Easily visualize event data with Streamlit
As the business world becomes more data-focused, companies are recognizing the importance of leveraging data and analytics to stay ahead of the curve. One key area where this is evident is in analyzing how events can impact a business's daily operations.
Intelligent event data – about concerts, conferences, severe weather, and more – is one data set that businesses across industries are turning to reveal how events impact demand for their products and services. But businesses need a user-friendly way to integrate the data into their existing tools and processes in order to analyze, explore, and visualize the data. Let’s dive into how Streamlit bridges the gap between event data and existing tools and processes for data-driven businesses across industries.
What is Streamlit?
Streamlit is an open-source Python framework for building interactive data science web applications. The framework powers the ability for data scientists and machine learning engineers to easily create custom, aesthetically pleasing, interactive web applications that can be shared with others – without the need for prior front-end experience.
Streamlit is designed to be user-friendly and easy to learn for those with little web development experience. It provides a simple way to create interactive web applications with minimal setup or configuration, allowing data scientists to focus on what matters most.
With Streamlit, you can write Python code to create interactive components to be displayed in a web browser (think buttons, charts, etc.), and custom web interfaces for your machine learning models, data visualizations, and other data science projects.
Create an interactive map of events
Combine Streamlit with intelligent event data to create custom web applications where you and your team can interact with and visualize this data to reveal deeper business insights and make informed decisions.
For example, you can use Streamlit to create custom visualizations of intelligent event data, such as an interactive map displaying impactful events around a particular location of interest, which makes it easy to filter for events which are in close proximity to your location, and are most likely to impact demand:
While this example was created for a parking garage, this type of visualization is commonly used by parking organizations, food and beverage operations, hotel and accommodation providers, and more.
4 steps to visualize event data with Streamlit
Streamlit provides a simple, efficient way to create interactive web applications used to explore and visualize data. By leveraging the power of Python and the simplicity of Streamlit's API, data scientists can create custom web applications without needing to learn complex web development frameworks. Here’s how to create data visualizations with Streamlit in four simple steps:
1. Define your application
Using Streamlit, you first write Python code to define your web application. This code can include interactive components such as sliders, buttons, text boxes, and charts.
2. Run your application
After defining your application, you can run it using the Streamlit command-line interface. This will launch a web server that serves your application and allows it to be accessed from a web browser.
3. Interact with your application
With your application running, you can interact with it through a web browser. This can include adjusting the values of sliders, entering text into text boxes, and clicking buttons to trigger actions.
4. Visualize your data
As you interact with your application, Streamlit handles the visualization of your data. This can include creating charts, maps, and other visualizations that update in real-time as you interact with the application.
In terms of data input, Streamlit provides a range of built-in functions for loading and processing data from various sources. For example, you can use Streamlit to load data from:
a CSV file
a SQL database
an API endpoint
Once your data is loaded, you can use Streamlit to visualize it and interact with it through your web application.
Start creating custom visualizations today
Data visualizations such as event mapping make it easier to understand at a glance where the highest concentration of events are taking place, and how this may impact operations for different stores, restaurants, or other locations of interest.
Interact with Streamlit yourself with example visualizations for restaurants, accommodation providers, and parking operators:
Get started customizing your own web application with Steamlit today.