A step-by-step guide, packed with examples of practical numerical analysis that will give you a comprehensive, but concise overview of NumPy. This book is for programmers, scientists, or engineers, who have basic Python knowledge and would like to be able to do numerical computations with Python.
This book is for programmers, scientists, and engineers who have knowledge of the Python language and know the basics of data science. It is for those who wish to learn different data analysis methods using Python and its libraries. This book contains all the basic ingredients you need to become an expert data analyst.
Over 140 practical recipes to help you make sense of your data with ease and build production-ready data apps About This Book Analyze Big Data sets, create attractive visualizations, and manipulate and process various data types Packed with rich recipes to help you learn and explore amazing algorithms for statistics and machine learning Authored by Ivan Idris, expert in python programming and proud author of eight highly reviewed books Who This Book Is For This book teaches Python data analysis at an intermediate level with the goal of transforming you from journeyman to master. Basic Python and data analysis skills and affinity are assumed. What You Will Learn Set up reproducible data analysis Clean and transform data Apply advanced statistical analysis Create attractive data visualizations Web scrape and work with databases, Hadoop, and Spark Analyze images and time series data Mine text and analyze social networks Use machine learning and evaluate the results Take advantage of parallelism and concurrency In Detail Data analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns. As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning. Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You'll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining. In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code. By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios. Style and Approach The book is written in “cookbook” style striving for high realism in data analysis. Through the recipe-based format, you can read each recipe separately as required and immediately apply the knowledge gained.
In today's world of science and technology, it's all about speed and flexibility. When it comes to scientific computing, NumPy tops the list. NumPy will give you both speed and high productivity. This book will walk you through NumPy with clear, step-by-step examples and just the right amount of theory. The book focuses on the fundamentals of NumPy, including array objects, functions, and matrices, each of them explained with practical examples. You will then learn about different NumPy modules while performing mathematical operations such as calculating the Fourier transform, finding the inverse of a matrix, and determining eigenvalues, among many others. This book is a one-stop solution to knowing the ins and outs of the vast NumPy library, empowering you to use its wide range of mathematical features to build efficient, high-speed programs.
Written in Cookbook style, the code examples will take your Numpy skills to the next level. This book will take Python developers with basic Numpy skills to the next level through some practical recipes.
Leverage the power of Python to clean, scrape, analyze, and visualize your data About This Book Clean, format, and explore your data using the popular Python libraries and get valuable insights from it Analyze big data sets; create attractive visualizations; manipulate and process various data types using NumPy, SciPy, and matplotlib; and more Packed with easy-to-follow examples to develop advanced computational skills for the analysis of complex data Who This Book Is For This course is for developers, analysts, and data scientists who want to learn data analysis from scratch. This course will provide you with a solid foundation from which to analyze data with varying complexity. A working knowledge of Python (and a strong interest in playing with your data) is recommended. What You Will Learn Understand the importance of data analysis and master its processing steps Get comfortable using Python and its associated data analysis libraries such as Pandas, NumPy, and SciPy Clean and transform your data and apply advanced statistical analysis to create attractive visualizations Analyze images and time series data Mine text and analyze social networks Perform web scraping and work with different databases, Hadoop, and Spark Use statistical models to discover patterns in data Detect similarities and differences in data with clustering Work with Jupyter Notebook to produce publication-ready figures to be included in reports In Detail Data analysis is the process of applying logical and analytical reasoning to study each component of data present in the system. Python is a multi-domain, high-level, programming language that offers a range of tools and libraries suitable for all purposes, it has slowly evolved as one of the primary languages for data science. Have you ever imagined becoming an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? If yes, look no further, this is the course you need! In this course, we will get you started with Python data analysis by introducing the basics of data analysis and supported Python libraries such as matplotlib, NumPy, and pandas. Create visualizations by choosing color maps, different shapes, sizes, and palettes then delve into statistical data analysis using distribution algorithms and correlations. You'll then find your way around different data and numerical problems, get to grips with Spark and HDFS, and set up migration scripts for web mining. You'll be able to quickly and accurately perform hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making. Finally, you will delve into advanced techniques such as performing regression, quantifying cause and effect using Bayesian methods, and discovering how to use Python's tools for supervised machine learning. The course provides you with highly practical content explaining data analysis with Python, from the following Packt books: Getting Started with Python Data Analysis. Python Data Analysis Cookbook. Mastering Python Data Analysis. By the end of this course, you will have all the knowledge you need to analyze your data with varying complexity levels, and turn it into actionable insights. Style and approach Learn Python data analysis using engaging examples and fun exercises, and with a gentle and friendly but comprehensive "learn-by-doing" approach. It offers you a useful way of analyzing the data that's specific to this course, but that can also be applied to any other data. This course is designed to be both a guide and a reference for moving beyond the basics of data analysis.
The book is written in beginner's guide style with each aspect of NumPy demonstrated by real world examples. There is appropriate explained code with the required screenshots thrown in for the novice. This book is for the programmer, scientist or engineer, who has basic Python knowledge and would like to be able to do numerical computations with Python.
The book is written in beginner’s guide style with each aspect of NumPy demonstrated with real world examples and required screenshots.If you are a programmer, scientist, or engineer who has basic Python knowledge and would like to be able to do numerical computations with Python, this book is for you. No prior knowledge of NumPy is required.
Filled with practical, step-by-step instructions and clear explanations for the most important and useful tasks .This book is a step-by-step, short and fast paced tutorial packed with powerful recipes that will teach you how to create exciting games.This book is aimed at Python Game Developers who want to create games with Pygame quickly and easily and get familiar with important aspects of it. Experience with Python is assumed. Basic Game development experience would help but isn't necessary.
Leverage the power of Python to clean, scrape, analyze, and visualize your data About This Book Clean, format, and explore your data using the popular Python libraries and get valuable insights from it Analyze big data sets; create attractive visualizations; manipulate and process various data types using NumPy, SciPy, and matplotlib; and more Packed with easy-to-follow examples to develop advanced computational skills for the analysis of complex data Who This Book Is For This course is for developers, analysts, and data scientists who want to learn data analysis from scratch. This course will provide you with a solid foundation from which to analyze data with varying complexity. A working knowledge of Python (and a strong interest in playing with your data) is recommended. What You Will Learn Understand the importance of data analysis and master its processing steps Get comfortable using Python and its associated data analysis libraries such as Pandas, NumPy, and SciPy Clean and transform your data and apply advanced statistical analysis to create attractive visualizations Analyze images and time series data Mine text and analyze social networks Perform web scraping and work with different databases, Hadoop, and Spark Use statistical models to discover patterns in data Detect similarities and differences in data with clustering Work with Jupyter Notebook to produce publication-ready figures to be included in reports In Detail Data analysis is the process of applying logical and analytical reasoning to study each component of data present in the system. Python is a multi-domain, high-level, programming language that offers a range of tools and libraries suitable for all purposes, it has slowly evolved as one of the primary languages for data science. Have you ever imagined becoming an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? If yes, look no further, this is the course you need! In this course, we will get you started with Python data analysis by introducing the basics of data analysis and supported Python libraries such as matplotlib, NumPy, and pandas. Create visualizations by choosing color maps, different shapes, sizes, and palettes then delve into statistical data analysis using distribution algorithms and correlations. You'll then find your way around different data and numerical problems, get to grips with Spark and HDFS, and set up migration scripts for web mining. You'll be able to quickly and accurately perform hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making. Finally, you will delve into advanced techniques such as performing regression, quantifying cause and effect using Bayesian methods, and discovering how to use Python's tools for supervised machine learning. The course provides you with highly practical content explaining data analysis with Python, from the following Packt books: Getting Started with Python Data Analysis. Python Data Analysis Cookbook. Mastering Python Data Analysis. By the end of this course, you will have all the knowledge you need to analyze your data with varying complexity levels, and turn it into actionable insights. Style and approach Learn Python data analysis using engaging examples and fun exercises, and with a gentle and friendly but comprehensive "learn-by-doing" approach. It offers you a useful way of analyzing the data that's specific to this course, but that can also be applied to any other data. This course is designed to be both a guide and a reference for moving beyond the basics of data analysis.
Written in Cookbook style, the code examples will take your Numpy skills to the next level. This book will take Python developers with basic Numpy skills to the next level through some practical recipes.
In today's world of science and technology, it's all about speed and flexibility. When it comes to scientific computing, NumPy tops the list. NumPy will give you both speed and high productivity. This book will walk you through NumPy with clear, step-by-step examples and just the right amount of theory. The book focuses on the fundamentals of NumPy, including array objects, functions, and matrices, each of them explained with practical examples. You will then learn about different NumPy modules while performing mathematical operations such as calculating the Fourier transform, finding the inverse of a matrix, and determining eigenvalues, among many others. This book is a one-stop solution to knowing the ins and outs of the vast NumPy library, empowering you to use its wide range of mathematical features to build efficient, high-speed programs.
Understand data analysis pipelines using machine learning algorithms and techniques with this practical guide Key FeaturesPrepare and clean your data to use it for exploratory analysis, data manipulation, and data wranglingDiscover supervised, unsupervised, probabilistic, and Bayesian machine learning methodsGet to grips with graph processing and sentiment analysisBook Description Data analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you'll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines. Starting with the essential statistical and data analysis fundamentals using Python, you'll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You'll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you'll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you'll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask. By the end of this data analysis book, you'll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data. What you will learnExplore data science and its various process modelsPerform data manipulation using NumPy and pandas for aggregating, cleaning, and handling missing valuesCreate interactive visualizations using Matplotlib, Seaborn, and BokehRetrieve, process, and store data in a wide range of formatsUnderstand data preprocessing and feature engineering using pandas and scikit-learnPerform time series analysis and signal processing using sunspot cycle dataAnalyze textual data and image data to perform advanced analysisGet up to speed with parallel computing using DaskWho this book is for This book is for data analysts, business analysts, statisticians, and data scientists looking to learn how to use Python for data analysis. Students and academic faculties will also find this book useful for learning and teaching Python data analysis using a hands-on approach. A basic understanding of math and working knowledge of the Python programming language will help you get started with this book.
This book is for programmers, scientists, and engineers who have knowledge of the Python language and know the basics of data science. It is for those who wish to learn different data analysis methods using Python and its libraries. This book contains all the basic ingredients you need to become an expert data analyst.
Over 140 practical recipes to help you make sense of your data with ease and build production-ready data apps About This Book Analyze Big Data sets, create attractive visualizations, and manipulate and process various data types Packed with rich recipes to help you learn and explore amazing algorithms for statistics and machine learning Authored by Ivan Idris, expert in python programming and proud author of eight highly reviewed books Who This Book Is For This book teaches Python data analysis at an intermediate level with the goal of transforming you from journeyman to master. Basic Python and data analysis skills and affinity are assumed. What You Will Learn Set up reproducible data analysis Clean and transform data Apply advanced statistical analysis Create attractive data visualizations Web scrape and work with databases, Hadoop, and Spark Analyze images and time series data Mine text and analyze social networks Use machine learning and evaluate the results Take advantage of parallelism and concurrency In Detail Data analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns. As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning. Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You'll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining. In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code. By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios. Style and Approach The book is written in “cookbook” style striving for high realism in data analysis. Through the recipe-based format, you can read each recipe separately as required and immediately apply the knowledge gained.
The book is written in beginner’s guide style with each aspect of NumPy demonstrated with real world examples and required screenshots.If you are a programmer, scientist, or engineer who has basic Python knowledge and would like to be able to do numerical computations with Python, this book is for you. No prior knowledge of NumPy is required.
Every story ever told really happened...' (The Doctor, 'Hell Bent', 2015) Stories are, fundamentally what Doctor Who is all about. In Once Upon a Time Lord, Ivan Phillips explores a wide range of perspectives on these stories and presents a lively and richly-varied analysis of the accumulated tales that constitute this popular modern mythology. Concerned equally with 'classic' and 'new' Who, Phillips looks at how aspects of the Time Lord's story have been developed on television and beyond, tracing lines of connection and divergence across various media. He discusses Doctor Who as a mythology that has drawn on its own past in often complex ways, at the same time reworking elements from many other sources, whether literary, cinematic, televisual or historical. Once Upon A Time Lord offers an original take on this singular hero's journey, reading the unsettled enigma of the Doctor in relation to the characters, narratives and locations that he has encountered across more than half a century.
Various opinion polls, both in the USA and Great Britain have revealed that a large proportion of citizens believe that their countries are heading in the wrong direction. The book generally describes the trends in the governance of the West that have been gradually changing Individualistic free societies to Collectivist societies of subservient people. This progression has been carried out by the so called Political elite. In practical terms we can see that there has been a growth of governments and their bureaucracies, as well as an encroachment of government's influence on what used to be citizen's individual decision. This occurrence has driven the attempt to "manage" entire societies. Examples can be noted in relation to: management of the economy, social engineering, the use of media and education to impose collectivist ideologies, extensive surveillance of citizens, and the general aggrandizement of governments and their rulers. These have all lead to the transformation of free individuals into subjects of the State (that is ruling elite) Alongside this transformation, the governments in the West are currently living beyond their means and are accruing enormous debts. The book compares the present Statism of the West with the Soviet Socialism, and how they are gradually drawing closer.
The engaging, witty, fascinating memoir of one of New Zealand's most eminent neurologists and winemakers. It all began when Ivan Donaldson's girlfriend, Chris, gave him Hugo Johnson's book Wine in 1966. A light bulb went off in the mind of the talented, ambitious young doctor. A fascination with wine started when he and that girfriend, now his wife of 46 years, started making fruit wines, then wine made with table grapes from her parents' garden. Things got more serious when he was working in London in the early 1970s and they were able to head off to France in their rackety old car to tour vineyards. Things got more serious still when, in the late 1970s, he and a group of Christchurch doctors planted out Mountainview vineyard in Halswell. And things became very serious indeed when, in 1984, Ivan and Chris Donaldson bought a parcel of land in the Waipara Valley on which to start Pegasus Bay Wines. It's now one of New Zealand's best-known and most awarded small wineries, still owned and run by the family and making magnificent wine using sustainable methods. It's highly sought after in overseas markets. Somehow, in between all this Ivan Donaldson has managed to carve out an impressive medical career. This engaging memoir tells how he has integrated the two great loves of his life. It's the story of one of this country's wine pioneers but also the fascinating account of a life in medicine, spent plumbing the deep mysteries of the human brain.
Nuclear Analytical Methods in the Life Sciences •1994 is a forefront survey of key presentations from the 1993 International Conference on Nuclear Analytical Methods in the Life Sciences. Sponsored by the International Atomic Energy Agency (IAEA), this useful volume covers the spectrum of multidisciplinary research on both the methodological aspects and the development of nuclear analytical methods and their applications in the life sciences. The book is divided into six sections covering related material. These sections are: Methodology of Nuclear Analytical Methods; Environmental Applications; Biomedical Applications; Analysis of Biological Samples; Quality Assurance and Comparison with Other Methods; and a section dealing with miscellaneous issues, such as programs offered by the IAEA.
In The Tale of the Prophet Isaiah. The Destiny and Meanings of an Apocryphal Text Ivan Biliarsky proposes an edition of the original text of the medieval apocryphon, together with images of the single manuscript copy. The author also includes a large commentary on the otherwise quite unclear narrative concerning its origins, its development, a prosopography of the mentioned persons, an interpretation of its meaning and of the stages of its continuous creation. This completely new approach profoundly revises the source with a strong focus on its biblical roots. Ivan Biliarsky abandons the “national” understanding of the apocryphon and introduces evidence about its significance for the enforcement of the Byzantine-Slavic/Bulgarian Commonwealth and solidarity.
The book gives an insight into how the quality of health care may improve through the model of knowledge management and a multi-contingency approach to organizational design. The author assesses the relational triangle between knowledge management, organizational design, and the health system in Montenegro. Montenegrin health care system is presented through macroeconomic, managerial, and organizational-legal factors. The author focuses on the importance of knowledge management, leadership, organizational strategy, structure, culture and climate of health organizations. The author’s research covered public and private health institutions of Montenegro and included data collection from managers, union members, doctors, technicians, and finally, users of health services. A special part is dedicated to organizational challenges in the context of COVID-19 pandemic. The author explains how political agenda confronted with knowledge and profession and made Montenegro found itself in downward spiral in its fight against the pandemic. An abundance of diverse approaches to the quality of health services - from the point of view of service providers and users, decision makers and employees, management and trade union representatives, and private and public sector, makes the book stimulating and useful for professionals in health management, policy makers, patients, and the general audience.
By examining historical applications of the compounds found in plants, this five-volume series serves as a reference for quality assurance, research, product development, and regulatory guidance of the compounds found in plant-based medicines. This work supports the growing consumers' interest in herbal medicine for wellness and health. Plant-Based Therapeutics, Volume 1: Cannabis sativa, the first in the series, covers a unique plant species and provides the framework to integrate its evidence-based scientific discoveries with healthcare therapies. Cannabis has been used in religious ceremonies and medical purposes for thousands of years. Cannabidiol (CBD), the main non-psychoactive component of Cannabis, was isolated in the 1940s, and its structure was established in the 1960s. In 1964 tetrahydrocannabinol (THC), the psychoactive component, was isolated. Cannabis has more than 500 components, of which 104 cannabinoids have been identified. Two of them, THC and CBD, have been the primary components of scientific investigations. They were approved by the FDA for chemotherapy-induced nausea and vomiting in 1985; for appetite stimulation in wasting conditions, such as AIDS, in 1992, and in 2018 for treating two forms of pediatric epilepsy, Dravet syndrome and Lennox-Gastaut syndrome. Beyond the indications for which cannabinoids are FDA-approved, the evidence reveals that cannabinoid receptors are present throughout the body, embedded in cell membranes, and are believed to be more numerous than any other receptor system. When cannabinoid receptors are stimulated, a variety of physiologic processes ensue. Thus, other constituents of Cannabis are extremely promising either as individual compounds or their potential synergistic or entourage effects in the treatment of numerous medical conditions.
A step-by-step guide, packed with examples of practical numerical analysis that will give you a comprehensive, but concise overview of NumPy. This book is for programmers, scientists, or engineers, who have basic Python knowledge and would like to be able to do numerical computations with Python.
An extraordinary compendium of information on herbal medicine, Medicinal Plants of the World, Volume 3 comprehensively documents the medicinal value of 16 major plant species widely used around the world in medical formulations. The book's exhaustive summary of available scientific data for the plants provides detailed information on how each plant is used in different countries, describing both traditional therapeutic applications and what is known from its use in clinical trials. A comprehensive bibliography of over 3000 references cites the literature available from a wide range of disciplines. This book offers an unprecedented collection of vital scientific information for pharmacologists, herbal medicine practitioners, drug developers, medicinal chemists, phytochemists, toxicologists, and researchers who want to explore the use of plant materials for medicinal and related purposes.
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