001453622 000__ 06026cam\a2200565\i\4500
001453622 001__ 1453622
001453622 003__ OCoLC
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001453622 019__ $$a1355565535
001453622 020__ $$a9781484289839$$q(electronic bk.)
001453622 020__ $$a1484289838$$q(electronic bk.)
001453622 020__ $$z9781484289822
001453622 020__ $$z148428982X
001453622 0247_ $$a10.1007/978-1-4842-8983-9$$2doi
001453622 035__ $$aSP(OCoLC)1355545249
001453622 040__ $$aORMDA$$beng$$erda$$epn$$cORMDA$$dEBLCP$$dGW5XE$$dYDX$$dTOH$$dOCLCQ$$dOCLCF$$dUKAHL
001453622 049__ $$aISEA
001453622 050_4 $$aTK5105.875.I6
001453622 08204 $$a006.7/8$$223/eng/20221220
001453622 1001_ $$aRaghavendra, Sujay,$$eauthor.
001453622 24510 $$aBeginner's guide to Streamlit with Python :$$bbuild web-based data and machine learning applications /$$cSujay Raghavendra.
001453622 250__ $$a[First edition].
001453622 264_1 $$aNew York, NY :$$bApress,$$c[2023]
001453622 300__ $$a1 online resource (215 pages) :$$billustrations
001453622 336__ $$atext$$btxt$$2rdacontent
001453622 337__ $$acomputer$$bc$$2rdamedia
001453622 338__ $$aonline resource$$bcr$$2rdacarrier
001453622 500__ $$aIncludes index.
001453622 5050_ $$aIntro -- 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
001453622 5058_ $$aComparing Streamlit to  Alternative Frameworks -- Installing Python -- Installing Streamlit on Windows -- Installing Streamlit on Linux -- Installing Streamlit on macOS -- Testing the Streamlit Installation -- Creating Our First App -- Summary -- Chapter 2: Text and Table Elements -- Text Elements -- Titles -- Headers -- Subheaders -- Captions -- Plain Text -- Markdown -- LaTeX -- Code -- Data Elements -- Dataframes -- Tables -- Metrics -- JSON -- The write() Function as a Superfunction -- Magic -- Summary -- Chapter 3: Visualization -- The Importance of Visualization
001453622 5058_ $$aVisualization 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
001453622 5058_ $$aProgress Bars -- Spinners -- Summary -- Chapter 6: Forms -- Text Box -- Text Area -- Number Input -- Time -- Date -- Color -- File Upload -- Text/Docx Document -- PDF Upload -- Dataset Upload -- Image Upload -- Uploading Multiple Images -- Saving Uploaded Documents -- Submit Button -- Summary -- Chapter 7: Columns and Navigation -- Columns -- Spaced-Out Columns -- Columns with Padding -- Grids -- Expanders/Accordions -- Containers -- Empty Containers -- Sidebars -- Multipage Navigation -- Main Page -- Pages -- Summary -- Chapter 8: Control Flow and Advanced Features -- Alert Box -- st.info()
001453622 5058_ $$aSt.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
001453622 506__ $$aAccess limited to authorized users.
001453622 520__ $$aThis 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.
001453622 650_0 $$aWeb applications.
001453622 650_0 $$aArtificial intelligence.
001453622 650_0 $$aMachine learning.
001453622 650_0 $$aPython (Computer program language)
001453622 655_0 $$aElectronic books.
001453622 77608 $$iPrint version: $$z148428982X$$z9781484289822$$w(OCoLC)1346349404
001453622 852__ $$bebk
001453622 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-8983-9$$zOnline Access$$91397441.1
001453622 909CO $$ooai:library.usi.edu:1453622$$pGLOBAL_SET
001453622 980__ $$aBIB
001453622 980__ $$aEBOOK
001453622 982__ $$aEbook
001453622 983__ $$aOnline
001453622 994__ $$a92$$bISE