# Define some CSS to control our custom labelsįont-family:Arial, Helvetica, sans-serif Here is what the code will look like: # CODE TO ADD Add tooltips for even more interactivityĪdd tooltips to see X and Y coordinates for even more interactivity (it’s why we’ve added markers). Here is what it’ll look like with panning, zooming, and resetting: # Replace st.pyplot(two_subplot_fig) with this code below!įig_html = mpld3.fig_to_html(two_subplot_fig) Make the static graph interactive with mpld3: You might be thinking, “Why are we adding markers? It doesn’t look beautiful.” I’ll explain why below! Step 2. Two_subplot_fig = plt.figure(figsize=(6,6)) Start out by statically rendering the graph: # Imports for all of the code Want to explore it yourself? See the app deployed live here.įor more mpld3’s plugins, check out mpld3’s website and documentation. Now your users can pan, zoom, reset, and explore the details of your chart! □ Embed this HTML snippet in your app via Streamlit’s custom component API: import matplotlib.pyplot as plt It’ll convert the Matplotlib figure ( fig) into an interactive Javascript representation and return it as HTML. Making this chart interactive is super simple. #create your figure and get the figure object returnedĪnd here’s what your app should look like now: Here is what the code will look like: import streamlit as st Create a basic Matplotlib chartįirst, create a basic Matplotlib chart and add it to your Streamlit app (you’ll add interactivity later). With a few lines of code, you can add panning, zooming, resetting, and rendering! 1. TLDR? Use mpld3 and render pyplots with Streamlit’s built-in st.pyplot command. In this post, I'll show you how to make them interactive: Just use st.pyplot!īut Matplotlib charts are static images. It’s also a popular way to add charts to your Streamlit apps. Matplotlib is one of the most popular charting libraries in Python.
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