Introducing Python: Modern Computing in Simple Packages

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O'Reilly Media #ad - This book also shows you how to use Python for applications in business, and the arts, science, using various Python tools and open source packages. Learn simple data types, apis, and other object-oriented featuresexamine storage from flat files to relational databases and NoSQLUse Python to build web clients, and basic math and text operationsUse data-wrangling techniques with Python’s built-in data structuresExplore Python code structure, classes, including the use of functionsWrite large programs in Python, servers, processes, with modules and packagesDive into objects, and servicesManage system tasks such as programs, and threadsUnderstand the basics of concurrency and network programming O reilly Media.

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Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

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O'Reilly Media #ad - Updated for Python 3. 6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. Python for data analysis wes mckinney. Data files and related material are available on GitHub. Use the ipython shell and jupyter notebook for exploratory computinglearn basic and advanced features in NumPy Numerical PythonGet started with data analysis tools in the pandas libraryUse flexible tools to load, and summarize datasetsAnalyze and manipulate regular and irregular time series dataLearn how to solve real-world data analysis problems with thorough, dice, clean, transform, and reshape dataCreate informative visualizations with matplotlibApply the pandas groupby facility to slice, merge, detailed examples Python for data analysis.

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Best Practices in Data Cleaning: A Complete Guide to Everything You Need to Do Before and After Collecting Your Data

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Database Systems: A Practical Approach to Design, Implementation, and Management 6th Edition

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Pearson #ad - Used book in Good Condition. It provides: database design methodology that can be used by Both Technical and Non-technical Readers A Comprehensive Introduction to the Theory behind Databases A Clear Presentation that Supports Learning Python for data analysis. The methodology for relational database management Systems is presented in simple, logical, step-by-step instructions in conjunction with a realistic worked example using three explicit phases—conceptual, and physical database design.

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Python for data analysis wes mckinney. This best-selling text introduces the theory behind databases in a concise yet comprehensive manner, providing database design methodology that can be used by both technical and non-technical readers.

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Python Pocket Reference: Python In Your Pocket Pocket Reference O'Reilly

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O'Reilly Media #ad - Python for data analysis wes mckinney. Updated for both Python 3. 4 and 2. 7, this convenient pocket guide is the perfect on-the-job quick reference. The handy index lets you pinpoint exactly what you need. Written by mark lutz—widely recognized as the world’s leading Python trainer—Python Pocket Reference is an ideal companion to O’Reilly’s classic Python tutorials, Learning Python and Programming Python, also written by Mark.

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Learning Python, 5th Edition

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O'Reilly Media #ad - You’ll also learn some advanced language features that recently have become more common in Python code. Explore python’s major built-in object types such as numbers, functions, metaclasses, including decorators, descriptors, and other tools into larger components with modulesDive into classes: Python’s object-oriented programming tool for structuring codeWrite large programs with Python’s exception-handling model and development toolsLearn advanced Python tools, and dictionariesCreate and process objects with Python statements, and learn Python’s general syntax modelUse functions to avoid code redundancy and package code for reuseOrganize statements, lists, and Unicode processing Python for data analysis.

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Essence of Decision: Explaining the Cuban Missile Crisis 2nd Edition

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Python Cookbook, Third edition

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Introduction to Machine Learning with Python: A Guide for Data Scientists

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O'Reilly Media #ad - O'reilly Media. Authors andreas müller and sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Pearson. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, including which data aspects to focus onadvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, you’ll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills Python for data analysis.

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Fluent Python: Clear, Concise, and Effective Programming

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Network Programmability and Automation: Skills for the Next-Generation Network Engineer

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O'Reilly Media #ad - O'reilly Media. O reilly Media. Like sysadmins before them, network engineers are finding that they cannot do their work manually anymore. Used book in Good Condition. As the field faces new protocols, delivery models, technologies, and a pressing need for businesses to be more agile and flexible, network automation is becoming essential.

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Network Programmability and Automation: Skills for the Next-Generation Network Engineer #ad - Through the course of the book, you’ll learn the basic skills and tools you need to make this critical transition. This book covers:python programming basics: data types, yaml, and moduleslinux fundamentals to provide the foundation you need on your network automation journeyData formats and models: JSON, and YANG for networkingJinja templating and its applicability for creating network device configurationsThe role of application programming interfaces APIs in network automationSource control with Git to manage code changes during the automation processHow Ansible, XML, conditionals, Salt, loops, functions, classes, and StackStorm open source automation tools can be used to automate network devicesKey tools and technologies required for a Continuous Integration CI pipeline in network operations Python for data analysis.

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