logologo
  • AI Tools

    DB Query GeneratorMock InterviewResume BuilderLearning Path GeneratorCheatsheet GeneratorAgentic Prompt GeneratorCompany ResearchCover Letter Generator
  • XpertoAI
  • MVP Ready
  • Resources

    CertificationsTopicsExpertsCollectionsArticlesQuestionsVideosJobs
logologo

Elevate Your Coding with our comprehensive articles and niche collections.

Useful Links

  • Contact Us
  • Privacy Policy
  • Terms & Conditions
  • Refund & Cancellation
  • About Us

Resources

  • Xperto-AI
  • Certifications
  • Python
  • GenAI
  • Machine Learning

Interviews

  • DSA
  • System Design
  • Design Patterns
  • Frontend System Design
  • ReactJS

Procodebase © 2024. All rights reserved.

Level Up Your Skills with Xperto-AI

A multi-AI agent platform that helps you level up your development skills and ace your interview preparation to secure your dream job.

Launch Xperto-AI

Introduction to Streamlit

author
Generated by
ProCodebase AI

15/11/2024

python

Sign in to read full article

What is Streamlit?

Streamlit is an open-source Python library that allows data scientists and developers to create beautiful, interactive web applications with minimal effort. It's designed to turn data scripts into shareable web apps in just a few lines of code, making it an ideal tool for quickly prototyping and sharing machine learning models, data visualizations, and other data-driven applications.

Why Use Streamlit?

Streamlit offers several advantages:

  1. Simplicity: You can create complex apps with just a few lines of Python code.
  2. No frontend experience required: Streamlit handles all the UI components for you.
  3. Instant deployment: Your app updates in real-time as you save your code.
  4. Wide range of components: From simple widgets to complex charts, Streamlit has it all.
  5. Integration with popular data science libraries: Works seamlessly with pandas, numpy, matplotlib, and more.

Getting Started with Streamlit

Installation

To get started with Streamlit, you'll need to install it first. Open your terminal and run:

pip install streamlit

Your First Streamlit App

Let's create a simple Streamlit app to get a feel for how it works. Create a new Python file called hello_streamlit.py and add the following code:

import streamlit as st st.title("My First Streamlit App") st.write("Hello, World!") name = st.text_input("Enter your name") if name: st.write(f"Hello, {name}!")

To run your app, open your terminal, navigate to the directory containing your Python file, and run:

streamlit run hello_streamlit.py

Your default web browser should open automatically, displaying your Streamlit app.

Key Streamlit Components

Streamlit provides a variety of components to help you build interactive apps. Here are some of the most commonly used ones:

Text Elements

  • st.title(): Displays text in title formatting
  • st.header(): Displays text in header formatting
  • st.subheader(): Displays text in subheader formatting
  • st.text(): Displays plain text
  • st.markdown(): Renders Markdown-formatted text

Input Widgets

  • st.text_input(): Creates a single-line text input field
  • st.number_input(): Creates a numeric input field
  • st.slider(): Creates a slider for selecting a value within a range
  • st.selectbox(): Creates a dropdown menu for selecting from a list of options
  • st.checkbox(): Creates a checkbox for boolean input

Data Display Elements

  • st.dataframe(): Displays a pandas DataFrame
  • st.table(): Displays static tables
  • st.json(): Displays JSON-formatted data

Charts and Plots

  • st.line_chart(): Creates a line chart
  • st.bar_chart(): Creates a bar chart
  • st.pyplot(): Displays matplotlib plots

A More Complex Example

Let's create a slightly more advanced app that demonstrates some of Streamlit's capabilities:

import streamlit as st import pandas as pd import numpy as np import matplotlib.pyplot as plt # Set page title st.set_page_config(page_title="Data Visualization App") # Add a title st.title("Data Visualization with Streamlit") # Create a sample DataFrame df = pd.DataFrame(np.random.randn(50, 3), columns=["A", "B", "C"]) # Display the DataFrame st.subheader("Sample Data") st.dataframe(df) # Create a line chart st.subheader("Line Chart") st.line_chart(df) # Create a selectbox for choosing a column column = st.selectbox("Select a column for the histogram", df.columns) # Create a histogram st.subheader("Histogram") fig, ax = plt.subplots() ax.hist(df[column], bins=20) ax.set_xlabel(column) ax.set_ylabel("Frequency") st.pyplot(fig) # Add a slider for data filtering threshold = st.slider("Filter data by threshold", float(df.min().min()), float(df.max().max())) filtered_df = df[df[column] > threshold] # Display filtered data st.subheader("Filtered Data") st.dataframe(filtered_df)

This app demonstrates how to display data, create charts, add interactive elements like dropdowns and sliders, and update the display based on user input.

Conclusion

Streamlit offers an intuitive and powerful way to create interactive web applications using Python. Its simplicity and integration with popular data science libraries make it an excellent choice for quickly prototyping and sharing your data projects. As you continue to explore Streamlit, you'll discover even more features and possibilities for creating engaging, data-driven web apps.

Popular Tags

pythonstreamlitweb development

Share now!

Like & Bookmark!

Related Collections

  • Python Advanced Mastery: Beyond the Basics

    13/01/2025 | Python

  • Mastering NumPy: From Basics to Advanced

    25/09/2024 | Python

  • Mastering LangGraph: Stateful, Orchestration Framework

    17/11/2024 | Python

  • Streamlit Mastery: From Basics to Advanced

    15/11/2024 | Python

  • Mastering Scikit-learn from Basics to Advanced

    15/11/2024 | Python

Related Articles

  • LangChain and Large Language Models

    26/10/2024 | Python

  • Mastering NumPy

    25/09/2024 | Python

  • Mastering Pandas

    25/09/2024 | Python

  • Mastering NumPy Broadcasting

    25/09/2024 | Python

  • Supercharging Named Entity Recognition with Transformers in Python

    14/11/2024 | Python

  • Bar Charts and Histograms Explained

    05/10/2024 | Python

  • Unleashing Real-Time Power

    15/10/2024 | Python

Popular Category

  • Python
  • Generative AI
  • Machine Learning
  • ReactJS
  • System Design