Детальная информация
| Название | The data visualization workshop: a self-paced, practical approach to transforming your complex data into compelling, captivating graphics. — Third edition. |
|---|---|
| Авторы | Döbler Mario. ; Großmann Tim. |
| Коллекция | Электронные книги зарубежных издательств ; Общая коллекция |
| Тематика | Information visualization — Computer programs. ; Python (Computer program language) ; EBSCO eBooks |
| Тип документа | Другой |
| Тип файла | |
| Язык | Английский |
| Права доступа | Доступ по паролю из сети Интернет (чтение, печать, копирование) |
| Ключ записи | on1181993746 |
| Дата создания записи | 06.08.2020 |
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- Cover
- FM
- Copyright
- Table of Contents
- Preface
- Chapter 1: The Importance of Data Visualization and Data Exploration
- Introduction
- Introduction to Data Visualization
- The Importance of Data Visualization
- Data Wrangling
- Tools and Libraries for Visualization
- Overview of Statistics
- Measures of Central Tendency
- Measures of Dispersion
- Correlation
- Types of Data
- Summary Statistics
- NumPy
- Exercise 1.01: Loading a Sample Dataset and Calculating the Mean Using NumPy
- Activity 1.01: Using NumPy to Compute the Mean, Median, Variance, and Standard Deviation of a Dataset
- Basic NumPy Operations
- Indexing
- Slicing
- Splitting
- Iterating
- Exercise 1.02: Indexing, Slicing, Splitting, and Iterating
- Advanced NumPy Operations
- Filtering
- Sorting
- Combining
- Reshaping
- Exercise 1.03: Filtering, Sorting, Combining, and Reshaping
- pandas
- Advantages of pandas over NumPy
- Disadvantages of pandas
- Exercise 1.04 Loading a Sample Dataset and Calculating the Mean using Pandas
- Exercise 1.05: Using pandas to Compute the Mean, Median, and Variance of a Dataset
- Basic Operations of pandas
- Indexing
- Slicing
- Iterating
- Series
- Exercise 1.06: Indexing, Slicing, and Iterating Using pandas
- Advanced pandas Operations
- Filtering
- Sorting
- Reshaping
- Exercise 1.07: Filtering, Sorting, and Reshaping
- Activity 1.02: Forest Fire Size and Temperature Analysis
- Summary
- Introduction
- Chapter 2: All You Need to Know about Plots
- Introduction
- Comparison Plots
- Line Chart
- Uses
- Example
- Design Practices
- Bar Chart
- Use
- Don’ts of Bar Charts
- Examples
- Design Practices
- Radar Chart
- Uses
- Examples
- Design Practices
- Activity 2.01: Employee Skill Comparison
- Line Chart
- Relation Plots
- Scatter Plot
- Uses
- Examples
- Design Practices
- Variants: Scatter Plots with Marginal Histograms
- Examples
- Bubble Plot
- Use
- Example
- Design Practices
- Correlogram
- Examples
- Design Practices
- Heatmap
- Use
- Examples
- Design Practice
- Activity 2.02: Road Accidents Occurring over Two Decades
- Scatter Plot
- Composition Plots
- Pie Chart
- Use
- Examples
- Design Practices
- Variants: Donut Chart
- Design Practice
- Stacked Bar Chart
- Use
- Examples
- Design Practices
- Stacked Area Chart
- Use
- Examples
- Design Practice
- Activity 2.03: Smartphone Sales Units
- Venn Diagram
- Use
- Example
- Design Practice
- Pie Chart
- Distribution Plots
- Histogram
- Use
- Example
- Design Practice
- Density Plot
- Use
- Example
- Design Practice
- Box Plot
- Use
- Examples
- Violin Plot
- Use
- Examples
- Design Practice
- Activity 2.04: Frequency of Trains during Different Time Intervals
- Histogram
- Geoplots
- Dot Map
- Use
- Example
- Design Practices
- Choropleth Map
- Use
- Example
- Design Practices
- Connection Map
- Use
- Examples
- Design Practices
- Dot Map
- What Makes a Good Visualization?
- Common Design Practices
- Activity 2.05: Analyzing Visualizations
- Activity 2.06: Choosing a Suitable Visualization
- Summary
- Chapter 3: A Deep Dive into Matplotlib
- Introduction
- Overview of Plots in Matplotlib
- Pyplot Basics
- Creating Figures
- Closing Figures
- Format Strings
- Plotting
- Plotting Using pandas DataFrames
- Ticks
- Displaying Figures
- Saving Figures
- Exercise 3.01: Creating a Simple Visualization
- Basic Text and Legend Functions
- Labels
- Titles
- Text
- Annotations
- Legends
- Activity 3.01: Visualizing Stock Trends by Using a Line Plot
- Basic Plots
- Bar Chart
- Activity 3.02: Creating a Bar Plot for Movie Comparison
- Pie Chart
- Exercise 3.02: Creating a Pie Chart for Water Usage
- Stacked Bar Chart
- Activity 3.03: Creating a Stacked Bar Plot to Visualize Restaurant Performance
- Stacked Area Chart
- Activity 3.04: Comparing Smartphone Sales Units Using a Stacked Area Chart
- Histogram
- Box Plot
- Activity 3.05: Using a Histogram and a Box Plot to Visualize Intelligence Quotient
- Scatter Plot
- Exercise 3.03: Using a Scatter Plot to Visualize Correlation between Various Animals
- Bubble Plot
- Layouts
- Subplots
- Tight Layout
- Radar Charts
- Exercise 3.04: Working on Radar Charts
- GridSpec
- Activity 3.06: Creating a Scatter Plot with Marginal Histograms
- Images
- Basic Image Operations
- Activity 3.07: Plotting Multiple Images in a Grid
- Writing Mathematical Expressions
- Summary
- Chapter 4: Simplifying Visualizations Using Seaborn
- Introduction
- Advantages of Seaborn
- Controlling Figure Aesthetics
- Seaborn Figure Styles
- Removing Axes Spines
- Controlling the Scale of Plot Elements
- Exercise 4.01: Comparing IQ Scores for Different Test Groups by Using a Box Plot
- Color Palettes
- Categorical Color Palettes
- Sequential Color Palettes
- Diverging Color Palettes
- Exercise 4.02: Surface Temperature Analysis
- Activity 4.01: Using Heatmaps to Find Patterns in Flight Passengers' Data
- Advanced Plots in Seaborn
- Bar Plots
- Activity 4.02: Movie Comparison Revisited
- Kernel Density Estimation
- Plotting Bivariate Distributions
- Visualizing Pairwise Relationships
- Violin Plots
- Activity 4.03: Comparing IQ Scores for Different Test Groups by Using a Violin Plot
- Multi-Plots in Seaborn
- FacetGrid
- Activity 4.04: Visualizing the Top 30 Music YouTube Channels Using Seaborn's FacetGrid
- Regression Plots
- Activity 4.05: Linear Regression for Animal Attribute Relations
- Squarify
- Exercise 4.03: Water Usage Revisited
- Activity 4.06: Visualizing the Impact of Education on Annual Salary and Weekly Working Hours
- Summary
- Introduction
- Chapter 5: Plotting Geospatial Data
- Introduction
- The Design Principles of geoplotlib
- Geospatial Visualizations
- Voronoi Tessellation
- Delaunay Triangulation
- Choropleth Plot
- Exercise 5.01: Plotting Poaching Density Using Dot Density and Histograms
- Activity 5.01: Plotting Geospatial Data on a Map
- The GeoJSON Format
- Exercise 5.02: Creating a Choropleth Plot with GeoJSON Data
- Tile Providers
- Exercise 5.03: Visually Comparing Different Tile Providers
- Custom Layers
- Exercise 5.04: Plotting the Movement of an Aircraft with a Custom Layer
- Activity 5.02: Visualizing City Density by the First Letter Using an Interactive Custom Layer
- Summary
- Introduction
- Chapter 6: Making Things Interactive with Bokeh
- Introduction
- Concepts of Bokeh
- Interfaces in Bokeh
- Output
- Bokeh Server
- Presentation
- Integrating
- Basic Plotting
- Exercise 6.01: Plotting with Bokeh
- Exercise 6.02: Comparing the Plotting and Models Interfaces
- Activity 6.01: Plotting Mean Car Prices of Manufacturers
- Adding Widgets
- Exercise 6.03: Building a Simple Plot Using Basic Interactivity Widgets
- Exercise 6.04: Plotting Stock Price Data in Tabs
- Activity 6.02: Extending Plots with Widgets
- Summary
- Introduction
- Chapter 7: Combining What We Have Learned
- Introduction
- Activity 7.01: Implementing Matplotlib and Seaborn on the New York City Database
- Bokeh
- Activity 7.02: Visualizing Stock Prices with Bokeh
- Geoplotlib
- Activity 7.03: Analyzing Airbnb Data with Geoplotlib
- Summary
- Introduction
- Appendix
- Index
