The normal distribution

               I have been investigating how to make dashboards as that seems to be the current
               thing. I was very suprised to see how easy it was to make one with Streamlit
               Below, is an intereactive plot of the normal/Gaussian distribution hosted by streamlit and written
               in Python.


            import streamlit as st
            import numpy as np
            import seaborn as sns
            import matplotlib.pyplot as plt

            st.title("Normal distribution")
            samples = st.slider("samples", 0,10_000, value=100)
            mu = st.slider("mu",0.0,1.0,step=0.001, value=0.5)
            sigma = st.slider("sigma",0.0,1.0, step=0.001, value=0.1)
            sample = np.random.normal(mu, sigma, samples)
            bins = st.slider("bins",1,1000, value=10)
            fig, ax  = plt.subplots()
            sns.histplot(sample, bins=bins, kde=True, ax=ax)
            st.pyplot(fig)

           To view your app in a local server you can use the code below. Streamlit can use a github
           repository to product the app from and will automatically update when the repository is updated
           the code for this example is host Here


            : streamlit run myapp.py