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.
- Clone this repository to your local machine:
git clone https://github.com/donmaruko/Flask-Data-Analysis.git
- Install the required Python packages listed in the
requirements.txt
file:
pip install -r requirements.txt
- Start the Flask application
python API.py
- The application will be accessible at
http://localhost:8000
.
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.
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.
/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_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.
Contributions are welcome! If you have any suggestions, bug reports, or feature requests, please create an issue or submit a pull request.