Python for Data Analysis

Data Wrangling with Pandas, NumPy, and IPython


Author: Wes McKinney

Publisher: "O'Reilly Media, Inc."

ISBN: 1491957611

Category: Computers

Page: 550

View: 5539

Get 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 computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

Introducing Python

Modern Computing in Simple Packages


Author: Bill Lubanovic

Publisher: "O'Reilly Media, Inc."

ISBN: 1449361196

Category: Computers

Page: 484

View: 8894

Annotation With 'Introducing Python', Bill Lubanovic brings years of knowledge as a programmer, system administrator and author to a book of impressive depth that's fun to read and simple enough for non-programmers to use. Along with providing a strong foundation in the language itself, Lubanovic shows you how to use Python for a range of applications in business, science and the arts, drawing on the rich collection of open source packages developed by Python fans.

Python for Data Analysis

Master the Basics of Data Analysis in Python Using Numpy, Pandas and Ipython


Author: Samuel Samuel Burns

Publisher: N.A

ISBN: 9781796231663


Page: 232

View: 6665

If you buy a new print edition of this book (or purchased one in the past), you can buy the Kindle Edition for FREE. Print edition purchase must be sold by Amazon!You want to learn Python for data analysis using NumPy, Pandas, and IPython, and you don't know how to start? You don't need a big boring and expensive textbook. This book is the best one for everyone.Order your book Now!! Why this book is the best guide for everyone? Here are the reasons:The author has explored everything about python for data analysis using pandas, NumPy, Ipython and Matplotlib libraries from the basics. A simple language has been used. Many examples have been given, both theoretically and programmatically. Screenshots showing program outputs have been added. The book is written chronologically, in a step-by-step manner. Book Objectives: The Aims and Objectives of the Book: To help you understand why you should choose Python for data analysis tasks. To help you know the various data analysis libraries supported by Python and how to use them. To help you know how to analyze your business data and draw meaningful insights for effective decision making. To equip you with data analysis skills using Python programming language. To help you know where data analysis is applied today and how to use it in your everyday life. Who is this Book is for? : Here are the target readers for this book: Anybody who is a complete beginner to data analysis with Python or data analysis in general. Anybody who wants to advance their data analysis skills with Python programming language. Anybody who wants to know how to use data analysis for the benefit of their business or brand. Professionals in data science, computer programming, computer scientist. Professors, lecturers or tutors who are looking to find better ways to explain python for data analysis to their students in the simplest and easiest way. Students and academicians, especially those focusing on python programming, computer science,neural networks, machine learning, and deep learning. What do you need for this Book? : You are required to have installed the following on your computer: Python 3.X Numpy Pandas Matplotlib The Author guides you on how to install and configure the rest of the Python libraries that are required for data analysis. What is inside the book? : Why Python for Data Analysis? Exploring the Libraries Installation and Setup Using IPython Numpy Arrays and Vectorized Computation Pandas Library Data Wrangling Data Visualization Data Aggregation Working with Time Series Data Applications of Data Analysis Today The content of this book is all about data analysis with Python programming language using NumPy, Pandas, and IPython. It has been grouped into chapters, with each chapter exploring a different aspect of data analysis. The author has provided Python codes for doing different data analysis tasks. All these codes have been tested to ensure they are working correctly. Corresponding explanations have also been provided alongside each piece of code to help the reader understand the meaning of the various lines of the code. In addition to this, screenshots showing the output that each code should return have been given. The author has used a simple language to make it easy even for beginners to understand. The author begins by exploring the basic to the complex tasks in data analysis.

Python for Programmers

with Big Data and Artificial Intelligence Case Studies


Author: Paul J. Deitel,Harvey Deitel

Publisher: Prentice Hall

ISBN: 0135231345

Category: Computers

Page: 640

View: 4411

The professional programmer’s Deitel® guide to Python® with introductory artificial intelligence case studies Written for programmers with a background in another high-level language, Python for Programmers uses hands-on instruction to teach today’s most compelling, leading-edge computing technologies and programming in Python–one of the world’s most popular and fastest-growing languages. Please read the Table of Contents diagram inside the front cover and the Preface for more details. In the context of 500+, real-world examples ranging from individual snippets to 40 large scripts and full implementation case studies, you’ll use the interactive IPython interpreter with code in Jupyter Notebooks to quickly master the latest Python coding idioms. After covering Python Chapters 1-5 and a few key parts of Chapters 6-7, you’ll be able to handle significant portions of the hands-on introductory AI case studies in Chapters 11-16, which are loaded with cool, powerful, contemporary examples. These include natural language processing, data mining Twitter® for sentiment analysis, cognitive computing with IBM® Watson™, supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning and convolutional neural networks, deep learning with recurrent neural networks, big data with Hadoop®, Spark™ and NoSQL databases, the Internet of Things and more. You’ll also work directly or indirectly with cloud-based services, including Twitter, Google Translate™, IBM Watson, Microsoft® Azure®, OpenMapQuest, PubNub and more. Features 500+ hands-on, real-world, live-code examples from snippets to case studies IPython + code in Jupyter® Notebooks Library-focused: Uses Python Standard Library and data science libraries to accomplish significant tasks with minimal code Rich Python coverage: Control statements, functions, strings, files, JSON serialization, CSV, exceptions Procedural, functional-style and object-oriented programming Collections: Lists, tuples, dictionaries, sets, NumPy arrays, pandas Series & DataFrames Static, dynamic and interactive visualizations Data experiences with real-world datasets and data sources Intro to Data Science sections: AI, basic stats, simulation, animation, random variables, data wrangling, regression AI, big data and cloud data science case studies: NLP, data mining Twitter®, IBM® Watson™, machine learning, deep learning, computer vision, Hadoop®, Spark™, NoSQL, IoT Open-source libraries: NumPy, pandas, Matplotlib, Seaborn, Folium, SciPy, NLTK, TextBlob, spaCy, Textatistic, Tweepy, scikit-learn®, Keras and more Accompanying code examples are available here: Register your product for convenient access to downloads, updates, and/or corrections as they become available. See inside book for more information.

Python for Data Science For Dummies


Author: John Paul Mueller,Luca Massaron

Publisher: John Wiley & Sons

ISBN: 1118843983

Category: Computers

Page: 432

View: 7169

Unleash the power of Python for your data analysis projectswith For Dummies! Python is the preferred programming language for data scientistsand combines the best features of Matlab, Mathematica, and R intolibraries specific to data analysis and visualization. Pythonfor Data Science For Dummies shows you how to take advantage ofPython programming to acquire, organize, process, and analyze largeamounts of information and use basic statistics concepts toidentify trends and patterns. You’ll get familiar with thePython development environment, manipulate data, design compellingvisualizations, and solve scientific computing challenges as youwork your way through this user-friendly guide. Covers the fundamentals of Python data analysis programming andstatistics to help you build a solid foundation in data scienceconcepts like probability, random distributions, hypothesistesting, and regression models Explains objects, functions, modules, and libraries and theirrole in data analysis Walks you through some of the most widely-used libraries,including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib Whether you’re new to data analysis or just new to Python,Python for Data Science For Dummies is your practical guideto getting a grip on data overload and doing interesting thingswith the oodles of information you uncover.

Learning Predictive Analytics with Python


Author: Ashish Kumar

Publisher: Packt Publishing Ltd

ISBN: 1783983272

Category: Computers

Page: 354

View: 3766

Gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python About This Book A step-by-step guide to predictive modeling including lots of tips, tricks, and best practices Get to grips with the basics of Predictive Analytics with Python Learn how to use the popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and Clustering Who This Book Is For If you wish to learn how to implement Predictive Analytics algorithms using Python libraries, then this is the book for you. If you are familiar with coding in Python (or some other programming/statistical/scripting language) but have never used or read about Predictive Analytics algorithms, this book will also help you. The book will be beneficial to and can be read by any Data Science enthusiasts. Some familiarity with Python will be useful to get the most out of this book, but it is certainly not a prerequisite. What You Will Learn Understand the statistical and mathematical concepts behind Predictive Analytics algorithms and implement Predictive Analytics algorithms using Python libraries Analyze the result parameters arising from the implementation of Predictive Analytics algorithms Write Python modules/functions from scratch to execute segments or the whole of these algorithms Recognize and mitigate various contingencies and issues related to the implementation of Predictive Analytics algorithms Get to know various methods of importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and numpy Create dummy datasets and simple mathematical simulations using the Python numpy and pandas libraries Understand the best practices while handling datasets in Python and creating predictive models out of them In Detail Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form - It needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Learning to predict who would win, lose, buy, lie, or die with Python is an indispensable skill set to have in this data age. This book is your guide to getting started with Predictive Analytics using Python. You will see how to process data and make predictive models from it. We balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and numpy. You'll start by getting an understanding of the basics of predictive modeling, then you will see how to cleanse your data of impurities and get it ready it for predictive modeling. You will also learn more about the best predictive modeling algorithms such as Linear Regression, Decision Trees, and Logistic Regression. Finally, you will see the best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. Style and approach All the concepts in this book been explained and illustrated using a dataset, and in a step-by-step manner. The Python code snippet to implement a method or concept is followed by the output, such as charts, dataset heads, pictures, and so on. The statistical concepts are explained in detail wherever required.

SciPy Recipes

A cookbook with over 110 proven recipes for performing mathematical and scientific computations


Author: V Kishore Ayyadevara,Luiz Felipe Martins,Ruben Oliva Ramos

Publisher: Packt Publishing Ltd

ISBN: 1788295811

Category: Computers

Page: 386

View: 5265

Tackle the most sophisticated problems associated with scientific computing and data manipulation using SciPy Key Features Covers a wide range of data science tasks using SciPy, NumPy, pandas, and matplotlib Effective recipes on advanced scientific computations, statistics, data wrangling, data visualization, and more A must-have book if you're looking to solve your data-related problems using SciPy, on-the-go Book Description With the SciPy Stack, you get the power to effectively process, manipulate, and visualize your data using the popular Python language. Utilizing SciPy correctly can sometimes be a very tricky proposition. This book provides the right techniques so you can use SciPy to perform different data science tasks with ease. This book includes hands-on recipes for using the different components of the SciPy Stack such as NumPy, SciPy, matplotlib, and pandas, among others. You will use these libraries to solve real-world problems in linear algebra, numerical analysis, data visualization, and much more. The recipes included in the book will ensure you get a practical understanding not only of how a particular feature in SciPy Stack works, but also of its application to real-world problems. The independent nature of the recipes also ensure that you can pick up any one and learn about a particular feature of SciPy without reading through the other recipes, thus making the book a very handy and useful guide. What you will learn Get a solid foundation in scientific computing using Python Master common tasks related to SciPy and associated libraries such as NumPy, pandas, and matplotlib Perform mathematical operations such as linear algebra and work with the statistical and probability functions in SciPy Master advanced computing such as Discrete Fourier Transform and K-means with the SciPy Stack Implement data wrangling tasks efficiently using pandas Visualize your data through various graphs and charts using matplotlib Who this book is for Python developers, aspiring data scientists, and analysts who want to get started with scientific computing using Python will find this book an indispensable resource. If you want to learn how to manipulate and visualize your data using the SciPy Stack, this book will also help you. A basic understanding of Python programming is all you need to get started.