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https://github.com/andreypomortsev/statistical-list.pop-performance-evaluation
This repository contains a performance comparison of list.pop() versus list.pop(-2) in Python. The project involves measuring execution times of these list operations, performing statistical tests to evaluate the significance of differences, and visualizing the results using histograms and box plots.
https://github.com/andreypomortsev/statistical-list.pop-performance-evaluation
jupyter-notebook scipy statistics visualization
Last synced: 23 days ago
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This repository contains a performance comparison of list.pop() versus list.pop(-2) in Python. The project involves measuring execution times of these list operations, performing statistical tests to evaluate the significance of differences, and visualizing the results using histograms and box plots.
- Host: GitHub
- URL: https://github.com/andreypomortsev/statistical-list.pop-performance-evaluation
- Owner: andreypomortsev
- License: mit
- Created: 2024-07-22T11:04:04.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-07-25T06:58:58.000Z (4 months ago)
- Last Synced: 2024-10-02T06:02:24.727Z (about 1 month ago)
- Topics: jupyter-notebook, scipy, statistics, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 587 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Statistical list.pop Performance Evaluation
## Overview
This repository contains a comprehensive analysis of the performance of the `list.pop()` and `list.pop(-2)` operations in Python. The analysis involves statistical tests to compare execution times and determine if there is a significant difference between the two operations. The project includes detailed explanations of the tests conducted, visualizations, and conclusions based on the results.
## Repository Structure
- `data/`: Contains samples data used for performance evaluation.
- `notebooks/`: Jupyter notebook with detailed analysis and visualization.
- `plots/`: Generated plot from the analysis.
## Installation
To get started with this project, follow these steps:
1. **Clone the Repository:**
```bash
git clone https://github.com/andreypomortsev/statistical-list.pop-performance-evaluation.git
cd statistical-list.pop-performance-evaluation
```
2. **Create a Virtual Environment:**
```bash
python3 -m venv .venv
```
3. **Activate the Virtual Environment:**
- On macOS and Linux:
```bash
source .venv/bin/activate
```
- On Windows:
```bash
.venv\Scripts\activate
```
4. **Install Dependencies:**
```bash
pip install -r requirements.txt
```
These steps will set up the environment and install the necessary packages for running the analysis.
## Usage
1. **Run the Analysis:**
Execute the main script or Jupyter notebook to perform the performance evaluation and statistical analysis.
2. **View Results:**
Results and visualizations will be saved in the `plots/` directory.
## Statistical Tests Performed
1. **Shapiro-Wilk Test:** To check the normality of the data distributions.
2. **Levene's Test:** To check the equality of variances between the two operations.
3. **Mann-Whitney U Test:** Non-parametric test used when normality assumptions are not met.
### Conclusion
Based on the results of the statistical tests, we can conclude whether there is a significant difference in the performance of `list.pop()` compared to `list.pop(-2)`. Detailed explanations and percentage differences are provided in the analysis.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
## Contact
For any questions or suggestions, please reach out to [Andrei Pomortsev](https://www.linkedin.com/in/andreypomortsev/).