This course will teach you how to develop web apps using the Streamlit library. With just a few lines of code, you can have your app up and running in no time.

streamlit.tar.gz

Streamlit

This course will teach you how to develop web apps using the Streamlit library. With just a few lines of code, you can have your app up and running in no time.

We'll start off with learning about the various components of Streamlit, before finally building an app that you can deploy and have your users access.

Develop Web Apps with Streamlit

Streamlit has the option to add data to an existing dataframe or chart. This is useful in scenarios where you may already have data on hand, but further work results in more data being collected. Instead of having to set up a new dataframe from scratch, Streamlit allows you to use the add_rows() method to update the existing dataframe.

df1 = pd.DataFrame({"A": [94, 25, 70, 90, 26],
               "B": [17,  46,  59, 195, 126],
               "C": [96, 267, 214, 209,  31]})

df2 = pd.DataFrame({"A": [69, 42, 51],
               "B": [192, 136,  33],
               "C": [145, 263, 125]})

st.dataframe(df1)
st.dataframe(df2)
df = st.dataframe(df1)
df.add_rows(df2)

First Things First

Introduction to Streamlit

Display Information with Streamlit

Display Interactive Widgets with Streamlit

Streamlit Layout

Mutate Data with Streamlit

Streamlit Miscallenous Items

Deploy Streamlit Apps

Mutate Tabular Data with Streamlit