Why Streamlit is the Best Choice for Fast and Easy Data App Development
Streamlit as an open-source Python framework enables the easy, fast creation of interactive data apps, making it particularly useful for data scientists and machine learning engineers who lack extensive knowledge in web development.
Developers using Streamlit are able to build and share visually appealing user interfaces and deploy models without needing a strong background in front-end. This is an all-Python framework, which is free as well as open source, meaning that one can develop web applications that can be shared globally within minutes.
Why not try Streamlit if you need to make a quick prototype, SaaS, analytics dashboard or just some project for friends? It starts immediately and has many templates ready for your work; so you will spend several minutes on your frontend. Moreover, it’s very simple to share!
This library isn’t suitable if you want something big like scalable or feature-rich. Streamlit primarily focuses on building simple websites with a single page that demonstrate one particular feature; hence it is not advisable to use it for creating social networks or startups.
Additionally, quite a few users have stated how difficult customizing Streamlit can be. Once you need adding some other things apart from what was said in the documentations provided earlier then this becomes a tough task.
Streamlit Installation
Open a terminal and run command :
$ pip install streamlit $ streamlit hello
This will open a Streamlit Hello App in Browser.
Simple Example
import streamlit as st
import pandas as pd
# Title of the app
st.title('Simple Streamlit App')
# Load a sample dataset
df = pd.DataFrame({
'Column A': [1, 2, 3, 4],
'Column B': [10, 20, 30, 40]
})
# Display the dataset
st.write(df)
# Interactive widget
number = st.slider('Select a number', 1, 10)
st.write('You selected:', number)
This app shows how it is easy to create element like slider and tables.
