How to Use Streamlit with Seaborn: A Quick Guide

Name

Olaf Källström

Published on 7/24/2023

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In the realm of Python data visualization, two names often stand out: Streamlit and Seaborn. These powerful libraries have revolutionized the way we visualize data, making it more accessible and insightful. In this comprehensive guide, we'll delve into the integration of Streamlit and Seaborn, showcasing how they can work together to create stunning visualizations.

Whether you're a seasoned data scientist or a beginner stepping into the world of data visualization, this guide offers valuable insights. We'll start with the basics, defining what Streamlit and Seaborn are, and gradually move into more complex territories, such as their integration and practical examples.

Introducing Streamlit

Ever felt like you're drowning in code while trying to build a data visualization app? Enter Streamlit. This open-source Python library is a breath of fresh air for developers, making it a breeze to create interactive web applications for machine learning and data science.

Streamlit's charm lies in its simplicity. Imagine building a fully functional web application with just a few lines of code. Sounds too good to be true, right? But with Streamlit, it's a reality.

Here's a quick peek at what Streamlit brings to the table:

• Simplicity: Streamlit's user-friendly interface lets you focus more on the data and less on the coding.
• Customizability: Want your app to stand out? Streamlit allows you to tweak your app to fit your specific needs.
• Integration: Streamlit plays well with other Python libraries, including Seaborn, Matplotlib, and Altair. This makes it a Swiss Army knife in a data scientist's toolkit.

And What is Seaborn?

If Streamlit is the stage, Seaborn is the performer. This Python data visualization library, based on Matplotlib, is like an artist, turning raw data into attractive and informative statistical graphics.

Seaborn shines when it comes to visualizing complex datasets with multiple variables. It's like having a magnifying glass that reveals patterns and relationships in your data that might otherwise go unnoticed.

Here are some of Seaborn's standout features:

• Intricate Plot Types: Ever heard of pair plots and correlation plots? These are Seaborn's specialties, providing valuable insights into your data.
• Integration with Pandas: Seaborn and Pandas go together like peanut butter and jelly. This integration makes Seaborn an excellent tool for exploratory data analysis.

Streamlit and Seaborn: A Dynamic Duo in Data Visualization

When used separately, Streamlit and Seaborn are like superheroes in the world of data visualization. But when they join forces, they become an unstoppable force. The integration of Streamlit and Seaborn allows you to create interactive data visualizations that can be shared and accessed through a web application.

This powerful combination opens up a world of possibilities. For instance, you can use Seaborn to create a complex pairplot, then use Streamlit to display that plot in a web application. This way, you can share your findings with others, even if they don't have Python or Seaborn installed on their machines.

But like any dynamic duo, Streamlit and Seaborn have their challenges. You might encounter issues, such as the infamous 'ImportError' or the 'savefig' error. But don't worry, we've got you covered. We'll tackle these issues and their solutions in the following sections.

However, integrating Streamlit and Seaborn isn't always straightforward. You might encounter issues, such as the infamous 'ImportError' or the 'savefig' error. But don't worry, we'll cover these issues and their solutions in the following sections.

Examples of How to Use Streamlit with Seaborn

Now that we've covered the basics, let's dive into some practical examples. In this section, we'll walk you through the process of creating a Streamlit-Seaborn pairplot and a correlation plot. We'll also address the 'savefig' error that often occurs when integrating Streamlit and Seaborn.

Streamlit and Seaborn Integration

Integrating Streamlit and Seaborn involves a few steps. First, you'll need to import the necessary libraries. Then, you'll create your Seaborn plot and use Streamlit to display it. Here's a simple example:

import streamlit as st
import seaborn as sns
import pandas as pd
 
# Load your data
df = pd.read_csv('your_data.csv')
 
# Create a Seaborn pairplot
plot = sns.pairplot(df)
 
# Display the plot in Streamlit
st.pyplot(plot.fig)

In this example, we first import the necessary libraries (Streamlit, Seaborn, and Pandas). We then load our data using Pandas. Next, we create a pairplot using Seaborn. Finally, we display the plot in Streamlit using the st.pyplot() function.

Displaying Seaborn Charts in Streamlit

Displaying Seaborn charts in Streamlit is straightforward. You simply create your Seaborn plot as usual, then use the st.pyplot() function to display it. Here's an example:

import streamlit as st
import seaborn as sns
import pandas as pd
 
# Load your data
df = pd.read_csv('your_data.csv')
 
# Create a Seaborn correlation plot
plot = sns.heatmap(df.corr(), annot=True)
 
# Display the plot in Streamlit
st.pyplot(plot.get_figure())

In this example, we create a correlation plot using Seaborn's heatmap() function. We then display the plot in Streamlit using the st.pyplot() function. Note that we use the get_figure() method to get the Matplotlib figure object from the Seaborn plot.

Create a Streamlit Data Visualization App with PyGWalker

PyGWalker (opens in a new tab) is also another awesome tool with No Code. You can easily use this Open Source Python Library to create easily Streamlit Data Visualization apps.

PyGWalker + Streamlit Online Demo (opens in a new tab)

PyGWalker (opens in a new tab) is a Python Library that helps you easily embed a Tableau-like UI into your own Streamlit app effortlessly.

Check out this amazing video produced by Sven from Coding is Fun (opens in a new tab) demonstrating the detailed steps for empowering your Streamlit app with this powerful Data Visualization Python Library!

Special Thanks to Sven and his great contribution (opens in a new tab) to PyGWalker community!

Additionally, you can also check out these resources:

• How to Explore Data and Share Findings with Pygwalker and Streamlit (opens in a new tab)
• PyGWalker GitHub Page (opens in a new tab) for more PyGWalker examples.

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Advanced Example: Build a Streamlit Application for Machine learning

Let's focus on creating a machine learning application with Streamlit. We'll use a simple example: a sentiment analysis app. This app will take user input, predict the sentiment of the input using a pre-trained model, and display the result.

Step 1: Import Necessary Libraries

First off, we'll need to import the necessary libraries. We'll need Streamlit for the app itself, and a machine learning library for the sentiment analysis. For this example, we'll use TextBlob.

import streamlit as st
from textblob import TextBlob

Step 2: Create a Function for Sentiment Analysis

Next, we'll create a function that takes a text string as input and returns the sentiment of the text. TextBlob's sentiment.polarity method returns a float between -1 and 1, where -1 is negative sentiment, 1 is positive sentiment, and 0 is neutral sentiment.

def analyze_sentiment(text):
    return TextBlob(text).sentiment.polarity

Step 3: Create Streamlit App

Now, we'll create the Streamlit app. We'll start by adding a title and a text input field where users can enter their text. Then, we'll add a button that, when clicked, will analyze the sentiment of the input text and display the result.

st.title('Sentiment Analysis App')
 
user_input = st.text_input("Enter text here")
 
if st.button('Analyze'):
    sentiment = analyze_sentiment(user_input)
    if sentiment < 0:
        st.write('The sentiment of this text is negative.')
    elif sentiment > 0:
        st.write('The sentiment of this text is positive.')
    else:
        st.write('The sentiment of this text is neutral.')

Step 4: Run the App

Finally, you can run the app by typing streamlit run app.py in your terminal, where app.py is the name of your Python file.

And voila! You've got yourself a sentiment analysis app. The user can input any text, and the app will analyze and display the sentiment of the text. This is just a simple example, but you can create much more complex machine learning applications with Streamlit. The sky's the limit!

Conclusion

The integration of Streamlit and Seaborn is a game-changer in the world of data visualization. It combines the simplicity and interactivity of Streamlit with the power and flexibility of Seaborn, opening up a world of possibilities for data scientists and developers.

Whether you're creating a complex pairplot or a simple correlation plot, Streamlit and Seaborn have got you covered. And with the practical examples and tutorials in this guide, you're well on your way to mastering Streamlit and Seaborn integration.

So go ahead, give it a shot. Dive into the world of Streamlit and Seaborn, and unleash the power of data visualization!

Frequently Asked Questions

1. Can I use Seaborn on Streamlit?

Absolutely! Streamlit integrates seamlessly with Seaborn, allowing you to create interactive data visualizations that can be shared and accessed through a web application.

2. How do you show the Seaborn plot in Streamlit?

You can display a Seaborn plot in Streamlit using the st.pyplot() function. Simply create your Seaborn plot as usual, then pass the Matplotlib figure object to st.pyplot().

3. Is Seaborn better than Matplotlib?

Seaborn and Matplotlib serve different purposes and are often used together. Seaborn provides a high-level interface for drawing attractive statistical graphics, while Matplotlib offers more control over the finer details of the plot. Depending on your needs, one may be more suitable than the other.

4. What is Streamlit good for?

Streamlit is an open-source Python library that allows developers to create interactive web applications for machine learning and data science. It's designed to simplify the process of building and sharing machine learning tools, making it an excellent choice for data visualization.