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DataVisualisation project

Data exploration and cleaning

  1. The data exploring and cleaning step was done in ./dataVisu.ipynb. I've worked on the 2020 and 2019 dataset.
  2. After cleaning the data the results were saved in df2020.csv df2019.csv



Streamlit app

  1. The streamlit app have three main feature.
    a. 2019 analysis : This feature allows you to have an overview of the 2019 data and to focus on a particular municipality

    b. 2020 analysis : This feature allows you to have an overview of the 2020 data and to focus on a particular municipality

    c. filter feature : This feature helps you to have an overview of the 2020 data with a filter. You can also download the filtered data after for your own purpose.

  2. Files architecture:
    a. ./prj_utils.py contains all the function that were used to process/visualize the data

    b. ./project.py is the main file of the app

  3. If you are struggling to run the app, you can access it on the stremlit servers with this link --> https://aure-m-datavisualisationprj-project-sf88ef.streamlitapp.com