Python Data Visualization Cookbook
This book is written in a Cookbook style targeted towards an advanced audience. It covers the advanced topics of data visualization in Python.Python Data Visualization Cookbook is for developers that already know about Python programming in general. If you have heard about data visualization but you...
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| Médium: | E-kniha |
| Jazyk: | angličtina |
| Vydáno: |
Birmingham
Packt Publishing, Limited
2013
Packt Publishing |
| Vydání: | 1 |
| Témata: | |
| ISBN: | 9781782163367, 1782163360 |
| On-line přístup: | Získat plný text |
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| Abstract | This book is written in a Cookbook style targeted towards an advanced audience. It covers the advanced topics of data visualization in Python.Python Data Visualization Cookbook is for developers that already know about Python programming in general. If you have heard about data visualization but you don't know where to start, then this book will guide you from the start and help you understand data, data formats, data visualization, and how to use Python to visualize data.You will need to know some general programming concepts, and any kind of programming experience will be helpful, but the code in this book is explained almost line by line. You don't need maths for this book, every concept that is introduced is thoroughly explained in plain English, and references are available for further interest in the topic. |
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| AbstractList | In DetailToday, data visualization is a hot topic as a direct result of the vast amount of data created every second. Transforming that data into information is a complex task for data visualization professionals, who, at the same time, try to understand the data and objectively transfer that understanding to others. This book is a set of practical recipes that strive to help the reader get a firm grasp of the area of data visualization using Python and its popular visualization and data libraries.Python Data Visualization Cookbook will progress the reader from the point of installing and setting up a Python environment for data manipulation and visualization all the way to 3D animations using Python libraries. Readers will benefit from over 60 precise and reproducible recipes that guide the reader towards a better understanding of data concepts and the building blocks for subsequent and sometimes more advanced concepts.Python Data Visualization Cookbook starts by showing you how to set up matplotlib and the related libraries that are required for most parts of the book, before moving on to discuss some of the lesser-used diagrams and charts such as Gantt Charts or Sankey diagrams. During the book, we go from simple plots and charts to more advanced ones, thoroughly explaining why we used them and how not to use them. As we go through the book, we will also discuss 3D diagrams. We will peep into animations just to show you what it takes to go into that area. Maps are irreplaceable for displaying geo-spatial data, so we also show you how to build them. In the last chapter, we show you how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python.This book will help those who already know how to program in Python to explore a new field - one of data visualization. As this book is all about recipes that explain how to do something, code samples are abundant, and they are followed by visual diagrams and charts to help you understand the logic and compare your own results with what is explained in the book.ApproachThis book is written in a Cookbook style targeted towards an advanced audience. It covers the advanced topics of data visualization in Python.Who this book is forPython Data Visualization Cookbook is for developers that already know about Python programming in general. If you have heard about data visualization but you don't know where to start, then this book will guide you from the start and help you understand data, data formats, data visualization, and how to use Python to visualize data.You will need to know some general programming concepts, and any kind of programming experience will be helpful, but the code in this book is explained almost line by line. You don't need maths for this book, every concept that is introduced is thoroughly explained in plain English, and references are available for further interest in the topic. This book is written in a Cookbook style targeted towards an advanced audience. It covers the advanced topics of data visualization in Python.Python Data Visualization Cookbook is for developers that already know about Python programming in general. If you have heard about data visualization but you don't know where to start, then this book will guide you from the start and help you understand data, data formats, data visualization, and how to use Python to visualize data.You will need to know some general programming concepts, and any kind of programming experience will be helpful, but the code in this book is explained almost line by line. You don't need maths for this book, every concept that is introduced is thoroughly explained in plain English, and references are available for further interest in the topic. |
| Author | Milovanovic, Igor |
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| Snippet | This book is written in a Cookbook style targeted towards an advanced audience. It covers the advanced topics of data visualization in Python.Python Data... In DetailToday, data visualization is a hot topic as a direct result of the vast amount of data created every second. Transforming that data into information... |
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| SubjectTerms | Python (Computer program language) Scripting languages (Computer science) |
| TableOfContents | Filling an under-plot area -- Drawing polar plots -- Visualizing the file system tree using a polar bar -- Chapter 5 : Making 3D Visualizations -- Introduction -- Creating 3D bars -- Creating 3D histograms -- Animating in matplotlib -- Animating with OpenGL -- Chapter 6 : Plotting Charts with Images and Maps -- Introduction -- Processing images with PIL -- Plotting with images -- Displaying image with other plots in the figure -- Plotting data on a map using Basemap -- Plotting data on a map using Google Map API -- Generating CAPTCHA images -- Chapter 7 : Using Right Plots to Understand Data -- Introduction -- Understanding logarithmic plots -- Understanding spectrograms -- Creating a stem plot -- Drawing streamlines of vector flow -- Using colormaps -- Using scatter plots and histograms -- Plotting the cross-correlation between two variables -- Importance of autocorrelation -- Chapter 8 : More on Matplotlib Gems -- Introduction -- Drawing barbs -- Making a box and whisker plot -- Making Gantt charts -- Making errorbars -- Making use of text and font properties -- Rendering text with LaTeX -- Understanding the difference between pyplot and OO API -- Index Cover -- Copyright -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: Preparing Your Working Environment -- Introduction -- Installing matplotlib, NumPy, and SciPy -- Installing virtualenv and virtualenvwrapper -- Installing matplotlib on Mac OS X -- Installing matplotlib on Windows -- Installing Python Imaging Library (PIL) for image processing -- Installing a requests module -- Customizing matplotlib's parameters in code -- Customizing matplotlib's parameters per project -- Chapter 2 : Knowing Your Data -- Introduction -- Importing data from CSV -- Importing data from Microsoft Excel files -- Importing data from fixed-width data files -- Importing data from tab-delimited files -- Importing data from a JSON resource -- Exporting data to JSON, CSV, and Excel -- Importing data from a database -- Cleaning up data from outliers -- Reading files in chunks -- Reading streaming data sources -- Importing image data into NumPy arrays -- Generating controlled random datasets -- Smoothing the noise in real-world data -- Chapter 3 : Drawing Your First Plots and Customizing Them -- Introduction -- Defining plot types - bar, line, and stacked charts -- Drawing simple sine and cosine plot -- Defining axis lengths and limits -- Defining plot line styles, properties, and format strings -- Setting ticks, labels, and grids -- Adding legend and annotations -- Moving spines to the center -- Making histograms -- Making bar charts with error bars -- Making pie charts count -- Plotting with filled areas -- Drawing scatter plots with colored markers -- Chapter 4 : More Plots and Customizations -- Introduction -- Setting the transparency and size of axis labels -- Adding a shadow to the chart line -- Adding a data table to the figure -- Using subplots -- Customizing grids -- Creating contour plots |
| Title | Python Data Visualization Cookbook |
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