Data Wrangling with Python

Tips and Tools to Make Your Life Easier

Author: Jacqueline Kazil,Katharine Jarmul

Publisher: "O'Reilly Media, Inc."

ISBN: 1491956801

Category: Computers

Page: 508

View: 8257

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
Posted in Computers

Data Wrangling with Python

Tips and Tools to Make Your Life Easier

Author: Jacqueline Kazil,Katharine Jarmul

Publisher: "O'Reilly Media, Inc."

ISBN: 1491948779

Category: Computers

Page: 508

View: 3000

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
Posted in Computers

Principles of Data Wrangling

Practical Techniques for Data Preparation

Author: Tye Rattenbury,Joseph M. Hellerstein,Jeffrey Heer,Sean Kandel,Connor Carreras

Publisher: "O'Reilly Media, Inc."

ISBN: 1491938870

Category: Computers

Page: 94

View: 8283

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
Posted in Computers

Bad Data Handbook

Cleaning Up The Data So You Can Get Back To Work

Author: Q. Ethan McCallum

Publisher: "O'Reilly Media, Inc."

ISBN: 1449324975

Category: Computers

Page: 264

View: 2873

What is bad data? Some people consider it a technical phenomenon, like missing values or malformed records, but bad data includes a lot more. In this handbook, data expert Q. Ethan McCallum has gathered 19 colleagues from every corner of the data arena to reveal how they’ve recovered from nasty data problems. From cranky storage to poor representation to misguided policy, there are many paths to bad data. Bottom line? Bad data is data that gets in the way. This book explains effective ways to get around it. Among the many topics covered, you’ll discover how to: Test drive your data to see if it’s ready for analysis Work spreadsheet data into a usable form Handle encoding problems that lurk in text data Develop a successful web-scraping effort Use NLP tools to reveal the real sentiment of online reviews Address cloud computing issues that can impact your analysis effort Avoid policies that create data analysis roadblocks Take a systematic approach to data quality analysis
Posted in Computers

Clean Data

Author: Megan Squire

Publisher: Packt Publishing Ltd

ISBN: 1785289039

Category: Computers

Page: 272

View: 3816

If you are a data scientist of any level, beginners included, and interested in cleaning up your data, this is the book for you! Experience with Python or PHP is assumed, but no previous knowledge of data cleaning is needed.
Posted in Computers

Practical Data Wrangling

Expert techniques for transforming your raw data into a valuable source for analytics

Author: Allan Visochek

Publisher: Packt Publishing Ltd

ISBN: 1787283674

Category: Computers

Page: 204

View: 7608

Turn your noisy data into relevant, insight-ready information by leveraging the data wrangling techniques in Python and R About This Book This easy-to-follow guide takes you through every step of the data wrangling process in the best possible way Work with different types of datasets, and reshape the layout of your data to make it easier for analysis Get simple examples and real-life data wrangling solutions for data pre-processing Who This Book Is For If you are a data scientist, data analyst, or a statistician who wants to learn how to wrangle your data for analysis in the best possible manner, this book is for you. As this book covers both R and Python, some understanding of them will be beneficial. What You Will Learn Read a csv file into python and R, and print out some statistics on the data Gain knowledge of the data formats and programming structures involved in retrieving API data Make effective use of regular expressions in the data wrangling process Explore the tools and packages available to prepare numerical data for analysis Find out how to have better control over manipulating the structure of the data Create a dexterity to programmatically read, audit, correct, and shape data Write and complete programs to take in, format, and output data sets In Detail Around 80% of time in data analysis is spent on cleaning and preparing data for analysis. This is, however, an important task, and is a prerequisite to the rest of the data analysis workflow, including visualization, analysis and reporting. Python and R are considered a popular choice of tool for data analysis, and have packages that can be best used to manipulate different kinds of data, as per your requirements. This book will show you the different data wrangling techniques, and how you can leverage the power of Python and R packages to implement them. You'll start by understanding the data wrangling process and get a solid foundation to work with different types of data. You'll work with different data structures and acquire and parse data from various locations. You'll also see how to reshape the layout of data and manipulate, summarize, and join data sets. Finally, we conclude with a quick primer on accessing and processing data from databases, conducting data exploration, and storing and retrieving data quickly using databases. The book includes practical examples on each of these points using simple and real-world data sets to give you an easier understanding. By the end of the book, you'll have a thorough understanding of all the data wrangling concepts and how to implement them in the best possible way. Style and approach This is a practical book on data wrangling designed to give you an insight into the practical application of data wrangling. It takes you through complex concepts and tasks in an accessible way, featuring information on a wide range of data wrangling techniques with Python and R
Posted in Computers

Data Visualization with Python and JavaScript

Scrape, Clean, Explore & Transform Your Data

Author: Kyran Dale

Publisher: "O'Reilly Media, Inc."

ISBN: 1491920548

Category: Computers

Page: 592

View: 1915

Learn how to turn raw data into rich, interactive web visualizations with the powerful combination of Python and JavaScript. With this hands-on guide, author Kyran Dale teaches you how build a basic dataviz toolchain with best-of-breed Python and JavaScript libraries—including Scrapy, Matplotlib, Pandas, Flask, and D3—for crafting engaging, browser-based visualizations. As a working example, throughout the book Dale walks you through transforming Wikipedia’s table-based list of Nobel Prize winners into an interactive visualization. You’ll examine steps along the entire toolchain, from scraping, cleaning, exploring, and delivering data to building the visualization with JavaScript’s D3 library. If you’re ready to create your own web-based data visualizations—and know either Python or JavaScript— this is the book for you. Learn how to manipulate data with Python Understand the commonalities between Python and JavaScript 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 Create engaging, interactive web visualizations with JavaScript’s D3 library
Posted in Computers

Python for Data Analysis

Data Wrangling with Pandas, NumPy, and IPython

Author: Wes McKinney

Publisher: "O'Reilly Media, Inc."

ISBN: 1491957611

Category: Computers

Page: 550

View: 9356

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
Posted in Computers

Learning the Pandas Library

Python Tools for Data Munging, Analysis, and Visual

Author: Matt Harrison

Publisher: Createspace Independent Publishing Platform

ISBN: 9781533598240

Category: Data mining

Page: 212

View: 6637

Python is one of the top 3 tools that Data Scientists use. One of the tools in their arsenal is the Pandas library. This tool is popular because it gives you so much functionality out of the box. In addition, you can use all the power of Python to make the hard stuff easy! Learning the Pandas Library is designed to bring developers and aspiring data scientists who are anxious to learn Pandas up to speed quickly. It starts with the fundamentals of the data structures. Then, it covers the essential functionality. It includes many examples, graphics, code samples, and plots from real world examples. The Content Covers: Installation Data Structures Series CRUD Series Indexing Series Methods Series Plotting Series Examples DataFrame Methods DataFrame Statistics Grouping, Pivoting, and Reshaping Dealing with Missing Data Joining DataFrames DataFrame Examples Preliminary Reviews This is an excellent introduction benefitting from clear writing and simple examples. The pandas documentation itself is large and sometimes assumes too much knowledge, in my opinion. Learning the Pandas Library bridges this gap for new users and even for those with some pandas experience such as me. -Garry C. I have finished reading Learning the Pandas Library and I liked it... very useful and helpful tips even for people who use pandas regularly. -Tom Z.
Posted in Data mining

Complex Network Analysis in Python

Recognize - Construct - Visualize - Analyze - Interpret

Author: Dmitry Zinoviev

Publisher: Pragmatic Bookshelf

ISBN: 1680505408

Category: Computers

Page: 262

View: 4511

Construct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If you're a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you'll increase your productivity exponentially. Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience. Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics. Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer. What You Need: You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems.
Posted in Computers

Python Data Science Essentials

Author: Alberto Boschetti,Luca Massaron

Publisher: Packt Publishing Ltd

ISBN: 1786462834

Category: Computers

Page: 378

View: 2590

Become an efficient data science practitioner by understanding Python's key concepts About This Book Quickly get familiar with data science using Python 3.5 Save time (and effort) with all the essential tools explained Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience Who This Book Is For If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills. What You Will Learn Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux Get data ready for your data science project Manipulate, fix, and explore data in order to solve data science problems Set up an experimental pipeline to test your data science hypotheses Choose the most effective and scalable learning algorithm for your data science tasks Optimize your machine learning models to get the best performance Explore and cluster graphs, taking advantage of interconnections and links in your data In Detail Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users. Style and approach The book is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.
Posted in Computers

Introducing Python

Modern Computing in Simple Packages

Author: Bill Lubanovic

Publisher: "O'Reilly Media, Inc."

ISBN: 1449361188

Category: Computers

Page: 484

View: 9288

Easy to understand and fun to read, Introducing Python is ideal for beginning programmers as well as those new to the language. Author Bill Lubanovic takes you from the basics to more involved and varied topics, mixing tutorials with cookbook-style code recipes to explain concepts in Python 3. End-of-chapter exercises help you practice what you’ve learned. You’ll gain a strong foundation in the language, including best practices for testing, debugging, code reuse, and other development tips. This book also shows you how to use Python for applications in business, science, and the arts, using various Python tools and open source packages. Learn simple data types, and basic math and text operations Use data-wrangling techniques with Python’s built-in data structures Explore Python code structure, including the use of functions Write large programs in Python, with modules and packages Dive into objects, classes, and other object-oriented features Examine storage from flat files to relational databases and NoSQL Use Python to build web clients, servers, APIs, and services Manage system tasks such as programs, processes, and threads Understand the basics of concurrency and network programming
Posted in Computers

R for Data Science

Import, Tidy, Transform, Visualize, and Model Data

Author: Hadley Wickham,Garrett Grolemund

Publisher: "O'Reilly Media, Inc."

ISBN: 1491910364

Category: Computers

Page: 520

View: 2276

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Posted in Computers

Data Science from Scratch

First Principles with Python

Author: Joel Grus

Publisher: "O'Reilly Media, Inc."

ISBN: 1491904402

Category: BUSINESS & ECONOMICS

Page: 330

View: 3627

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
Posted in BUSINESS & ECONOMICS

Slaying Excel Dragons

A Beginners Guide to Conquering Excel's Frustrations and Making Excel Fun

Author: Mike Girvin

Publisher: Tickling Keys, Inc.

ISBN: 1615473025

Category: Computers

Page: 532

View: 5542

This enthusiastic introduction provides support for Excel beginners and focuses on using the program immediately for maximum efficiency. With 1,104 screenshots and explicit information on everything from rows, columns, and cells to subtotaling, sorting, and pivot tables, this guide aims to alleviate the frustrations that come with using the program for the first time. This manual offers strategies for avoiding problems and streamlining efficiency and assists readers from start to finish, turning Excel 2010 novices into experts.
Posted in Computers

Learn Python 3 the Hard Way

A Very Simple Introduction to the Terrifyingly Beautiful World of Computers and Code

Author: Zed A. Shaw

Publisher: Addison-Wesley Professional

ISBN: 0134693906

Category: Computers

Page: 320

View: 8007

You Will Learn Python 3! Zed Shaw has perfected the world’s best system for learning Python 3. Follow it and you will succeed—just like the millions of beginners Zed has taught to date! You bring the discipline, commitment, and persistence; the author supplies everything else. In Learn Python 3 the Hard Way, 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 a computer works; what good programs look like; and how to read, write, and think about code. Zed then teaches you even more in 5+ hours of video where he shows you how to break, fix, and debug your code—live, as he’s doing the exercises. Install a complete Python environment Organize and write code Fix and break code Basic mathematics Variables Strings and text Interact with users Work 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 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 course 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. This Book Is Perfect For Total beginners with zero programming experience Junior developers who know one or two languages Returning professionals who haven’t written code in years Seasoned professionals looking for a fast, simple, crash course in Python 3
Posted in Computers

Python for Data Science For Dummies

Author: John Paul Mueller,Luca Massaron

Publisher: John Wiley & Sons

ISBN: 1118843983

Category: Computers

Page: 432

View: 2762

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.
Posted in Computers

Machine Learning with Python Cookbook

Practical Solutions from Preprocessing to Deep Learning

Author: Chris Albon

Publisher: "O'Reilly Media, Inc."

ISBN: 1491989335

Category: Computers

Page: 366

View: 7824

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
Posted in Computers

Python Tricks

A Buffet of Awesome Python Features

Author: Dan Bader

Publisher: BookBaby

ISBN: 177509331X

Category: Computers

Page: N.A

View: 783

“I don’t even feel like I’ve scratched the surface of what I can do with Python” With Python Tricks: The Book you’ll discover Python’s best practices and the power of beautiful & Pythonic code with simple examples and a step-by-step narrative. You'll get one step closer to mastering Python, so you can write beautiful and idiomatic code that comes to you naturally. Learning the ins and outs of Python is difficult—and with this book you'll be able to focus on the practical skills that really matter. Discover the “hidden gold” in Python’s standard library and start writing clean and Pythonic code today. Who Should Read This Book: - If you’re wondering which lesser known parts in Python you should know about, you’ll get a roadmap with this book. Discover cool (yet practical!) Python tricks and blow your coworkers’ minds in your next code review. - If you’ve got experience with legacy versions of Python, the book will get you up to speed with modern patterns and features introduced in Python 3 and backported to Python 2. - If you’ve worked with other programming languages and you want to get up to speed with Python, you’ll pick up the idioms and practical tips you need to become a confident and effective Pythonista. - If you want to make Python your own and learn how to write clean and Pythonic code, you’ll discover best practices and little-known tricks to round out your knowledge.
Posted in Computers

Gestión de la información web usando Python

Author: Sarasa Cabezuelo, Antonio

Publisher: Editorial UOC

ISBN: 8491164863

Category: Computers

Page: N.A

View: 8655

En este manual se realiza una introducción a un conjunto de herramientas y técnicas para el acceso y procesamiento de datos web, que se encuentran en formatos como XML, CSV o JSON, o bien en bases de datos tanto relacionales como NoSQL. El objetivo de esta obra es acercar al lector estos conocimientos a partir de las herramientas y librerías de un lenguaje de programación concreto como Python, el más utilizado hoy en el área del análisis de datos y big data. El primer capítulo constituye una introducción a Python, que sirve como lenguaje vehicular en el resto de los capítulos, los cuales se dedican a estudiar el acceso y procesamiento de datos en los formatos XML, JSON y CSV. Los siguientes capítulos abordan el acceso a bases de datos relacionales, SQLite y MySQL, y a la base de datos NoSQL MongoDB. En los dos últimos capítulos, se tratan técnicas de extracción de información usando web scraping y programación de páginas web con la framework Bottle. Cada capítulo contiene algunos ejercicios propuestos para fijar las ideas expuestas.
Posted in Computers