Data Science with Python and Dask

Name: Data Science with Python and Dask
Brand: Manning
Price: 37.67 EUR
Availability: OnlineOnly

Jesse Daniel(Autor*in)

Manning (Verlag)

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Erschienen am 8. Juli 2019

296 Seiten

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Beschreibung

Summary

Dask is a native parallel analytics tool designed to integrate seamlessly with the libraries you're already using, including Pandas, NumPy, and Scikit-Learn. With Dask you can crunch and work with huge datasets, using the tools you already have. And Data Science with Python and Dask is your guide to using Dask for your data projects without changing the way you work!

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. You'll find registration instructions inside the print book.

About the Technology

An efficient data pipeline means everything for the success of a data science project. Dask is a flexible library for parallel computing in Python that makes it easy to build intuitive workflows for ingesting and analyzing large, distributed datasets. Dask provides dynamic task scheduling and parallel collections that extend the functionality of NumPy, Pandas, and Scikit-learn, enabling users to scale their code from a single laptop to a cluster of hundreds of machines with ease.

About the Book

Data Science with Python and Dask teaches you to build scalable projects that can handle massive datasets. After meeting the Dask framework, you'll analyze data in the NYC Parking Ticket database and use DataFrames to streamline your process. Then, you'll create machine learning models using Dask-ML, build interactive visualizations, and build clusters using AWS and Docker.

What's inside

Working with large, structured and unstructured datasets
Visualization with Seaborn and Datashader
Implementing your own algorithms
Building distributed apps with Dask Distributed
Packaging and deploying Dask apps

About the Reader

For data scientists and developers with experience using Python and the PyData stack.

About the Author

Jesse Daniel is an experienced Python developer. He taught Python for Data Science at the University of Denver and leads a team of data scientists at a Denver-based media technology company.

Table of Contents

PART 1 - The Building Blocks of scalable computing

Why scalable computing matters
Introducing Dask

PART 2 - Working with Structured Data using Dask DataFrames

Introducing Dask DataFrames
Loading data into DataFrames
Cleaning and transforming DataFrames
Summarizing and analyzing DataFrames
Visualizing DataFrames with Seaborn
Visualizing location data with Datashader

PART 3 - Extending and deploying Dask

Working with Bags and Arrays
Machine learning with Dask-ML
Scaling and deploying Dask

Weitere Details

Weitere Ausgaben

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Inhalt

Intro
Titlepage
Copyright
Dedication
preface
acknowledgments
about this book
Who should read this book
How this book is organized: A roadmap
About the code
liveBook discussion forum
about the author
about the cover illustration
Part 1: The building blocks of scalable computing
Chapter 1: Why scalable computing matters
1.1 Why Dask?
1.2 Cooking with DAGs
1.3 Scaling out, concurrency, and recovery
1.3.1 Scaling up vs. scaling out
1.3.2 Concurrency and resource management
1.3.3 Recovering from failures
1.4 Introducing a companion dataset
Summary
Chapter 2: Introducing Dask
2.1 Hello Dask: A first look at the DataFrame API
2.1.1 Examining the metadata of Dask objects
2.1.2 Running computations with the compute method
2.1.3 Making complex computations more efficient with persist
2.2 Visualizing DAGs
2.2.1 Visualizing a simple DAG using Dask Delayed objects
2.2.2 Visualizing more complex DAGs with loops and collections
2.2.3 Reducing DAG complexity with persist
2.3 Task scheduling
2.3.1 Lazy computations
2.3.2 Data locality
Summary
Part 2: Working with structured data using Dask DataFrames
Chapter 3: Introducing Dask DataFrames
3.1 Why use DataFrames?
3.2 Dask and Pandas
3.2.1 Managing DataFrame partitioning
3.2.2 What is the shuffle?
3.3 Limitations of Dask DataFrames
Summary
Chapter 4: Loading data into DataFrames
4.1 Reading data from text files
4.1.1 Using Dask datatypes
4.1.2 Creating schemas for Dask DataFrames
4.2 Reading data from relational databases
4.3 Reading data from HDFS and S3
4.4 Reading data in Parquet format
Summary
Chapter 5: Cleaning and transforming DataFrames
5.1 Working with indexes and axes
5.1.1 Selecting columns from a DataFrame
5.1.2 Dropping columns from a DataFrame
5.1.3 Renaming columns in a DataFrame
5.1.4 Selecting rows from a DataFrame
5.2 Dealing with missing values
5.2.1 Counting missing values in a DataFrame
5.2.2 Dropping columns with missing values
5.2.3 Imputing missing values
5.2.4 Dropping rows with missing data
5.2.5 Imputing multiple columns with missing values
5.3 Recoding data
5.4 Elementwise operations
5.5 Filtering and reindexing DataFrames
5.6 Joining and concatenating DataFrames
5.6.1 Joining two DataFrames
5.6.2 Unioning two DataFrames
5.7 Writing data to text files and Parquet files
5.7.1 Writing to delimited text files
5.7.2 Writing to Parquet files
Summary
Chapter 6: Summarizing and analyzing DataFrames
6.1 Descriptive statistics
6.1.1 What are descriptive statistics?
6.1.2 Calculating descriptive statistics with Dask
6.1.3 Using the describe method for descriptive statistics
6.2 Built-In aggregate functions
6.2.1 What is correlation?
6.2.2 Calculating correlations with Dask DataFrames
6.3 Custom aggregate functions
6.3.1 Testing categorical variables with the t-test
6.3.2 Using custom aggregates to implement the Brown-Forsythe test
6.4 Rolling (window) functions
6.4.1 Preparing data for a rolling function
6.4.2 Using the rolling method to apply a window function
Summary
Chapter 7: Visualizing DataFrames with Seaborn
7.1 The prepare-reduce-collect-plot pattern
7.2 Visualizing continuous relationships with scatterplot and regplot
7.2.1 Creating a scatterplot with Dask and Seaborn
7.2.2 Adding a linear regression line to the scatterplot
7.2.3 Adding a nonlinear regression line to a scatterplot
7.3 Visualizing categorical relationships with violinplot
7.3.1 Creating a violinplot with Dask and Seaborn
7.3.2 Randomly sampling data from a Dask DataFrame
7.4 Visualizing two categorical relationships with heatmap
Summary
Chapter 8: Visualizing location data with Datashader
8.1 What is Datashader and how does it work?
8.1.1 The five stages of the Datashader rendering pipeline
8.1.2 Creating a Datashader Visualization
8.2 Plotting location data as an interactive heatmap
8.2.1 Preparing geographic data for map tiling
8.2.2 Creating the interactive heatmap
Summary
Part 3: Extending and deploying Dask
Chapter 9: Working with Bags and Arrays
9.1 Reading and parsing unstructured data with Bags
9.1.1 Selecting and viewing data from a Bag
9.1.2 Common parsing issues and how to overcome them
9.1.3 Working with delimiters
9.2 Transforming, filtering, and folding elements
9.2.1 Transforming elements with the map method
9.2.2 Filtering Bags with the filter method
9.2.3 Calculating descriptive statistics on Bags
9.2.4 Creating aggregate functions using the foldby method
9.3 Building Arrays and DataFrames from Bags
9.4 Using Bags for parallel text analysis with NLTK
9.4.1 The basics of bigram analysis
9.4.2 Extracting tokens and filtering stopwords
9.4.3 Analyzing the bigrams
Summary
Chapter 10: Machine learning with Dask-ML
10.1 Building linear models with Dask-ML
10.1.1 Preparing the data with binary vectorization
10.1.2 Building a logistic regression model with Dask-ML
10.2 Evaluating and tuning Dask-ML models
10.2.1 Evaluating Dask-ML models with the score method
10.2.2 Building a naïve Bayes classifier with Dask-ML
10.2.3 Automatically tuning hyperparameters
10.3 Persisting Dask-ML models
Summary
Chapter 11: Scaling and deploying Dask
11.1 Building a Dask cluster on Amazon AWS with Docker
11.1.1 Getting started
11.1.2 Creating a security key
11.1.3 Creating the ECS cluster
11.1.4 Configuring the cluster's networking
11.1.5 Creating a shared data drive in Elastic File System
11.1.6 Allocating space for Docker images in Elastic Container Repository
11.1.7 Building and deploying images for scheduler, worker, and notebook
11.1.8 Connecting to the cluster
11.2 Running and monitoring Dask jobs on a cluster
11.3 Cleaning up the Dask cluster on AWS
Summary
appendix: Software installation
Installing additional packages with Anaconda
Installing packages without Anaconda
Starting a Jupyter Notebook server
Configuring NLTK
Index
Lists of Figures, Tables and Listings

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