semvec/pypi-packages huggingface dataset:
dashcamcsv 0.2.5
Helpful utilities for data science workflows.
Requires Python: >=3.8

# dashcamcsv

A simple Python package with helpful utilities for data science workflows.

## Features
- `load_and_describe(url: str)`: Load a CSV from a URL and print basic statistics.
- `missing_report(df)`: Show missing value counts and percentages.
- `info_summary(df)`: Print DataFrame shape, types, and memory usage.
- `clean_data(df, strategies=None)`: Perform basic data cleaning with customizable strategies.
- `quick_plot(df, column, figsize=(10, 6))`: Plot a histogram or bar plot for a column with custom figure size.
- `plot_advanced(df, x, y=None, kind='pairplot', figsize=(10, 8))`: Create advanced visualizations using seaborn with custom figure size.

## Installation
```bash
pip install dashcamcsv
```

## Usage
```python
from dashcamcsv import load_and_describe, missing_report, info_summary, clean_data, quick_plot, plot_advanced

# Basic example
df = load_and_describe("https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv")
missing_report(df)
info_summary(df)

# Data cleaning
# Apply different cleaning strategies for different columns
clean_df = clean_data(df, strategies={
    'sepal_length': 'median',  # Fill missing values with median
    'species': 'mode',         # Fill missing values with most common value
    'petal_width': 'drop',     # Drop rows where this column has missing values
    'sepal_width': 'value:5.0' # Fill missing values with a specific value
})

# Visualization
quick_plot(df, "sepal_length", figsize=(12, 6))  # Customize figure size

# Advanced visualizations with seaborn
plot_advanced(df, 'sepal_length', 'sepal_width', kind='scatter', figsize=(10, 8))  # Scatter plot with regression line
plot_advanced(df, 'species', 'sepal_length', kind='box', figsize=(8, 6))  # Box plot
plot_advanced(df, kind='heatmap', figsize=(12, 10))  # Correlation heatmap
```

## Changelog
- v0.2.5: Added clean_data function for flexible data cleaning
- v0.2.4: Fixed plotting bugs and improved error handling
- v0.2.3: Updated documentation and examples
- v0.2.2: Fixed plotting functions to handle categorical data better
- v0.2.1: Added compatibility with Python 3.8+
- v0.2.0: Added figsize parameter to plotting functions
- v0.1.0: Initial release
mod-dlm-server 1.1.0
mod-dlm-server is an open-source REST server for storing and querying element data described using the mod-dlm domain-specific language (DSL).
Keywords: OpenAPI mod-dlm API



<div align="center">
    <a href="https://mod.construction/" target="_blank">
        <img src="https://raw.githubusercontent.com/mod-construction/mod-dlm-server-fastapi/main/mod-dlm-fastapi-server.png" alt="mod-dlm-logo"/>
    </a>



# mod-dlm-fastapi-server

[![build status](https://github.com/mod-construction/mod-dlm-server-fastapi/actions/workflows/publish-pypi.yml/badge.svg)](https://github.com/mod-construction/mod-dlm-server-fastapi/actions/workflows/publish-pypi.yml)
[![PyPI - Version](https://img.shields.io/pypi/v/mod-dlm-server)](https://pypi.org/project/mod-dlm-server/)
[![License](https://img.shields.io/github/license/mod-construction/mod-dlm)](https://opensource.org/licenses/MIT)
</div>

**mod-dlm-server** is an open-source REST server for storing and querying element data described using the **[mod-dlm](https://github.com/mod-construction/mod-dlm)** domain-specific language (DSL).
This project provides tools for defining entity models and automatically generating CRUD APIs, enabling suppliers to create and manage catalogs of their products easily.

You can always find the most up-to-date API documentation here: [mod-dlm API Docs](https://mod-construction.github.io/mod-dlm/)


The server is generated automatically using the  [OpenAPI Generator](https://openapi-generator.tech).

- API version: 1.1.0
- Build date: 2025-06-04T08:15:21.458468935Z[Etc/UTC]
- Generator version: 7.14.0-SNAPSHOT
- Build package: org.openapitools.codegen.languages.PythonFastAPIServerCodegen

## Requirements.

Python >= 3.7

## Installation & Usage

To run the server, please execute the following from the root directory:

```bash
pip install -r requirements.txt
PYTHONPATH=src uvicorn mod_dlm_server.main:app --host 0.0.0.0 --port 8080
```

and open your browser at `http://localhost:8080/docs/` to see the docs.

## Running with Docker

To run the server on a Docker container, please execute the following from the root directory:

```bash
docker-compose up --build
```

## Tests

To run the tests:

```bash
pip install pytest
PYTHONPATH=src pytest tests
```