Creating a Web App with 雪花 and Streamlit
Streamlit provides an easy way for users to turn their data into web applications. The app framework is open-source, and everything is written in Python. This means that you do not have to bother learning other front-end technologies such as HTML or Javascript. Your web app will also update as you save your source file, providing real-time results. Web apps on Streamlit are quick, user-friendly, and easily sharable.
Let’s Create a Web App!
If you do not already have a dataset to use, I recommend navigating to 雪花’s Data Marketplace and downloading a free database. The Data Marketplace is perfect for finding third-party data to integrate with internal datasets or in this case, experiment with. For this example, I used ‘COVID-19 Epidemiological Data’ from Starschema.
The next step is to create a new repository in GitHub. GitHub is a platform used mainly for software development and version control. A GitHub repository contains all the files and folders that you use for a project. The repository for this project will be used when creating the web app, so I recommend writing a name relevant to your dataset.
Once your new repository is created, add a new file called ‘streamlit_app.py’. This will be the main (and only) file for our web app and it will be written in Python. 下一个, navigate to Streamlit. If you do not already have an account, I recommend signing up. Notice how it asks to connect to your GitHub account? This will make the process even easier! After you’ve finished logging in, click ‘New app’ in the right corner. Streamlit will ask for the repository you just created along with the main file (streamlit_app.py).
Once the web app is deployed, navigate back to the homepage, and access the settings for your new app. You will need to change the contents under the ‘Secrets’ tab. Streamlit needs your 雪花 account login information as well as your database con文本 to connect both platforms.
Now, let’s add some data! You will need to import Streamlit, Pandas, and the 雪花 Connector. Importing 雪花 Connector will connect Streamlit to any queries that you write with the database specified earlier. Pandas is a Python library that allows for easier data manipulation, especially with tables and data frames. 下一个, add a few lines of 文本. 我添加了一个标题, 文本, header, 还有留白, however you can add whatever is relevant to your data.
Now that you have the necessary imports added, you can connect to 雪花 and start querying your data. I decided to analyze the number of positive and negative COVID-19 cases per state.
After the data is queried, you can display the results in numerous ways. I converted my data to a data frame with Pandas in order to change the column names and reset the initial index column. I then displayed my data as a table. After you save your file, the web app should be updated to show your results.
To make the web app more interesting, let’s add a chart! The process will be extremely similar to that of the first visual, but instead of a table, I used a bar chart. I also queried different data so that the chart displays positive and negative COVID-19 cases monthly.
If you followed along, your web app should look something like the following:
结论
Using 雪花 to create a web app with Streamlit is a very simple process. It doesn’t take much scripting to end up with a great platform to display your data. Besides 雪花, APIs can also be incorporated to make web apps more dynamic. Streamlit offers input widgets, 媒体元素, 布局, and many other useful tools to customize web apps.
Links to Other Moser Webpages
http://qa8.aifengcai.com/data-analytics
参考文献
Inspiration received from the following websites:
Official Documentation -> Get started - Streamlit Docs
雪花 Training -> 雪花 Hands-On Essentials Workshops