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Data Wrangling with Python by Jacqueline Kazil,Katharine Jarmul Pdf
How do you take your data analysis skills beyond Excel to the next level? By learning just enough Python to get stuff done. This hands-on guide shows non-programmers like you how to process information that’s initially too messy or difficult to access. You don't need to know a thing about the Python programming language to get started. Through various step-by-step exercises, you’ll learn how to acquire, clean, analyze, and present data efficiently. You’ll also discover how to automate your data process, schedule file- editing and clean-up tasks, process larger datasets, and create compelling stories with data you obtain. Quickly learn basic Python syntax, data types, and language concepts Work with both machine-readable and human-consumable data Scrape websites and APIs to find a bounty of useful information Clean and format data to eliminate duplicates and errors in your datasets Learn when to standardize data and when to test and script data cleanup Explore and analyze your datasets with new Python libraries and techniques Use Python solutions to automate your entire data-wrangling process
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
Data Wrangling with Python by Dr. Tirthajyoti Sarkar,Shubhadeep Roychowdhury Pdf
Simplify your ETL processes with these hands-on data hygiene tips, tricks, and best practices. Key FeaturesFocus on the basics of data wranglingStudy various ways to extract the most out of your data in less timeBoost your learning curve with bonus topics like random data generation and data integrity checksBook Description For data to be useful and meaningful, it must be curated and refined. Data Wrangling with Python teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain. The book starts with the absolute basics of Python, focusing mainly on data structures. It then delves into the fundamental tools of data wrangling like NumPy and Pandas libraries. You’ll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python. This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you’ll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The book will further help you grasp concepts through real-world examples and datasets. By the end of this book, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently. What you will learnUse and manipulate complex and simple data structuresHarness the full potential of DataFrames and numpy.array at run timePerform web scraping with BeautifulSoup4 and html5libExecute advanced string search and manipulation with RegEXHandle outliers and perform data imputation with PandasUse descriptive statistics and plotting techniquesPractice data wrangling and modeling using data generation techniquesWho this book is for Data Wrangling with Python is designed for developers, data analysts, and business analysts who are keen to pursue a career as a full-fledged data scientist or analytics expert. Although, this book is for beginners, prior working knowledge of Python is necessary to easily grasp the concepts covered here. It will also help to have rudimentary knowledge of relational database and SQL.
Practical Python Data Wrangling and Data Quality by Susan E. McGregor Pdf
The world around us is full of data that holds unique insights and valuable stories, and this book will help you uncover them. Whether you already work with data or want to learn more about its possibilities, the examples and techniques in this practical book will help you more easily clean, evaluate, and analyze data so that you can generate meaningful insights and compelling visualizations. Complementing foundational concepts with expert advice, author Susan E. McGregor provides the resources you need to extract, evaluate, and analyze a wide variety of data sources and formats, along with the tools to communicate your findings effectively. This book delivers a methodical, jargon-free way for data practitioners at any level, from true novices to seasoned professionals, to harness the power of data. Use Python 3.8+ to read, write, and transform data from a variety of sources Understand and use programming basics in Python to wrangle data at scale Organize, document, and structure your code using best practices Collect data from structured data files, web pages, and APIs Perform basic statistical analyses to make meaning from datasets Visualize and present data in clear and compelling ways
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
Data Wrangling with JavaScript by Ashley Davis Pdf
Summary Data Wrangling with JavaScript is hands-on guide that will teach you how to create a JavaScript-based data processing pipeline, handle common and exotic data, and master practical troubleshooting strategies. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Why not handle your data analysis in JavaScript? Modern libraries and data handling techniques mean you can collect, clean, process, store, visualize, and present web application data while enjoying the efficiency of a single-language pipeline and data-centric web applications that stay in JavaScript end to end. About the Book Data Wrangling with JavaScript promotes JavaScript to the center of the data analysis stage! With this hands-on guide, you'll create a JavaScript-based data processing pipeline, handle common and exotic data, and master practical troubleshooting strategies. You'll also build interactive visualizations and deploy your apps to production. Each valuable chapter provides a new component for your reusable data wrangling toolkit. What's inside Establishing a data pipeline Acquisition, storage, and retrieval Handling unusual data sets Cleaning and preparing raw dataInteractive visualizations with D3 About the Reader Written for intermediate JavaScript developers. No data analysis experience required. About the Author Ashley Davis is a software developer, entrepreneur, author, and the creator of Data-Forge and Data-Forge Notebook, software for data transformation, analysis, and visualization in JavaScript. Table of Contents Getting started: establishing your data pipeline Getting started with Node.js Acquisition, storage, and retrieval Working with unusual data Exploratory coding Clean and prepare Dealing with huge data files Working with a mountain of data Practical data analysis Browser-based visualization Server-side visualization Live data Advanced visualization with D3 Getting to production
Machine Learning with Python Cookbook by Chris Albon Pdf
This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. You’ll find recipes for: Vectors, matrices, and arrays Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Support vector machines (SVM), naïve Bayes, clustering, and neural networks Saving and loading trained models
The Data Wrangling Workshop by Brian Lipp,Shubhadeep Roychowdhury,Dr. Tirthajyoti Sarkar Pdf
A beginner's guide to simplifying Extract, Transform, Load (ETL) processes with the help of hands-on tips, tricks, and best practices, in a fun and interactive way Key FeaturesExplore data wrangling with the help of real-world examples and business use casesStudy various ways to extract the most value from your data in minimal timeBoost your knowledge with bonus topics, such as random data generation and data integrity checksBook Description While a huge amount of data is readily available to us, it is not useful in its raw form. For data to be meaningful, it must be curated and refined. If you're a beginner, then The Data Wrangling Workshop will help to break down the process for you. You'll start with the basics and build your knowledge, progressing from the core aspects behind data wrangling, to using the most popular tools and techniques. This book starts by showing you how to work with data structures using Python. Through examples and activities, you'll understand why you should stay away from traditional methods of data cleaning used in other languages and take advantage of the specialized pre-built routines in Python. Later, you'll learn how to use the same Python backend to extract and transform data from an array of sources, including the internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, the book teaches you how to handle missing or incorrect data, and reformat it based on the requirements from your downstream analytics tool. By the end of this book, you will have developed a solid understanding of how to perform data wrangling with Python, and learned several techniques and best practices to extract, clean, transform, and format your data efficiently, from a diverse array of sources. What you will learnGet to grips with the fundamentals of data wranglingUnderstand how to model data with random data generation and data integrity checksDiscover how to examine data with descriptive statistics and plotting techniquesExplore how to search and retrieve information with regular expressionsDelve into commonly-used Python data science librariesBecome well-versed with how to handle and compensate for missing dataWho this book is for The Data Wrangling Workshop is designed for developers, data analysts, and business analysts who are looking to pursue a career as a full-fledged data scientist or analytics expert. Although this book is for beginners who want to start data wrangling, prior working knowledge of the Python programming language is necessary to easily grasp the concepts covered here. It will also help to have a rudimentary knowledge of relational databases and SQL.
Practical Python Data Wrangling and Data Quality by Susan E. McGregor Pdf
There are awesome discoveries to be made and valuable stories to be told in datasets--and this book will help you uncover them. Whether you already work with data or just want to understand its possibilities, the techniques and advice in this practical book will help you learn how to better clean, evaluate, and analyze data to generate meaningful insights and compelling visualizations. Through foundational concepts and worked examples, author Susan McGregor provides the concepts and tools you need to evaluate and analyze all kinds of data and communicate your findings effectively. This book provides a methodical, jargon-free way for practitioners of all levels to harness the power of data. Use Python 3.8+ to read, write, and transform data from a variety of sources Understand and use programming basics in Python to wrangle data at scale Organize, document, and structure your code using best practices Complete exercises either on your own machine or on the web Collect data from structured data files, web pages, and APIs Perform basic statistical analysis to make meaning from data sets Visualize and present data in clear and compelling ways.
Python Data Science Handbook by Jake VanderPlas Pdf
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
Data Wrangling with R by Bradley C. Boehmke, Ph.D. Pdf
This guide for practicing statisticians, data scientists, and R users and programmers will teach the essentials of preprocessing: data leveraging the R programming language to easily and quickly turn noisy data into usable pieces of information. Data wrangling, which is also commonly referred to as data munging, transformation, manipulation, janitor work, etc., can be a painstakingly laborious process. Roughly 80% of data analysis is spent on cleaning and preparing data; however, being a prerequisite to the rest of the data analysis workflow (visualization, analysis, reporting), it is essential that one become fluent and efficient in data wrangling techniques. This book will guide the user through the data wrangling process via a step-by-step tutorial approach and provide a solid foundation for working with data in R. The author's goal is to teach the user how to easily wrangle data in order to spend more time on understanding the content of the data. By the end of the book, the user will have learned: How to work with different types of data such as numerics, characters, regular expressions, factors, and dates The difference between different data structures and how to create, add additional components to, and subset each data structure How to acquire and parse data from locations previously inaccessible How to develop functions and use loop control structures to reduce code redundancy How to use pipe operators to simplify code and make it more readable How to reshape the layout of data and manipulate, summarize, and join data sets
Hands-On Data Analysis with Pandas by Stefanie Molin Pdf
Get to grips with pandas by working with real datasets and master data discovery, data manipulation, data preparation, and handling data for analytical tasks Key Features Perform efficient data analysis and manipulation tasks using pandas 1.x Apply pandas to different real-world domains with the help of step-by-step examples Make the most of pandas as an effective data exploration tool Book DescriptionExtracting valuable business insights is no longer a ‘nice-to-have’, but an essential skill for anyone who handles data in their enterprise. Hands-On Data Analysis with Pandas is here to help beginners and those who are migrating their skills into data science get up to speed in no time. This book will show you how to analyze your data, get started with machine learning, and work effectively with the Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. This updated edition will equip you with the skills you need to use pandas 1.x to efficiently perform various data manipulation tasks, reliably reproduce analyses, and visualize your data for effective decision making – valuable knowledge that can be applied across multiple domains.What you will learn Understand how data analysts and scientists gather and analyze data Perform data analysis and data wrangling using Python Combine, group, and aggregate data from multiple sources Create data visualizations with pandas, matplotlib, and seaborn Apply machine learning algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets Solve common data representation and analysis problems using pandas Build Python scripts, modules, and packages for reusable analysis code Who this book is for This book is for data science beginners, data analysts, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. Data scientists looking to implement pandas in their machine learning workflow will also find plenty of valuable know-how as they progress. You’ll find it easier to follow along with this book if you have a working knowledge of the Python programming language, but a Python crash-course tutorial is provided in the code bundle for anyone who needs a refresher.
Hands-On Data Analysis with Pandas by Stefanie Molin Pdf
Get to grips with pandas—a versatile and high-performance Python library for data manipulation, analysis, and discovery Key FeaturesPerform efficient data analysis and manipulation tasks using pandasApply pandas to different real-world domains using step-by-step demonstrationsGet accustomed to using pandas as an effective data exploration toolBook Description Data analysis has become a necessary skill in a variety of positions where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification, using scikit-learn, to make predictions based on past data. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. What you will learnUnderstand how data analysts and scientists gather and analyze dataPerform data analysis and data wrangling in PythonCombine, group, and aggregate data from multiple sourcesCreate data visualizations with pandas, matplotlib, and seabornApply machine learning (ML) algorithms to identify patterns and make predictionsUse Python data science libraries to analyze real-world datasetsUse pandas to solve common data representation and analysis problemsBuild Python scripts, modules, and packages for reusable analysis codeWho this book is for This book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist who is looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial.
Data Science Projects with Python by Stephen Klosterman Pdf
Gain hands-on experience with industry-standard data analysis and machine learning tools in Python Key FeaturesTackle data science problems by identifying the problem to be solvedIllustrate patterns in data using appropriate visualizationsImplement suitable machine learning algorithms to gain insights from dataBook Description Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools, by applying them to realistic data problems. You will learn how to use pandas and Matplotlib to critically examine datasets with summary statistics and graphs, and extract the insights you seek to derive. You will build your knowledge as you prepare data using the scikit-learn package and feed it to machine learning algorithms such as regularized logistic regression and random forest. You’ll discover how to tune algorithms to provide the most accurate predictions on new and unseen data. As you progress, you’ll gain insights into the working and output of these algorithms, building your understanding of both the predictive capabilities of the models and why they make these predictions. By then end of this book, you will have the necessary skills to confidently use machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data. What you will learnInstall the required packages to set up a data science coding environmentLoad data into a Jupyter notebook running PythonUse Matplotlib to create data visualizationsFit machine learning models using scikit-learnUse lasso and ridge regression to regularize your modelsCompare performance between models to find the best outcomesUse k-fold cross-validation to select model hyperparametersWho this book is for If you are a data analyst, data scientist, or business analyst who wants to get started using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of Python and data analytics will help you get the most from this book. Familiarity with mathematical concepts such as algebra and basic statistics will also be useful.
Principles of Data Wrangling by Tye Rattenbury,Joseph M. Hellerstein,Jeffrey Heer,Sean Kandel,Connor Carreras Pdf
A key task that any aspiring data-driven organization needs to learn is data wrangling, the process of converting raw data into something truly useful. This practical guide provides business analysts with an overview of various data wrangling techniques and tools, and puts the practice of data wrangling into context by asking, "What are you trying to do and why?" Wrangling data consumes roughly 50-80% of an analyst’s time before any kind of analysis is possible. Written by key executives at Trifacta, this book walks you through the wrangling process by exploring several factors—time, granularity, scope, and structure—that you need to consider as you begin to work with data. You’ll learn a shared language and a comprehensive understanding of data wrangling, with an emphasis on recent agile analytic processes used by many of today’s data-driven organizations. Appreciate the importance—and the satisfaction—of wrangling data the right way. Understand what kind of data is available Choose which data to use and at what level of detail Meaningfully combine multiple sources of data Decide how to distill the results to a size and shape that can drive downstream analysis