Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you’ll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.
Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It’s ideal for analysts new to Python and for Python programmers new to scientific computing.
- Use the IPython interactive shell as your primary development environment
- Learn basic and advanced NumPy (Numerical Python) features
- Get started with data analysis tools in the pandas library
- Use high-performance tools to load, clean, transform, merge, and reshape data
- Create scatter plots and static or interactive visualizations with matplotlib
- Apply the pandas groupby facility to slice, dice, and summarize datasets
- Measure data by points in time, whether it’s specific instances, fixed periods, or intervals
- Learn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples
Author(s): Wes McKinney
2. Pandas Cookbook: Recipes for Scientific Computing, Time Series Analysis and Data Visualization using Python (2017)
- Use the power of pandas to solve most complex scientific computing problems with ease
- Leverage fast, robust data structures in pandas to gain useful insights from your data
- Practical, easy to implement recipes for quick solutions to common problems in data using pandas
Pandas is one of the most powerful, flexible, and efficient scientific computing packages in Python. With this book, you will explore data in pandas through dozens of practice problems with detailed solutions in iPython notebooks.
This book will provide you with clean, clear recipes, and solutions that explain how to handle common data manipulation and scientific computing tasks with pandas. You will work with different types of datasets, and perform data manipulation and data wrangling effectively. You will explore the power of pandas DataFrames and find out about boolean and multi-indexing. Tasks related to statistical and time series computations, and how to implement them in financial and scientific applications are also covered in this book.
By the end of this book, you will have all the knowledge you need to master pandas, and perform fast and accurate scientific computing.
What you will learn
- Master the fundamentals of pandas to quickly begin exploring any dataset
- Isolate any subset of data by properly selecting and querying the data
- Split data into independent groups before applying aggregations and transformations to each group
- Restructure data into a tidy form to make data analysis and visualization easier
- Prepare messy real-world datasets for machine learning
- Combine and merge data from different sources through pandas SQL-like operations
- Utilize pandas unparalleled time series functionality
- Create beautiful and insightful visualizations through pandas direct hooks to matplotlib and seaborn
About the Author
Theodore Petrou is a data scientist and the founder of Dunder Data, a professional educational company focusing on exploratory data analysis. He is also the head of Houston Data Science, a meetup group with more than 2,000 members that has the primary goal of getting local data enthusiasts together in the same room to practice data science. Before founding Dunder Data, Ted was a data scientist at Schlumberger, a large oil services company, where he spent the vast majority of his time exploring data.
Some of his projects included using targeted sentiment analysis to discover the root cause of part failure from engineer text, developing customized client/server dashboarding applications, and real-time web services to avoid the mispricing of sales items. Ted received his masters degree in statistics from Rice University, and used his analytical skills to play poker professionally and teach math before becoming a data scientist. Ted is a strong supporter of learning through practice and can often be found answering questions about pandas on Stack Overflow.
Table of Contents
- Pandas Foundations
- Essential DataFrame Operations
- Beginning Data Analysis
- Selecting Subsets of Data
- Boolean Indexing
- Index Alignment
- Grouping for Aggregation, Filtration and Transformation
- Restructuring Data into Tidy Form
- Joining multiple pandas objects
- Time Series
Author(s): Theodore Petrou
- Employ the use of pandas for data analysis closely to focus more on analysis and less on programming
- Get programmers comfortable in performing data exploration and analysis on Python using pandas
- Step-by-step demonstration of using Python and pandas with interactive and incremental examples to facilitate learning
This learner’s guide will help you understand how to use the features of pandas for interactive data manipulation and analysis.
This book is your ideal guide to learning about pandas, all the way from installing it to creating one- and two-dimensional indexed data structures, indexing and slicing-and-dicing that data to derive results, loading data from local and Internet-based resources, and finally creating effective visualizations to form quick insights. You start with an overview of pandas and NumPy and then dive into the details of pandas, covering pandas’ Series and DataFrame objects, before ending with a quick review of using pandas for several problems in finance.
With the knowledge you gain from this book, you will be able to quickly begin your journey into the exciting world of data science and analysis.
What You Will Learn
- Install pandas on Windows, Mac, and Linux using the Anaconda Python distribution
- Learn how pandas builds on NumPy to implement flexible indexed data
- Adopt pandas’ Series and DataFrame objects to represent one- and two-dimensional data constructs
- Index, slice, and transform data to derive meaning from information
- Load data from files, databases, and web services
- Manipulate dates, times, and time series data
- Group, aggregate, and summarize data
- Visualize techniques for pandas and statistical data
About the Author
Michael Heydt is an independent consultant, educator, and trainer with nearly 30 years of professional software development experience, during which time, he focused on Agile software design and implementation using advanced technologies in multiple verticals, including media, finance, energy, and healthcare. Since 2005, he has specialized in building energy and financial trading systems for major investment banks on Wall Street and for several global energy-trading companies, utilizing .NET, C#, WPF, TPL, DataFlow, Python, R, Mono, iOS, and Android. His current interests include creating seamless applications using desktop, mobile, and wearable technologies, which utilize high-concurrency, high-availability, and real-time data analytics; augmented and virtual reality; cloud services; messaging; computer vision; natural user interfaces; and software-defined networks. He is the author of numerous technology articles, papers, and books. He is a frequent speaker at .NET user groups and various mobile and cloud conferences, and he regularly delivers webinars and conducts training courses on emerging and advanced technologies.
Table of Content
- A Tour of pandas
- Installing pandas
- Numpy for pandas
- The pandas Series Object
- The pandas Dataframe Object
- Accessing Data
- Tidying up Your Data
- Combining and Reshaping Data
- Grouping and Aggregating Data
- Time-series Data
- Applications to Finance
Author(s): Michael Heydt
The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python
Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.
Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems.
Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes.
Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem.
- Work with DataFrames and Series, and import or export data
- Create plots with matplotlib, seaborn, and pandas
- Combine datasets and handle missing data
- Reshape, tidy, and clean datasets so they’re easier to work with
- Convert data types and manipulate text strings
- Apply functions to scale data manipulations
- Aggregate, transform, and filter large datasets with groupby
- Leverage Pandas’ advanced date and time capabilities
- Fit linear models using statsmodels and scikit-learn libraries
- Use generalized linear modeling to fit models with different response variables
- Compare multiple models to select the “best”
- Regularize to overcome overfitting and improve performance
- Use clustering in unsupervised machine learning
Register your product at informit.com/register for convenient access to downloads, updates, and/or corrections as they become available.
Author(s): Daniel Y. Chen
For many researchers, Python is a first-class tool mainly 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 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 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 to use:
- IPython and Jupyter: provide computational environments for data scientists using Python
- NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python
- Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python
- Matplotlib: includes capabilities for a flexible range of data visualizations in Python
- Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
Author(s): Jake VanderPlas
Unleash the power of Python for your data analysis projects with For Dummies!
Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide.
- Covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models
- Explains objects, functions, modules, and libraries and their role 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 guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover.
Author(s): John Paul Mueller, Luca Massaron
You’ll start by learning core concepts such as the Model-View-Controller architectural pattern, and then work your way toward advanced topics. The authors demonstrate ASP.NET MVC 4 best practices and techniques by building a sample online auction site (“EBuy”) throughout the book.
- Learn the similarities between ASP.NET MVC 4 and Web Forms
- Use Entity Framework to create and maintain an application database
- Create rich web applications, using jQuery for client-side development
- Incorporate AJAX techniques into your web applications
- Learn how to create and expose ASP.NET Web API services
- Deliver a rich and consistent experience for mobile devices
- Apply techniques for error handling, automated testing, and build automation
- Use various options to deploy your ASP.NET MVC 4 application
Author(s): Jess Chadwick, Todd Snyder
- Learn how to manipulate data with Python
- Extract information from websites by using Python’s web-scraping tools, BeautifulSoup and Scrapy
- Clean and explore data with Python’s Pandas, Matplotlib, and Numpy libraries
- Serve data and create RESTful web APIs with Python’s Flask framework
Author(s): Kyran Dale
9. EJB 3 in Action (2014)
Building on the bestselling first edition, EJB 3 in Action, Second Edition tackles EJB 3.2 head-on, through numerous code samples, real-life scenarios, and illustrations. This book is a fast-paced tutorial for Java EE 6 business component development using EJB 3.2, JPA 2, and CDI. Besides covering the basics of EJB 3.2, this book includes in-depth EJB 3.2 internal implementation details, best practices, design patterns, and performance tuning tips.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the Book
The EJB 3 framework provides a standard way to capture business logic in manageable server-side modules, making it easier to write, maintain, and extend Java EE applications. EJB 3.2 provides more enhancements and intelligent defaults and integrates more fully with other Java technologies, such as CDI, to make development even easier.
EJB 3 in Action, Second Edition is a fast-paced tutorial for Java EE business component developers using EJB 3.2, JPA, and CDI. It tackles EJB head-on through numerous code samples, real-life scenarios, and illustrations. Beyond the basics, this book includes internal implementation details, best practices, design patterns, performance tuning tips, and various means of access including Web Services, REST Services, and WebSockets.
Readers need to know Java. No prior experience with EJB or Java EE is assumed.
- Fully revised for EJB 3.2
- POJO persistence with JPA 2.1
- Dependency injection and bean management with CDI 1.1
- Interactive application with WebSocket 1.0
About the Authors
Debu Panda, Reza Rahman, Ryan Cuprak, and Michael Remijan are seasoned Java architects, developers, authors, and community leaders. Debu and Reza coauthored the first edition of EJB 3 in Action.
Table of Contents
- What’s what in EJB 3
- A first taste of EJB
- Building business logic with session beans
- Messaging and developing MDBs
- EJB runtime context, dependency injection,and crosscutting logic
- Transactions and security
- Scheduling and timers
- Exposing EJBs as web services
- JPA entities
- Managing entities
- Using CDI with EJB 3
- Packaging EJB 3 applications
- Using WebSockets with EJB 3
- Testing and EJB
PART 1 OVERVIEW OF THE EJB LANDSCAPE
PART 2 WORKING WITH EJB COMPONENTS
PART 3 USING EJB WITH JPA AND CDI
PART 4 PUTTING EJB INTO ACTION
Author(s): Debu Panda, Reza Rahman
10. Cython: A Guide for Python Programmers (2015)
Build software that combines Python’s expressivity with the performance and control of C (and C++). It’s possible with Cython, the compiler and hybrid programming language used by foundational packages such as NumPy, and prominent in projects including Pandas, h5py, and scikits-learn. In this practical guide, you’ll learn how to use Cython to improve Python’s performance—up to 3000x— and to wrap C and C++ libraries in Python with ease.
Author Kurt Smith takes you through Cython’s capabilities, with sample code and in-depth practice exercises. If you’re just starting with Cython, or want to go deeper, you’ll learn how this language is an essential part of any performance-oriented Python programmer’s arsenal.
- Use Cython’s static typing to speed up Python code
- Gain hands-on experience using Cython features to boost your numeric-heavy Python
- Create new types with Cython—and see how fast object-oriented programming in Python can be
- Effectively organize Cython code into separate modules and packages without sacrificing performance
- Use Cython to give Pythonic interfaces to C and C++ libraries
- Optimize code with Cython’s runtime and compile-time profiling tools
- Use Cython’s prange function to parallelize loops transparently with OpenMP
Author(s): Kurt W. Smith
11. Easy Kaleidoscope Coloring Book for Adult: Basic design of mandala, animals, birds, bear, dog and friend for beginner Easy to color (2017)
AMAZON BEST SELLER | BEST GIFT IDEAS
This incredible adult coloring book by best-selling artist is the perfect way to relieve stress and aid relaxation while enjoying beautiful and highly detailed images. Each coloring page will transport you into a world of your own while your responsibilities will seem to fade away…
Use Any of Your Favorite Tools
Including colored pencils, pens, and fine-tipped markers.
One Image Per Page
Each image is printed on black-backed pages to prevent bleed-through.
Display Your Artwork
You can display your artwork with a standard 8.5″ x 11” frame.
Two Copies of Every Image
Enjoy coloring your favorite images a second time, color with a friend, or have an extra copy in case you make a mistake.
As a special bonus, you can download a PDF and print your favorite images to as many times as you want.
Now on Sale
Regular Price: $9.99 | SAVE $6.99, 60% OFF | Limited time only.
Makes the Perfect Gift
Surprise that special someone in your life and make them smile. Buy two copies and enjoy coloring together.
Buy Now, Start Coloring, and Relax…
Scroll to the top of the page and click the buy button.
Author(s): Adult Coloring Book