- Table View
- List View
Python Challenges: 100 Proven Programming Tasks Designed to Prepare You for Anything
by Michael IndenAugment your knowledge of Python with this entertaining learning guide, which features 100 exercises and programming puzzles and solutions. Python Challenges will help prepare you for your next exam or a job interview, and covers numerous practical topics such as strings, data structures, recursion, arrays, and more. Each topic is addressed in its own separate chapter, starting with an introduction to the basics and followed by 10 to 15 exercises of various degrees of difficulty, helping you to improve your programming skills effectively. Detailed sample solutions, including the algorithms used for all tasks, are included to maximize your understanding of each area. Author Michael Inden also describes alternative solutions and analyzes possible pitfalls and typical errors. Three appendices round out the book: the first covers the Python command line interpreter, which is often helpful for trying out the code snippets and examples in the book, followed by an overview of Pytest for unit testing and checking the solutions. The last explains the O notation for estimating performance. After reading this book, you'll be prepared to take the next step in your career or tackle your next personal project. All source code is freely available for download via the Apress website. What You Will Learn Improve your Python knowledge by solving enjoyable but challenging programming puzzles Solve mathematical problems, recursions, strings, arrays and more Manage data processing and data structures like lists, sets, maps Handle advanced recursion as well as binary trees, sorting and searching Gamify key fundamentals for fun and easier reinforcement Who this book is for: Programmers, software developers who are either professionals or makers, as well as students and teachers. At least some prior experience with the Python programming is recommended.
Python Concurrency with asyncio
by Matthew FowlerLearn how to speed up slow Python code with concurrent programming and the cutting-edge asyncio library. Use coroutines and tasks alongside async/await syntax to run code concurrently Build web APIs and make concurrency web requests with aiohttp Run thousands of SQL queries concurrently Create a map-reduce job that can process gigabytes of data concurrently Use threading with asyncio to mix blocking code with asyncio code Python is flexible, versatile, and easy to learn. It can also be very slow compared to lower-level languages. Python Concurrency with asyncio teaches you how to boost Python's performance by applying a variety of concurrency techniques. You'll learn how the complex-but-powerful asyncio library can achieve concurrency with just a single thread and use asyncio's APIs to run multiple web requests and database queries simultaneously. The book covers using asyncio with the entire Python concurrency landscape, including multiprocessing and multithreading. About the technology It&’s easy to overload standard Python and watch your programs slow to a crawl. Th e asyncio library was built to solve these problems by making it easy to divide and schedule tasks. It seamlessly handles multiple operations concurrently, leading to apps that are lightning fast and scalable. About the book Python Concurrency with asyncio introduces asynchronous, parallel, and concurrent programming through hands-on Python examples. Hard-to-grok concurrency topics are broken down into simple flowcharts that make it easy to see how your tasks are running. You&’ll learn how to overcome the limitations of Python using asyncio to speed up slow web servers and microservices. You&’ll even combine asyncio with traditional multiprocessing techniques for huge improvements to performance. What's inside Build web APIs and make concurrency web requests with aiohttp Run thousands of SQL queries concurrently Create a map-reduce job that can process gigabytes of data concurrently Use threading with asyncio to mix blocking code with asyncio code About the reader For intermediate Python programmers. No previous experience of concurrency required. About the author Matthew Fowler has over 15 years of software engineering experience in roles from architect to engineering director. Table of Contents 1 Getting to know asyncio 2 asyncio basics 3 A first asyncio application 4 Concurrent web requests 5 Non-blocking database drivers 6 Handling CPU-bound work 7 Handling blocking work with threads 8 Streams 9 Web applications 10 Microservices 11 Synchronization 12 Asynchronous queues 13 Managing subprocesses 14 Advanced asyncio
Python Continuous Integration and Delivery: A Concise Guide with Examples
by Moritz LenzGain the techniques and tools that enable a smooth and efficient software development process in this quick and practical guide on Python continuous integration (CI) and continuous delivery (CD). Based on example applications, this book introduces various kinds of testing and shows you how to set up automated systems that run these tests, and install applications in different environments in controlled ways. Python Continuous Integration and Delivery tackles the technical problems related to software development that are typically glossed over in pure programming texts.After reading this book, you’ll see that in today's fast-moving world, no software project can afford to go through development, then an integration phase of unpredictable length and complexity, and finally be shipped to the customer -- just to find out that the resulting application didn't quite fill their need. Instead, you’ll discover that practicing continuous integration and continuous delivery reduces the risks by keeping changes small and automating otherwise painful processes. What You Will LearnCarry out various kinds of testing, including unit testing and continuous integration testing, of your Python code using JenkinsBuild packages and manage repositoriesIncorporate Ansible and Go for automated packaging and other deploymentsManage more complex and robust deploymentsWho This Book Is ForPython programmers and operating staff that work with Python applications.
Python Cookbook
by David Ascher Alex MartelliThe Python Cookbook is a collection of problems, solutions, and practical examples for Python programmers, written by Python programmers. It contains over two hundred recipes for text manipulation, object oriented programming, XML processing, system administration, and much more. This book is a treasure trove of useful code for both novices and advanced practitioners, with contributions from such Python luminaries as Guido Van Rossum, Tim Peters, Paul Prescod, and Mark Hammond.
Python Cookbook
by David Ascher Alex Martelli Anna RavenscroftPortable, powerful, and a breeze to use, Python is the popular open source object-oriented programming language used for both standalone programs and scripting applications. It is now being used by an increasing number of major organizations, including NASA and Google. Updated for Python 2.4, The Python Cookbook, 2nd Edition offers a wealth of useful code for all Python programmers, not just advanced practitioners. Like its predecessor, the new edition provides solutions to problems that Python programmers face everyday. It now includes over 200 recipes that range from simple tasks, such as working with dictionaries and list comprehensions, to complex tasks, such as monitoring a network and building a templating system. This revised version also includes new chapters on topics such as time, money, and metaprogramming. Here's a list of additional topics covered: Manipulating text Searching and sorting Working with files and the filesystem Object-oriented programming Dealing with threads and processes System administration Interacting with databases Creating user interfaces Network and web programming Processing XML Distributed programming Debugging and testing Another advantage of The Python Cookbook, 2nd Edition is its trio of authors--three well-known Python programming experts, who are highly visible on email lists and in newsgroups, and speak often at Python conferences. With scores of practical examples and pertinent background information, The Python Cookbook, 2nd Edition is the one source you need if you're looking to build efficient, flexible, scalable, and well-integrated systems.
Python Cookbook, 2nd Edition
by David Ascher Alex Martelli Anna RavenscroftPortable, powerful, and a breeze to use, Python is the popular open source object-oriented programming language used for both standalone programs and scripting applications. It is now being used by an increasing number of major organizations, including NASA and Google.Updated for Python 2.4, The Python Cookbook, 2nd Edition offers a wealth of useful code for all Python programmers, not just advanced practitioners. Like its predecessor, the new edition provides solutions to problems that Python programmers face everyday.It now includes over 200 recipes that range from simple tasks, such as working with dictionaries and list comprehensions, to complex tasks, such as monitoring a network and building a templating system. This revised version also includes new chapters on topics such as time, money, and metaprogramming.Here's a list of additional topics covered:Manipulating textSearching and sortingWorking with files and the filesystemObject-oriented programmingDealing with threads and processesSystem administrationInteracting with databasesCreating user interfacesNetwork and web programmingProcessing XMLDistributed programming Debugging and testing Another advantage of The Python Cookbook, 2nd Edition is its trio of authors--three well-known Python programming experts, who are highly visible on email lists and in newsgroups, and speak often at Python conferences.With scores of practical examples and pertinent background information, The Python Cookbook, 2nd Edition is the one source you need if you're looking to build efficient, flexible, scalable, and well-integrated systems.
Python Cookbook: Recipes for Mastering Python 3
by Brian K. Jones David BeazleyIf you need help writing programs in Python 3, or want to update older Python 2 code, this book is just the ticket. Packed with practical recipes written and tested with Python 3.3, this unique cookbook is for experienced Python programmers who want to focus on modern tools and idioms.Inside, youâ??ll find complete recipes for more than a dozen topics, covering the core Python language as well as tasks common to a wide variety of application domains. Each recipe contains code samples you can use in your projects right away, along with a discussion about how and why the solution works.Topics include:Data Structures and AlgorithmsStrings and TextNumbers, Dates, and TimesIterators and GeneratorsFiles and I/OData Encoding and ProcessingFunctionsClasses and ObjectsMetaprogrammingModules and PackagesNetwork and Web ProgrammingConcurrencyUtility Scripting and System AdministrationTesting, Debugging, and ExceptionsC Extensions
Python Crash Course, 2nd Edition: A Hands-On, Project-Based Introduction to Programming
by Eric MatthesSecond edition of the best selling Python book in the world. A fast-paced, no-nonsense guide to programming in Python. This book teaches beginners the basics of programming in Python with a focus on real projects.This is the second edition of the best selling Python book in the world. Python Crash Course, 2nd Edition is a straightforward introduction to the core of Python programming. Author Eric Matthes dispenses with the sort of tedious, unnecessary information that can get in the way of learning how to program, choosing instead to provide a foundation in general programming concepts, Python fundamentals, and problem solving. Three real world projects in the second part of the book allow readers to apply their knowledge in useful ways. Readers will learn how to create a simple video game, use data visualization techniques to make graphs and charts, and build and deploy an interactive web application. Python Crash Course, 2nd Edition teaches beginners the essentials of Python quickly so that they can build practical programs and develop powerful programming techniques.
Python Crash Course, 3rd Edition: A Hands-On, Project-Based Introduction to Programming
by Eric MatthesPython Crash Course is the world&’s bestselling programming book, with over 1,500,000 copies sold to date!Python Crash Course is the world&’s best-selling guide to the Python programming language. This fast-paced, thorough introduction will have you writing programs, solving problems, and developing functioning applications in no time.You&’ll start by learning basic programming concepts, such as variables, lists, classes, and loops, and practice writing clean code with exercises for each topic. You&’ll also learn how to make your programs interactive and test your code safely before adding it to a project. You&’ll put your new knowledge into practice by creating a Space Invaders–inspired arcade game, building a set of data visualizations with Python&’s handy libraries, and deploying a simple application online.As you work through the book, you&’ll learn how to:Use powerful Python libraries and tools, including pytest, Pygame, Matplotlib, Plotly, and DjangoMake increasingly complex 2D games that respond to keypresses and mouse clicksGenerate interactive data visualizations using a variety of datasetsBuild apps that allow users to create accounts and manage their own data, and deploy your apps online Troubleshoot coding errors and solve common programming problems New to this edition: This third edition is completely revised to reflect the latest in Python code. New and updated coverage includes VS Code for text editing, the pathlib module for file handling, pytest for testing your code, as well as the latest features of Matplotlib, Plotly, and Django.If you&’ve been thinking about digging into programming, Python Crash Course will provide you with the skills to write real programs fast. Why wait any longer? Start your engines and code!Covers Python 3.x
Python Crash Course: A Hands-On, Project-Based Introduction to Programming
by Eric MatthesPython Crash Course is a fast-paced, thorough introduction to Python that will have you writing programs, solving problems, and making things that work in no time.In the first half of the book, you’ll learn about basic programming concepts, such as lists, dictionaries, classes, and loops, and practice writing clean and readable code with exercises for each topic. You’ll also learn how to make your programs interactive and how to test your code safely before adding it to a project. In the second half of the book, you’ll put your new knowledge into practice with three substantial projects: a Space Invaders–inspired arcade game, data visualizations with Python’s super-handy libraries, and a simple web app you can deploy online.As you work through Python Crash Course you’ll learn how to:–Use powerful Python libraries and tools, including matplotlib, NumPy, and Pygal–Make 2D games that respond to keypresses and mouse clicks, and that grow more difficult as the game progresses–Work with data to generate interactive visualizations–Create and customize Web apps and deploy them safely online–Deal with mistakes and errors so you can solve your own programming problemsIf you’ve been thinking seriously about digging into programming, Python Crash Course will get you up to speed and have you writing real programs fast. Why wait any longer? Start your engines and code!Uses Python 2 and 3
Python Data Analysis
by Ivan IdrisThis 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.
Python Data Analysis - Second Edition
by Armando FandangoLearn how to apply powerful data analysis techniques with popular open source Python modules About This Book • Find, manipulate, and analyze your data using the Python 3.5 libraries • Perform advanced, high-performance linear algebra and mathematical calculations with clean and efficient Python code • An easy-to-follow guide with realistic examples that are frequently used in real-world data analysis projects. Who This Book Is For This book is for programmers, scientists, and engineers who have the knowledge of Python and know the basics of data science. It is for those who wish to learn different data analysis methods using Python 3.5 and its libraries. This book contains all the basic ingredients you need to become an expert data analyst. What You Will Learn • Install open source Python modules such NumPy, SciPy, Pandas, stasmodels, scikit-learn,theano, keras, and tensorflow on various platforms • Prepare and clean your data, and use it for exploratory analysis • Manipulate your data with Pandas • Retrieve and store your data from RDBMS, NoSQL, and distributed filesystems such as HDFS and HDF5 • Visualize your data with open source libraries such as matplotlib, bokeh, and plotly • Learn about various machine learning methods such as supervised, unsupervised, probabilistic, and Bayesian • Understand signal processing and time series data analysis • Get to grips with graph processing and social network analysis In Detail Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries. Style and approach The book takes a very comprehensive approach to enhance your understanding of data analysis. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work. Packed with clear, easy to follow examples, this book will turn you into an ace data analyst in no time.
Python Data Analysis Cookbook
by Ivan IdrisOver 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.
Python Data Analysis: Perform data collection, data processing, wrangling, visualization, and model building using Python, 3rd Edition
by Ivan Idris Armando Fandango Avinash NavlaniUnderstand data analysis pipelines using machine learning algorithms and techniques with this practical guideKey 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 DescriptionData 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 forThis 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.
Python Data Analytics
by Fabio NelliPython Data Analytics will help you tackle the world of data acquisition and analysis using the power of the Python language. At the heart of this book lies the coverage of pandas, an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Author Fabio Nelli expertly shows the strength of the Python programming language when applied to processing, managing and retrieving information. Inside, you will see how intuitive and flexible it is to discover and communicate meaningful patterns of data using Python scripts, reporting systems, and data export. This book examines how to go about obtaining, processing, storing, managing and analyzing data using the Python programming language. You will use Python and other open source tools to wrangle data and tease out interesting and important trends in that data that will allow you to predict future patterns. Whether you are dealing with sales data, investment data (stocks, bonds, etc. ), medical data, web page usage, or any other type of data set, Python can be used to interpret, analyze, and glean information from a pile of numbers and statistics. This book is an invaluable reference with its examples of storing and accessing data in a database; it walks you through the process of report generation; it provides three real world case studies or examples that you can take with you for your everyday analysis needs. What you'll learn How to set up your environment and use automation scripts Where and how to get your data How to analyze your data with Python's data analysis library, called pandas How to carry out visualization with matplotlib How to analyze text data using Python's natural language processing Who this book is for This book is for the mid to experienced level programmer who already knows the basics of Python programming. It is for programmers who want to know how to use database data and reporting tools to manipulate raw data into coherent useful information. Table of Contents PART I 1. Setup Your Environment 2. Automation Scripts PART II 3. Where and How to Get Data 4. Introduction to pandas 5. pandas In-Depth 6. Python Data Visualizations with matplotlib 7. Designing Beautiful Graphs 8. Analyze Text Data with Natural Language Processing PART III 9. Full Example project 1 10. Full Example project 2 11. Full Example project 3 Appendix A Appendix B
Python Data Analytics: With Pandas, NumPy, and Matplotlib
by Fabio NelliExplore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis. You'll review scientific computing with NumPy, visualization with matplotlib, and machine learning with scikit-learn. This revision is fully updated with new content on social media data analysis, image analysis with OpenCV, and deep learning libraries. Each chapter includes multiple examples demonstrating how to work with each library. At its heart lies the coverage of pandas, for high-performance, easy-to-use data structures and tools for data manipulationAuthor Fabio Nelli expertly demonstrates using Python for data processing, management, and information retrieval. Later chapters apply what you've learned to handwriting recognition and extending graphical capabilities with the JavaScript D3 library. Whether you are dealing with sales data, investment data, medical data, web page usage, or other data sets, Python Data Analytics, Second Edition is an invaluable reference with its examples of storing, accessing, and analyzing data.What You'll LearnUnderstand the core concepts of data analysis and the Python ecosystemGo in depth with pandas for reading, writing, and processing dataUse tools and techniques for data visualization and image analysisExamine popular deep learning libraries Keras, Theano,TensorFlow, and PyTorchWho This Book Is ForExperienced Python developers who need to learn about Pythonic tools for data analysis
Python Data Analytics: With Pandas, NumPy, and Matplotlib
by Fabio NelliExplore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis. You'll review scientific computing with NumPy, visualization with matplotlib, and machine learning with scikit-learn. This third edition is fully updated for the latest version of Python and its related libraries, and includes coverage of social media data analysis, image analysis with OpenCV, and deep learning libraries. Each chapter includes multiple examples demonstrating how to work with each library. At its heart lies the coverage of pandas, for high-performance, easy-to-use data structures and tools for data manipulationAuthor Fabio Nelli expertly demonstrates using Python for data processing, management, and information retrieval. Later chapters apply what you've learned to handwriting recognition and extending graphical capabilities with the JavaScript D3 library. Whether you are dealing with sales data, investment data, medical data, web page usage, or other data sets, Python Data Analytics, Third Edition is an invaluable reference with its examples of storing, accessing, and analyzing data.What You'll LearnUnderstand the core concepts of data analysis and the Python ecosystemGo in depth with pandas for reading, writing, and processing dataUse tools and techniques for data visualization and image analysisExamine popular deep learning libraries Keras, Theano,TensorFlow, and PyTorchWho This Book Is ForExperienced Python developers who need to learn about Pythonic tools for data analysis
Python Data Cleaning Cookbook: Prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn, and OpenAI
by Michael WalkerLearn the intricacies of data description, issue identification, and practical problem-solving, armed with essential techniques and expert tips.Key FeaturesGet to grips with new techniques for data preprocessing and cleaning for machine learning and NLP modelsUse new and updated AI tools and techniques for data cleaning tasksClean, monitor, and validate large data volumes to diagnose problems using cutting-edge methodologies including Machine learning and AIBook DescriptionJumping into data analysis without proper data cleaning will certainly lead to incorrect results. The Python Data Cleaning Cookbook - Second Edition will show you tools and techniques for cleaning and handling data with Python for better outcomes. Fully updated to the latest version of Python and all relevant tools, this book will teach you how to manipulate and clean data to get it into a useful form. he current edition focuses on advanced techniques like machine learning and AI-specific approaches and tools for data cleaning along with the conventional ones. The book also delves into tips and techniques to process and clean data for ML, AI, and NLP models. You will learn how to filter and summarize data to gain insights and better understand what makes sense and what does not, along with discovering how to operate on data to address the issues you've identified. Next, you’ll cover recipes for using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors and generate visualizations for exploratory data analysis (EDA) to identify unexpected values. Finally, you’ll build functions and classes that you can reuse without modification when you have new data. By the end of this Data Cleaning book, you'll know how to clean data and diagnose problems within it.What you will learnUsing OpenAI tools for various data cleaning tasks Producing summaries of the attributes of datasets, columns, and rowsAnticipating data-cleaning issues when importing tabular data into pandasApplying validation techniques for imported tabular data Improving your productivity in pandas by using method chainingRecognizing and resolving common issues like dates and IDs Setting up indexes to streamline data issue identificationUsing data cleaning to prepare your data for ML and AI modelsWho this book is forThis book is for anyone looking for ways to handle messy, duplicate, and poor data using different Python tools and techniques. The book takes a recipe-based approach to help you to learn how to clean and manage data with practical examples. Working knowledge of Python programming is all you need to get the most out of the book.
Python Data Mining Quick Start Guide: A beginner's guide to extracting valuable insights from your data
by Nathan GreeneltchExplore the different data mining techniques using the libraries and packages offered by PythonKey FeaturesGrasp the basics of data loading, cleaning, analysis, and visualizationUse the popular Python libraries such as NumPy, pandas, matplotlib, and scikit-learn for data miningYour one-stop guide to build efficient data mining pipelines without going into too much theoryBook DescriptionData mining is a necessary and predictable response to the dawn of the information age. It is typically defined as the pattern and/ or trend discovery phase in the data mining pipeline, and Python is a popular tool for performing these tasks as it offers a wide variety of tools for data mining.This book will serve as a quick introduction to the concept of data mining and putting it to practical use with the help of popular Python packages and libraries. You will get a hands-on demonstration of working with different real-world datasets and extracting useful insights from them using popular Python libraries such as NumPy, pandas, scikit-learn, and matplotlib. You will then learn the different stages of data mining such as data loading, cleaning, analysis, and visualization. You will also get a full conceptual description of popular data transformation, clustering, and classification techniques.By the end of this book, you will be able to build an efficient data mining pipeline using Python without any hassle.What you will learnExplore the methods for summarizing datasets and visualizing/plotting dataCollect and format data for analytical workAssign data points into groups and visualize clustering patternsLearn how to predict continuous and categorical outputs for dataClean, filter noise from, and reduce the dimensions of dataSerialize a data processing model using scikit-learn’s pipeline featureDeploy the data processing model using Python’s pickle moduleWho this book is forPython developers interested in getting started with data mining will love this book. Budding data scientists and data analysts looking to quickly get to grips with practical data mining with Python will also find this book to be useful. Knowledge of Python programming is all you need to get started.
Python Data Science Cookbook
by Gopi SubramanianOver 60 practical recipes to help you explore Python and its robust data science capabilities About This Book * The book is packed with simple and concise Python code examples to effectively demonstrate advanced concepts in action * Explore concepts such as programming, data mining, data analysis, data visualization, and machine learning using Python * Get up to speed on machine learning algorithms with the help of easy-to-follow, insightful recipes Who This Book Is For This book is intended for all levels of Data Science professionals, both students and practitioners, starting from novice to experts. Novices can spend their time in the first five chapters getting themselves acquainted with Data Science. Experts can refer to the chapters starting from 6 to understand how advanced techniques are implemented using Python. People from non-Python backgrounds can also effectively use this book, but it would be helpful if you have some prior basic programming experience. What You Will Learn * Explore the complete range of Data Science algorithms * Get to know the tricks used by industry engineers to create the most accurate data science models * Manage and use Python libraries such as numpy, scipy, scikit learn, and matplotlib effectively * Create meaningful features to solve real-world problems * Take a look at Advanced Regression methods for model building and variable selection * Get a thorough understanding of the underlying concepts and implementation of Ensemble methods * Solve real-world problems using a variety of different datasets from numerical and text data modalities * Get accustomed to modern state-of-the art algorithms such as Gradient Boosting, Random Forest, Rotation Forest, and so on In Detail Python is increasingly becoming the language for data science. It is overtaking R in terms of adoption, it is widely known by many developers, and has a strong set of libraries such as Numpy, Pandas, scikit-learn, Matplotlib, Ipython and Scipy, to support its usage in this field. Data Science is the emerging new hot tech field, which is an amalgamation of different disciplines including statistics, machine learning, and computer science. It's a disruptive technology changing the face of today's business and altering the economy of various verticals including retail, manufacturing, online ventures, and hospitality, to name a few, in a big way. This book will walk you through the various steps, starting from simple to the most complex algorithms available in the Data Science arsenal, to effectively mine data and derive intelligence from it. At every step, we provide simple and efficient Python recipes that will not only show you how to implement these algorithms, but also clarify the underlying concept thoroughly. The book begins by introducing you to using Python for Data Science, followed by working with Python environments. You will then learn how to analyse your data with Python. The book then teaches you the concepts of data mining followed by an extensive coverage of machine learning methods. It introduces you to a number of Python libraries available to help implement machine learning and data mining routines effectively. It also covers the principles of shrinkage, ensemble methods, random forest, rotation forest, and extreme trees, which are a must-have for any successful Data Science Professional. Style and approach This is a step-by-step recipe-based approach to Data Science algorithms, introducing the math philosophy behind these algorithms.
Python Data Science Essentials
by Alberto Boschetti Luca MassaronIf you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills.
Python Data Science Essentials
by Alberto Boschetti Luca MassaronBecome an efficient data science practitioner by understanding Python's key concepts About This Book Quickly get familiar with data science using Python 3.5 Save time (and effort) with all the essential tools explained Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience Who This Book Is For If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills. What You Will Learn Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux Get data ready for your data science project Manipulate, fix, and explore data in order to solve data science problems Set up an experimental pipeline to test your data science hypotheses Choose the most effective and scalable learning algorithm for your data science tasks Optimize your machine learning models to get the best performance Explore and cluster graphs, taking advantage of interconnections and links in your data In Detail Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users. Style and approach The book is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.
Python Data Science Essentials - Second Edition
by Alberto Boschetti Luca MassaronBecome an efficient data science practitioner by understanding Python's key concepts About This Book * Quickly get familiar with data science using Python 3.5 * Save time (and effort) with all the essential tools explained * Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience Who This Book Is For If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills. What You Will Learn * Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux * Get data ready for your data science project * Manipulate, fix, and explore data in order to solve data science problems * Set up an experimental pipeline to test your data science hypotheses * Choose the most effective and scalable learning algorithm for your data science tasks * Optimize your machine learning models to get the best performance * Explore and cluster graphs, taking advantage of interconnections and links in your data In Detail Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users. Style and approach The book is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.
Python Data Science Essentials: A practitioner’s guide covering essential data science principles, tools, and techniques, 3rd Edition
by Alberto Boschetti Luca MassaronGain useful insights from your data using popular data science toolsKey FeaturesA one-stop guide to Python libraries such as pandas and NumPyComprehensive coverage of data science operations such as data cleaning and data manipulationChoose scalable learning algorithms for your data science tasksBook DescriptionFully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn.The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost.By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business usersWhat you will learnSet up your data science toolbox on Windows, Mac, and LinuxUse the core machine learning methods offered by the scikit-learn libraryManipulate, fix, and explore data to solve data science problemsLearn advanced explorative and manipulative techniques to solve data operationsOptimize your machine learning models for optimized performanceExplore and cluster graphs, taking advantage of interconnections and links in your dataWho this book is forIf you’re a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.
Python Data Science Handbook: Essential Tools for Working with Data
by Jake VanderPlasPython is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all--IPython, NumPy, pandas, Matplotlib, scikit-learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you'll learn how:IPython and Jupyter provide computational environments for scientists using PythonNumPy includes the ndarray for efficient storage and manipulation of dense data arraysPandas contains the DataFrame for efficient storage and manipulation of labeled/columnar dataMatplotlib includes capabilities for a flexible range of data visualizationsScikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms