object Notation (TOON) parser for Python
Requires Python: >=3.7

# TOON Parser (Python)

TOON (Token-Oriented Object Notation) is a human-readable data format optimized for Large Language Models (LLMs). This is the Python implementation of the TOON parser.

## Installation

```bash
pip install toon-format-parser
```

## Quick Start

```python
import toon_parser

# Encode Python data to TOON format
data = { 
  "sessionId": "abc-123",
  "users": [
    { "name": "Alice", "role": "admin" },
    { "name": "Bob", "role": "user" }
  ]
}

toon_string = toon_parser.encode(data)
print(toon_string)
# Output:
# sessionId: abc-123
# users: items[2]{name,role}:
#   Alice,admin
#   Bob,user

# Decode back to Python
decoded = toon_parser.decode(toon_string)
print(decoded)
```

## Features

- **Collection Markers**: Full support for `items[N]:` and `items[N]{headers}:`.
- **Tabular Arrays**: Efficient CSV-like representation for object arrays.
- **Ambiguity Resolution**: Robust parsing for nested structures.
- **LLM Optimized**: Minimizes tokens by removing redundant brackets and quotes.