StreamLit - Data Scientists tool for developing web apps

  • StreamLit is a tool for Data Scientists and Machine Learning Engineers to create the front end of the web application.
  • StreamLit is a wrapper around JavaScript, HTML, and CSS.
  • It helps developers with no expertise in JavaScript, HTML, and CSS create interactive web-based front ends for the models.

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Pros and Cons of Streamlit


  • Interactive
  • Customizable, and does not require any front end experience.​
  • Deployment is easy​
  • Active Community​


  • The front-end interface is fixed, and developers cannot adjust the position of controls​.
  • Early stages of development​.

Getting Started with StreamLit

→ Downloading StreamLit

pip install streamlit

→ Running the file

streamlit run 

Setting up the web App

import streamlit as st
import pandas as pd
  • Title:
    Let’s start with the title of the web app, use your python script to write the codes.
st.title("First StreamLit App")
  • Header:
st.header("Abhigyan's App")
  • Subheader:
st.subheader("I am a Data Science Researcher at INSAID")
  • Text and markdown:
st.text("INSAID: India’s No.1 Online School to Learn Data Science & AI")
  1. You can pass in multiple arguments, all of which will be written.
  2. Its behaviour depends on the input types as follows.
  3. It returns None, so it’s “slot” in the App cannot be reused.
st.write(pd.DataFrame({'A': [1, 2, 3, 4],
'B': [5, 6, 7, 8]}))

Interactive widgets

  • Button:
if st.button("Start"):
st.text("Hi! Welcome to the Web App")
  • Checkbox:
st.checkbox(label, value = False)
yes = st.checkbox("Move Forward")if yes:
st.text("Great Choice!")
  • Radio:, options, index = 0)
options = ['Beginner', 'Intermediate', 'Master', 'GODLY']
genre ="How skilled are you at Data Science", options, index = 2)
if genre == 'Beginner':
st.text("Keep pushing yourself, if you want a boost check out:")
elif genre == 'Intermediate':
st.text("Keep learning and you will succeed.")
elif genre == 'Master':
st.write("Awesome! *Master Shifu* :sunglasses:")
elif genre == 'GODLY':
st.write("Welcome to my webpage")
st.write("You didn't select anything")
  • Selectbox:
st.selectbox(label, options, index = 0)
exp = ["0-1", "2-5", "6-11", "> 12"]
option = st.selectbox("What is your experience level", exp)
st.write("You have a experience of:", option)
  • Multiselect:
st.multiselect(label, options, default = None)
mem = ['Father', 'Mother', 'Son', 'Daughter', 'Grand Mother',
'Grand Father']
option = st.multiselect("How many members in your family", mem,
['Father, 'Mother'])
st.write('You selected:')
for i in option:
  • Slider:
st.slider(label, min_value, max_value, value, step, format)
age = st.slider("How old are you?", 6, 100, 1)
st.write("I am", age, "years old")
st.text_input(label, value, max_chars, type)
comp = st.text_input("Your Company name")
st.write("your comany name is", comp)
  • Text Area:
st.text_area(label, value, height, max_chars)
text = st.text_area("Write about Yourself")
st.write('About you:', text)

Displaying Charts

  • Line Chart:
st.line_chart(data, width, height, use_container_width)
chart_data = pd.DataFrame(np.random.randn(20, 3),
columns=['a', 'b', 'c'])
  • Area chart:
st.area_chart(data, width, height, use_container_width)
chart_data = pd.DataFrame(np.random.randn(20, 3),
columns=['a', 'b', 'c'])
  • Similarly you can plot bar chart by using the function st.bar_chart().
  • Pyplot:
st.pyplot(fig, clear_figure)
import matplotlib.pyplot as plt
import numpy as np

arr = np.random.normal(1, 1, size=100)
fig, ax = plt.subplots()
ax.hist(arr, bins=20)

Display Media

  • Image:
st.image(image, caption, width, use_column_width, clamp, channels, output_format)
st.image(image, caption, use_column_width = True)

Sharing web apps

  1. Push your app in your GitHub repository, with the requirements.txt will all the packages that need to be installed for a smooth flow of the web app.
  2. Create an account in
  3. Paste your GitHub repo’s URL after clicking on ‘Deploy an app’.

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