Creating a Sales Analysis Application with Streamlit: A Practical Approach to Business Intelligence

TELL IVAN CASILLA MAQUERA — Fri, 19 Apr 2024 22:11:56 +0000

Introduction:
Streamlit is a powerful open-source tool that allows you to create interactive web applications for data analysis and visualization with just a little Python code. In this article, we'll explore how to use Streamlit to build a sales analysis application, providing a practical approach to business intelligence. We'll learn how to generate sample data, visualize key information, create interactive charts, and deploy our application to the cloud.

Setting up the Environment:
To get started, we'll need to install the following Python libraries:

-Streamlit is the main library that will allow us to
create the web application.
-Pandas will be used for data manipulation and
analysis.
-Plotly will help us create attractive and
interactive data visualizations.

Let's explore the code step by step:

In this section, we import the necessary libraries and generate a sample dataset using a Pandas DataFrame. This DataFrame contains information about different products, such as their name, category, sales, costs, and region.

Here, we configure the Streamlit application page by setting a title and a "wide" layout to better utilize the available space. Then, we display the sales data in our application using st.header and st.write.

In this section, we create an interactive bar chart that displays total sales by region. First, we group the data by region and sum the sales using Pandas. Then, we use the plotly.express library to create the chart and display it in our application with st.plotly_chart.

Similarly, we create an interactive pie chart that shows the distribution of sales by product category.

Finally, we display some key performance indicators (KPIs) important for a business, such as total sales, total cost, and net profit. We use st.columns to divide the screen into three columns and display a KPI in each one.

Running the Application:
To run the application locally, follow these steps:

1.-Save the above code in a file called ejemplo.py.

2.-Open a terminal or command prompt and navigate to the directory
where app.py is located.

3.-Run the following command: streamlit run app.py.

4.-The application will open in your local web browser, and you'll
be able to interact with the charts and explore the data from
different perspectives.

Deploying to Streamlit Cloud:
Streamlit Cloud is a service that allows us to deploy our Streamlit applications to the cloud effortlessly. To do so, follow these steps:

1.-Create a repository on GitHub and upload the app.py file.

2.-Go to https://streamlit.io, log in, and create a new app from your GitHub repository.

3.-Streamlit Cloud will deploy your application and provide you with a public URL to access it from anywhere.

https://app-b8tbb4n9vpnghxjbnktkpm.streamlit.app/

Next Steps:
This application is just a basic example, but you can improve and expand it in several ways, such as:

-Loading real data from different sources (databases,CSV files,
APIs, etc.).
-Adding more analyses and visualizations, such as line charts,scatter plots, heatmaps, etc.
-Implementing data filtering and sorting functions.
-Generating automatic reports in different formats (PDF, Excel, etc.).
-Integrating user authentication and access control.

The flexibility and potential of Streamlit are
enormous, allowing you to create more complex and
customized business intelligence applications based
on your specific needs.

Conclusion:
In this article, we've explored how Streamlit can simplify the creation of interactive web applications for data analysis and business intelligence. We've built an application that analyzes sales data, displays key information through interactive charts and KPIs, and learned how to deploy it to the cloud using Streamlit Cloud.

Streamlit is a powerful tool that combines ease of use with the ability to create complex applications. I encourage you to explore Streamlit and discover how it can help you transform your data into valuable information for decision-making in your business.

I hope this article has been helpful and inspires you to begin your own journey with Streamlit. Enjoy creating interactive and attractive business intelligence applications!

DEV Community: TELL IVAN CASILLA MAQUERA

Creating a Sales Analysis Application with Streamlit: A Practical Approach to Business Intelligence