Data Exploration

Practical Data Analysis Cookbook by Tomasz Drabas Over 60 practical recipes on data exploration and analysisKey Features[] Clean dirty data, extract accurate information, and explore the relationships between variables[] Forecast the output of an electric plant and the water flow of American rivers using pandas, NumPy, Statsmodels, and scikit-learn[] Find and extract the most important features from your dataset using the most efficient Python librariesBook DescriptionData analysis is the process of systematically applying statistical and logical techniques to describe and illustrate, condense and recap, and evaluate data. Its importance has been most visible in the sector of information and communication technologies. It is an employee asset in almost all economy sectors. This book provides a rich set of independent recipes that dive into the world of data analytics and modeling using a variety of approaches, tools, and algorithms. You will learn the basics of data handling and modeling, and will build your skills gradually toward more advanced topics such as simulations, raw text processing, social interactions analysis, and more. First, you will learn some easy-to-follow practical techniques on how to read, write, clean, reformat, explore, and understand your data—arguably the most time-consuming (and the most important) tasks for any data scientist. In the second section, different independent recipes delve into intermediate topics such as classification, clustering, predicting, and more. With the help of these easy-to-follow recipes, you will also learn techniques that can easily be expanded to solve other real-life problems such as building recommendation engines or predictive models. In the third section, you will explore more advanced topics: from the field of graph theory through natural language processing, discrete choice modeling to simulations. You will also get to expand your knowledge on identifying fraud origin with the help of a graph, scrape Internet websites, and classify movies based on their reviews. By the end of this book, you will be able to efficiently use the vast array of tools that the Python environment has to offer.What you will learn[] Read, clean, transform, and store your data usng Pandas and OpenRefine[] Understand your data and explore the relationships between variables using Pandas and D3.js[] Explore a variety of techniques to classify and cluster outbound marketing campaign calls data of a bank using Pandas, mlpy, NumPy, and Statsmodels[] Reduce the dimensionality of your dataset and extract the most important features with pandas, NumPy, and mlpy[] Predict the output of a power plant with regression models and forecast water flow of American rivers with time series methods using pandas, NumPy, Statsmodels, and scikit-learn[] Explore social interactions and identify fraudulent activities with graph theory concepts using NetworkX and Gephi[] Scrape Internet web pages using urlib and BeautifulSoup and get to know natural language processing techniques to classify movies ratings using NLTK[] Study simulation techniques in an example of a gas station with agent-based modelingWho this book is forIf you are a beginner or intermediate-level professional who is looking to solve your day-to-day, analytical problems with Python, this book is for you. Even with no prior programming and data analytics experience, you will be able to finish each recipe and learn while doing so.

Become a Python Data Analyst by Alvaro Fuentes Enhance your data analysis and predictive modeling skills using popular Python toolsKey FeaturesCover all fundamental libraries for operation and manipulation of Python for data analysisImplement real-world datasets to perform predictive analytics with PythonAccess modern data analysis techniques and detailed code with scikit-learn and SciPyBook DescriptionPython is one of the most common and popular languages preferred by leading data analysts and statisticians for working with massive datasets and complex data visualizations.Become a Python Data Analyst introduces Python's most essential tools and libraries necessary to work with the data analysis process, right from preparing data to performing simple statistical analyses and creating meaningful data visualizations.In this book, we will cover Python libraries such as NumPy, pandas, matplotlib, seaborn, SciPy, and scikit-learn, and apply them in practical data analysis and statistics examples. As you make your way through the chapters, you will learn to efficiently use the Jupyter Notebook to operate and manipulate data using NumPy and the pandas library. In the concluding chapters, you will gain experience in building simple predictive models and carrying out statistical computation and analysis using rich Python tools and proven data analysis techniques.By the end of this book, you will have hands-on experience performing data analysis with Python.What you will learnExplore important Python libraries and learn to install Anaconda distributionUnderstand the basics of NumPyProduce informative and useful visualizations for analyzing dataPerform common statistical calculationsBuild predictive models and understand the principles of predictive analyticsWho this book is forBecome a Python Data Analyst is for entry-level data analysts, data engineers, and BI professionals who want to make complete use of Python tools for performing efficient data analysis. Prior knowledge of Python programming is necessary to understand the concepts covered in this book

Pandas 1.X Cookbook by Matt Harrison; Theodore Petrou Use the power of pandas to solve most complex scientific computing problems with ease. Revised for pandas 1.x.Key FeaturesThis is the first book on pandas 1.x Practical, easy to implement recipes for quick solutions to common problems in data using pandasMaster the fundamentals of pandas to quickly begin exploring any datasetBook DescriptionThe pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter.This new updated and revised edition provides you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. Many advanced recipes combine several different features across the pandas library to generate results.What you will learnMaster data exploration in pandas through dozens of practice problemsGroup, aggregate, transform, reshape, and filter dataMerge data from different sources through pandas SQL-like operationsCreate visualizations via pandas hooks to matplotlib and seabornUse pandas, time series functionality to perform powerful analysesImport, clean, and prepare real-world datasets for machine learningCreate workflows for processing big data that doesn't fit in memoryWho this book is forThis book is for Python developers, data scientists, engineers, and analysts. Pandas is the ideal tool for manipulating structured data with Python and this book provides ample instruction and examples. Not only does it cover the basics required to be proficient, but it goes into the details of idiomatic pandas.

Hands-On Exploratory Data Analysis with R by Radhika Datar; Harish Garg Learn exploratory data analysis concepts using powerful R packages to enhance your R data analysis skillsKey FeaturesSpeed up your data analysis projects using powerful R packages and techniquesCreate multiple hands-on data analysis projects using real-world dataDiscover and practice graphical exploratory analysis techniques across domainsBook DescriptionHands-On Exploratory Data Analysis with R will help you build not just a foundation but also expertise in the elementary ways to analyze data. You will learn how to understand your data and summarize its main characteristics. You'll also uncover the structure of your data, and you'll learn graphical and numerical techniques using the R language.This book covers the entire exploratory data analysis (EDA) process—data collection, generating statistics, distribution, and invalidating the hypothesis. As you progress through the book, you will learn how to set up a data analysis environment with tools such as ggplot2, knitr, and R Markdown, using tools such as DOE Scatter Plot and SML2010 for multifactor, optimization, and regression data problems.By the end of this book, you will be able to successfully carry out a preliminary investigation on any dataset, identify hidden insights, and present your results in a business context.What you will learnLearn powerful R techniques to speed up your data analysis projectsImport, clean, and explore data using powerful R packagesPractice graphical exploratory analysis techniquesCreate informative data analysis reports using ggplot2Identify and clean missing and erroneous dataExplore data analysis techniques to analyze multi-factor datasetsWho this book is forHands-On Exploratory Data Analysis with R is for data enthusiasts who want to build a strong foundation for data analysis. If you are a data analyst, data engineer, software engineer, or product manager, this book will sharpen your skills in the complete workflow of exploratory data analysis.

Exploratory Data Analysis by John W. Tukey The approach in this introductory book is that of informal study of the data. Methods range from plotting picture-drawing techniques to rather elaborate numerical summaries. Several of the methods are the original creations of the author, and all can be carried out either with pencil or aided by hand-held calculator.

Graphical Exploratory Data Analysis by Du Toit, S. H. C.; Steyn, A. G. W.; Stumpf, R. H.

Exploratory Data Analysis by Frederick Hartwig; Brian E. Dearing An Introduction to the underlying principles, central concepts, and basic techniques for conducting and understanding exploratory data analysis - with numerous social science examples.