In today's world of science and technology, it's all about speed and flexibility. When it comes to scientific computing, NumPy tops the list. NumPy will give you both speed and high productivity. This book will walk you through NumPy with clear, step-by-step examples and just the right amount of theory. The book focuses on the fundamentals of NumPy, including array objects, functions, and matrices, each of them explained with practical examples. You will then learn about different NumPy modules while performing mathematical operations such as calculating the Fourier transform, finding the inverse of a matrix, and determining eigenvalues, among many others. This book is a one-stop solution to knowing the ins and outs of the vast NumPy library, empowering you to use its wide range of mathematical features to build efficient, high-speed programs.

The book is written in beginner’s guide style with each aspect of NumPy demonstrated with real world examples and required screenshots.If you are a programmer, scientist, or engineer who has basic Python knowledge and would like to be able to do numerical computations with Python, this book is for you. No prior knowledge of NumPy is required.

Want complete instructions on the Python library and its elements? Get solutions with practical case studies and implications of python in data analysis through this book. “A BEGINNER’S GUIDE TO PYTHON FOR DATA ANALYTICS” will help you to learn about the different aspects of python along with its implementation in data analysis in different industries.

Over 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.

This book targets programmers and scientists who have basic Python knowledge and who are keen to perform scientific and numerical computations with SciPy.

The book is specifically intended for scientists, engineers, and engineering students who have taken a course on numeric methods and wish to comprehend and learn the subject through programming. The book's chapters are written methodically (step-by-step) so that programming becomes simple. More emphasis is placed on computationally modelling the methodologies and discussing the numerical method. Python is chosen as the programming language because it is simple to comprehend and use compared to other programming languages. The book allows readers to use and experiment with the approaches it describes. With very few adjustments, many of the programmes in the book can be utilised for applications in science and engineering.

Annotation Your work demands results, and you don't have time for tedious, repetitive mathematical tasks. Sage is a free, open-source software package that automates symbolic and numerical calculations with the power of the Python programming language, so you can focus on the analytical and creative aspects of your work or studies. Sage Beginner's Guide shows you how to do calculations with Sage. Each concept is illustrated with a complete example that you can use as a starting point for your own work. You will learn how to use many of the functions that are built in to Sage, and how to use Python to write sophisticated programs that utilize the power of Sage. This book starts by showing you how to download and install Sage, and introduces the command-line interface and the graphical notebook interface. It also includes an introduction to Python so you can start programming in Sage. Every major concept is illustrated with a practical example. After learning the fundamentals of variables and functions in Sage, you will learn how to symbolically simplify expressions, solve equations, perform integrals and derivatives, and manipulate vectors and matrices. You will learn how Sage can produce numerous kinds of plots and graphics. The book will demonstrate numerical methods in Sage, and explain how to use object-oriented programming to improve your code. Sage Beginner's Guide will give you the tools you need to unlock the full potential of Sage for simplifying and automating mathematical computing. Effectively use Sage to eliminate tedious algebra, speed up numerical calculations, implement algorithms and data structures, and illustrate your work with publication-quality plots and graphics.

The Python ecosystem with scikit-learn and pandas is required for operational machine learning. Python is the rising platform for professional machine learning because you can use the same code to explore different models in R&D then deploy it directly to production. In this Ebook, learn exactly how to get started and apply machine learning using the Python ecosystem.

Ready to learn Data Science through Python language? Python for Data Analysis is a step-by-step guide for beginners and dabblers-alike. This book is designed to offer working knowledge of Python and data science and some of the tools required to apply that knowledge. It’s possible that you have little experience with or knowledge of data analysis and are interested in it. You might have some experience in coding. You may have worked with data before and want to use Python. We have made this book in a way that will be helpful to all these groups and more besides in varying ways. This can serve as an introduction to the most current tools and functions of those tools used by data scientists. In this book You will learn: Data Science/Analysis and its applications IPython and Jupyter - an introduction to the basic tools and how to navigate and use them. You will also learn about its importance in a data scientist’s ecosystem. Pandas - a powerful data management Python library that lets you do interesting things with data. You will learn all the basics you need to get started. NumPy - a powerful numerical library for Python. You will learn more about its advantages. Get your copy now

This book introduces text analytics as a valuable method for deriving insights from text data. Unlike other text analytics publications, Practical Text Analytics: Maximizing the Value of Text Data makes technical concepts accessible to those without extensive experience in the field. Using text analytics, organizations can derive insights from content such as emails, documents, and social media. Practical Text Analytics is divided into five parts. The first part introduces text analytics, discusses the relationship with content analysis, and provides a general overview of text mining methodology. In the second part, the authors discuss the practice of text analytics, including data preparation and the overall planning process. The third part covers text analytics techniques such as cluster analysis, topic models, and machine learning. In the fourth part of the book, readers learn about techniques used to communicate insights from text analysis, including data storytelling. The final part of Practical Text Analytics offers examples of the application of software programs for text analytics, enabling readers to mine their own text data to uncover information.