{"id":19223,"date":"2024-10-15T16:53:50","date_gmt":"2024-10-15T11:23:50","guid":{"rendered":"https:\/\/opstree.com\/blog\/?p=19223"},"modified":"2026-02-18T15:09:05","modified_gmt":"2026-02-18T09:39:05","slug":"getting-started-with-streamlit-build-interactive-data-apps-in-python","status":"publish","type":"post","link":"https:\/\/opstree.com\/blog\/2024\/10\/15\/getting-started-with-streamlit-build-interactive-data-apps-in-python\/","title":{"rendered":"Getting Started with StreamLit: Build Interactive Data Apps in Python"},"content":{"rendered":"<p><span class=\"TextRun SCXW233843445 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW233843445 BCX0\">\u00a0<\/span><\/span> <span class=\"TextRun SCXW233843445 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW233843445 BCX0\">In this blog, we will explore the <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW233843445 BCX0\">Streamlit<\/span><span class=\"NormalTextRun SCXW233843445 BCX0\"> library, which <\/span><span class=\"NormalTextRun SCXW233843445 BCX0\">simplifies the creation of<\/span><span class=\"NormalTextRun SCXW233843445 BCX0\"> data-driven web applications without having <\/span><span class=\"NormalTextRun SCXW233843445 BCX0\">prior<\/span><span class=\"NormalTextRun SCXW233843445 BCX0\"> knowledge of <\/span><span class=\"NormalTextRun SCXW233843445 BCX0\">front-end <\/span><span class=\"NormalTextRun SCXW233843445 BCX0\">development<\/span><span class=\"NormalTextRun SCXW233843445 BCX0\">.\u00a0<\/span><\/span><\/p>\r\n<h5><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-19251 aligncenter\" src=\"https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/Screenshot-2024-10-09-at-12.25.08\u202fAM-e1728413860920-300x150.png\" alt=\"\" width=\"734\" height=\"367\" srcset=\"https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/Screenshot-2024-10-09-at-12.25.08\u202fAM-e1728413860920-300x150.png 300w, https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/Screenshot-2024-10-09-at-12.25.08\u202fAM-e1728413860920-1024x514.png 1024w, https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/Screenshot-2024-10-09-at-12.25.08\u202fAM-e1728413860920-768x385.png 768w, https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/Screenshot-2024-10-09-at-12.25.08\u202fAM-e1728413860920-1536x770.png 1536w, https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/Screenshot-2024-10-09-at-12.25.08\u202fAM-e1728413860920-2048x1027.png 2048w, https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/Screenshot-2024-10-09-at-12.25.08\u202fAM-e1728413860920-1200x602.png 1200w\" sizes=\"(max-width: 709px) 85vw, (max-width: 909px) 67vw, (max-width: 984px) 61vw, (max-width: 1362px) 45vw, 600px\" \/><\/h5>\r\n<h3><b><span data-contrast=\"auto\">INTRODUCTION<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/h3>\r\n<p><span data-contrast=\"auto\"><a href=\"https:\/\/streamlit.io\/\" target=\"_blank\" rel=\"noopener\">Streamlit<\/a> 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.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><!--more--><\/p>\r\n<h5><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-19246 aligncenter\" src=\"https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/1673640599539-300x99.png\" alt=\"\" width=\"685\" height=\"226\" srcset=\"https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/1673640599539-300x99.png 300w, https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/1673640599539-1024x339.png 1024w, https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/1673640599539-768x255.png 768w, https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/1673640599539.png 1080w\" sizes=\"(max-width: 709px) 85vw, (max-width: 909px) 67vw, (max-width: 984px) 61vw, (max-width: 1362px) 45vw, 600px\" \/><\/h5>\r\n<h3><b>KEY FEATURES<\/b><\/h3>\r\n<ul>\r\n<li style=\"list-style-type: none;\">\r\n<ul>\r\n<li><b><span data-contrast=\"auto\">Simplicity<\/span><\/b><span data-contrast=\"auto\">: You can build apps using just Python. There&#8217;s no need for HTML, CSS, or JavaScript.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\r\n<li><b><span data-contrast=\"auto\">Fast Development<\/span><\/b><span data-contrast=\"auto\">: With a few lines of code, you can create dashboards or web apps that automatically update as the Python script changes.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\r\n<li><b><span data-contrast=\"auto\">Interactive Widgets<\/span><\/b><span data-contrast=\"auto\">: Streamlit provides a range of widgets (e.g., sliders, buttons, textboxes) that make it easy to add interactivity to your app.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\r\n<li><b><span data-contrast=\"auto\">Data Visualizations<\/span><\/b><span data-contrast=\"auto\">: It integrates seamlessly with popular data visualization libraries like <a href=\"https:\/\/matplotlib.org\/\" target=\"_blank\" rel=\"noopener\">Matplotlib<\/a>, <a href=\"https:\/\/plotly.com\/\" target=\"_blank\" rel=\"noopener\">Plotly<\/a>, and <a href=\"https:\/\/seaborn.pydata.org\/\" target=\"_blank\" rel=\"noopener\">Seaborn<\/a>, allowing you to display graphs and charts effortlessly.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\r\n<li><b><span data-contrast=\"auto\">Live Updates<\/span><\/b><span data-contrast=\"auto\">: Streamlit apps can dynamically update in real time as data or inputs change.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\r\n<li><b><span data-contrast=\"auto\">Deployment<\/span><\/b><span data-contrast=\"auto\">: You can easily deploy Streamlit apps on platforms like <a href=\"https:\/\/streamlit.io\/cloud\" target=\"_blank\" rel=\"noopener\">Streamlit Cloud<\/a>, <a href=\"https:\/\/aws.amazon.com\/\" target=\"_blank\" rel=\"noopener\">AWS<\/a>, <a href=\"https:\/\/cloud.google.com\/\" target=\"_blank\" rel=\"noopener\">GCP<\/a> or any other cloud service.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>\r\n<h3><b><span data-contrast=\"none\">BUILD YOUR FIRST STREAMLIT APP<\/span><\/b><span data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:160,&quot;335559739&quot;:80}\">\u00a0<\/span><\/h3>\r\n<p><span data-contrast=\"auto\">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.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span> <span data-contrast=\"auto\">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.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span> <span data-contrast=\"auto\">1. Set up your python development environment.<\/span> <span data-contrast=\"auto\">2. Installation: <\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\r\n<pre><b><span data-contrast=\"auto\">pip install streamlit pandas plotly<\/span><\/b><\/pre>\r\n<p><span data-contrast=\"auto\">3. You can validate your Streamlit Installation by running the <strong>Hello app<\/strong>: <\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\r\n<pre><b><span data-contrast=\"auto\">Streamlit hello<\/span><\/b><\/pre>\r\n<p><span data-contrast=\"auto\">4. Create a <strong>crime_rates.py<\/strong> file and import necessary libraries as per the code snippet mentioned below<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;201341983&quot;:0,&quot;335551550&quot;:1,&quot;335551620&quot;:1,&quot;335559685&quot;:720,&quot;335559737&quot;:0,&quot;335559738&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:279,&quot;335559991&quot;:360}\">\u00a0<\/span><\/p>\r\n<pre><b><span data-contrast=\"auto\">import streamlit as st\r\n<\/span><\/b><b><span data-contrast=\"auto\">import pandas as pd<\/span><\/b>\r\n<b><span data-contrast=\"auto\">import plotly.graph_objects as go<\/span><\/b>\r\n\r\n<b><span data-contrast=\"auto\"># sample data<\/span><\/b> \r\n<b><span data-contrast=\"auto\">data = {<\/span><\/b> \r\n<b><span data-contrast=\"auto\">'City': ['Mumbai', 'Delhi', 'Bangalore', 'Hyderabad', 'Kolkata', 'Chennai', 'Ahmedabad', 'Pune', 'Jaipur', 'Lucknow'],<\/span><\/b> \r\n<b><span data-contrast=\"auto\">'Crime Rate': [105, 98, 75, 85, 88, 65, 80, 70, 82, 68], # Crime rates per 100k people (mock data)<\/span><\/b> \r\n<b><span data-contrast=\"auto\">'Latitude': [19.0760, 28.7041, 12.9716, 17.3850, 22.5726, 13.0827, 23.0225, 18.5204, 26.9124, 26.8467],<\/span><\/b> \r\n<b><span data-contrast=\"auto\">'Longitude': [72.8777, 77.1025, 77.5946, 78.4867, 88.3639, 80.2707, 72.5714, 73.8567, 75.7873, 80.9462]<\/span><\/b> \r\n<b><span data-contrast=\"auto\">}<\/span><\/b>\r\n\r\n<b><span data-contrast=\"auto\"># Create a DataFrame<\/span><\/b>\r\n<b><span data-contrast=\"auto\">df = pd.DataFrame(data)<\/span><\/b>\r\n\r\n<b><span data-contrast=\"auto\">#size of the datapoint<\/span><\/b>\r\n<b><span data-contrast=\"auto\">marker_size = df['Crime Rate'] \/ max(df['Crime Rate']) * 20<\/span><\/b>\r\n\r\n<b><span data-contrast=\"auto\"># Streamlit App<\/span><\/b>\r\n<b><span data-contrast=\"auto\">st.title(\"Crime Rates Across Indian Cities\")<\/span><\/b>\r\n\r\n<b><span data-contrast=\"auto\"># Show the DataFrame<\/span><\/b>\r\n<b><span data-contrast=\"auto\">st.write(\"Here is the crime rate data for various cities in India:\")<\/span><\/b>\r\n<b><span data-contrast=\"auto\">st.dataframe(df)<\/span><\/b>\r\n\r\n<b><span data-contrast=\"auto\">fig = go.Figure(go.Scattermapbox(<\/span><\/b>\r\n<b><span data-contrast=\"auto\">lat=df['Latitude'],<\/span><\/b>\r\n<b><span data-contrast=\"auto\">lon=df['Longitude'],<\/span><\/b>\r\n<b><span data-contrast=\"auto\">mode='markers',<\/span><\/b>\r\n<b><span data-contrast=\"auto\">marker=go.scattermapbox.Marker(<\/span><\/b>\r\n<b><span data-contrast=\"auto\">size=marker_size, # Adjust size of the markers dynamically based on\r\ncrime rate<\/span><\/b>\r\n<b><span data-contrast=\"auto\">color='red', # Set the color of the points<\/span><\/b>\r\n<b><span data-contrast=\"auto\">opacity=0.7 # Make the points slightly transparent for better\r\nvisualization<\/span><\/b>\r\n<b><span data-contrast=\"auto\">),<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;201341983&quot;:0,&quot;335557856&quot;:8355711,&quot;335559685&quot;:864,&quot;335559737&quot;:0,&quot;335559740&quot;:279}\">\u00a0<\/span>\r\n\r\n<b><span data-contrast=\"auto\">text=df['City'] + '&lt;br&gt;Crime Rate: ' + df['Crime Rate'].astype(str) + ' per 100k people',<\/span><\/b>\r\n<b><span data-contrast=\"auto\">hoverinfo='text'<\/span><\/b>\r\n<b><span data-contrast=\"auto\">))<\/span><\/b>\r\n\r\n<b><span data-contrast=\"auto\"># Set the layout for the map<\/span><\/b>\r\n<b><span data-contrast=\"auto\">fig.update_layout(<\/span><\/b>\r\n<b><span data-contrast=\"auto\">mapbox=dict(<\/span><\/b>\r\n<b><span data-contrast=\"auto\">style='open-street-map',<\/span><\/b>\r\n<b><span data-contrast=\"auto\">zoom=4,<\/span><\/b>\r\n<b><span data-contrast=\"auto\">center=dict(lat=20.5937, lon=78.9629) # Center of India<\/span><\/b>\r\n<b><span data-contrast=\"auto\">),<\/span><\/b>\r\n<b><span data-contrast=\"auto\">margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0} # Remove margins<\/span><\/b>\r\n<b><span data-contrast=\"auto\">)<\/span><\/b>\r\n\r\n<b><span data-contrast=\"auto\"># Display the Plotly map in Streamlit<\/span><\/b><b><span data-contrast=\"auto\">st.plotly_chart(fig)<\/span><\/b><\/pre>\r\n<p><span data-contrast=\"auto\">5. Navigate to the directory where your Python file is saved, and run the following command:<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;201341983&quot;:0,&quot;335551550&quot;:1,&quot;335551620&quot;:1,&quot;335559737&quot;:0,&quot;335559738&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:279}\">\u00a0<\/span><\/p>\r\n<pre><b><span data-contrast=\"auto\">streamlit run crime_rates.py<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;201341983&quot;:0,&quot;335557856&quot;:8355711,&quot;335559685&quot;:864,&quot;335559737&quot;:1440,&quot;335559740&quot;:279}\">\u00a0<\/span><\/pre>\r\n<p><span data-contrast=\"auto\">In this app, Streamlit is utilized to create an intuitive and engaging user experience.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\r\n<ul>\r\n<li style=\"list-style-type: none;\">\r\n<ul>\r\n<li><b><span data-contrast=\"auto\">Title Creation<\/span><\/b><span data-contrast=\"auto\">: The <a href=\"https:\/\/docs.streamlit.io\/develop\/api-reference\/text\/st.title\" target=\"_blank\" rel=\"noopener\">st.title()<\/a> function establishes a clear and prominent title, immediately informing users about the app&#8217;s focus on crime rates across Indian cities.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/li>\r\n<li><b><span data-contrast=\"auto\">Context and Description<\/span><\/b><span data-contrast=\"auto\">: The <a href=\"https:\/\/docs.streamlit.io\/develop\/api-reference\/write-magic\/st.write\" target=\"_blank\" rel=\"noopener\">st.write()<\/a> function provides essential context and descriptions, guiding users through the data displayed in the app.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/li>\r\n<li><b><span data-contrast=\"auto\">Data Presentation<\/span><\/b><span data-contrast=\"auto\">: Streamlit\u2019s <a href=\"https:\/\/docs.streamlit.io\/develop\/api-reference\/data\/st.dataframe\" target=\"_blank\" rel=\"noopener\">st.dataframe()<\/a> 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.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/li>\r\n<li><b><span data-contrast=\"auto\">Interactive Visualization<\/span><\/b><span data-contrast=\"auto\">: 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.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>\r\n<p><span data-contrast=\"auto\">Through these features, Streamlit effectively enhances the app&#8217;s usability, allowing users to interact with and understand crime data in a visually appealing manner.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\r\n<h3><strong>APP PREVIEW<\/strong><\/h3>\r\n<p><span data-contrast=\"auto\">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.\u00a0<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span> <img loading=\"lazy\" decoding=\"async\" class=\"wp-image-19236 aligncenter\" src=\"https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/Screenshot-2024-09-25-at-8.05.31\u202fPM-300x172.png\" alt=\"\" width=\"578\" height=\"331\" srcset=\"https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/Screenshot-2024-09-25-at-8.05.31\u202fPM-300x172.png 300w, https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/Screenshot-2024-09-25-at-8.05.31\u202fPM-1024x588.png 1024w, https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/Screenshot-2024-09-25-at-8.05.31\u202fPM-768x441.png 768w, https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/Screenshot-2024-09-25-at-8.05.31\u202fPM-1536x882.png 1536w, https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/Screenshot-2024-09-25-at-8.05.31\u202fPM-1200x689.png 1200w, https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/Screenshot-2024-09-25-at-8.05.31\u202fPM.png 1874w\" sizes=\"(max-width: 578px) 85vw, 578px\" \/> <span class=\"TextRun SCXW201562608 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW201562608 BCX0\">Users can hover over the markers on the map to view specific crime rates, making it easy to explore and analyze urban safety trends.<\/span><\/span><span class=\"EOP SCXW201562608 BCX0\" data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span> <img loading=\"lazy\" decoding=\"async\" class=\"wp-image-19237 aligncenter\" src=\"https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/Screenshot-2024-09-25-at-8.05.25\u202fPM-e1728410110191-300x192.png\" alt=\"\" width=\"580\" height=\"371\" srcset=\"https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/Screenshot-2024-09-25-at-8.05.25\u202fPM-e1728410110191-300x192.png 300w, https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/Screenshot-2024-09-25-at-8.05.25\u202fPM-e1728410110191-768x491.png 768w, https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/Screenshot-2024-09-25-at-8.05.25\u202fPM-e1728410110191.png 1004w\" sizes=\"(max-width: 580px) 85vw, 580px\" \/><\/p>\r\n<h3><strong><span class=\"TextRun SCXW179977330 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW179977330 BCX0\">CONCLUSION <\/span><\/span><\/strong><span class=\"EOP SCXW179977330 BCX0\" data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;201341983&quot;:0,&quot;335551550&quot;:1,&quot;335551620&quot;:1,&quot;335559685&quot;:0,&quot;335559737&quot;:0,&quot;335559738&quot;:240,&quot;335559739&quot;:240,&quot;335559740&quot;:279}\">\u00a0<\/span><\/h3>\r\n<p>In this tutorial, we&#8217;ve seen how to create a <strong>crime rates app<\/strong> for major Indian cities, offering real-time, interactive insights through just a few lines of Python code. By using Streamlit, you can quickly deploy and share similar apps with ease, transforming data into accessible formats for users.<\/p>\r\n<div class=\"ch bg ew ex ey ez\"><strong>Blog Pundits:<\/strong> <strong><a href=\"https:\/\/opstree.com\/blog\/author\/sandeep7c51ad81ba\/\">Sandeep Rawat<\/a> <\/strong> <!-- \/wp:list -->\r\n\r\n<!-- wp:paragraph --><\/div>\r\n<div>\u00a0<\/div>\r\n<div class=\"ch bg ew ex ey ez\"><a href=\"https:\/\/www.opstree.com\/contact-us?utm_source=wordpress&amp;utm_campaign=AWS-Gateway-LoadBalancer-A-Load-Balancer-that-we-deserve&amp;utm_id=Blog\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Opstree<\/strong> <\/a>is an End to End DevOps solution provider<\/div>\r\n<div>\u00a0<\/div>\r\n<div class=\"ch bg ew ex ey ez\"><!-- \/wp:paragraph -->\r\n\r\n<!-- wp:buttons -->\r\n<div class=\"wp-block-buttons\"><!-- wp:button {\"className\":\"is-style-fill\"} -->\r\n<div class=\"wp-block-button is-style-fill\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.opstree.com\/contact-us\" target=\"_blank\" rel=\"noreferrer noopener\">CONTACT US<\/a><\/div>\r\n<!-- \/wp:button --><\/div>\r\n<!-- \/wp:buttons -->\r\n\r\n<!-- wp:paragraph {\"align\":\"center\"} -->\r\n<p class=\"has-text-align-center\"><strong>Connect With Us<\/strong><\/p>\r\n<p>&nbsp;<\/p>\r\n<\/div>\r\n<section style=\"border: 2px solid #d1d1d1; padding: 25px; margin: 25px 0; background-color: #f9fafc; border-radius: 8px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);\">\r\n<h2 style=\"color: #004085; font-size: 26px; margin-bottom: 15px; text-align: center;\">\ud83d\ude80 Webinar: Maximize ROI with DevSecOps<\/h2>\r\n<p style=\"font-size: 18px; color: #333; margin-bottom: 15px; text-align: center; line-height: 1.6;\">Uncover strategies to accelerate releases, enhance security, and reduce costs with an internal DevSecOps platform. Master CI\/CD, automation, and ROI measurement in this value-packed session tailored for tech leaders and DevOps pros!<\/p>\r\n<div style=\"background-color: #e3f2fd; padding: 15px; border: 1px solid #b6d4fe; border-radius: 6px; margin: 15px 0;\">\r\n<p style=\"font-size: 16px; color: #0056b3; text-align: center; font-weight: bold;\">\ud83d\udcc5 Date &amp; Time:<\/p>\r\n<p style=\"font-size: 16px; color: #333; text-align: center; margin: 5px 0;\"><strong>ET:<\/strong> 11:00 AM &#8211; 01:00 PM<br \/><strong>IST:<\/strong> 8:30 PM &#8211; 10:30 PM<\/p>\r\n<\/div>\r\n<div style=\"text-align: center; margin-top: 20px;\">Reserve Your Spot Now<\/div>\r\n<\/section>","protected":false},"excerpt":{"rendered":"<p>\u00a0 In this blog, we will explore the Streamlit library, which simplifies the creation of data-driven web applications without having prior knowledge of front-end development.\u00a0 INTRODUCTION\u00a0 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 &hellip; <a href=\"https:\/\/opstree.com\/blog\/2024\/10\/15\/getting-started-with-streamlit-build-interactive-data-apps-in-python\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Getting Started with StreamLit: Build Interactive Data Apps in Python&#8221;<\/span><\/a><\/p>\n","protected":false},"author":244582683,"featured_media":19280,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false},"version":2}},"categories":[768739361],"tags":[768739385,768739342,832,768739386],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"https:\/\/opstree.com\/blog\/wp-content\/uploads\/2024\/10\/Getting-Started-with-StreamLit.png","jetpack_likes_enabled":true,"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/pfDBOm-503","jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/posts\/19223"}],"collection":[{"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/users\/244582683"}],"replies":[{"embeddable":true,"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/comments?post=19223"}],"version-history":[{"count":26,"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/posts\/19223\/revisions"}],"predecessor-version":[{"id":30831,"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/posts\/19223\/revisions\/30831"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/media\/19280"}],"wp:attachment":[{"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/media?parent=19223"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/categories?post=19223"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/tags?post=19223"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}