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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 2. Uploading, Downloading, and Manipulating Data FREE CHAPTER 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

Streamlit’s built-in graphing functions

There are four built-in functions for graphing – st.line_chart(), st.bar_chart(), st.area_chart(), and st.map(). They all work similarly by trying to figure out what variables you’re already trying to graph and then putting them into a line, bar, map, or area chart, respectively. In our dataset, we have a variable called dbh, which is the width of the tree at chest height. First, we can group our DataFrame by dbh, and then push that directly to the line chart, bar chart, and area chart. The following code should group our dataset by width, count the unique trees of each width, and then make a line, bar, and area chart of each:

import streamlit as st
import pandas as pd
st.title('SF Trees')
st.write(
    """This app analyzes trees in San Francisco using
    a dataset kindly provided by SF DPW"""
)
trees_df = pd.read_csv('trees.csv')
df_dbh_grouped = pd.DataFrame(trees_df...
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