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Streamlit for Data Science - Second Edition

You're reading from  Streamlit for Data Science - Second Edition

Product type Book
Published in Sep 2023
Publisher Packt
ISBN-13 9781803248226
Pages 300 pages
Edition 2nd Edition
Languages
Author (1):
Tyler Richards Tyler Richards
Profile icon Tyler Richards

Table of Contents (15) Chapters

Preface An Introduction to Streamlit Uploading, Downloading, and Manipulating Data Data Visualization Machine Learning and AI with Streamlit Deploying Streamlit with Streamlit Community Cloud Beautifying Streamlit Apps Exploring Streamlit Components Deploying Streamlit Apps with Hugging Face and Heroku Connecting to Databases Improving Job Applications with Streamlit The Data Project – Prototyping Projects in Streamlit Streamlit Power Users Other Books You May Enjoy
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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