Streamlit Tutorial for Data Science Projects

Streamlight Installation and Setup

Before building your first Streamlit app, you must install Streamlit on your local machine. This is easy to do using pip, Python’s package installer.

• Step 1: Install Streamlight

  To install Streamlit, run the following command in your terminal or command prompt:

  • Pip install streamlet

• Step 2: Verify Installation

  Once the installation is complete, you can verify that Streamlit is installed correctly by running this command:

  • Streamlight –version

This should return the version of Streamlit installed on your system.

• Step 3: Start Your First Streamlit App

  To create and run your first Streamlit app, create a Python Programming file (e.g., app.py) and add the following simple code:

  • Import streamlet as st
  • st.title(“Hello, Streamlit!”)
  • st.write(“This is your first Streamlit app!”)
  Now, run the app with the command:
  • Streamlight run app.py

  Your app will open in a web browser, and the text you wrote will be displayed on the page.

  
  

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  Key Features of Streamlight

  Simplicity in Code

  Streamlit’s API is straightforward and clean. A few lines of Python code allow you to add text, graphs, buttons, sliders, and more. The following are some essential Streamlit elements:

  • st.title(): Adds a title to the app.
  • st.write(): Displays text or outputs, such as data frames, charts, etc.
  • st.slider(): Adds an interactive slider.
  • st.button(): Adds a button to trigger an action.

  Real-time Interactivity

  Streamlit’s design ensures that when users interact with the app (e.g., using sliders, dropdown menus, etc.), the app automatically reruns and updates the output. This feature, especially valuable in Data science Training, enables real-time feedback and dynamic data visualizations.

  Easy Integration with Libraries

  Streamlit integrates smoothly with popular Python libraries. For example, it supports visualizing Pandas DataFrames, using Matplotlib and Plotly for interactive plots, and even embedding machine learning models for prediction.

  
  

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  Creating Your First Streamlit App

  Building a Basic Web App

  Here’s an example of building a simple web app that displays a dataset and a plot using Streamlit.

  • import streamlet as st
  • import streamlet as st
  • import pandas as PD
  • import numpy as np
  • import matplotlib.pyplot as plt
  • # Create a simple dataset
  • data = pd.DataFrame({
  • ‘x’: np.linspace(0, 10, 100),
  • ‘y’: np.sin(np.linspace(0, 10, 100))
  • })
  • # Display the data frame
  • st.write(“Here is a simple dataset:”)
  • st.write(data)
  • # Create a plot
  • st.write(“Here is a plot of the dataset:”)
  • fig, ax = plt.subplots()
  • ax.plot(data[‘x’], data[‘y’])
  • st.pyplot(fig)

  Running this code will display the dataset and the plot on your app’s interface.

  Adding Widgets for Interactivity

  Streamlit’s widgets allow you to add interactivity to your app. Here’s an example of using a slider to change a plot dynamically:

  • import streamlet as st
  • import numpy as np
  • import matplotlib.pyplot as plt
  • # Add a slider to control the frequency of the sine wave
  • freq = st.slider(‘Select frequency’, 1, 10, 2)
  • # Generate a sine wave with the selected frequency
  • x = np.linspace(0, 10, 100)
  • y = np.sin(freq * x)
  • # Create and display the plot
  • fig, ax = plt.subplots()
  • ax.plot(x, y)
  • st.pyplot(fig)

  With this app, users can change the sine wave frequency by interacting with the slider.

  

  Advanced Streamlit Features

  Streamlit provides advanced features, making it an even more powerful tool for building data-driven apps.

  Using Plotly and Matplotlib for Data Visualization

  You can integrate Plotly for highly interactive visualizations or use Matplotlib for basic plots. Here’s an example of using Plotly:

  • import streamlet as st
  • import plotly.express as px
  • # Create a simple Plotly scatter plot
  • df = px.data.iris()
  • fig = px.scatter(df, x=’sepal_width’, y=’sepal_length’, color=’species’)
  • st.plotly_chart(fig)

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  Displaying DataFrames and Tables

  Streamlit makes it easy to display data tables, which are especially useful for data science projects. You can display Pandas DataFrames directly:

  • import streamlet as st
  • import pandas as PD
  • # Create a sample data frame
  • df = pd.DataFrame({
  • ‘Name’: [‘John,’ ‘Alice,’ ‘Bob’],
  • ‘Age’: [25, 30, 22]
  • })
  • # Display the data frame
  • st. write(pdf)

  

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  Incorporating Machine Learning Models

  Streamlit makes it simple to integrate machine learning models. You can showcase models built in Scikit-learn or any other library, allowing users to interact with them. Here’s a quick example with a model prediction:

  • Import streamlit as st
  • from sklearn.datasets import load_iris
  • from sklearn. Ensemble import RandomForestClassifier
  • # Load Iris dataset
  • data = load_iris()
  • X, y = data.data, data.target
  • # Train a model
  • model = RandomForestClassifier()
  • model.fit(X, y)
  • # Input from user
  • sepal_length = st.slider(“Sepal Length”, 4.0, 8.0, 5.0)
  • # Predict the class
  • prediction = model.predict([[sepal_length, 3.2, 4.5, 1.5]])
  • st.write(f”Predicted class: {data.target_names[prediction[0]]}”)

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  Deployment of Streamlit Apps

  You can do another once you’ve built your Streamlit app. You can deploy your app in several ways:

  • Hosting on Streamlit Cloud: Streamlit offers a free platform for hosting and sharing apps. You can quickly deploy your app by pushing it to GitHub and linking it to Streamlit Cloud.
  • Deployment on Other Platforms (Heroku, AWS): You can also deploy your app on other cloud platforms like Heroku, AWS, or Google Cloud, which offer greater flexibility and control over the hosting environment.

  Conclusion

  Streamlit is a fantastic tool for Python developers, data scientists, and machine learning engineers who want to rapidly build and deploy interactive web applications without needing extensive knowledge of web development. Its simplicity, real-time interactivity, and easy integration with data science libraries make it a go-to solution for turning Python code into user-friendly apps, especially in Data Science Training. Whether you’re visualizing data, presenting machine learning models, or sharing insights, Streamlit allows you to do it with minimal effort. Start building your Streamlit apps today, and unlock the power of Python in the world of web development!