semvec/pypi-packages huggingface dataset:
mp4tolottie 0.1.1
Convert MP4 videos to vector-based Lottie JSON animations
Requires Python: >=3.7

# 🎞️ mp4tolottie

Convert MP4 video clips into lightweight Lottie animations automatically — frame-by-frame, vectorized, and optimized.

---

## 🚀 Features

- 🎥 Extract frames from MP4 videos using `ffmpeg`
- 🖼️ Convert raster frames to vector graphics (`SVG`) using `potrace`
- 📦 Generate optimized Lottie JSON animation
- 🧩 CLI interface for ease of use
- 🔧 Modular design for customization

---

## 📦 Installation

Install via PyPI:

```bash
pip install mp4tolottie
```

---

## ⚙️ Requirements

`mp4tolottie` depends on the following system tools:

### ✅ FFmpeg

Install FFmpeg and ensure it's accessible in your system's `PATH`.

#### macOS:
```bash
brew install ffmpeg
```

#### Ubuntu / Debian:
```bash
sudo apt update && sudo apt install ffmpeg
```

#### Windows:
1. Download from [ffmpeg.org/download](https://ffmpeg.org/download.html)
2. Extract the ZIP
3. Add the `bin/` folder to your System PATH

---

### ✅ Potrace

`potrace` is required to vectorize raster images into SVGs.

#### macOS:
```bash
brew install potrace
```

#### Ubuntu / Debian:
```bash
sudo apt install potrace
```

#### Windows:
Download from [potrace.sourceforge.net](http://potrace.sourceforge.net) and add it to PATH.
myawesomeeda 1.0.1
📊Simple yet cool EDA module
Requires Python: >=3.6

# My Awesome EDA Module

![Python3](https://img.shields.io/badge/Language-Python3-steelblue)
![Pandas](https://img.shields.io/badge/Dependecy-Pandas-steelblue)
![Seaborn](https://img.shields.io/badge/Dependecy-Seaborn-steelblue)
![Matplotlib](https://img.shields.io/badge/Dependecy-Matplotlib-steelblue)
![OS](https://img.shields.io/badge/OS-_Windows_|_Mac_|_Linux-steelblue)
![License](https://img.shields.io/badge/License-MIT-steelblue)
[![Downloads](https://static.pepy.tech/badge/myawesomeeda)](https://pepy.tech/project/myawesomeeda)
[![Tests](https://github.com/iliapopov17/myawesomeeda/workflows/Tests/badge.svg)](https://github.com/iliapopov17/myawesomeeda/actions?workflow=Tests)

> Welcome to the My Awesome EDA (Exploratory Data Analysis) Module! This Python module provides a set of tools for exploring and analyzing your dataset. Whether you're a data scientist, analyst, or enthusiast, this module will help you gain insights into your data quickly and efficiently.

## Features
- **Welcome Gif**: A fun welcome gif to kick off your exploration.
- **Basic Dataset Information**: Quickly get an overview of the number of observations (rows) and parameters (columns) in your dataset.
- **Data Type Summary**: Understand the data types of each column in your dataset.
- **Categorization of Features**: Categorize features into numerical, string, and categorical based on unique threshold.
- **Summary Statistics**: Get descriptive statistics for numerical features, including mean, standard deviation, minimum, 25th percentile, median, 75th percentile, and maximum values.
- **Outliers Detection**: Identify outliers in numerical features using the interquartile range (IQR) method.
- **Missing Values Analysis**: Investigate missing values in your dataset, including total missing values, rows with missing values, and columns with missing values.
- **Duplicate Rows Detection**: Identify duplicate rows in your dataset.
- **Visualizations**: Generate informative visualizations including bar plots of missing values by variable, correlation heatmap for numerical features, and histograms with boxplots for numerical features.

## Installation
```bash
pip install myawesomeeda
```

```python
from my_awesome_eda import run_eda
```

## Usage Guide

- Demonstrational python notebook is available in [GitHub repo](https://github.com/iliapopov17/MyAwesomeEDA) in `demo.ipynb` file<br>

🔗 Visit [MyAwesomeEDA wiki](https://github.com/iliapopov17/MyAwesomeEDA/wiki) page

## Contributing
Contributions are welcome! If you have any ideas, bug fixes, or enhancements, feel free to open an issue or submit a pull request.

## Contact
For any inquiries or support, feel free to contact me via [email](mailto:iljapopov17@gmail.com)

Happy data exploring! 💻🧐