Found insideLeading computer scientists Ian Foster and Dennis Gannon argue that it can, and in this book offer a guide to cloud computing for students, scientists, and engineers, with advice and many hands-on examples. Found inside – Page 208Dask. Bag. and. DataFrame. Dask provides other data structures for ... For example, you can create a Bag from a list using the from_sequence factory ... Found inside – Page iiThis book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. Found insideOver 95 hands-on recipes to leverage the power of pandas for efficient scientific computation and data analysis About This Book Use the power of pandas to solve most complex scientific computing problems with ease Leverage fast, robust data ... Found insideAnd 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 work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Found inside – Page 270For the sake of our example, we will use positions. ... We will now create a Dask DataFrame: all_ddf = dd.from_array(positions, columns=['POS']) ... Found insideProbability is the bedrock of machine learning. Found inside – Page iiiWritten for statisticians, computer scientists, geographers, research and applied scientists, and others interested in visualizing data, this book presents a unique foundation for producing almost every quantitative graphic found in ... Found inside – Page 416For example, in pandas: (a) boolean indexing can be assimilated to WHERE ... e.g. with the abstraction of a Dask dataframe that provides an interface that ... Found insideYour Python code may run correctly, but you need it to run faster. Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. Found inside – Page 324Dask provides replacements for most of the data structures from the Python scientific stack, such as NumPy arrays and Pandas DataFrames. Found inside – Page 329the NumPy array and the dask.dataframe for the Pandas DataFrame. ... For example, let's calculate the matrix multiplication of two random arrays a and b in ... Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists. Found insideAdvanced analytics on your Big Data with latest Apache Spark 2.x About This Book An advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities. Found insideWith this Learning Path, you will gain complete knowledge to solve problems by building high performing applications loaded with asynchronous, multithreaded code and proven design patterns. Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Found insideUsing clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of statistical methods to machine learning, summary stats, hypothesis testing, nonparametric stats, resampling methods, ... Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. This tutorial introduces the reader informally to the basic concepts and features of the python language and system. This book will help in learning python data structures and essential concepts such as Functions, Lambdas, List comprehensions, Datetime objects, etc. required for data engineering. Found insideDeep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Found inside – Page iWho This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. You are required to have a basic knowledge of Python development to get the most of this book. What you will learn Use Python to read and transform data into different formats Generate basic statistics and metrics using data on disk Work with computing tasks distributed over a cluster Convert data from various sources into storage or ... Found insideThis book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels. After reading this book, you’ll have the solid foundation you need to start a career in data science. This book is intended for Python programmers, mathematicians, and analysts who already have a basic understanding of Python and wish to learn about its data analysis capabilities in depth. Found insideThis book is a printed edition of the Special Issue "Air Quality Monitoring and Forecasting" that was published in Atmosphere Jill Lepore, best-selling author of These Truths, came across the company’s papers in MIT’s archives and set out to tell this forgotten history, the long-lost backstory to the methods, and the arrogance, of Silicon Valley. Presents case studies and instructions on how to solve data analysis problems using Python. Found insideTime series forecasting is different from other machine learning problems. This is exactly the topic of this book. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. Found insideExpanded from Tyler Akidau’s popular blog posts "Streaming 101" and "Streaming 102", this book takes you from an introductory level to a nuanced understanding of the what, where, when, and how of processing real-time data streams. Found insideThe Dask dataFrame copies the Pandas API. pyspark dataframe In Apache Spark ... activity: single example #index: 11-2 import pandas as molCoord import numpy ... Found insideAbout This Book Understand how Spark can be distributed across computing clusters Develop and run Spark jobs efficiently using Python A hands-on tutorial by Frank Kane with over 15 real-world examples teaching you Big Data processing with ... Found inside – Page 1Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Found insideThis book is about making machine learning models and their decisions interpretable. Found inside – Page 27Let's look at some of these cases: • Requesting a sample: For example, ... Note that if we call compute directly on a Dask DataFrame instead, ... Found insideThis practical guide shows ambitious non-programmers how to automate and scale the processing and analysis of data in different formats—by using Python. Found inside – Page iThis book thoroughly addresses these and other considerations, leaving institutional investors and risk managers with a basis of knowledge that will enable them to extract the maximum value from alternative data. Key Features This is the first book on pandas 1.x Practical, easy to implement recipes for quick solutions to common problems in data using pandas Master the fundamentals of pandas to quickly begin exploring any dataset Book Description The ... Using the hands-on recipes in this book, you'll be able to do practical research and analysis in computational biology with Python. Found inside – Page iThis book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. The Hitchhiker's Guide to Python takes the journeyman Pythonista to true expertise. Found insideThis book is an indispensable guide for integrating SAS and Python workflows. Found inside – Page 1Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Found inside – Page 58The example below uses Dask, but it would look exactly the same with Modin except for the final .compute() and an initial import modin.pandas as pd. Found inside – Page 125If you are a regular pandas and NumPy user, you'll love Dask. ... For example, you could install only the arrays or DataFrames module, but it's a good idea ... Found inside – Page 418This results in the following output: In the preceding example, we grouped the ... Converting a pandas DataFrame into a Dask DataFrame Dask DataFrames are ... Inside – Page 418This results in the following output: in the preceding example, we grouped.... Abstraction of a Dask DataFrame that provides an interface that... found inside – Page 270For the sake of example! Technique right now, you 'll love Dask Python language and system 3, this edition. Performance bottlenecks and significantly speed up your code in high-data-volume programs inside Page! 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