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‎README.md

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# Rust EDA
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# 📚 Rust Data Analysis
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<div align="center">
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[![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://github.com/wiseaidev)
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[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
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[![made-with-rust](https://img.shields.io/badge/Made%20with-Rust-1f425f.svg?logo=rust&logoColor=white)](https://www.rust-lang.org/)
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[![made-with-latex](https://img.shields.io/badge/Made%20with-LaTeX-1f425f.svg)](https://www.latex-project.org/)
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[![Jupyter Notebook](https://img.shields.io/badge/Jupyter-Notebook-blue.svg?logo=Jupyter&logoColor=orange)](https://jupyter.org/)
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[![GitHub contributors](https://badgen.net/github/contributors/wiseaidev/rust-data-analysis)](https://github.com/wiseaidev/rust-data-analysis/graphs/contributors/)
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[![GitHub issues](https://img.shields.io/github/issues/wiseaidev/rust-data-analysis.svg)](https://github.com/wiseaidev/rust-data-analysis/issues/)
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![Jupyter Notebook](https://img.shields.io/badge/jupyter-%23FA0F00.svg?logo=jupyter&logoColor=white)
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![Rust](https://img.shields.io/badge/rust-%23000000.svg?logo=rust&logoColor=white)
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Welcome to the Rust Data Analysis repository! This collection of Jupyter notebooks provides a comprehensive exploration of data analysis using Rust. Powered by a Rust kernel, these notebooks allow you to dive deep into the realm of data analysis, leveraging the capabilities of the Rust programming language. With the help of various Rust libraries, such as ndarray, plotters, and more, you'll be able to extract valuable insights from different datasets with ease.
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</div>
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## 📝 Table of Contents
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This repository is a collection of Jupyter notebooks, all powered by a Rust kernel. With these notebooks, you'll be able to dive deep into the realm of data analysis with Rust, exploring different datasets and extracting insights with ease using different Rust libraries such as ndarray, plotters, and much more.
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- [Installation](#-installation)
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- [Tutorials](#-tutorials)
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- [Contributing](#-contributing)
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- [Licence](#-licence)
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- [Star History](#-star-history)
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## Steps
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## 🚀 Installation
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- Install a Rust toolchain (e.g. nightly):
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To use the notebooks in this repository, you need to set up your environment. Follow these steps to get started:
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```sh
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curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y --default-toolchain nightly
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```
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1. Clone the repository to your local machine:
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- Install [`Jupyter`](https://jupyter.org/install).
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```sh
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git clone https://github.com/wiseaidev/rust-data-analysis.git
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```
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- Install [`evcxr_jupyter`](https://github.com/evcxr/evcxr/blob/main/evcxr_jupyter/README.md)
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1. Install the required dependencies and libraries. Make sure you have [`Rust`](https://rustup.rs/), [`Jupyter Notebook`](https://jupyter.org/install), and [`evcxr_jupyter`](https://github.com/evcxr/evcxr/blob/main/evcxr_jupyter/README.md) installed on your system.
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```sh
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cargo install evcxr_jupyter
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evcxr_jupyter --install
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```
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```sh
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# Install a Rust toolchain (e.g. nightly):
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curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y --default-toolchain nightly
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- Run Jupyter:
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# Install Jupyter Notebook
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pip install notebook
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```sh
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jupyter notebook
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```
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# Install evcxr_jupyter
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cargo install evcxr_jupyter
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evcxr_jupyter --install
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```
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## Resources
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1. Navigate to the cloned repository:
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I have written a series of articles titled:
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```sh
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cd rust-data-analysis
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```
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1. [Rust: The Next Big Thing in Data Science](https://towardsdatascience.com/rust-the-next-big-thing-in-data-science-319a03305883?source): In this article, you will delve into the exciting world of Rust libraries for exploratory data analysis on the famed iris dataset. You can open up [this notebook](./1-iris-data-analysis-rust.ipynb) associated with this article to follow along.
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1. Start Jupyter Notebook:
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1. [The Ultimate Ndarray Handbook: Mastering the Art of Scientific Computing with Rust](https://towardsdatascience.com/the-ultimate-ndarray-handbook-mastering-the-art-of-scientific-computing-with-rust-ef5ab767212a): This article is an overview of different Rust’s built-in data structures and a deep dive into the Ndarray library. You can open up [this notebook](./2-ndarray-tutorial.ipynb) associated with this article to follow along.
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```sh
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jupyter notebook
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```
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1. [Rust Polars: Unlocking High-Performance Data Analysis — Part 1](https://towardsdatascience.com/rust-polars-unlocking-high-performance-data-analysis-part-1-ce42af370ece). This article is part 1 of exploring the Polars library. You can open up [this notebook](./3-polars-tutorial-part-1.ipynb) associated with this article to follow along.
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1. Access the notebooks in your web browser by clicking on the notebook file you want to explore.
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1. [Rust Polars: Unlocking High-Performance Data Analysis — Part 2](https://towardsdatascience.com/rust-polars-unlocking-high-performance-data-analysis-part-2-7c58a3cb7a1f). This article is part 2 of exploring the Polars library. You can open up [this notebook](./4-polars-tutorial-part-2.ipynb) associated with this article to follow along.
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## 📌 Tutorials
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## Star History
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| ID | Article | Thumbnail | Description | Open on GitHub | Launch on Binder |
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|----|---------------|-----------|-------------|----------------|------------------|
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| 1 | [Rust: The Next Big Thing in Data Science](https://towardsdatascience.com/rust-the-next-big-thing-in-data-science-319a03305883) | ![Article 1](https://miro.medium.com/v2/resize:fit:720/format:webp/1*2jSP2n1KukVJYKVg2u4RuA.png) | A Contextual Guide for Data Scientists and Analysts. | [GitHub](./1-iris-data-analysis-rust.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/wiseaidev/rust-data-analysis/main?filepath=1-iris-data-analysis-rust.ipynb) |
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| 2 | [The Ultimate Ndarray Handbook: Mastering the Art of Scientific Computing with Rust](https://towardsdatascience.com/the-ultimate-ndarray-handbook-mastering-the-art-of-scientific-computing-with-rust-ef5ab767212a) | ![Article 2](https://miro.medium.com/v2/resize:fit:720/format:webp/1*bgmO2hUgZXpCHPC1XaBy3w.png) | This article is an overview of different Rust’s built-in data structures and a deep dive into the Ndarray library. | [GitHub](./2-ndarray-tutorial.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/wiseaidev/rust-data-analysis/main?filepath=2-ndarray-tutorial.ipynb) |
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| 3 | [Rust Polars: Unlocking High-Performance Data Analysis — Part 1](https://towardsdatascience.com/rust-polars-unlocking-high-performance-data-analysis-part-1-ce42af370ece) | ![Article 3](https://miro.medium.com/v2/resize:fit:720/0*Le8YYCDuEhc4A7tN) | This article is part 1 of exploring the Polars library. | [GitHub](./3-polars-tutorial-part-1.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/wiseaidev/rust-data-analysis/main?filepath=3-polars-tutorial-part-1.ipynb) |
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| 4 | [Rust Polars: Unlocking High-Performance Data Analysis — Part 2](https://towardsdatascience.com/rust-polars-unlocking-high-performance-data-analysis-part-1-ce42af370ece) | ![Article 4](https://miro.medium.com/v2/resize:fit:720/format:webp/1*wbXTzoBWnmGXH7WVkAu4PQ.jpeg) | This article is part 2 of exploring the Polars library. | [GitHub](./4-polars-tutorial-part-2.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/wiseaidev/rust-data-analysis/main?filepath=4-polars-tutorial-part-2.ipynb) |
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| 5 | TODO | TODO | This article is a deep dive into probability theory with Rust. | [GitHub](./5-probability-theory-tutorial.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/wiseaidev/rust-data-analysis/main?filepath=5-probability-theory-tutorial.ipynb) |
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[![Star History Chart](https://api.star-history.com/svg?repos=wiseaidev/rust-data-analysis&type=Date)](https://star-history.com/#wiseaidev/rust-data-analysis&Date)
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## 🤝 Contributing
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We welcome contributions to enhance the Rust Data Analysis repository! To contribute, please follow these CONTRIBUING.md file guidelines. Thank you for helping make this project better!
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## 📜 License
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This project is licensed under the [Apache License 2.0](https://opensource.org/licenses/Apache-2.0). For more details, You can refer to the [LICENSE](LICENSE) file.
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## 📈 Star History
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[![Star History Chart](https://api.star-history.com/svg?repos=wiseaidev/rust-data-analysis&type=Date)](https://star-history.com/#wiseaidev/rust-data-analysis&Date)

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