Streamlit is an open-source Python library that simplifies the creation of interactive web apps for data science and machine learning projects. It is highly user-friendly, with minimal coding required to turn Python scripts into shareable web apps. It allows developers and data scientists to create interactive, visually appealing applications with minimal effort by focusing on writing Python code rather than dealing with front-end development.
KEY FEATURES
Simplicity: You can build apps using just Python. There’s no need for HTML, CSS, or JavaScript.
Fast Development: With a few lines of code, you can create dashboards or web apps that automatically update as the Python script changes.
Interactive Widgets: Streamlit provides a range of widgets (e.g., sliders, buttons, textboxes) that make it easy to add interactivity to your app.
Data Visualizations: It integrates seamlessly with popular data visualization libraries like Matplotlib, Plotly, and Seaborn, allowing you to display graphs and charts effortlessly.
Live Updates: Streamlit apps can dynamically update in real time as data or inputs change.
Deployment: You can easily deploy Streamlit apps on platforms like Streamlit Cloud, AWS, GCP or any other cloud service.
BUILD YOUR FIRST STREAMLIT APP
The primary objective of this Streamlit app is to provide an intuitive and interactive visualization of crime rates across major cities in India. By leveraging real-time data visualization tools, this application aims to provide users with insights into the varying crime rates in different urban areas. The data used in this app is sample data, designed to illustrate the functionality and capabilities of the application. You are encouraged to replace this sample dataset with your own real-time data to create tailored visualizations that reflect the most relevant information. 1. Set up your python development environment. 2. Installation:
pip install streamlit pandas plotly3. You can validate your Streamlit Installation by running the Hello app:
Streamlit hello4. Create a crime_rates.py file and import necessary libraries as per the code snippet mentioned below
import streamlit as st
import pandas as pd
import plotly.graph_objects as go
# sample data
data = {
'City': ['Mumbai', 'Delhi', 'Bangalore', 'Hyderabad', 'Kolkata', 'Chennai', 'Ahmedabad', 'Pune', 'Jaipur', 'Lucknow'],
'Crime Rate': [105, 98, 75, 85, 88, 65, 80, 70, 82, 68], # Crime rates per 100k people (mock data)
'Latitude': [19.0760, 28.7041, 12.9716, 17.3850, 22.5726, 13.0827, 23.0225, 18.5204, 26.9124, 26.8467],
'Longitude': [72.8777, 77.1025, 77.5946, 78.4867, 88.3639, 80.2707, 72.5714, 73.8567, 75.7873, 80.9462]
}
# Create a DataFrame
df = pd.DataFrame(data)
#size of the datapoint
marker_size = df['Crime Rate'] / max(df['Crime Rate']) * 20
# Streamlit App
st.title("Crime Rates Across Indian Cities")
# Show the DataFrame
st.write("Here is the crime rate data for various cities in India:")
st.dataframe(df)
fig = go.Figure(go.Scattermapbox(
lat=df['Latitude'],
lon=df['Longitude'],
mode='markers',
marker=go.scattermapbox.Marker(
size=marker_size, # Adjust size of the markers dynamically based on
crime rate
color='red', # Set the color of the points
opacity=0.7 # Make the points slightly transparent for better
visualization
),
text=df['City'] + '<br>Crime Rate: ' + df['Crime Rate'].astype(str) + ' per 100k people',
hoverinfo='text'
))
# Set the layout for the map
fig.update_layout(
mapbox=dict(
style='open-street-map',
zoom=4,
center=dict(lat=20.5937, lon=78.9629) # Center of India
),
margin={"r":0,"t":0,"l":0,"b":0} # Remove margins
)
# Display the Plotly map in Streamlitst.plotly_chart(fig)5. Navigate to the directory where your Python file is saved, and run the following command:
streamlit run crime_rates.py In this app, Streamlit is utilized to create an intuitive and engaging user experience.
Title Creation: The st.title() function establishes a clear and prominent title, immediately informing users about the app’s focus on crime rates across Indian cities.
Context and Description: The st.write() function provides essential context and descriptions, guiding users through the data displayed in the app.
Data Presentation: Streamlit’s st.dataframe() function presents the underlying crime rate data in a well-structured, scrollable table, making it easy for users to explore and compare crime rates among cities.
Interactive Visualization: Finally, the app employs Plotly mapping features supported by Streamlit, enabling the interactive display of a scatter map. Users can hover over the markers to view detailed information about each city, including the specific crime rate.
Through these features, Streamlit effectively enhances the app’s usability, allowing users to interact with and understand crime data in a visually appealing manner.
[ Good Read: ETL vs. ELT ]
APP PREVIEW
Once the app is running, you will see an interactive map displaying crime rates across various Indian cities, accompanied by a detailed table of the underlying data. Users can hover over the markers on the map to view specific crime rates, making it easy to explore and analyze urban safety trends.
You can check more info about: StreamLit.
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