semvec/pypi-packages huggingface dataset:
rp-django-cli 0.1.0
Rapid Django project generator
License: MIT
Requires Python: >=3.8
Keywords: django, cli, generator, template, project-generator

# RP Django CLI

Rapid Django project generator - Create Django projects from templates in seconds.

## Installation

```bash
pip install rp-django-cli
```

## Usage

```bash
# Create a new Django project
rp create myproject

# Create project in a specific folder
rp create myproject --folder custom-folder

# Use a specific template
rp create myproject --template default
```

## Features

- 🚀 Fast project creation from templates
- 📦 Automatic dependency setup
- 🔧 Smart placeholder replacement
- ✨ Clean project structure

## Requirements

- Python >= 3.8
- Git

## License

MIT

## Author

Mazharul Islam (tamim.mazharul28@gmail.com)

## Company

Vzure - Team Raptors
LinearRegressionHAFA 0.1
A simple linear regression model implemented from scratch.
License: MIT
Requires Python: >=3.6

# LinearRegression_pkg

`LinearRegression_pkg` is a Python library for Linear Regression

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install `package_name`.

```bash
pip install LinearRegression_pkg
```

## Usage

```python
import LinearRegression

#
LinearRegression.fit(epochs , lr)

# returns prediction for inp
LinearRegression.predict(inp)

# returns 'phenomenon'
LinearRegression.module_name('phenomena')
```

## License

[MIT](https://choosealicense.com/licenses/mit/)
SnoScience 2.1.0
A package optimised for building small neural networks fast.
License: MIT
Requires Python: <3.13,>=3.10

# SnoScience

The source code, documentation, and CI/CD scripts for the SnoScience package are stored in this repository.

## Installation

1. Clone the repository with the following command:
    
    ```shell
    git clone https://snomax@dev.azure.com/snomax/SnoScience/_git/SnoScience
    ```

2. Install poetry:
    
    ```shell
    pip install poetry
    ```

3. Install the package:
    
    ```shell
    poetry install
    ```

## Documentation

Both the user and developer documentation can be found [here](https://thesecondsnomax.github.io/SnoScience).
vahidlari-dummypkg 4.1.0
A dummy Python package for testing CI workflows
License: MIT
Requires Python: >=3.8

# Dummy Package

A dummy Python package for testing CI workflows and release automation.

## Features

- Simple utility functions for demonstration
- Automatic version management with setuptools_scm
- Modern Python packaging with pyproject.toml

## Installation

```bash
pip install -e .
```

## Usage

```python
from vahidlari_dummypkg import greet, calculate_sum, format_message, get_package_info

# Greet someone
greet("Alice")

# Calculate sum
result = calculate_sum(2, 3)

# Format a message
formatted = format_message("Hello world")

# Get package info
info = get_package_info()
```

## Development

This package is designed for testing CI/CD workflows and is not intended for production use.
pandascompare 0.6.3
Generates comparison reports for pandas DataFrames.
License: MIT
Requires Python: <4.0,>=3.8

# pandascompare

<div>

[![Package version](https://img.shields.io/pypi/v/pandascompare?color=%2334D058&label=pypi)](https://pypi.org/project/pandascompare/)
[![License](https://img.shields.io/github/license/zteinck/pandascompare)](https://github.com/zteinck/pandascompare/blob/master/LICENSE)

</div>

`pandascompare` is a Python package designed to compare `DataFrame` objects, enabling you to quickly identify the differences between two datasets.

## Installation
```sh
pip install pandascompare
```

## Main Features
The `PandasCompare` class compares any two `DataFrame` objects along the following dimensions:
- `Rows` ➔ missing rows based on the join key(s) specified via the `join_on` argument.
- `Columns` ➔ name differences or missing columns.
- `Values` ➔ value mismatches in content or type.


## Example Usage
Please refer to the documentation within the code for more information.

### Imports
```python
from pandascompare import PandasCompare
```

### Create DataFrames
First, let's create two sample `DataFrame` objects to compare.
```python
import pandas as pd
import numpy as np

# February Data
left_df = pd.DataFrame({
    'id': [1, 2, 3],
    'date': [pd.to_datetime('2024-02-29')] * 3,
    'first_name': ['Alice', 'Mike', 'John'],
    'amount': [10.5, 5.3, 33.77],
    })

# January Data
right_df = pd.DataFrame({
    'id': [1, 2, 9],
    'date': [pd.to_datetime('2024-01-31')] * 3,
    'first_name': ['Alice', 'Michael', 'Zachary'],
    'last_name': ['Jones', 'Smith', 'Einck'],
    'amount': [11.1, np.nan, 14],
    })
```

### Compare DataFrames
Next, we will initialize a `PandasCompare` instance to perform the comparison. Please consult the in-code documentation for a comprehensive list of available arguments.
```python
pc = PandasCompare(
    left_data=left_df,
    right_data=right_df,
    left_label='feb',
    right_label='jan',
    join_on='id',
    left_ref=['first_name'],
    include_delta=True,
    verbose=True,
    )
```

### Export to Excel
Finally, let's export the compare report to an Excel file to view the results.
```python
pc.to_excel()
```