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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

Data manipulation in Streamlit

Streamlit runs our Python file from the top down as a script, so we can perform data manipulation with powerful libraries such as pandas in the same way that we might in a Jupyter notebook or a regular Python script. As we’ve discussed before, we can do all our regular data manipulation as normal. For our Palmer’s Penguins app, what if we wanted the user to be able to filter out penguins based on their gender? The following code filters our DataFrame using pandas:

import streamlit as st
import pandas as pd
import altair as alt 
import seaborn as sns
st.title("Palmer's Penguins")
st.markdown('Use this Streamlit app to make your own scatterplot about penguins!')
penguin_file = st.file_uploader(
    'Select Your Local Penguins CSV (default provided)')
if penguin_file is not None:
    penguins_df = pd.read_csv(penguin_file)
else:
    penguins_df = pd.read_csv('penguins.csv')
selected_x_var =...
You have been reading a chapter from
Streamlit for Data Science - Second Edition
Published in: Sep 2023
Publisher: Packt
ISBN-13: 9781803248226
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