Python for Data Analysis

Python for Data Analysis

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.

Author: Wes McKinney

Publisher: "O'Reilly Media, Inc."

ISBN: 9781491957639

Category: Computers

Page: 550

View: 337

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
Categories: Computers

Python for Data Analysis

Python for Data Analysis

Presents case studies and instructions on how to solve data analysis problems using Python.

Author: Wes McKinney

Publisher: "O'Reilly Media, Inc."

ISBN: 9781449319793

Category: Computers

Page: 452

View: 861

Presents case studies and instructions on how to solve data analysis problems using Python.
Categories: Computers

Neuronale Netze Selbst Programmieren

Neuronale Netze Selbst Programmieren

- Tariq Rashid hat eine besondere Fähigkeit, schwierige Konzepte verständlich zu erklären, dadurch werden Neuronale Netze für jeden Interessierten zugänglich und praktisch nachvollziehbar.

Author: Tariq Rashid

Publisher:

ISBN: 1492064041

Category:

Page: 232

View: 235

Neuronale Netze sind Schlüsselelemente des Deep Learning und der Künstlichen Intelligenz, die heute zu Erstaunlichem in der Lage sind. Dennoch verstehen nur wenige, wie Neuronale Netze tatsächlich funktionieren. Dieses Buch nimmt Sie mit auf eine unterhaltsame Reise, die mit ganz einfachen Ideen beginnt und Ihnen Schritt für Schritt zeigt, wie Neuronale Netze arbeiten. Dafür brauchen Sie keine tieferen Mathematik-Kenntnisse, denn alle mathematischen Konzepte werden behutsam und mit vielen Illustrationen erläutert. Dann geht es in die Praxis: Sie programmieren Ihr eigenes Neuronales Netz mit Python und bringen ihm bei, handgeschriebene Zahlen zu erkennen, bis es eine Performance wie ein professionell entwickeltes Netz erreicht. Zum Schluss lassen Sie das Netz noch auf einem Raspberry Pi Zero laufen. - Tariq Rashid hat eine besondere Fähigkeit, schwierige Konzepte verständlich zu erklären, dadurch werden Neuronale Netze für jeden Interessierten zugänglich und praktisch nachvollziehbar.
Categories:

Python for Data Analysis

Python for Data Analysis

Learn Python for data analysis using. This Book will take you from the basics of Python to exploring many different types of data analysis.

Author: William KRYSTAL

Publisher:

ISBN: 9798670122801

Category:

Page: 515

View: 921

Learn Python for data analysis using. This Book will take you from the basics of Python to exploring many different types of data analysis. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more! ★Topics covered★ ★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 Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library and we will use some of its machine learning algorithms to build smart models and make cool predictions.
Categories:

Python for Data Analysis

Python for Data Analysis

Why this book is the best guide for everyone? Here are the reasons: The author has explored everything about python for data analysis using pandas, NumPy, Ipython and Matplotlib libraries from the basics. A simple language has been used.

Author: Samuel Samuel Burns

Publisher:

ISBN: 1796231665

Category:

Page: 232

View: 393

If you buy a new print edition of this book (or purchased one in the past), you can buy the Kindle Edition for FREE. Print edition purchase must be sold by Amazon!You want to learn Python for data analysis using NumPy, Pandas, and IPython, and you don't know how to start? You don't need a big boring and expensive textbook. This book is the best one for everyone.Order your book Now!! Why this book is the best guide for everyone? Here are the reasons:The author has explored everything about python for data analysis using pandas, NumPy, Ipython and Matplotlib libraries from the basics. A simple language has been used. Many examples have been given, both theoretically and programmatically. Screenshots showing program outputs have been added. The book is written chronologically, in a step-by-step manner. Book Objectives: The Aims and Objectives of the Book: To help you understand why you should choose Python for data analysis tasks. To help you know the various data analysis libraries supported by Python and how to use them. To help you know how to analyze your business data and draw meaningful insights for effective decision making. To equip you with data analysis skills using Python programming language. To help you know where data analysis is applied today and how to use it in your everyday life. Who is this Book is for? : Here are the target readers for this book: Anybody who is a complete beginner to data analysis with Python or data analysis in general. Anybody who wants to advance their data analysis skills with Python programming language. Anybody who wants to know how to use data analysis for the benefit of their business or brand. Professionals in data science, computer programming, computer scientist. Professors, lecturers or tutors who are looking to find better ways to explain python for data analysis to their students in the simplest and easiest way. Students and academicians, especially those focusing on python programming, computer science,neural networks, machine learning, and deep learning. What do you need for this Book? : You are required to have installed the following on your computer: Python 3.X Numpy Pandas Matplotlib The Author guides you on how to install and configure the rest of the Python libraries that are required for data analysis. What is inside the book? : Why Python for Data Analysis? Exploring the Libraries Installation and Setup Using IPython Numpy Arrays and Vectorized Computation Pandas Library Data Wrangling Data Visualization Data Aggregation Working with Time Series Data Applications of Data Analysis Today The content of this book is all about data analysis with Python programming language using NumPy, Pandas, and IPython. It has been grouped into chapters, with each chapter exploring a different aspect of data analysis. The author has provided Python codes for doing different data analysis tasks. All these codes have been tested to ensure they are working correctly. Corresponding explanations have also been provided alongside each piece of code to help the reader understand the meaning of the various lines of the code. In addition to this, screenshots showing the output that each code should return have been given. The author has used a simple language to make it easy even for beginners to understand. The author begins by exploring the basic to the complex tasks in data analysis.
Categories:

Python For Data Analysis

Python For Data Analysis

This book is the best one for everyone.Get your copy Now!!Why this book? Here are the reasons:The author has explored everything about python for data analysis using pandas, NumPy, Ipython and Matplotlib libraries from the basics.

Author: Samuel Burns

Publisher:

ISBN: 1699031991

Category:

Page: 184

View: 502

You want to learn Python for data analysis using NumPy, Pandas, and IPython, and you don't know how to start? You don't need a big boring and expensive textbook. This book is the best one for everyone.Get your copy Now!!Why this book? Here are the reasons: The author has explored everything about python for data analysis using pandas, NumPy, Ipython and Matplotlib libraries from the basics. A simple language has been used. Many examples have been given, both theoretically and programmatically. Screenshots showing program outputs have been added. The book is written chronologically, in a step-by-step manner.Book Objectives: The Aims and Objectives of the Book: To help you understand why you should choose Python for data analysis tasks. To help you know the various data analysis libraries supported by Python and how to use them. To help you know how to analyze your business data and draw meaningful insights for effective decision making. To equip you with data analysis skills using Python programming language. To help you know where data analysis is applied today and how to use it in your everyday life. Who is this Book is for?: Here are the target readers for this book: Anybody who is a complete beginner to data analysis with Python or data analysis in general. Anybody who wants to advance their data analysis skills with Python programming language. Anybody who wants to know how to use data analysis for the benefit of their business or brand. Professionals in data science, computer programming, computer scientist. Professors, lecturers or tutors who are looking to find better ways to explain python for data analysis to their students in the simplest and easiest way. Students and academicians, especially those focusing on python programming, computer science, neural networks, machine learning, and deep learning. What do you need for this Book?: You are required to have installed the following on your computer: Python 3.X Numpy Pandas Matplotlib The Author guides you on how to install and configure the rest of the Python libraries that are required for data analysis.What is inside the book?: Why Python for Data Analysis? Exploring the Libraries Installation and Setup Using IPython Numpy Arrays and Vectorized Computation Pandas Library Data Wrangling Data Visualization Data Aggregation Working with Time Series Data Applications of Data Analysis Today The content of this book is all about data analysis with Python programming language using NumPy, Pandas, and IPython. It has been grouped into chapters, with each chapter exploring a different aspect of data analysis. The author has provided Python codes for doing different data analysis tasks. All these codes have been tested to ensure they are working correctly. Corresponding explanations have also been provided alongside each piece of code to help the reader understand the meaning of the various lines of the code. In addition to this, screenshots showing the output that each code should return have been given. The author has used a simple language to make it easy even for beginners to understand. The author begins by exploring the basic to the complex tasks in data analysis.
Categories:

Generatives Deep Learning

Generatives Deep Learning

David Foster veranschaulicht die Funktionsweise jeder Methode, beginnend mit den Grundlagen des Deep Learning mit Keras, bevor er zu einigen der modernsten Algorithmen auf diesem Gebiet vorstößt.

Author: David Foster

Publisher:

ISBN: OCLC:1151051275

Category:

Page: 310

View: 833

Generative Modelle haben sich zu einem der spannendsten Themenbereiche der Künstlichen Intelligenz entwickelt: Mit generativem Deep Learning ist es inzwischen möglich, einer Maschine das Malen, Schreiben oder auch das Komponieren von Musik beizubringen - kreative Fähigkeiten, die bisher dem Menschen vorbehalten waren. Mit diesem praxisnahen Buch können Data Scientists einige der eindrucksvollsten generativen Deep-Learning-Modelle nachbilden wie z.B. Generative Adversarial Networks (GANs), Variational Autoencoder (VAEs), Encoder-Decoder- sowie World-Modelle. David Foster veranschaulicht die Funktionsweise jeder Methode, beginnend mit den Grundlagen des Deep Learning mit Keras, bevor er zu einigen der modernsten Algorithmen auf diesem Gebiet vorstößt. Die zahlreichen praktischen Beispiele und Tipps helfen dem Leser herauszufinden, wie seine Modelle noch effizienter lernen und noch kreativer werden können.
Categories:

Python for Data Analysis

Python for Data Analysis

This book is the best one for every readers.Grap your copy now!Why this book?There are several reasons: The author has explored everything about python for data analysis using pandas, NumPy, Ipython and Matplotlib libraries from the basics.

Author: Eric Marston

Publisher:

ISBN: 1077024274

Category:

Page: 160

View: 700

Do you want to learn Python for data analysis using NumPy, Pandas, and IPython ?You don't know how to begin? You don't need a boring and expensive textbook. This book is the best one for every readers.Grap your copy now!Why this book?There are several reasons: The author has explored everything about python for data analysis using pandas, NumPy, Ipython and Matplotlib libraries from the basics. A simple language has been used. Many examples have been given, both theoretically and programmatically. Screenshots showing program outputs have been added.The book is written step-by-step for beginners.Book Objectives: The Aims and Objectives of the Book: To help you understand why you should choose Python for data analysis tasks. To help you know the various data analysis libraries supported by Python and how to use them. To help you know how to analyze your business data and draw meaningful insights for effective decision making. To equip you with data analysis skills using Python programming language. To help you know where data analysis is applied today and how to use it in your everyday life.Who is this Book is for?: Here are the target readers for this book: Anybody who is a complete beginner to data analysis with Python or data analysis in general. Anybody who wants to advance their data analysis skills with Python programming language. Anybody who wants to know how to use data analysis for the benefit of their business or brand. Professionals in data science, computer programming, computer scientist. Professors, lecturers or tutors who are looking to find better ways to explain python for data analysis to their students in the simplest and easiest way. Students and academicians, especially those focusing on python programming, computer science, neural networks, machine learning, and deep learning. What do you need for this Book?: You are required to have installed the following on your computer: Python 3.X Numpy Pandas MatplotlibThe Author guides you on how to install and configure the rest of the Python libraries that are required for data analysis.What is inside the book?: Why Python for Data Analysis? Exploring the Libraries Installation and Setup Using IPython Numpy Arrays and Vectorized Computation Pandas Library Data Wrangling Data Visualization Data Aggregation Working with Time Series Data Applications of Data Analysis Today The content of this book is all about data analysis with Python programming language using NumPy, Pandas, and IPython. It has been grouped into chapters, with each chapter exploring a different aspect of data analysis. The author has provided Python codes for doing different data analysis tasks. All these codes have been tested to ensure they are working correctly. Corresponding explanations have also been provided alongside each piece of code to help the reader understand the meaning of the various lines of the code. In addition to this, screenshots showing the output that each code should return have been given. The author has used a simple language to make it easy even for beginners to understand. The author begins by exploring the basic to the complex tasks in data analysis.
Categories:

Introducing Python

Introducing Python

Scientists are starting to use IPython notebooks to publish their research, including all the code and data used to reach ... The book Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney (O'Reilly) ...

Author: Bill Lubanovic

Publisher: "O'Reilly Media, Inc."

ISBN: 9781449361198

Category: Computers

Page: 484

View: 960

Annotation With 'Introducing Python', Bill Lubanovic brings years of knowledge as a programmer, system administrator and author to a book of impressive depth that's fun to read and simple enough for non-programmers to use. Along with providing a strong foundation in the language itself, Lubanovic shows you how to use Python for a range of applications in business, science and the arts, drawing on the rich collection of open source packages developed by Python fans.
Categories: Computers

Humanities Data Analysis

Humanities Data Analysis

Data Structures for Statistical Computing in Python . ” In Proceedings of the 9th Python in Science Conference , 51–56 . 2012b . Python for Data Analysis : Data Wrangling with Pandas , NumPy , and IPython . 1st ed .

Author: Folgert Karsdorp

Publisher: Princeton University Press

ISBN: 9780691200330

Category: Computers

Page: 360

View: 844

A practical guide to data-intensive humanities research using the Python programming language The use of quantitative methods in the humanities and related social sciences has increased considerably in recent years, allowing researchers to discover patterns in a vast range of source materials. Despite this growth, there are few resources addressed to students and scholars who wish to take advantage of these powerful tools. Humanities Data Analysis offers the first intermediate-level guide to quantitative data analysis for humanities students and scholars using the Python programming language. This practical textbook, which assumes a basic knowledge of Python, teaches readers the necessary skills for conducting humanities research in the rapidly developing digital environment. The book begins with an overview of the place of data science in the humanities, and proceeds to cover data carpentry: the essential techniques for gathering, cleaning, representing, and transforming textual and tabular data. Then, drawing from real-world, publicly available data sets that cover a variety of scholarly domains, the book delves into detailed case studies. Focusing on textual data analysis, the authors explore such diverse topics as network analysis, genre theory, onomastics, literacy, author attribution, mapping, stylometry, topic modeling, and time series analysis. Exercises and resources for further reading are provided at the end of each chapter. An ideal resource for humanities students and scholars aiming to take their Python skills to the next level, Humanities Data Analysis illustrates the benefits that quantitative methods can bring to complex research questions. Appropriate for advanced undergraduates, graduate students, and scholars with a basic knowledge of Python Applicable to many humanities disciplines, including history, literature, and sociology Offers real-world case studies using publicly available data sets Provides exercises at the end of each chapter for students to test acquired skills Emphasizes visual storytelling via data visualizations
Categories: Computers