Python Data Analytics: With Pandas, NumPy, and Matplotlib, Edition 2

Apress
Free sample

Explore 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 manipulation
Author 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 Learn
  • Understand the core concepts of data analysis and the Python ecosystem
  • Go in depth with pandas for reading, writing, and processing data
  • Use tools and techniques for data visualization and image analysis
  • Examine popular deep learning libraries Keras, Theano,TensorFlow, and PyTorch
Who This Book Is For

Experienced Python developers who need to learn about Pythonic tools for data analysis
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About the author

Fabio Nelli is an IT Scientific Application Specialist at IRBM Science Park, a private research center in Pomezia, Roma, Italy. He has been a computer consultant for many years at IBM, EDS, Merck Sharp, and Dohme, along with several banks and insurance companies. He has an Organic Chemistry degree and many years of experience in Information technologies and Automation systems applied to Life Sciences (Tech Specialist at Beckman Coulter Italy and Spain). He is currently developing Java applications that interface Oracle databases with scientific instrumentation generating data and web server applications providing analysis of the results to researchers in real time.
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Additional Information

Publisher
Apress
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Published on
Sep 27, 2018
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Pages
569
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ISBN
9781484239131
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Language
English
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Genres
Computers / Databases / General
Computers / Intelligence (AI) & Semantics
Computers / Programming / General
Computers / Programming Languages / General
Computers / Programming Languages / Python
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Content Protection
This content is DRM protected.
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Read Aloud
Available on Android devices
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Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. This second edition has gone through a major revamp and introduces several significant changes and new topics based on the recent trends in NLP.

You’ll see how to use the latest state-of-the-art frameworks in NLP, coupled with machine learning and deep learning models for supervised sentiment analysis powered by Python to solve actual case studies. Start by reviewing Python for NLP fundamentals on strings and text data and move on to engineering representation methods for text data, including both traditional statistical models and newer deep learning-based embedding models. Improved techniques and new methods around parsing and processing text are discussed as well.

Text summarization and topic models have been overhauled so the book showcases how to build, tune, and interpret topic models in the context of an interest dataset on NIPS conference papers. Additionally, the book covers text similarity techniques with a real-world example of movie recommenders, along with sentiment analysis using supervised and unsupervised techniques.

There is also a chapter dedicated to semantic analysis where you’ll see how to build your own named entity recognition (NER) system from scratch. While the overall structure of the book remains the same, the entire code base, modules, and chapters has been updated to the latest Python 3.x release.


What You'll Learn

• Understand NLP and text syntax, semantics and structure• Discover text cleaning and feature engineering• Review text classification and text clustering • Assess text summarization and topic models• Study deep learning for NLP
Who This Book Is For
IT professionals, data analysts, developers, linguistic experts, data scientists and engineers and basically anyone with a keen interest in linguistics, analytics and generating insights from textual data.
Embrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problems. The book uses a hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines will enable your business processes to achieve efficiencies on minimal time and resources.
Python Machine Learning Case Studies takes you through the steps to improve business processes and determine the pivotal points that frame strategies. You’ll see machine learning techniques that you can use to support your products and services. Moreover you’ll learn the pros and cons of each of the machine learning concepts to help you decide which one best suits your needs.
By taking a step-by-step approach to coding in Python you’ll be able to understand the rationale behind model selection and decisions within the machine learning process. The book is equipped with practical examples along with code snippets to ensure that you understand the data science approach to solving real-world problems.
What You Will LearnGain insights into machine learning concepts
Work on real-world applications of machine learningLearn concepts of model selection and optimizationGet a hands-on overview of Python from a machine learning point of view

Who This Book Is For
Data scientists, data analysts, artificial intelligence engineers, big data enthusiasts, computer scientists, computer sciences students, and capital market analysts.

Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more.

Numerical Python, Second Edition, presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis.

After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning.

What You'll Learn

Work with vectors and matrices using NumPy
Plot and visualize data with Matplotlib
Perform data analysis tasks with Pandas and SciPy
Review statistical modeling and machine learning with statsmodels and scikit-learn
Optimize Python code using Numba and Cython
Who This Book Is For

Developers who want to understand how to use Python and its related ecosystem for numerical computing.

You Will Learn Python!

Zed Shaw has perfected the world's best system for learning Python. Follow it and you will succeed-just like the hundreds of thousands of beginners Zed has taught to date! You bring the discipline, commitment, and persistence; the author supplies everything else.

In Learn Python the Hard Way, Third Edition, you'll learn Python by working through 52 brilliantly crafted exercises. Read them. Type their code precisely. (No copying and pasting!) Fix your mistakes. Watch the programs run. As you do, you'll learn how software works; what good programs look like; how to read, write, and think about code; and how to find and fix your mistakes using tricks professional programmers use. Most importantly, you'll learn the following, which you need to start writing excellent Python software of your own:

Installing a complete Python environment Organizing and writing code Basic mathematics Variables Strings and text Interacting with users Working with files Looping and logic Data structures using lists and dictionaries Program design Object-oriented programming Inheritance and composition Modules, classes, and objects Python packaging Debugging Automated testing Basic game development Basic web development

It'll be hard at first. But soon, you'll just get it-and that will feel great!

This tutorial will reward you for every minute you put into it. Soon, you'll know one of the world's most powerful, popular programming languages. You'll be a Python programmer.

Watch Zed, too! The accompanying DVD contains 5+ hours of passionate, powerful teaching: a complete Python video course!

Beginning JavaScript Charts shows how to convert your data into eye-catching, innovative, animated, and highly interactive browser-based charts. This book is suitable for developers of all experience levels and needs: for those who love fast and effective solutions, you can use the jqPlot library to generate charts with amazing effects and animations using only a few lines of code; if you want more power and need to create data visualization beyond traditional charts, then D3 is the JavaScript library for you; finally, if you need a high-performance, professional solution for interactive charts, then the Highcharts library is also covered.

If you are an experienced developer and want to take things further, then Beginning JavaScript Charts also shows you how to develop your own graphics library starting from scratch using jQuery. At the end of the book, you will have a good knowledge of all the elements needed to manage data from every possible source, from high-end scientific instruments to Arduino boards, from PHP SQL databases queries to simple HTML tables, and from Matlab calculations to reports in Excel. You will be able to provide cutting-edge charts exploiting the growing power of modern browsers.

Create all kinds of charts using the latest technologies available on browsers (HTML5, CSS3, jQuery, jqPlot, D3, Highcharts, and SVG) Full of step-by-step examples, Beginning JavaScript Charts introduces you gradually to all aspects of chart development, from the data source to the choice of which solution to apply. This book provides a number of tools that can be the starting point for any project requiring graphical representations of data, whether using commercial libraries or your own
“Each item in Slatkin’s Effective Python teaches a self-contained lesson with its own source code. This makes the book random-access: Items are easy to browse and study in whatever order the reader needs. I will be recommending Effective Python to students as an admirably compact source of mainstream advice on a very broad range of topics for the intermediate Python programmer.”

—Brandon Rhodes, software engineer at Dropbox and chair of PyCon 2016-2017

It’s easy to start coding with Python, which is why the language is so popular. However, Python’s unique strengths, charms, and expressiveness can be hard to grasp, and there are hidden pitfalls that can easily trip you up.

Effective Python will help you master a truly “Pythonic” approach to programming, harnessing Python’s full power to write exceptionally robust and well-performing code. Using the concise, scenario-driven style pioneered in Scott Meyers’ best-selling Effective C++, Brett Slatkin brings together 59 Python best practices, tips, and shortcuts, and explains them with realistic code examples.

Drawing on years of experience building Python infrastructure at Google, Slatkin uncovers little-known quirks and idioms that powerfully impact code behavior and performance. You’ll learn the best way to accomplish key tasks, so you can write code that’s easier to understand, maintain, and improve.

Key features include

Actionable guidelines for all major areas of Python 3.x and 2.x development, with detailed explanations and examples Best practices for writing functions that clarify intention, promote reuse, and avoid bugs Coverage of how to accurately express behaviors with classes and objects Guidance on how to avoid pitfalls with metaclasses and dynamic attributes More efficient approaches to concurrency and parallelism Better techniques and idioms for using Python’s built-in modules Tools and best practices for collaborative development Solutions for debugging, testing, and optimization in order to improve quality and performance
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