Learning IPython for Interactive Computing and Data Visualization - Second Edition
eBook - ePub

Learning IPython for Interactive Computing and Data Visualization - Second Edition

Cyrille Rossant

Buch teilen
  1. 200 Seiten
  2. English
  3. ePUB (handyfreundlich)
  4. Über iOS und Android verfügbar
eBook - ePub

Learning IPython for Interactive Computing and Data Visualization - Second Edition

Cyrille Rossant

Angaben zum Buch
Buchvorschau
Inhaltsverzeichnis
Quellenangaben

Über dieses Buch

Get started with Python for data analysis and numerical computing in the Jupyter notebookAbout This Book• Learn the basics of Python in the Jupyter Notebook• Analyze and visualize data with pandas, NumPy, matplotlib, and seaborn• Perform highly-efficient numerical computations with Numba, Cython, and ipyparallelWho This Book Is ForThis book targets students, teachers, researchers, engineers, analysts, journalists, hobbyists, and all data enthusiasts who are interested in analyzing and visualizing real-world datasets. If you are new to programming and data analysis, this book is exactly for you. If you're already familiar with another language or analysis software, you will also appreciate this introduction to the Python data analysis platform. Finally, there are more technical topics for advanced readers. No prior experience is required; this book contains everything you need to know.What You Will Learn• Install Anaconda and code in Python in the Jupyter Notebook• Load and explore datasets interactively• Perform complex data manipulations effectively with pandas• Create engaging data visualizations with matplotlib and seaborn• Simulate mathematical models with NumPy• Visualize and process images interactively in the Jupyter Notebook with scikit-image• Accelerate your code with Numba, Cython, and IPython.parallel• Extend the Notebook interface with HTML, JavaScript, and D3In DetailPython is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while the Jupyter Notebook is a rich environment well-adapted to data science and visualization. Together, these open source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors.This book is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and the Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in the Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this book, you will be able to perform in-depth analyses of all sorts of data.Style and approachThis is a hands-on beginner-friendly guide to analyze and visualize data on real-world examples with Python and the Jupyter Notebook.

Häufig gestellte Fragen

Wie kann ich mein Abo kündigen?
Gehe einfach zum Kontobereich in den Einstellungen und klicke auf „Abo kündigen“ – ganz einfach. Nachdem du gekündigt hast, bleibt deine Mitgliedschaft für den verbleibenden Abozeitraum, den du bereits bezahlt hast, aktiv. Mehr Informationen hier.
(Wie) Kann ich Bücher herunterladen?
Derzeit stehen all unsere auf Mobilgeräte reagierenden ePub-Bücher zum Download über die App zur Verfügung. Die meisten unserer PDFs stehen ebenfalls zum Download bereit; wir arbeiten daran, auch die übrigen PDFs zum Download anzubieten, bei denen dies aktuell noch nicht möglich ist. Weitere Informationen hier.
Welcher Unterschied besteht bei den Preisen zwischen den Aboplänen?
Mit beiden Aboplänen erhältst du vollen Zugang zur Bibliothek und allen Funktionen von Perlego. Die einzigen Unterschiede bestehen im Preis und dem Abozeitraum: Mit dem Jahresabo sparst du auf 12 Monate gerechnet im Vergleich zum Monatsabo rund 30 %.
Was ist Perlego?
Wir sind ein Online-Abodienst für Lehrbücher, bei dem du für weniger als den Preis eines einzelnen Buches pro Monat Zugang zu einer ganzen Online-Bibliothek erhältst. Mit über 1 Million Büchern zu über 1.000 verschiedenen Themen haben wir bestimmt alles, was du brauchst! Weitere Informationen hier.
Unterstützt Perlego Text-zu-Sprache?
Achte auf das Symbol zum Vorlesen in deinem nächsten Buch, um zu sehen, ob du es dir auch anhören kannst. Bei diesem Tool wird dir Text laut vorgelesen, wobei der Text beim Vorlesen auch grafisch hervorgehoben wird. Du kannst das Vorlesen jederzeit anhalten, beschleunigen und verlangsamen. Weitere Informationen hier.
Ist Learning IPython for Interactive Computing and Data Visualization - Second Edition als Online-PDF/ePub verfügbar?
Ja, du hast Zugang zu Learning IPython for Interactive Computing and Data Visualization - Second Edition von Cyrille Rossant im PDF- und/oder ePub-Format sowie zu anderen beliebten Büchern aus Computer Science & Data Visualisation. Aus unserem Katalog stehen dir über 1 Million Bücher zur Verfügung.

Information

Jahr
2015
ISBN
9781783986996

Learning IPython for Interactive Computing and Data Visualization Second Edition


Table of Contents

Learning IPython for Interactive Computing and Data Visualization Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Support files, eBooks, discount offers, and more
Why subscribe?
Free access for Packt account holders
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
1. Getting Started with IPython
What are Python, IPython, and Jupyter?
Jupyter and IPython
What this book covers
References
Installing Python with Anaconda
Downloading Anaconda
Installing Anaconda
Before you get started...
Opening a terminal
Finding your home directory
Manipulating your system path
Testing your installation
Managing environments
Common conda commands
References
Downloading the notebooks
Introducing the Notebook
Launching the IPython console
Launching the Jupyter Notebook
The Notebook dashboard
The Notebook user interface
Structure of a notebook cell
Markdown cells
Code cells
The Notebook modal interface
Keyboard shortcuts available in both modes
Keyboard shortcuts available in the edit mode
Keyboard shortcuts available in the command mode
References
A crash course on Python
Hello world
Variables
String escaping
Lists
Loops
Indentation
Conditional branches
Functions
Positional and keyword arguments
Passage by assignment
Errors
Object-oriented programming
Functional programming
Python 2 and 3
Going beyond the basics
Ten Jupyter/IPython essentials
Using IPython as an extended shell
Learning magic commands
Mastering tab completion
Writing interactive documents in the Notebook with Markdown
Creating interactive widgets in the Notebook
Running Python scripts from IPython
Introspecting Python objects
Debugging Python code
Benchmarking Python code
Profiling Python code
Summary
2. Interactive Data Analysis with pandas
Exploring a dataset in the Notebook
Provenance of the data
Downloading and loading a dataset
Making plots with matplotlib
Descriptive statistics with pandas and seaborn
Manipulating data
Selecting data
Selecting columns
Selecting rows
Filtering with boolean indexing
Computing with numbers
Working with text
Working with dates and times
Handling missing data
Complex operations
Group-by
Joins
Summary
3. Numerical Computing with NumPy
A primer to vector computing
Multidimensional arrays
The ndarray
Vector operations on ndarrays
How fast are vector computations in NumPy?
How an ndarray is stored in memory
Why operations on ndarrays are fast
Creating and loading arrays
Creating arrays
Loading arrays from files
Basic array manipulations
Computing with NumPy arrays
Selection and indexing
Boolean operations on arrays
Mathematical operations on arrays
A density map with NumPy
Other topics
Summary
4. Interactive Plotting and Graphical Interfaces
Choosing a plotting backend
Inline plots
Exported figures
GUI toolkits
Dynamic inline plots
Web-based visualization
matplotlib and seaborn essentials
Common plots with matplotlib
Customizing matplotlib figures
Interacting with matplotlib figures in the Notebook
High-level plotting with seaborn
Image processing
Further plotting and visualization libraries
High-level plotting
Bokeh
Vincent and Vega
Plotly
Maps and geometry
The matplotlib Basemap toolkit
GeoPandas
Leaflet wrappers: folium and mplleaflet
3D visualization
Mayavi
VisPy
Summary
5. High-Performance and Parallel Computing
Accelerating Python code with Numba
Random walk
Universal functions
Writing C in Python with Cython
Installing Cython and a C compiler for Python
Implementing the Eratosthenes Sieve in Python and Cython
Distributing tasks on several cores with IPython.parallel
Direct interface
Load-balanced interface
Further high-performance computing techniques
MPI
Distributed computing
C/C++ with Python
GPU computing
PyPy
Julia
Summary
6. Customizing IPython
Creating a custom magic command in an IPython extension
Writing a new Jupyter kernel
Displaying rich HTML elements in the Notebook
Displaying SVG in the Notebook
JavaScript and D3 in the Notebook
Customizing the Notebook interface with JavaScript
Summary
Index
2323__perlego__chapter_divider...

Inhaltsverzeichnis