loading reading lists ...
');
function adjustReadingListIcon(isInReadingList){
$readingListToggle.toggleClass("fa-plus", !isInReadingList);
$readingListToggle.toggleClass("fa-check", isInReadingList);
var tooltipMessage = isInReadingList ? "edit in reading lists" : "add to reading list";
$readingListToggle.attr("title", tooltipMessage);
$readingListToggle.attr("data-original-title", tooltipMessage);
}
$.ajax({
url: "/readingList/isInReadingList",
data: {
productId: 1276
}
}).done(function (data) {
adjustReadingListIcon(data && data.hasProductInReadingList);
}).catch(function(e){
console.log(e);
adjustReadingListIcon(false);
});
$readingListToggle.on("click", function(){
if(codePromise == null){ showToast() }
loadCode().then(function(store){
store.requestReadingListSpecificationForProduct({
id: window.readingListsServerVars.externalId,
manningId: window.readingListsServerVars.productId,
title: window.readingListsServerVars.title
});
ReadingLists.ReactDOM.render(
ReadingLists.React.createElement(ReadingLists.ManningOnlineReadingListModal, {
store: store,
}),
document.getElementById("reading-lists-modal")
);
}).catch(function(e){
console.log("Error loading code reading list code");
});
});
var codePromise
var readingListStore
function loadCode(){
if(codePromise) { return codePromise }
return codePromise = new Promise(function (resolve, reject){
$.getScript(window.readingListsServerVars.libraryLocation).done(function(){
hideToast()
readingListStore = new ReadingLists.ReadingListStore(
new ReadingLists.ReadingListProvider(
new ReadingLists.ReadingListWebProvider(
ReadingLists.SourceApp.marketplace,
getDeploymentType()
)
)
);
readingListStore.onReadingListChange(handleChange);
readingListStore.onReadingListModalChange(handleChange);
resolve(readingListStore);
}).catch(function(){
hideToast();
console.log("Error downloading reading lists source");
$readingListToggle.css("display", "none");
reject();
});
});
}
function handleChange(){
if(readingListStore != null) {
adjustReadingListIcon(readingListStore.isInAtLeastOneReadingList({
id: window.readingListsServerVars.externalId,
manningId: window.readingListsServerVars.productId
}));
}
}
var $readingListToast = $("#reading-list-toast");
function showToast(){
$readingListToast.css("display", "flex");
setTimeout(function(){
$readingListToast.addClass("shown");
}, 16);
}
function hideToast(){
$readingListToast.removeClass("shown");
setTimeout(function(){
$readingListToast.css("display", "none");
}, 150);
}
function getDeploymentType(){
switch(window.readingListsServerVars.deploymentType){
case "development":
case "test":
return ReadingLists.DeploymentType.dev;
case "qa":
return ReadingLists.DeploymentType.qa;
case "production":
return ReadingLists.DeploymentType.prod;
case "docker":
return ReadingLists.DeploymentType.docker;
default:
console.error("Unknown deployment environment, defaulting to production");
return ReadingLists.DeploymentType.prod;
}
}
});
}
});
pro $24.99 per month
- access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
- choose one free eBook per month to keep
- exclusive 50% discount on all purchases
lite $19.99 per month
- access to all Manning books, including MEAPs!
team
5, 10 or 20 seats+ for your team - learn more
Look inside
Think big about your data! PySpark brings the powerful Spark big data processing engine to the Python ecosystem, letting you seamlessly scale up your data tasks and create lightning-fast pipelines.
In Data Analysis with Python and PySpark you will learn how to:
Data Analysis with Python and PySpark is your guide to delivering successful Python-driven data projects. Packed with relevant examples and essential techniques, this practical book teaches you to build pipelines for reporting, machine learning, and other data-centric tasks. Quick exercises in every chapter help you practice what you’ve learned, and rapidly start implementing PySpark into your data systems. No previous knowledge of Spark is required.
In Data Analysis with Python and PySpark you will learn how to:
- Manage your data as it scales across multiple machines
- Scale up your data programs with full confidence
- Read and write data to and from a variety of sources and formats
- Deal with messy data with PySpark’s data manipulation functionality
- Discover new data sets and perform exploratory data analysis
- Build automated data pipelines that transform, summarize, and get insights from data
- Troubleshoot common PySpark errors
- Creating reliable long-running jobs
Data Analysis with Python and PySpark is your guide to delivering successful Python-driven data projects. Packed with relevant examples and essential techniques, this practical book teaches you to build pipelines for reporting, machine learning, and other data-centric tasks. Quick exercises in every chapter help you practice what you’ve learned, and rapidly start implementing PySpark into your data systems. No previous knowledge of Spark is required.
about the technology
The Spark data processing engine is an amazing analytics factory: raw data comes in, insight comes out. PySpark wraps Spark’s core engine with a Python-based API. It helps simplify Spark’s steep learning curve and makes this powerful tool available to anyone working in the Python data ecosystem.about the book
Data Analysis with Python and PySpark helps you solve the daily challenges of data science with PySpark. You’ll learn how to scale your processing capabilities across multiple machines while ingesting data from any source—whether that’s Hadoop clusters, cloud data storage, or local data files. Once you’ve covered the fundamentals, you’ll explore the full versatility of PySpark by building machine learning pipelines, and blending Python, pandas, and PySpark code.what's inside
- Organizing your PySpark code
- Managing your data, no matter the size
- Scale up your data programs with full confidence
- Troubleshooting common data pipeline problems
- Creating reliable long-running jobs
A clear and in-depth introduction for truly tackling big data with Python.
The perfect way to learn how to analyze and master huge datasets.
Covers both basic and more advanced topics of PySpark, with a good balance between theory and hands-on.
For beginner to pro, a well-written book to help understand PySpark.
Data Analysis with Python and PySpark ebook for free
