Streamlit for data science create interactive data apps in Python
An easy-to-follow and comprehensive guide to creating data apps with Streamlit, including how-to guides for working with cloud data warehouses like Snowflake, using pretrained Hugging Face and OpenAI models, and creating apps for job interviews. If you work with data in Python and are looking to cre...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing
[2023]
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| Edición: | Second edition |
| Colección: | Expert insight
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009769034806719 |
Tabla de Contenidos:
- Cover
- Copyright Page
- Contributors
- Table of Contents
- Preface
- Chapter 1: An Introduction to Streamlit
- Technical requirements
- Why Streamlit?
- Installing Streamlit
- Organizing Streamlit apps
- Streamlit plotting demo
- Making an app from scratch
- Using user input in Streamlit apps
- Finishing touches - adding text to Streamlit
- Summary
- Chapter 2: Uploading, Downloading, and Manipulating Data
- Technical requirements
- The setup - Palmer's Penguins
- Exploring Palmer's Penguins
- Flow control in Streamlit
- Debugging Streamlit apps
- Developing in Streamlit
- Exploring in Jupyter and then copying to Streamlit
- Data manipulation in Streamlit
- An introduction to caching
- Persistence with Session State
- Summary
- Chapter 3: Data Visualization
- Technical requirements
- San Francisco Trees - a new dataset
- Streamlit visualization use cases
- Streamlit's built-in graphing functions
- Streamlit's built-in visualization options
- Plotly
- Matplotlib and Seaborn
- Bokeh
- Altair
- PyDeck
- Configuration options
- Summary
- Chapter 4: Machine Learning and AI with Streamlit
- Technical requirements
- The standard ML workflow
- Predicting penguin species
- Utilizing a pre-trained ML model in Streamlit
- Training models inside Streamlit apps
- Understanding ML results
- Integrating external ML libraries - a Hugging Face example
- Integrating external AI libraries - an OpenAI example
- Authenticating with OpenAI
- OpenAI API cost
- Streamlit and OpenAI
- Summary
- Chapter 5: Deploying Streamlit with Streamlit Community Cloud
- Technical requirements
- Getting started with Streamlit Community Cloud
- A quick primer on GitHub
- Deploying with Streamlit Community Cloud
- Debugging Streamlit Community Cloud
- Streamlit Secrets
- Summary
- Chapter 6: Beautifying Streamlit Apps.
- Technical requirements
- Setting up the SF Trees dataset
- Working with columns in Streamlit
- Exploring page configuration
- Using Streamlit tabs
- Using the Streamlit sidebar
- Picking colors with a color picker
- Multi-page apps
- Editable DataFrames
- Summary
- Chapter 7: Exploring Streamlit Components
- Technical requirements
- Adding editable dataframes with streamlit-aggrid
- Creating drill-down graphs with streamlit-plotly-events
- Using Streamlit Components - streamlit-lottie
- Using Streamlit Components - streamlit-pandas-profiling
- Interactive maps with st-folium
- Helpful mini-functions with streamlit-extras
- Finding more Components
- Summary
- Chapter 8: Deploying Streamlit Apps with Hugging Face and Heroku
- Technical requirements
- Choosing between Streamlit Community Cloud, Hugging Face, and Heroku
- Deploying Streamlit with Hugging Face
- Deploying Streamlit with Heroku
- Setting up and logging in to Heroku
- Cloning and configuring our local repository
- Deploying to Heroku
- Summary
- Chapter 9: Connecting to Databases
- Technical requirements
- Connecting to Snowflake with Streamlit
- Connecting to BigQuery with Streamlit
- Adding user input to queries
- Organizing queries
- Summary
- Chapter 10: Improving Job Applications with Streamlit
- Technical requirements
- Using Streamlit for proof-of-skill data projects
- Machine learning - the Penguins app
- Visualization - the Pretty Trees app
- Improving job applications in Streamlit
- Questions
- Answering Question 1
- Answering Question 2
- Summary
- Chapter 11: The Data Project - Prototyping Projects in Streamlit
- Technical requirements
- Data science ideation
- Collecting and cleaning data
- Making an MVP
- How many books do I read each year?
- How long does it take for me to finish a book that I have started?.
- How long are the books that I have read?
- How old are the books that I have read?
- How do I rate books compared to other Goodreads users?
- Iterative improvement
- Beautification via animation
- Organization using columns and width
- Narrative building through text and additional statistics
- Hosting and promotion
- Summary
- Chapter 12: Streamlit Power Users
- Fanilo Andrianasolo
- Adrien Treuille
- Gerard Bentley
- Arnaud Miribel and Zachary Blackwood
- Yuichiro Tachibana
- Summary
- PacktPage
- Other Books You May Enjoy
- Index.
