/Data-Visualize-Demo

Primary LanguagePython

README.md

This code provides a data explorer and visualizations for a given dataset. The following is a brief explanation of the code:

  1. Importing required libraries:

    • streamlit: used for building interactive web apps
    • pandas: used for data manipulation and analysis
    • matplotlib: used for creating visualizations
    • seaborn: used for creating statistical graphics

  2. Loading the dataset:

    • The function load_data() is used to load the dataset from a CSV file ("data.csv").
    • The @st.cache decorator is used to cache the results of the function for better performance.

  3. Selecting columns:

    • A sidebar is created using st.sidebar.title() to select columns of the dataset to be displayed.
    • st.sidebar.multiselect() is used to create a multiselect widget to choose columns of the dataset.

  4. Displaying data:

    • The selected columns of the dataset are displayed using st.write() and df[columns].

  5. Creating visualizations:

    • Two types of visualizations are created using matplotlib and seaborn libraries: Histogram and Scatterplot.
    • A histogram of the first selected column is created using plt.subplots() and ax.hist(). The visualization is then displayed using st.pyplot().
    • A scatterplot is created using sns.scatterplot() with the first two selected columns of the dataset. The visualization is then displayed using st.pyplot().

To run the code, the following steps should be followed:

  • Install the required libraries using pip install streamlit pandas matplotlib seaborn
  • Save the dataset as a CSV file named "data.csv" in the same directory as the code file.
  • Run the code using streamlit run <filename>.py in the terminal/command prompt.

Sure, here's an example README.md file for this code:

Usage

Once the app is running, you'll see a sidebar on the left where you can select the columns you want to explore. Simply check the boxes next to the column names, and the app will display the corresponding data.

You can also switch to the "Visualizations" tab to see histograms and scatterplots of the selected columns.

Data

This app uses a sample dataset called data.csv. You can replace this file with your own CSV file by updating the load_data() function in app.py.

Acknowledgements

This app was built using the following libraries:

License

This project is licensed under the MIT License - see the LICENSE file for details.