Python Data Analysis 2nd (Packt) azw3
Python Data Analysis 2nd by Armando Fandango 2017 azw3格式
Python Data Analysis 2nd (Packt) epub2018-01-29
Python Data Analysis 2nd by Armando Fandango 2017
Python Data Analysis 2nd (Packt)2018-01-29
Python Data Analysis 2nd by Armando Fandango 2017 转化版的PDF
Python Data Analysis, 2nd Edition-Packt Publishing(2017).epub2018-03-11
Data analysis has a rich history in natural, biomedical, and social sciences. In almost every area of industry, data analysis has gained popularity lately due to the hype around Data Science. Data analysis and Data Science attempt to extract information from data. For that purpose, we use techniques from statistics, machine learning, signal processing, natural language processing, and computer science. A mind map visualizing Python software that can be used for data analysis can be found in first chapter of this book. The first noticeable thing is that the Python ecosystem is very mature, diverse and rich. It includes famous packages such as NumPy, SciPy, and matplotlib. This should not come as a surprise since Python has been around since 1989. Python is easy to learn and use, less verbose than other programming languages, and very readable. Even if you don39;t know Python, you can pick up the basics within days, especially if you have experience in another programming language. To enjoy this book, you don39;t need more than the basics. There are plenty of books, courses, and online tutorials that teach Python.
Python Data Analysis(PACKT,2014)2015-09-02
Dive deeper into data analysis with the flexibility of Python and learn how its extensive range of scientific and mathematical libraries can be used to solve some of the toughest challenges in data analysis. Build your confidence and expertise and develop valuable skills in high demand in a world driven by Big Data with this expert data analysis book. This data science tutorial will help you learn how to effectively retrieve, clean, manipulate, and visualize data and establish a successful data analysis workflow. Apply the impressive functionality of Python39;s data mining tools and scientific and numerical libraries to a range of the most important tasks within data analysis and data science, and develop strategies and ideas to take control your own data analysis projects. Get to grips with statistical analysis using NumPy and SciPy, visualize data with Matplotlib, and uncover sophisticated insights through predictive analytics and machine learning with SciKit-Learn. You will also learn how to use the tools needed to work with databases and find out how Python can be used to analyze textual and social media data, as you work through this essential data science tutorial.
Python_End-to-end Data Analysis-Packt Publishing(2017).pdf2018-03-07
The use of Python for data analysis and visualization has only increased in popularity in the last few years. The aim of this book is to develop skills to effectively approach almost any data analysis problem, and extract all of the available information. This is done by introducing a range of varying techniques and methods such as uni- and multi- variate linear regression, cluster finding, Bayesian analysis, machine learning, and time series analysis. Exploratory data analysis is a key aspect to get a sense of what can be done and to maximize the insights that are gained from the data. Additionally, emphasis is put on presentation-ready figures that are clear and easy to interpret.
Python Data Analysis Cookbook(PACKT,2016)2016-08-13
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. You39;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.
Python: End-to-end Data Analysis.azw3电子书下载2017-06-13
Python: End-to-end Data Analysis by Phuong Vothihong English | 31 May 2017 | ASIN: B072M6868D | 1321 Pages | AZW3 | 27.07 MB 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. You39;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. You39;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 Python39;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 that39;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.
Oreilly Python for Data Analysis 2nd 2017原版2018-01-17
Oreilly Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython Oct 20, 2017 最终版，完整版，清晰版,原版
Python Data Analysis Cookbook2016-07-29
Python Data Analysis Cookbook by Ivan Idris-P2P Packt Publishing | 22 July 2016 | English | ISBN: 178528228X | 462 pages 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.
Python Data Visualization(PACKT,2ed,2015)2016-01-25
Python Data Visualization Cookbook will progress the reader from the point of installing and setting up a Python environment for data manipulation and visualization all the way to 3D animations using Python libraries. Readers will benefit from over 60 precise and reproducible recipes that will guide the reader towards a better understanding of data concepts and the building blocks for subsequent and sometimes more advanced concepts. Python Data Visualization Cookbook starts by showing how to set up matplotlib and the related libraries that are required for most parts of the book, before moving on to discuss some of the lesser-used diagrams and charts such as Gantt Charts or Sankey diagrams. Initially it uses simple plots and charts to more advanced ones, to make it easy to understand for readers. As the readers will go through the book, they will get to know about the 3D diagrams and animations. Maps are irreplaceable for displaying geo-spatial data, so this book will also show how to build them. In the last chapter, it includes explanation on how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python.
Mastering Python Data Visualization(PACKT,2015)2016-09-05
About This Book Explore various tools and their strengths while building meaningful representations that can make it easier to understand data Packed with computational methods and algorithms in diverse fields of science Written in an easy-to-follow categorical style, this book discusses some niche
Practical Data Analysis(PACKT,2ed,2016)2016-10-13
Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. This book explains the basic data algorithms without the theoretical jargon, and you’ll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark. What you will learn Acquire, format, and visualize your data Build an image-similarity search engine Generate meaningful visualizations anyone can understand Get started with analyzing social network graphs Find out how to implement sentiment text analysis Install data analysis tools such as Pandas, MongoDB, and Apache Spark Get to grips with Apache Spark Implement machine learning algorithms such as classification or forecasting
Learning Python Data Visualization(PACKT,2014)2015-04-10
Today, data visualization is a hot topic as a direct result of the vast amount of data created every second. Transforming that data into information is a complex task for data visualization professionals, who, at the same time, try to understand the data and objectively transfer that understanding to others. This book is a set of practical recipes that strive to help the reader get a firm grasp of the area of data visualization using Python and its popular visualization and data libraries. Python Data Visualization Cookbook will progress the reader from the point of installing and setting up a Python environment for data manipulation and visualization all the way to 3D animations using Python libraries. Readers will benefit from over 60 precise and reproducible recipes that guide the reader towards a better understanding of data concepts and the building blocks for subsequent and sometimes more advanced concepts.
2017 Deep Learning Theano-Packt azw3 kindle 版本2018-01-05
2017 Deep Learning Theano-Packt azw3 适合于 Kindle 阅读
Python GUI Programming Cookbook 2nd(AZW3+PDF)2018-09-10
Title: Python GUI Programming Cookbook 2nd Author(s): Burkhard A. Meier Publisher: Packt Publishing Year: 2017 Edition: 2nd Language: English Pages (biblio\tech): 422\0 ISBN: 1787129454, 9781787129450 Key FeaturesUse object-oriented programming to develop amazing GUIs in PythonCreate a working GUI project as a central resource for developing your Python GUIsEasy-to-follow recipes to help you develop code using the latest released version of PythonBook Description Python is a multi-domain, interpreted programming language. It is a widely used general-purpose, high-level programming language. It is often used as a scripting language because of its forgiving syntax and compatibility with a wide variety of different eco-systems. Python GUI Programming Cookbook follows a task-based approach to help you create beautiful and very effective GUIs with the least amount of code necessary. This book will guide you through the very basics of creating a fully functional GUI in Python with only a few lines of code. Each and every recipe adds more widgets to the GUIs we are creating. While the cookbook recipes all stand on their own, there is a common theme running through all of them. As our GUIs keep expanding, using more and more widgets, we start to talk to networks, databases, and graphical libraries that greatly enhance our GUI39;s functionality. This book is what you need to expand your knowledge on the subject of GUIs, and make sure you39;re not missing out in the long run. What you will learnCreate the GUI Form and add widgetsArrange the widgets using layout managersUse object-oriented programming to create GUIsCreate Matplotlib chartsUse threads and talking to networksTalk to a MySQL database via the GUIPerform unit-testing and internationalizing the GUIExtend the GUI with third-party graphical librariesGet to know the best practices to create GUIsAbout the Author Burkhard A. Meier has more than 17 years of professional experience working in the software industry as
Python Geospatial Analysis Cookbook(PACKT,2015)2015-12-17
Geospatial development links your data to places on the Earth39;s surface. Its analysis is used in almost every industry to answer location type questions. Combined with the power of the Python programming language, which is becoming the de facto spatial scripting choice for developers and analysts worldwide, this technology will help you to solve real-world spatial problems. This book begins by tackling the installation of the necessary software dependencies and libraries needed to perform spatial analysis with Python. From there, the next logical step is to prepare our data for analysis; we will do this by building up our tool box to deal with data preparation, transformations, and projections. Now that our data is ready for analysis, we will tackle the most common analysis methods for vector and raster data. To check or validate our results, we will explore how to use topology checks to ensure top-quality results. This is followed with network routing analysis focused on constructing indoor routes within buildings, over different levels. Finally, we put several recipes together in a GeoDjango web application that demonstrates a working indoor routing spatial analysis application. The round trip will provide you all the pieces you need to accomplish your own spatial analysis application to suit your requirements.
Learning Haskell Data Analysis(PACKT,2015)2015-08-26
Haskell is trending in the field of data science by providing a powerful platform for robust data science practices. This book provides you with the skills to handle large amounts of data, even if that data is in a less than perfect state. Each chapter in the book helps to build a small library of code that will be used to solve a problem for that chapter. The book starts with creating databases out of existing datasets, cleaning that data, and interacting with databases within Haskell in order to produce charts for publications. It then moves towards more theoretical concepts that are fundamental to introductory data analysis, but in a context of a real-world problem with real-world data. As you progress in the book, you will be relying on code from previous chapters in order to help create new solutions quickly. By the end of the book, you will be able to manipulate, find, and analyze large and small sets of data using your own Haskell libraries.
Python Machine Learning(PACKT,2015)2015-10-29
Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world39;s leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable.
Python Data Visualization Cookbook2016-01-08
Python Data Visualization Cookbook Packt 英文原版电子书
ISO/IEC/IEEE 15939：2017 系统和软件工程—测量过程 - 完整英文电子版（46页）.pdf
ISO/IEC/IEEE 15939：2017 系统和软件工程—测量过程 - 完整英文电子版（46页）.pdf