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Python. Der Sprachkurs für Einsteiger und Individualisten: Der Sprachkurs Fur Einsteiger Und Individualisten
by Arnold V. WillemerPython – einfach und leistungsfähig Sie haben schon viel Gutes über Python gehört und möchten auch in Python programmieren? Dann brauchen Sie dieses Buch. Vorwissen hingegen brauchen Sie nicht. Arnold Willemer erklärt Ihnen zu Beginn, was ein Programmierer überhaupt macht und wie ein Computer mit Zahlen und Texten umgeht. Danach erarbeiten Sie sich mit ihm Schritt für Schritt die Kunst des Programmierens in Python. Die witzige und gut gelaunte Schreibe des Autors wirkt zusätzlich motivierend. Und Ihren Lernerfolg können Sie anhand vieler Aufgaben und Musterlösungen überprüfen. So ermöglicht Ihnen das Buch zuverlässig den schnellen Einstieg in Python.Aus dem Inhalt• Programmieren für Einsteiger• Beschaffung und Installation der notwendigen Werkzeuge• Variablen, Abfragen, Schleifen, Funktionen• Objektorientierte Programmierung• Sequenzen, Tupel und Listen• Grafi sche Oberfl ächen mit Tkinter• Visualisieren mit dem Canvas-Widget• Module und Bibliotheken• Datenbankprogrammierung• Kommunikation in Netzwerken und mit dem Betriebssystem
Python Descriptors
by Jacob ZimmermanThis short book on Python descriptors is a collection of knowledge and ideas from many sources on dealing with and creating descriptors. And, after going through the things all descriptors have in common, the author explores ideas that have multiple ways of being implemented as well as completely new ideas never seen elsewhere before. This truly is a comprehensive guide to creating Python descriptors. As a bonus: A pip install-able library, descriptor_tools, was written alongside this book and is an open source library on GitHub. There aren't many good resources out there for writing Python descriptors, and extremely few books. This is a sad state of affairs, as it makes it difficult for Python developers to get a really good understanding of how descriptors work and the techniques to avoid the big gotchas associated with working with them. What You Will Learn Discover descriptor protocols Master attribute access and how it applies to descriptors Make descriptors and discover why you should Store attributes Create read-only descriptors and _delete() Explore the descriptor classes Apply the other uses of descriptors and more Who This Book Is For Experienced Python coders, programmers and developers.
Python Descriptors: Understanding and Using the Descriptor Protocol
by Jacob ZimmermanCreate descriptors and see ideas and examples of how to use descriptors effectively. In this short book, you’ll explore descriptors in general, with a deep explanation of what descriptors are, how they work, and how they're used. Once you understand the simplicity of the descriptor protocol, the author delves into using and creating descriptors in practice, with plenty of tips, patterns, and real-world guidance. Because descriptors are inherently flexible, you’ll work with multiple examples illustrating how to best take advantage of them.This second edition includes additions throughout, including new material covering the set_name_() descriptors, new and improved flowcharts to explain the inner workings of descriptors, and a completely new chapter to address instance-level attributes, the easiest way to create descriptors correctly the first time. Although brief, Python Descriptors is a comprehensive guide to creating Python descriptors, including a pip install-able library called descriptor_tools, which was written alongside this book and is an open source library on GitHub. After reading this book, you will have a solid understanding of how descriptors work and the techniques to avoid the big gotchas associated with working with them.What You Will LearnDiscover descriptor protocolsMaster attribute access and how it applies to descriptorsBuild your own descriptorsUse descriptors to store attributesCreate read-only descriptors Explore the descriptor classesApply the other uses of descriptors Who This Book Is ForExperienced Python coders, programmers, and developers.
Python Digital Forensics Cookbook
by Preston Miller Chapin BryceOver 60 recipes to help you learn digital forensics and leverage Python scripts to amplify your examinations About This Book • Develop code that extracts vital information from everyday forensic acquisitions. • Increase the quality and efficiency of your forensic analysis. • Leverage the latest resources and capabilities available to the forensic community. Who This Book Is For If you are a digital forensics examiner, cyber security specialist, or analyst at heart, understand the basics of Python, and want to take it to the next level, this is the book for you. Along the way, you will be introduced to a number of libraries suitable for parsing forensic artifacts. Readers will be able to use and build upon the scripts we develop to elevate their analysis. What You Will Learn • Understand how Python can enhance digital forensics and investigations • Learn to access the contents of, and process, forensic evidence containers • Explore malware through automated static analysis • Extract and review message contents from a variety of email formats • Add depth and context to discovered IP addresses and domains through various Application Program Interfaces (APIs) • Delve into mobile forensics and recover deleted messages from SQLite databases • Index large logs into a platform to better query and visualize datasets In Detail Technology plays an increasingly large role in our daily lives and shows no sign of stopping. Now, more than ever, it is paramount that an investigator develops programming expertise to deal with increasingly large datasets. By leveraging the Python recipes explored throughout this book, we make the complex simple, quickly extracting relevant information from large datasets. You will explore, develop, and deploy Python code and libraries to provide meaningful results that can be immediately applied to your investigations. Throughout the Python Digital Forensics Cookbook, recipes include topics such as working with forensic evidence containers, parsing mobile and desktop operating system artifacts, extracting embedded metadata from documents and executables, and identifying indicators of compromise. You will also learn to integrate scripts with Application Program Interfaces (APIs) such as VirusTotal and PassiveTotal, and tools such as Axiom, Cellebrite, and EnCase. By the end of the book, you will have a sound understanding of Python and how you can use it to process artifacts in your investigations. Style and approach Our succinct recipes take a no-frills approach to solving common challenges faced in investigations. The code in this book covers a wide range of artifacts and data sources. These examples will help improve the accuracy and efficiency of your analysis—no matter the situation.
Python Essentials
by Steven F. LottThis book is designed for Python 2 developers who want to get to grips with Python 3 in a short period of time. It covers the key features of Python, assuming you are familiar with the fundamentals of Python 2.
Python Essentials For Dummies
by John C. Shovic Alan SimpsonThe no-nonsense way to get started coding in the Python programming language Python Essentials For Dummies is a quick reference to all the core concepts in Python, the multifaceted general-purpose language used for everything from building websites to creating apps. This book gets right to the point, with no excess review, wordy explanations, or fluff, making it perfect as a desk reference on the job or as a brush-up as you expand your skills in related areas. Focusing on just the essential topics you need to know to brush up or level up your Python skill, this is the reliable little book you can always turn to for answers. Get a quick and thorough intro to the basic concepts of coding in Python Review what you've already learned or pick up essential new skills Create websites, software, machine learning, and automation for school or work Keep this concise reference book handy for jogging your memory as you code This portable Dummies Essentials book focuses on the key topics you need to know about the popular Python language. Great for supplementing a course, reviewing for a certification, or staying knowledgeable on the job.
Python Ethical Hacking from Scratch: Think like an ethical hacker, avoid detection, and successfully develop, deploy, detect, and avoid malware
by Fahad Ali SarwarExplore the world of practical ethical hacking by developing custom network scanning and remote access tools that will help you test the system security of your organizationKey FeaturesGet hands-on with ethical hacking and learn to think like a real-life hackerBuild practical ethical hacking tools from scratch with the help of real-world examplesLeverage Python 3 to develop malware and modify its complexitiesBook DescriptionPenetration testing enables you to evaluate the security or strength of a computer system, network, or web application that an attacker can exploit. With this book, you'll understand why Python is one of the fastest-growing programming languages for penetration testing. You'll find out how to harness the power of Python and pentesting to enhance your system security. Developers working with Python will be able to put their knowledge and experience to work with this practical guide. Complete with step-by-step explanations of essential concepts and practical examples, this book takes a hands-on approach to help you build your own pentesting tools for testing the security level of systems and networks. You'll learn how to develop your own ethical hacking tools using Python and explore hacking techniques to exploit vulnerabilities in networks and systems. Finally, you'll be able to get remote access to target systems and networks using the tools you develop and modify as per your own requirements. By the end of this ethical hacking book, you'll have developed the skills needed for building cybersecurity tools and learned how to secure your systems by thinking like a hacker.What you will learnUnderstand the core concepts of ethical hackingDevelop custom hacking tools from scratch to be used for ethical hacking purposesDiscover ways to test the cybersecurity of an organization by bypassing protection schemesDevelop attack vectors used in real cybersecurity testsTest the system security of an organization or subject by identifying and exploiting its weaknessesGain and maintain remote access to target systemsFind ways to stay undetected on target systems and local networksWho this book is forIf you want to learn ethical hacking by developing your own tools instead of just using the prebuilt tools, this book is for you. A solid understanding of fundamental Python concepts is expected. Some complex Python concepts are explained in the book, but the goal is to teach ethical hacking, not Python.
Python Feature Engineering Cookbook: Over 70 recipes for creating, engineering, and transforming features to build machine learning models
by Soledad GalliExtract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries Key Features Discover solutions for feature generation, feature extraction, and feature selection Uncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasets Implement modern feature extraction techniques using Python's pandas, scikit-learn, SciPy and NumPy libraries Book Description Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code. Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you'll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You'll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains. By the end of this book, you'll have discovered tips and practical solutions to all of your feature engineering problems. What you will learn Simplify your feature engineering pipelines with powerful Python packages Get to grips with imputing missing values Encode categorical variables with a wide set of techniques Extract insights from text quickly and effortlessly Develop features from transactional data and time series data Derive new features by combining existing variables Understand how to transform, discretize, and scale your variables Create informative variables from date and time Who this book is for This book is for machine learning professionals, AI engineers, data scientists, and NLP and reinforcement learning engineers who want to optimize and enrich their machine learning models with the best features. Knowledge of machine learning and Python coding will assist you with understanding the concepts covered in this book.
Python Feature Engineering Cookbook: Over 70 recipes for creating, engineering, and transforming features to build machine learning models, 2nd Edition
by Soledad GalliCreate end-to-end, reproducible feature engineering pipelines that can be deployed into production using open-source Python librariesKey FeaturesLearn and implement feature engineering best practicesReinforce your learning with the help of multiple hands-on recipesBuild end-to-end feature engineering pipelines that are performant and reproducibleBook DescriptionFeature engineering, the process of transforming variables and creating features, albeit time-consuming, ensures that your machine learning models perform seamlessly. This second edition of Python Feature Engineering Cookbook will take the struggle out of feature engineering by showing you how to use open source Python libraries to accelerate the process via a plethora of practical, hands-on recipes.This updated edition begins by addressing fundamental data challenges such as missing data and categorical values, before moving on to strategies for dealing with skewed distributions and outliers. The concluding chapters show you how to develop new features from various types of data, including text, time series, and relational databases. With the help of numerous open source Python libraries, you'll learn how to implement each feature engineering method in a performant, reproducible, and elegant manner.By the end of this Python book, you will have the tools and expertise needed to confidently build end-to-end and reproducible feature engineering pipelines that can be deployed into production.What you will learnImpute missing data using various univariate and multivariate methodsEncode categorical variables with one-hot, ordinal, and count encodingHandle highly cardinal categorical variablesTransform, discretize, and scale your variablesCreate variables from date and time with pandas and Feature-engineCombine variables into new featuresExtract features from text as well as from transactional data with FeaturetoolsCreate features from time series data with tsfreshWho this book is forThis book is for machine learning and data science students and professionals, as well as software engineers working on machine learning model deployment, who want to learn more about how to transform their data and create new features to train machine learning models in a better way.
Python Feature Engineering Cookbook: Over 70 recipes for creating, engineering, and transforming features to build machine learning models
by Soledad GalliCreate end-to-end, reproducible feature engineering pipelines that can be deployed into production using open-source Python librariesKey FeaturesLearn and implement feature engineering best practicesReinforce your learning with the help of multiple hands-on recipesBuild end-to-end feature engineering pipelines that are performant and reproducibleBook DescriptionFeature engineering, the process of transforming variables and creating features, albeit time-consuming, ensures that your machine learning models perform seamlessly. This second edition of Python Feature Engineering Cookbook will take the struggle out of feature engineering by showing you how to use open source Python libraries to accelerate the process via a plethora of practical, hands-on recipes. This updated edition begins by addressing fundamental data challenges such as missing data and categorical values, before moving on to strategies for dealing with skewed distributions and outliers. The concluding chapters show you how to develop new features from various types of data, including text, time series, and relational databases. With the help of numerous open source Python libraries, you'll learn how to implement each feature engineering method in a performant, reproducible, and elegant manner. By the end of this Python book, you will have the tools and expertise needed to confidently build end-to-end and reproducible feature engineering pipelines that can be deployed into production.What you will learnImpute missing data using various univariate and multivariate methodsEncode categorical variables with one-hot, ordinal, and count encodingHandle highly cardinal categorical variablesTransform, discretize, and scale your variablesCreate variables from date and time with pandas and Feature-engineCombine variables into new featuresExtract features from text as well as from transactional data with FeaturetoolsCreate features from time series data with tsfreshWho this book is forThis book is for machine learning and data science students and professionals, as well as software engineers working on machine learning model deployment, who want to learn more about how to transform their data and create new features to train machine learning models in a better way.
Python for Accounting and Finance: An Integrative Approach to Using Python for Research
by Sunil KumarThis book is a comprehensive guide to the application of Python in accounting, finance, and other business disciplines. This book is more than a Python tutorial; it is an integrative approach to using Python for practical research in these fields. The book begins with an introduction to Python and its key libraries. It then covers real-world applications of Python, covering data acquisition, cleaning, exploratory data analysis, visualization, and advanced topics like natural language processing, machine learning, predictive analytics, and deep learning. What sets this book apart is its unique blend of theoretical knowledge and real-world examples, supplemented with ready-to-use code. It doesn't stop at the syntax; it shows how to apply Python to tackle actual analytical problems. The book uses case studies to illustrate how Python can enhance traditional research methods in accounting and finance, not only allowing the reader to gain a firm understanding of Pythonprogramming but also equipping them with the skills to apply Python to accounting, finance, and broader business research. Whether you are a PhD student, a professor, an industry professional, or a financial researcher, this book provides the key to unlocking the full potential of Python in research.
Python for Algorithmic Trading
by Yves HilpischAlgorithmic trading, once the exclusive domain of institutional players, is now open to small organizations and individual traders using online platforms. The tool of choice for many traders today is Python and its ecosystem of powerful packages. In this practical book, author Yves Hilpisch shows students, academics, and practitioners how to use Python in the fascinating field of algorithmic trading.You'll learn several ways to apply Python to different aspects of algorithmic trading, such as backtesting trading strategies and interacting with online trading platforms. Some of the biggest buy- and sell-side institutions make heavy use of Python. By exploring options for systematically building and deploying automated algorithmic trading strategies, this book will help you level the playing field.Set up a proper Python environment for algorithmic tradingLearn how to retrieve financial data from public and proprietary data sourcesExplore vectorization for financial analytics with NumPy and pandasMaster vectorized backtesting of different algorithmic trading strategiesGenerate market predictions by using machine learning and deep learningTackle real-time processing of streaming data with socket programming toolsImplement automated algorithmic trading strategies with the OANDA and FXCM trading platforms
Python For ArcGIS
by Laura TateosianThis book introduces Python scripting for geographic information science (GIS) workflow optimization using ArcGIS. It builds essential programming skills for automating GIS analysis. Over 200 sample Python scripts and 175 classroom-tested exercises reinforce the learning objectives. Readers will learn to: * Write and run Python in the ArcGIS Python Window, the PythonWin IDE, and the PyScripter IDE * Work with Python syntax and data types * Call ArcToolbox tools, batch process GIS datasets, and manipulate map documents using the arcpy package * Read and modify proprietary and ASCII text GIS data * Parse HTML web pages and KML datasets * Create Web pages and fetch GIS data from Web sources. * Build user-interfaces with the native Python file dialog toolkit or the ArcGIS Script tools and PyToolboxes Python for ArcGIS is designed as a primary textbook for advanced-level students in GIS. Researchers, government specialists and professionals working in GIS will also find this book useful as a reference.
Python for ArcGIS Pro: Automate cartography and data analysis using ArcPy, ArcGIS API for Python, Notebooks, and pandas
by Silas Toms Bill Parker Dr. Christopher Tucker Rene RubalcavaExtend your ArcGIS expertise by unlocking the world of Python programming. A fully hands-on guide that takes you through exercise after exercise using real data and real problems.Key FeaturesLearn the core components of the two Python modules for ArcGIS: ArcPy and ArcGIS API for PythonUse ArcPy, pandas, NumPy, and ArcGIS in ArcGIS Pro Notebooks to manage and analyze geospatial data at scaleIntegrate with ArcGIS Online using Python to publish and manage dataBook DescriptionIntegrating Python into your day-to-day ArcGIS work is highly recommended when dealing with large amounts of geospatial data. Python for ArcGIS Pro aims to help you get your work done faster, with greater repeatability and higher confidence in your results. Starting from programming basics and building in complexity, two experienced ArcGIS professionals-turned-Python programmers teach you how to incorporate scripting at each step: automating the production of maps for print, managing data between ArcGIS Pro and ArcGIS Online, creating custom script tools for sharing, and then running data analysis and visualization on top of the ArcGIS geospatial library, all using Python. You'll use ArcGIS Pro Notebooks to explore and analyze geospatial data, and write data engineering scripts to manage ongoing data processing and data transfers. This exercise-based book also includes three rich real-world case studies, giving you an opportunity to apply and extend the concepts you studied earlier. Irrespective of your expertise level with Esri software or the Python language, you'll benefit from this book's hands-on approach, which takes you through the major uses of Python for ArcGIS Pro to boost your ArcGIS productivity.What you will learnAutomate map production to make and edit maps at scale, cutting down on repetitive tasksPublish map layer data to ArcGIS OnlineAutomate data updates using the ArcPy Data Access module and cursorsTurn your scripts into script tools for ArcGIS ProLearn how to manage data on ArcGIS OnlineQuery, edit, and append to feature layers and create symbology with renderers and colorizersApply pandas and NumPy to raster and vector analysisLearn new tricks to manage data for entire cities or large companiesWho this book is forThis book is ideal for anyone looking to add Python to their ArcGIS Pro workflows, even if you have no prior experience with programming. This includes ArcGIS professionals, intermediate ArcGIS Pro users, ArcGIS Pro power users, students, and people who want to move from being a GIS Technician to GIS Analyst; GIS Analyst to GIS Programmer; or GIS Developer/Programmer to a GIS Architect. Basic familiarity with geospatial/GIS syntax, ArcGIS, and data science (pandas) is helpful, though not necessary.
Python for Beginners
by Kuldeep Singh Kaswan Jagjit Singh Dhatterwal B BalamuruganPython is an amazing programming language. It can be applied to almost any programming task. It allows for rapid development and debugging. Getting started with Python is like learning any new skill: it’s important to find a resource you connect with to guide your learning. Luckily, there’s no shortage of excellent books that can help you learn both the basic concepts of programming and the specifics of programming in Python. With the abundance of resources, it can be difficult to identify which book would be best for your situation. Python for Beginners is a concise single point of reference for all material on python. • Provides concise, need-to-know information on Python types and statements, special method names, built-in functions and exceptions, commonly used standard library modules, and other prominent Python tools • Offers practical advice for each major area of development with both Python 3.x and Python 2.x • Based on the latest research in cognitive science and learning theory • Helps the reader learn how to write effective, idiomatic Python code by leveraging its best—and possibly most neglected—features This book focuses on enthusiastic research aspirants who work on scripting languages for automating the modules and tools, development of web applications, handling big data, complex calculations, workflow creation, rapid prototyping, and other software development purposes. It also targets graduates, postgraduates in computer science, information technology, academicians, practitioners, and research scholars.
Python for Cybersecurity: Using Python for Cyber Offense and Defense
by Howard E. Poston IIIDiscover an up-to-date and authoritative exploration of Python cybersecurity strategies Python For Cybersecurity: Using Python for Cyber Offense and Defense delivers an intuitive and hands-on explanation of using Python for cybersecurity. It relies on the MITRE ATT&CK framework to structure its exploration of cyberattack techniques, attack defenses, and the key cybersecurity challenges facing network administrators and other stakeholders today. Offering downloadable sample code, the book is written to help you discover how to use Python in a wide variety of cybersecurity situations, including: Reconnaissance, resource development, initial access, and execution Persistence, privilege escalation, defense evasion, and credential access Discovery, lateral movement, collection, and command and control Exfiltration and impact Each chapter includes discussions of several techniques and sub-techniques that could be used to achieve an attacker's objectives in any of these use cases. The ideal resource for anyone with a professional or personal interest in cybersecurity, Python For Cybersecurity offers in-depth information about a wide variety of attacks and effective, Python-based defenses against them.
Python for Data Analysis
by Wes Mckinney<p>Despite the explosive growth of data in industry after industry, learning and accessing data analysis tools has remained a challenge. This pragmatic guide demonstrates the nuts and bolts of manipulating, processing, cleaning, and crunching data with Python. It also serves as a modern introduction to scientific computing in Python for data-intensive applications.</p>
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (Oreilly And Associate Ser.)
by Wes MckinneyGet complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. 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. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process.Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. 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 Python)Get started with data analysis tools in the pandas libraryUse flexible tools to load, clean, transform, merge, and reshape dataCreate informative visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsAnalyze and manipulate regular and irregular time series dataLearn how to solve real-world data analysis problems with thorough, detailed examples
Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter
by Wes McKinneyGet the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third 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. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process.Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.Use the Jupyter notebook and IPython shell for exploratory computingLearn basic and advanced features in NumPyGet started with data analysis tools in the pandas libraryUse flexible tools to load, clean, transform, merge, and reshape dataCreate informative visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsAnalyze and manipulate regular and irregular time series dataLearn how to solve real-world data analysis problems with thorough, detailed examples
Python for Data & Analytics: A Business-Oriented Approach
by Daniel H. GronerPython for Data & Analytics introduces essential programming concepts using Python, pandas, and other packages for the purpose of accessing data, performing analyses, and developing applications. The presentation presumes no prior programming experience. It is designed for introductory and more advanced programming courses in a data analytics, information systems, or business program. To this end, business examples are emphasized throughout. Various topics often covered in a computer-science-oriented course are purposefully not included in this textbook including topics like recursion and sorting algorithms.
Python for Data Mining Quick Syntax Reference
by Valentina PorcuLearn how to use Python and its structures, how to install Python, and which tools are best suited for data analyst work. This book provides you with a handy reference and tutorial on topics ranging from basic Python concepts through to data mining, manipulating and importing datasets, and data analysis.Python for Data Mining Quick Syntax Reference covers each concept concisely, with many illustrative examples. You'll be introduced to several data mining packages, with examples of how to use each of them. The first part covers core Python including objects, lists, functions, modules, and error handling. The second part covers Python's most important data mining packages: NumPy and SciPy for mathematical functions and random data generation, pandas for dataframe management and data import, Matplotlib for drawing charts, and scikitlearn for machine learning. What You'll LearnInstall Python and choose a development environmentUnderstand the basic concepts of object-oriented programmingImport, open, and edit filesReview the differences between Python 2.x and 3.xWho This Book Is ForProgrammers new to Python's data mining packages or with experience in other languages, who want a quick guide to Pythonic tools and techniques.
Python for Data Science
by A. Lakshmi Muddana Sandhya VinayakamThe book is designed to serve as a textbook for courses offered to undergraduate and graduate students enrolled in data science. This book aims to help the readers understand the basic and advanced concepts for developing simple programs and the fundamentals required for building machine learning models. The book covers basic concepts like data types, operators, and statements that enable the reader to solve simple problems. As functions are the core of any programming, a detailed illustration of defining & invoking functions and recursive functions is covered. Built-in data structures of Python, such as strings, lists, tuples, sets, and dictionary structures, are discussed in detail with examples and exercise problems. Files are an integrated part of programming when dealing with large data. File handling operations are illustrated with examples and a case study at the end of the chapter. Widely used Python packages for data science, such as Pandas, Data Visualization libraries, and regular expressions, are discussed with examples and case studies at the end of the chapters. The book also contains a chapter on SQLite3, a small relational database management system of Python, to understand how to create and manage databases. As AI applications are becoming popular for developing intelligent solutions to various problems, the book includes chapters on Machine Learning and Deep Learning. They cover the basic concepts, example applications, and case studies using popular frameworks such as SKLearn and Keras on public datasets
Python for Data Science: A Hands-On Introduction
by Yuli VasilievA hands-on, real-world introduction to data analysis with the Python programming language, loaded with wide-ranging examples.Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. Python for Data Science introduces you to the Pythonic world of data analysis with a learn-by-doing approach rooted in practical examples and hands-on activities. You&’ll learn how to write Python code to obtain, transform, and analyze data, practicing state-of-the-art data processing techniques for use cases in business management, marketing, and decision support.You will discover Python&’s rich set of built-in data structures for basic operations, as well as its robust ecosystem of open-source libraries for data science, including NumPy, pandas, scikit-learn, matplotlib, and more. Examples show how to load data in various formats, how to streamline, group, and aggregate data sets, and how to create charts, maps, and other visualizations. Later chapters go in-depth with demonstrations of real-world data applications, including using location data to power a taxi service, market basket analysis to identify items commonly purchased together, and machine learning to predict stock prices.
Python for Data Science For Dummies
by John Paul Mueller Luca Massaronof the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLibWhether you're new to data analysis or just new to Python, Python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover.
Python for Data Science For Dummies
by John Paul Mueller Luca MassaronThe fast and easy way to learn Python programming and statistics Python is a general-purpose programming language created in the late 1980s—and named after Monty Python—that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library. Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud. Get started with data science and Python Visualize information Wrangle data Learn from data The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.