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Beginner's guide to Streamlit with Python : build web-based data and machine learning applications /
This book will teach you the basics of Streamlit, a Python-based application framework used to build interactive dashboards and machine learning web apps. Streamlit reduces development time for web-based application prototypes of data and machine learning models. As you’ll see, Streamlit...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Apress,
[2023]
|
| Edición: | [First edition]. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Raghavendra, Sujay, |e author. | |
| 245 | 1 | 0 | |a Beginner's guide to Streamlit with Python : |b build web-based data and machine learning applications / |c Sujay Raghavendra. |
| 250 | |a [First edition]. | ||
| 264 | 1 | |a New York, NY : |b Apress, |c [2023] | |
| 300 | |a 1 online resource (215 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 500 | |a Includes index. | ||
| 520 | |a This book will teach you the basics of Streamlit, a Python-based application framework used to build interactive dashboards and machine learning web apps. Streamlit reduces development time for web-based application prototypes of data and machine learning models. As you’ll see, Streamlit helps develop data-enhanced analytics, build dynamic user experiences, and showcases data for data science and machine learning models. Beginner's Guide to Streamlit with Python begins with the basics of Streamlit by demonstrating how to build a basic application and advances to visualization techniques and their features. Next, it covers the various aspects of a typical Streamlit web application, and explains how to manage flow control and status elements. You’ll also explore performance optimization techniques necessary for data modules in a Streamlit application. Following this, you’ll see how to deploy Streamlit applications on various platforms. The book concludes with a few prototype natural language processing apps with computer vision implemented using Streamlit. After reading this book, you will understand the concepts, functionalities, and performance of Streamlit, and be able to develop dynamic Streamlit web-based data and machine learning applications of your own. | ||
| 505 | 0 | |a Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: Introduction to Streamlit -- What Is Streamlit? -- Why Streamlit? -- Why Streamlit for Data Science and ML Engineers? -- Features of Streamlit -- Open Source -- Platforms -- Ease of Development -- Interactive Applications -- Reduced Time of Development -- No Core Web Development Knowledge -- Easy to Learn -- Model Implementation -- Compatibility -- Literate Programming Document -- Streamlit Cloud -- Optimize Change -- Error Notifications | |
| 505 | 8 | |a Comparing Streamlit to Alternative Frameworks -- Installing Python -- Installing Streamlit on Windows -- Installing Streamlit on Linux -- Installing Streamlit on macOS -- Testing the Streamlit Installation -- Creating Our First App -- Summary -- Chapter 2: Text and Table Elements -- Text Elements -- Titles -- Headers -- Subheaders -- Captions -- Plain Text -- Markdown -- LaTeX -- Code -- Data Elements -- Dataframes -- Tables -- Metrics -- JSON -- The write() Function as a Superfunction -- Magic -- Summary -- Chapter 3: Visualization -- The Importance of Visualization | |
| 505 | 8 | |a Visualization in Streamlit -- Purpose of Visualization -- Streamlit Functions -- Bar -- Line -- Area -- Map -- Graphviz -- Seaborn -- Count -- Violin -- Strip -- Altair -- Boxplot -- Area -- Heatmap -- Plotly -- Pie -- Donut -- Scatter -- Line -- Bar -- Bar Horizontal -- Subplots -- Summary -- Chapter 4: Data and Media Elements -- Images -- Multiple Images -- Background Image -- Resizing an Image -- Audio -- Video -- Balloon -- Snowflake -- Emojis -- Summary -- Chapter 5: Buttons and Sliders -- Buttons -- Radio Buttons -- Check Boxes -- Drop-Downs -- Multiselects -- Download Buttons | |
| 505 | 8 | |a Progress Bars -- Spinners -- Summary -- Chapter 6: Forms -- Text Box -- Text Area -- Number Input -- Time -- Date -- Color -- File Upload -- Text/Docx Document -- PDF Upload -- Dataset Upload -- Image Upload -- Uploading Multiple Images -- Saving Uploaded Documents -- Submit Button -- Summary -- Chapter 7: Columns and Navigation -- Columns -- Spaced-Out Columns -- Columns with Padding -- Grids -- Expanders/Accordions -- Containers -- Empty Containers -- Sidebars -- Multipage Navigation -- Main Page -- Pages -- Summary -- Chapter 8: Control Flow and Advanced Features -- Alert Box -- st.info() | |
| 505 | 8 | |a St.warning() -- st.success() -- st.error() -- st.exception() -- Control Flow -- Stop Execution -- Rerun the Script -- st.form_submit_button -- Advanced Features -- Configuring the Page -- st.echo -- st.experimental_show -- Session State -- Performance -- Caching -- st.experimental_memo -- st.experimental_memo.clear() -- st.experimental_singleton -- st.experimental_singleton.clear -- Summary -- Chapter 9: Natural Language Processing -- NLP App Creation -- User Input -- Cleaning the Text -- Predictions -- Setting Up Files -- Requirement Text -- setup.sh -- Procfile -- GitHub Repository Creation | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Web applications. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Python (Computer program language) | |
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| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Python (Computer program language) |2 fast |0 (OCoLC)fst01084736 | |
| 650 | 7 | |a Web applications. |2 fast |0 (OCoLC)fst01895855 | |
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