https://github.com/shaadclt/numpy-exercises
This project provides a collection of Jupyter Notebook exercises for practicing NumPy, a fundamental library for numerical computing in Python. NumPy provides powerful data structures and functions for handling large, multi-dimensional arrays and matrices. Through this project, we aim to enhance our skills in NumPy.
https://github.com/shaadclt/numpy-exercises
Last synced: 6 months ago
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This project provides a collection of Jupyter Notebook exercises for practicing NumPy, a fundamental library for numerical computing in Python. NumPy provides powerful data structures and functions for handling large, multi-dimensional arrays and matrices. Through this project, we aim to enhance our skills in NumPy.
- Host: GitHub
- URL: https://github.com/shaadclt/numpy-exercises
- Owner: shaadclt
- Created: 2022-10-19T06:39:00.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-07-08T10:51:57.000Z (about 2 years ago)
- Last Synced: 2025-03-24T02:51:38.330Z (7 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 5.86 KB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# NumPy Exercises for Practice
This project provides a collection of Jupyter Notebook exercises for practicing NumPy, a fundamental library for numerical computing in Python. NumPy provides powerful data structures and functions for handling large, multi-dimensional arrays and matrices. Through this project, we aim to enhance our skills in NumPy and gain hands-on experience with array manipulation, mathematical operations, and data analysis.
## Prerequisites
Before running the code, make sure you have the following dependencies installed:
- Python (3.x)
- Jupyter Notebook
- NumPy
## Getting Started
To get started with the project, follow the steps below:
1. Clone the repository:
```bash
git clone https://github.com/shaadclt/NumPy-Exercises.git
```
2. Change into the project directory:
```bash
cd NumPy-Exercises
```
3. Install the required dependencies:
4. Run Jupyter Notebook:
```bash
jupyter notebook
```
5. Open the Jupyter Notebook files (*.ipynb) in Jupyter.
6. Follow the instructions in the notebooks to practice and explore different NumPy exercises.
## Project Overview
The project covers various NumPy exercises, including but not limited to:
1. Array Creation: Creating arrays using different techniques, such as linspace, arange, or random.
2. Array Manipulation: Modifying arrays by reshaping, slicing, or joining arrays.
3. Mathematical Operations: Performing mathematical calculations on arrays, such as addition, subtraction, multiplication, or division.
4. Array Indexing: Accessing and modifying specific elements or subsets of arrays.
5. Statistical Operations: Calculating statistical measures, such as mean, median, standard deviation, or correlation coefficients.
6. Broadcasting: Applying operations on arrays of different shapes through broadcasting.
7. File Input/Output: Reading from and writing to files using NumPy.
Each notebook includes code snippets, and practice exercises to reinforce the understanding of NumPy concepts.
## Results and Insights
The emphasis of this project is on practicing and implementing NumPy exercises rather than providing specific results or insights. Each notebook contains exercises and examples to apply the concepts learned and gain a deeper understanding of numerical computing using NumPy. Feel free to experiment with different datasets, modify the exercises, or explore additional NumPy functions beyond the provided exercises.
## Customization
You can customize the project by adding your own exercises, creating additional notebooks for specific topics, or expanding the exercises with more advanced NumPy concepts. This project serves as a starting point for you to practice and enhance your skills in numerical computing using NumPy.
## License
This project is licensed under the MIT License. See the `LICENSE` file for more information.
## Acknowledgments
- This project is created for the purpose of practicing NumPy exercises using Jupyter Notebook.
## Contributing
Contributions are welcome! If you find any issues, have suggestions for improvements, or want to add more exercises, please open an issue or submit a pull request.