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What is Streamlit Python?

What is Streamlit Python?
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This blog aims to introduce and explore the capabilities of Streamlit in Python, focusing on its role in developing dynamic web applications. We’ll cover the essential aspects, starting with the introduction to Streamlit and its significance. Following that, we will discuss its installation and applications.

Table of Contents:

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Definition of Streamlit Python

Streamlit is an open-source Python library designed for the rapid development of interactive web applications. It makes it easier for users to create web-based data applications by enabling them to easily transform Python scripts into shareable online applications. The main advantage of Streamlit is its user-friendly design, which provides a simple and easy way for both newbies and seasoned developers to create compelling apps.

Streamlit turns raw data into visually appealing and dynamic web experiences with just a few lines of code by integrating machine learning models and interactive widgets. Its adaptability makes it suitable for a broad range of users, including researchers, data analysts, and software engineers. It bridges the gap between technically complicated applications and user-friendly ones by offering a platform on which complex data can be presented and interacted with with ease.

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Why Use Streamlit Python?

For those looking for a user-friendly solution for data display and application development in Python, Streamlit is a recommended option due to its simplicity, versatility, and capacity for generating compelling, interactive web apps.

Below are some reasons why you should use Streamlit:

  • Easy to Use: With just a few lines of code, Streamlit makes it easier to create interactive web apps in Python. Its approachable nature makes it suitable for both beginners and experienced developers.
  • Quick Development: Developers may quickly prototype and implement apps with Streamlit. Because of its simplicity, testing and iteration may happen more quickly, speeding up the development process.
  • Interactive Visualization: By enabling the production of dynamic and interactive charts, graphs, and maps, the library effortlessly incorporates data visualizations, increasing the accessibility and interest level of the data.
  • Integration of Machine Learning Models: Streamlit makes it easier for users to present and engage with machine learning models inside of web apps, creating a more engaging user experience.
  • Diverse Applications: Streamlit is a flexible tool for a variety of use cases, with a broad range of applications across multiple industries, including data science and analytics, education, and beyond.


How to Install Streamlit

Installing Streamlit is an easy process, and it’s perfect for creating web apps in Python. Whether you’re a data enthusiast or a developer, Streamlit makes building data-driven apps effortless. To start, follow these steps to install Streamlit on your computer.

Step 1: Set Up Python

Make sure that you have a Python environment installed on your system. You can download and install Python from its official website, python.org.

Step 2: Install Streamlit

In the terminal or command prompt, type the following command to install Streamlit:

pip install streamlit
Install streamlit

Step 3: Verify Installation

Verify the installation by examining the Streamlit version once it has finished.

streamlit version
verify installation

Step 4: Creating a Sample Streamlit App

To verify the installation, create a sample Streamlit application. Make a new Python file, for example, stramlit.py, and use your favorite code editor to open it. 

Creating a Sample Streamlit App

Step 5: Running the Streamlit App

Run the Streamlit app by executing this command in your terminal:

Streamlit run stramlit.py

This command will run your application on the local server, and a new tab will open in your default browser.

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Running the Streamlit App

st.title(): With this function, you can add the app’s title.

st.header(): The section header can be set using this function.

st.subheader(): The sub-header of a section can be set using this function.

st.caption(): The caption is written using this function.

st.code(): One uses this function to set a code.

st.latex(): LaTeX-formatted mathematical expressions can be seen with this function.


This will give the output:


Applications of Streamlit

Streamlit can be used in many different areas to make quick and user-friendly web apps for tasks like showing data and analysis. Whether you’re in research, business, or development, Streamlit helps you build web applications efficiently. Let us see a few of the applications of Streamlit:

  • Data Visualization and Analysis: Streamlit is widely used for displaying data through interactive graphs, charts, and visualizations in data science and analytics. For the purpose of interpreting data, it helps analysts produce dynamic visual representations.
  • Development and Prototyping: It is a great tool for creating minimal viable products (MVPs) and prototyping quickly. Because of its simplicity of use, developers may quickly prototype and test concepts, allowing for rapid iteration.
  • Machine Learning Applications: Streamlit makes it possible to include machine learning models in online programs. This makes it useful for sharing ML projects, building interactive dashboards for data prediction, and exhibiting and presenting models.
  • Educational Tools: Streamlit is used in the education industry to develop interactive teaching aids, learning resources, and simulation tools. 
  • Data Sharing and Communication: Streamlit is used by experts in a range of fields to generate dynamic reports and presentations. It is used to share analysis and insights in a more dynamic and interesting way.
  • Dashboard Creation: Live, interactive dashboards that enable real-time data display and analysis are created using Streamlit. Different stakeholders can access and share these dashboards.

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You’ve built a basic app and installed Streamlit successfully. You can now use Streamlit to create interactive web applications for your projects involving data science and machine learning. Remember that Streamlit offers an active and supportive community. Please feel free to browse the documentation or the Streamlit community forums for more detailed instructions if you have any questions or need help.

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Meet Akash, a Principal Data Scientist who worked as a Supply Chain professional with expertise in demand planning, inventory management, and network optimization. With a master’s degree from IIT Kanpur, his areas of interest include machine learning and operations research.