Python data visualization cookbook :: over 70 recipes, based on the principal concepts of data visualization, to get you started with popular Python libraries /
Over 70 recipes to get you started with popular Python libraries based on the principal concepts of data visualization About This Book Learn how to set up an optimal Python environment for data visualization Understand how to import, clean and organize your data Determine different approaches to dat...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
2015.
|
| Ausgabe: | Second edition. |
| Schriftenreihe: | Quick answers to common problems.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Over 70 recipes to get you started with popular Python libraries based on the principal concepts of data visualization About This Book Learn how to set up an optimal Python environment for data visualization Understand how to import, clean and organize your data Determine different approaches to data visualization and how to choose the most appropriate for your needs Who This Book Is For If you already know about Python programming and want to understand data, data formats, data visualization, and how to use Python to visualize data then this book is for you. What You Will Learn Introduce yourself to the essential tooling to set up your working environment Explore your data using the capabilities of standard Python Data Library and Panda Library Draw your first chart and customize it Use the most popular data visualization Python libraries Make 3D visualizations mainly using mplot3d Create charts with images and maps Understand the most appropriate charts to describe your data Know the matplotlib hidden gems Use plot.ly to share your visualization online In Detail 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 will 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 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. Initially it uses simple plots and charts to more advanced ones, to make it easy to understand for readers. As the readers will go through the book, they will get to know about the 3D diagrams and animations. Maps are irreplaceable for displaying geo-spatial data, so this book will also show how to build them. In the last chapter, it includes explanation on how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python. Style and approach A step-by-step recipe based approach to data visualization. The topics are explained sequentially as cookbook recipes consisting of a code snippet and the resulting visualization. |
| Beschreibung: | Includes index. |
| Beschreibung: | 1 online resource : illustrations |
| ISBN: | 9781784394943 1784394947 1523106239 9781523106233 |
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| 100 | 1 | |a Milovanović, Igor, |e author. | |
| 245 | 1 | 0 | |a Python data visualization cookbook : |b over 70 recipes, based on the principal concepts of data visualization, to get you started with popular Python libraries / |c Igor Milovanović, Giuseppe Vettigli, Dimitry Foures. |
| 250 | |a Second edition. | ||
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2015. | |
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| 500 | |a Includes index. | ||
| 520 | |a Over 70 recipes to get you started with popular Python libraries based on the principal concepts of data visualization About This Book Learn how to set up an optimal Python environment for data visualization Understand how to import, clean and organize your data Determine different approaches to data visualization and how to choose the most appropriate for your needs Who This Book Is For If you already know about Python programming and want to understand data, data formats, data visualization, and how to use Python to visualize data then this book is for you. What You Will Learn Introduce yourself to the essential tooling to set up your working environment Explore your data using the capabilities of standard Python Data Library and Panda Library Draw your first chart and customize it Use the most popular data visualization Python libraries Make 3D visualizations mainly using mplot3d Create charts with images and maps Understand the most appropriate charts to describe your data Know the matplotlib hidden gems Use plot.ly to share your visualization online In Detail 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 will 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 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. Initially it uses simple plots and charts to more advanced ones, to make it easy to understand for readers. As the readers will go through the book, they will get to know about the 3D diagrams and animations. Maps are irreplaceable for displaying geo-spatial data, so this book will also show how to build them. In the last chapter, it includes explanation on how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python. Style and approach A step-by-step recipe based approach to data visualization. The topics are explained sequentially as cookbook recipes consisting of a code snippet and the resulting visualization. | ||
| 505 | 0 | |a Cover -- Copyright -- Credits -- About the Authors -- About the Reviewer -- 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 and manipulating data with Pandas -- 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 plots -- Defining axis lengths and limits -- Defining plot line styles, properties, and format strings -- Setting ticks, labels, and grids -- Adding legends and annotations -- Moving spines to the center -- Making histograms -- Making bar charts with error bars -- Making pie charts count -- Plotting with filled areas -- Making stacked plots -- 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. | |
| 505 | 8 | |a Adding a data table to the figure -- Using subplots -- Customizing grids -- Creating contour plots -- Filling an under-plot area -- Drawing polar plots -- Visualizing the filesystem tree using a polar bar -- Customizing matplotlib with style -- 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 images with other plots in the figure -- Plotting data on a map using Basemap -- Plotting data on a map using the Google Map API -- Generating CAPTCHA images -- Chapter 7: Using the Right Plots to Understand Data -- Introduction -- Understanding logarithmic plots -- Understanding spectrograms -- Creating 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 a whisker plot -- Making Gantt charts -- Making error bars -- Making use of text and font properties -- Rendering text with LaTeX -- Understanding the difference between pyplot and OO API -- Chapter 9: Visualizations in the Clouds with Plot.ly -- Introduction -- Creating line charts -- Creating bar charts -- Plotting a 3D trefoil knot -- Visualizing maps and bubbles -- Index. | |
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| 700 | 1 | |a Vettigli, Giuseppe, |e author. | |
| 700 | 1 | |a Foures, Dimitry, |e author. | |
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| contents | Cover -- Copyright -- Credits -- About the Authors -- About the Reviewer -- 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 and manipulating data with Pandas -- 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 plots -- Defining axis lengths and limits -- Defining plot line styles, properties, and format strings -- Setting ticks, labels, and grids -- Adding legends and annotations -- Moving spines to the center -- Making histograms -- Making bar charts with error bars -- Making pie charts count -- Plotting with filled areas -- Making stacked plots -- 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 -- Filling an under-plot area -- Drawing polar plots -- Visualizing the filesystem tree using a polar bar -- Customizing matplotlib with style -- 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 images with other plots in the figure -- Plotting data on a map using Basemap -- Plotting data on a map using the Google Map API -- Generating CAPTCHA images -- Chapter 7: Using the Right Plots to Understand Data -- Introduction -- Understanding logarithmic plots -- Understanding spectrograms -- Creating 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 a whisker plot -- Making Gantt charts -- Making error bars -- Making use of text and font properties -- Rendering text with LaTeX -- Understanding the difference between pyplot and OO API -- Chapter 9: Visualizations in the Clouds with Plot.ly -- Introduction -- Creating line charts -- Creating bar charts -- Plotting a 3D trefoil knot -- Visualizing maps and bubbles -- Index. |
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tag="520" ind1=" " ind2=" "><subfield code="a">Over 70 recipes to get you started with popular Python libraries based on the principal concepts of data visualization About This Book Learn how to set up an optimal Python environment for data visualization Understand how to import, clean and organize your data Determine different approaches to data visualization and how to choose the most appropriate for your needs Who This Book Is For If you already know about Python programming and want to understand data, data formats, data visualization, and how to use Python to visualize data then this book is for you. What You Will Learn Introduce yourself to the essential tooling to set up your working environment Explore your data using the capabilities of standard Python Data Library and Panda Library Draw your first chart and customize it Use the most popular data visualization Python libraries Make 3D visualizations mainly using mplot3d Create charts with images and maps Understand the most appropriate charts to describe your data Know the matplotlib hidden gems Use plot.ly to share your visualization online In Detail 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 will 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 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. Initially it uses simple plots and charts to more advanced ones, to make it easy to understand for readers. As the readers will go through the book, they will get to know about the 3D diagrams and animations. Maps are irreplaceable for displaying geo-spatial data, so this book will also show how to build them. In the last chapter, it includes explanation on how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python. Style and approach A step-by-step recipe based approach to data visualization. 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labels -- Adding a shadow to the chart line.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Adding a data table to the figure -- Using subplots -- Customizing grids -- Creating contour plots -- Filling an under-plot area -- Drawing polar plots -- Visualizing the filesystem tree using a polar bar -- Customizing matplotlib with style -- 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 images with other plots in the figure -- Plotting data on a map using Basemap -- Plotting data on a map using the Google Map API -- Generating CAPTCHA images -- Chapter 7: Using the Right Plots to Understand Data -- Introduction -- Understanding logarithmic plots -- Understanding spectrograms -- Creating stem plot -- Drawing streamlines of 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| id | ZDB-4-EBA-ocn932304549 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:26:57Z |
| institution | BVB |
| isbn | 9781784394943 1784394947 1523106239 9781523106233 |
| language | English |
| oclc_num | 932304549 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Packt Publishing, |
| record_format | marc |
| series | Quick answers to common problems. |
| series2 | Quick answers to common problems |
| spelling | Milovanović, Igor, author. Python data visualization cookbook : over 70 recipes, based on the principal concepts of data visualization, to get you started with popular Python libraries / Igor Milovanović, Giuseppe Vettigli, Dimitry Foures. Second edition. Birmingham, UK : Packt Publishing, 2015. 1 online resource : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier text file Quick answers to common problems Online resource; title from cover page (Safari, viewed December 15, 2015). Includes index. Over 70 recipes to get you started with popular Python libraries based on the principal concepts of data visualization About This Book Learn how to set up an optimal Python environment for data visualization Understand how to import, clean and organize your data Determine different approaches to data visualization and how to choose the most appropriate for your needs Who This Book Is For If you already know about Python programming and want to understand data, data formats, data visualization, and how to use Python to visualize data then this book is for you. What You Will Learn Introduce yourself to the essential tooling to set up your working environment Explore your data using the capabilities of standard Python Data Library and Panda Library Draw your first chart and customize it Use the most popular data visualization Python libraries Make 3D visualizations mainly using mplot3d Create charts with images and maps Understand the most appropriate charts to describe your data Know the matplotlib hidden gems Use plot.ly to share your visualization online In Detail 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 will 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 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. Initially it uses simple plots and charts to more advanced ones, to make it easy to understand for readers. As the readers will go through the book, they will get to know about the 3D diagrams and animations. Maps are irreplaceable for displaying geo-spatial data, so this book will also show how to build them. In the last chapter, it includes explanation on how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python. Style and approach A step-by-step recipe based approach to data visualization. The topics are explained sequentially as cookbook recipes consisting of a code snippet and the resulting visualization. Cover -- Copyright -- Credits -- About the Authors -- About the Reviewer -- 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 and manipulating data with Pandas -- 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 plots -- Defining axis lengths and limits -- Defining plot line styles, properties, and format strings -- Setting ticks, labels, and grids -- Adding legends and annotations -- Moving spines to the center -- Making histograms -- Making bar charts with error bars -- Making pie charts count -- Plotting with filled areas -- Making stacked plots -- 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 -- Filling an under-plot area -- Drawing polar plots -- Visualizing the filesystem tree using a polar bar -- Customizing matplotlib with style -- 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 images with other plots in the figure -- Plotting data on a map using Basemap -- Plotting data on a map using the Google Map API -- Generating CAPTCHA images -- Chapter 7: Using the Right Plots to Understand Data -- Introduction -- Understanding logarithmic plots -- Understanding spectrograms -- Creating 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 a whisker plot -- Making Gantt charts -- Making error bars -- Making use of text and font properties -- Rendering text with LaTeX -- Understanding the difference between pyplot and OO API -- Chapter 9: Visualizations in the Clouds with Plot.ly -- Introduction -- Creating line charts -- Creating bar charts -- Plotting a 3D trefoil knot -- Visualizing maps and bubbles -- Index. Information visualization. http://id.loc.gov/authorities/subjects/sh2002000243 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Visualisation de l'information. Python (Langage de programmation) COMPUTERS Data Visualization. bisacsh COMPUTERS Programming Languages Python. bisacsh Information visualization fast Python (Computer program language) fast Vettigli, Giuseppe, author. Foures, Dimitry, author. has work: Python data visualization cookbook (Text) https://id.oclc.org/worldcat/entity/E39PD3BR9qDpbjJWvWrDwt4VT3 https://id.oclc.org/worldcat/ontology/hasWork Erscheint auch als: Druck-Ausgabe Igor Milovanovi?, Dimitry Foures, Giuseppe Vettigli. Python Data Visualization Cookbook - Second Edition Quick answers to common problems. http://id.loc.gov/authorities/names/no2015091434 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1104919 Volltext |
| spellingShingle | Milovanović, Igor Vettigli, Giuseppe Foures, Dimitry Python data visualization cookbook : over 70 recipes, based on the principal concepts of data visualization, to get you started with popular Python libraries / Quick answers to common problems. Cover -- Copyright -- Credits -- About the Authors -- About the Reviewer -- 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 and manipulating data with Pandas -- 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 plots -- Defining axis lengths and limits -- Defining plot line styles, properties, and format strings -- Setting ticks, labels, and grids -- Adding legends and annotations -- Moving spines to the center -- Making histograms -- Making bar charts with error bars -- Making pie charts count -- Plotting with filled areas -- Making stacked plots -- 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 -- Filling an under-plot area -- Drawing polar plots -- Visualizing the filesystem tree using a polar bar -- Customizing matplotlib with style -- 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 images with other plots in the figure -- Plotting data on a map using Basemap -- Plotting data on a map using the Google Map API -- Generating CAPTCHA images -- Chapter 7: Using the Right Plots to Understand Data -- Introduction -- Understanding logarithmic plots -- Understanding spectrograms -- Creating 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 a whisker plot -- Making Gantt charts -- Making error bars -- Making use of text and font properties -- Rendering text with LaTeX -- Understanding the difference between pyplot and OO API -- Chapter 9: Visualizations in the Clouds with Plot.ly -- Introduction -- Creating line charts -- Creating bar charts -- Plotting a 3D trefoil knot -- Visualizing maps and bubbles -- Index. Information visualization. http://id.loc.gov/authorities/subjects/sh2002000243 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Visualisation de l'information. Python (Langage de programmation) COMPUTERS Data Visualization. bisacsh COMPUTERS Programming Languages Python. bisacsh Information visualization fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2002000243 http://id.loc.gov/authorities/subjects/sh96008834 |
| title | Python data visualization cookbook : over 70 recipes, based on the principal concepts of data visualization, to get you started with popular Python libraries / |
| title_auth | Python data visualization cookbook : over 70 recipes, based on the principal concepts of data visualization, to get you started with popular Python libraries / |
| title_exact_search | Python data visualization cookbook : over 70 recipes, based on the principal concepts of data visualization, to get you started with popular Python libraries / |
| title_full | Python data visualization cookbook : over 70 recipes, based on the principal concepts of data visualization, to get you started with popular Python libraries / Igor Milovanović, Giuseppe Vettigli, Dimitry Foures. |
| title_fullStr | Python data visualization cookbook : over 70 recipes, based on the principal concepts of data visualization, to get you started with popular Python libraries / Igor Milovanović, Giuseppe Vettigli, Dimitry Foures. |
| title_full_unstemmed | Python data visualization cookbook : over 70 recipes, based on the principal concepts of data visualization, to get you started with popular Python libraries / Igor Milovanović, Giuseppe Vettigli, Dimitry Foures. |
| title_short | Python data visualization cookbook : |
| title_sort | python data visualization cookbook over 70 recipes based on the principal concepts of data visualization to get you started with popular python libraries |
| title_sub | over 70 recipes, based on the principal concepts of data visualization, to get you started with popular Python libraries / |
| topic | Information visualization. http://id.loc.gov/authorities/subjects/sh2002000243 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Visualisation de l'information. Python (Langage de programmation) COMPUTERS Data Visualization. bisacsh COMPUTERS Programming Languages Python. bisacsh Information visualization fast Python (Computer program language) fast |
| topic_facet | Information visualization. Python (Computer program language) Visualisation de l'information. Python (Langage de programmation) COMPUTERS Data Visualization. COMPUTERS Programming Languages Python. Information visualization |
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