/medical-data-visualizer

Primary LanguagePython

Medical Data Visualizer

This project is adopted from the freeCodeCamp Data Analysis with Python course.

To start working on the project, you need to fork this repository to your own GitHub account. Then, clone the new repository to your computer and start working on the project.

Part 1: Data processing and visualization

Follow the freeCodeCamp instructions for building your project at https://www.freecodecamp.org/learn/data-analysis-with-python/data-analysis-with-python-projects/medical-data-visualizer

Part 2: Web application

In the second part, we will build a simple application using the [Streamlit]](https://streamlit.io/) library.

Quoting the Streamlit web site:

A faster way to build and share data apps

Streamlit turns data scripts into shareable web apps in minutes. All in pure Python. No front‑end experience required.

  • Streamlit is really efficient in creating web applications, mainly for data science projects.
  • The result is visually appealing.
  • It is easy to deploy.

1. Install Streamlit

If not already installed, install Streamlit using the official instructions.

2. Turn your code into a Streamlit app

  1. Turn medical_data_visualizer.py into a reusable module.

    1. Wrap the code that creates the df dataframe into a function called load_data(). This function should return the dataframe.
    2. Change the plotting functions to accept a dataframe as an argument.
    3. (Optional) Adapt the test to work with the new module.

  2. Using instructions at https://docs.streamlit.io/library/get-started/create-an-app, create your first Streamlit app 🎉 in a new app.py file:

    1. Import the medical_data_visualizer module.
    2. Add a title to your app.
    3. Load the dataframe using the load_data() function.
    4. Display the two figures using either the st.pyplot or st.write function. Optionally, you can add a header above each figure.

  3. Add some user controls (first, we need to make the functions in medical_data_visualizer accept arguments for the user controls):

    1. Change functions to accept these arguments:
      • load_data(bmi_overweight=25)
      • draw_cat_plot(df, split_by="cardio")
      • draw_heat_map(df, outlier_quantile=0.025)
    2. Add a sidebar to your app containing:
      • a slider for the bmi_overweight argument
      • a selectbox for the split_by argument
      • a number input for the outlier_quantile argument

  4. Publish the app using instructions at https://docs.streamlit.io/en/stable/deploy_streamlit_app.html.

You will need to: 1. Create a requirements.txt file containing the list of dependencies. See pip documentation for instructions. Remember you need to add all the primary dependencies, such as pandas or seaborn, and also streamlit. It is advisable to specify the versions of the dependencies, e.g. to pin the major versions using ~=. 2. Commit all changes and push to your GitHub repository. 3. Publish the app and share the link 😏

  1. Change the plotting library to Plotly Express and re-implement the plotting functions to use Plotly Express.