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Streamlit for Data Science

You're reading from   Streamlit for Data Science Create interactive data apps in Python

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781803248226
Length 300 pages
Edition 2nd Edition
Languages
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Author (1):
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Tyler Richards Tyler Richards
Author Profile Icon Tyler Richards
Tyler Richards
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Table of Contents (15) Chapters Close

Preface 1. An Introduction to Streamlit FREE CHAPTER 2. Uploading, Downloading, and Manipulating Data 3. Data Visualization 4. Machine Learning and AI with Streamlit 5. Deploying Streamlit with Streamlit Community Cloud 6. Beautifying Streamlit Apps 7. Exploring Streamlit Components 8. Deploying Streamlit Apps with Hugging Face and Heroku 9. Connecting to Databases 10. Improving Job Applications with Streamlit 11. The Data Project – Prototyping Projects in Streamlit 12. Streamlit Power Users 13. Other Books You May Enjoy
14. Index

Interactive maps with st-folium

Earlier in this chapter, we learned how critical it could be to add bidirectionality to visualizations through streamlit-plotly-events. Drilling down into graphs is an oft requested feature by business users, and maps are no exception to that! st-folim is very similar to streamlit-plotly-events, but for geospatial maps.

This example focuses on the trees dataset that we have used time and time again in this book, so go ahead and create a new file in the pages folder called folium_map.py, and we can get started. The following section of code loads the libraries, adds the data, creates a folium map, and adds that map to our Streamlit app. This is mostly a repeat of our previous graph, which maps the trees in SF but adds the Folium library:

import folium
import pandas as pd
import streamlit as st
from streamlit_folium import st_folium
st.title("SF Trees Map")
trees_df = pd.read_csv("trees.csv")
trees_df = trees_df.dropna(subset...
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