https://github.com/donmaruko/flask-data-analysis
Flask API for statistical calculations. Data analysis, cleansing, visualization, and manipulation. Documented by Swagger.
https://github.com/donmaruko/flask-data-analysis
api api-rest data-analysis data-science data-visualization datascience flasgger matplotlib pandas seaborn sqlite wordcloud
Last synced: about 2 months ago
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Flask API for statistical calculations. Data analysis, cleansing, visualization, and manipulation. Documented by Swagger.
- Host: GitHub
- URL: https://github.com/donmaruko/flask-data-analysis
- Owner: donmaruko
- Created: 2023-07-11T05:39:04.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-10-04T15:41:57.000Z (over 2 years ago)
- Last Synced: 2025-02-23T09:14:33.576Z (over 1 year ago)
- Topics: api, api-rest, data-analysis, data-science, data-visualization, datascience, flasgger, matplotlib, pandas, seaborn, sqlite, wordcloud
- Language: Python
- Homepage:
- Size: 14.6 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Data Analysis and Text Manipulation with Flask
This Flask application provides various data analysis and text manipulation functionalities through a RESTful API. It includes operations for cleansing text, performing statistical calculations, visualizing data, and more.
## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
- [Endpoints](#endpoints)
- [Contributing](#contributing)
## Installation
1. Clone this repository to your local machine:
```shell
git clone https://github.com/donmaruko/Flask-Data-Analysis.git
```
2. Install the required Python packages listed in the `requirements.txt` file:
```shell
pip install -r requirements.txt
```
3. Start the Flask application
```shell
python API.py
```
4. The application will be accessible at `http://localhost:8000`.
## Endpoints
The API endpoints provided by this application are documented using Swagger. You can access the Swagger documentation by visiting `http://localhost:8000/apidocs/` while the application is running.
### Text-Related Endpoints:
The application expects data files in CSV format for data analysis and text files for text manipulation. You can upload these files to the corresponding endpoints for processing.
- `/cleanse_text`: Cleanses text by removing non-alphanumeric characters.
- `/cleanse_text_file`: Cleanses text from an uploaded file.
- `/multiply_numbers`: Multiplies two numbers.
- `/reverse_text`: Reverses the order of characters in text.
- `/count_words`: Counts the number of words in text.
### Data-Visualization Endpoints:
- `/analyze_data`: Analyzes data from an uploaded CSV file.
- `/visualize_data`: Visualizes data distribution from an uploaded CSV file.
- `/generate_pie_chart`: Generates a pie chart for a specific column in a CSV file.
- `/generate_word_cloud`: Generates a word cloud from text data.
- `/frequency_word_cloud`: Generates a word cloud based on word frequencies.
- `/visualize_skewness`: Visualizes skewness for a specific column.
- `/visualize_kurtosis`: Visualizes kurtosis for a specific column.
### Git Endpoints:
- `/git_pull`: Performs a Git pull to update the repository.
- `/git_push`: Performs a Git push to push changes to the repository.
Please refer to the Swagger documentation for more details on how to use these endpoints.
## Contributing
Contributions are welcome! If you have any suggestions, bug reports, or feature requests, please create an issue or submit a pull request.