Interactive data visualization with Python: present your data as an effective and compelling story
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| Main Authors: | , , , |
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| Format: | Book |
| Language: | English |
| Published: |
Birmingham
Packt
April 2020
|
| Edition: | Second edition |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Physical Description: | vii, 332 Seiten Illustrationen, Diagramme, Karten |
| ISBN: | 9781800200944 |
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Table of Contents Preface ¡ Chapter 1 : Introduction to Visualization with Python - Basic and Customized Plotting_ լ Introduction. 2 Handling Data with pandas DataFrame. 3 Reading Data from Files.3 Exercise 1: Reading Data from Files. 3 Observing and Describing Data .4 Exercise 2: Observing and Describing Data.4 Selecting Columns from a DataFrame .8 Adding New Columns to a DataFrame.8 Exercise 3: Adding New Columns to the DataFrame.9 Applying Functions on DataFrame Columns. 10 Exercise 4: Applying Functions on DataFrame columns.11 Exercise 5: Applying Functions on Multiple Columns. 13 Deleting Columns from a DataFrame. 14 Exercise 6: Deleting Columns from a DataFrame .14 Writing a DataFrame to a
File.16 Exercise 7: Writing a DataFrame to a File . 16 Plotting with pandas and seaborn. 18 Creating Simple Plots to Visualize a Distribution of Variables.18 Exercise 8: Plotting and Analyzing a Histogram .19 Bar Plots. 25 Exercise 9: Creating a Bar Plot and Calculating the Mean Price Distribution .25
Exercise 10: Creating Bar Plots Grouped by a Specific Feature . 30 Tweaking Plot Parameters.31 Exercise 11 : Tweaking the Plot Parameters of a Grouped Bar Plot. 32 Annotations. 35 Exercise 12: Annotating a Bar Plot.36 Activity 1: Analyzing Different Scenarios and Generating the Appropriate Visualization .39 Summary.45 Chapter 2: Static Visualization ֊ Global Patterns and Summary Statistics_ 47 introduction.48 Creating Plots that Present Global Patterns in Data.48 Scatter Plots. 49 Exercise 13: Creating a Static Scatter Plot.50 Hexagonal Binning Plots. 51 Exercise 14: Creating a Static Hexagonal Binning Plot. 51 Contour
Plots. 53 Exercise 15: Creating a Static Contour Plot . 53 Line Plots. 54 Exercise 16: Creating a Static Line Plot . 55 Exercise 17: Presenting Data across Time with multiple Line Plots. 58 Heatmaps. 60 Exercise 18: Creating and Exploring a Static Heatmap . 60 The Concept of Linkage in Heatmaps. 66 Exercise 19: Creating Linkage in Static Heatmaps. ,. 66 Creating Plots That Present Summary Statistics of Your Data . 71 Histogram Revisited .71 Example 1: Histogram Revisited . 72
Box Plots . 73 Exercise 20: Creating and Exploring a Static Box Plot. 73 Violin Plots. 76 Exercise 21: Creating a Static Violin Plot.77 Activity 2: Design Static Visualization to Present Global Patterns and Summary Statistics . 78 Summary. 83 Chapter 3: From Static to Interactive Visualization_ 85 Introduction .86 Static versus Interactive Visualization . 88 Applications of Interactive Data Visualizations . 93 Getting Started with Interactive Data Visualizations. 95 Interactive Data Visualization with Bokeh . 98 Exercise 22: Preparing Our Dataset.99 Exercise 23: Creating the Base Static Plot for an Interactive Data Visualization. 104 Exercise 24:
Adding a Slider to the Static Plot. 107 Exercise 25: Adding a Hover Tool. 108 Interactive Data Visualization with Plotły Express . 113 Exercise 26: Creating an Interactive Scatter Plot. 113 Activity 3: Creating Different Interactive Visualizations Using Plotły Express. 117 Summary. 119 Chapter 4: Interactive Visualization of Data across Strata 121 Introduction. 122 Interactive Scatter Plots. 122 Exercise 27: Adding Zoom-In and Zoom-Out to a Static Scatter Plot . 124 Exercise 28: Adding Hover and Tooltip Functionality to a Scatter Plot. 127
Exercise 29: Exploring Select and Highlight Functionality on a Scatter Plot. 130 Exercise 30: Generating a Plot with Selection, Zoom, and Hover/Tooltip Functions. 133 Selection across Multiple Plots . 136 Exercise 31: Selection across Multiple Plots. 137 Selection Based on the Values of a Feature. 140 Exercise 32: Selection Based on the Values of a Feature. 141 Other Interactive Plots in altair . 143 Exercise 33: Adding a Zoom-In and Zoom-Out Feature and Calculating the Mean on a Static Bar Plot . 144 Exercise 34: An Alternative Shortcut for Representing the Mean on a Bar Plot. 150 Exercise 35: Adding a Zoom Feature on a Static Heatmap. 153 Exercise 36: Creating a Bar Plot and a Heatmap Next to Each Other. 157 Exercise 37: Dynamically Linkinga Bar Plot and a Heatmap . 160 Activity 4: Generate a Bar Plot and a Heatmap to Represent Content Rating Types in the Google Play Store Apps Dataset. 163
Summary.166 Chapter 5: Interactive Visualization of Data across Time 169 Introduction. 170 Temporal Data.170 Types of Temporal Data.171 Why Study Temporal Visualization? . 172 Understanding the Relation between Temporal Data and Time-Series Data . 174 Examples of Domains That Use Temporal Data . 175 Visualization of Temporal Data. 176 How Time-Series Data Is Manipulated and Visualized. 179
Date/Time Manipulation in pandas. 181 Building a DateTime Index. 182 Choosing the Right Aggregation Level for Temporal Data . 183 Exercise 38: Creating a Static Bar Plot and Calculating the Mean and Standard Deviation in Temporal Data . 185 Exercise 39: Calculating zscore to Find Outliers in Temporal Data . 190 Resampling in Temporal Data.194 Common Pitfalls of Upsampling and Downsampling. 194 Exercise 40: Upsampling and Downsampling in Temporal Data . 194 Using shift and tshift to Introduce a Lag in Time-Series Data. 199 Exercise 41: Using shift and tshift to Shift Time in Data. 199 Autocorrelation in Time Series. 201 Interactive Temporal Visualization. 203 Bokeh Basics. 204 Advantages of Using Bokeh. 204 Exercise 42: Adding Interactivity to Static Line Plots Using Bokeh. 206 Exercise 43: Changing the Line Color and Width on a Line Plot. 208 Exercise 44: Adding Box Annotations to Find Anomalies in a Dataset. 210
Interactivity in Bokeh . 212 Activity 5: Create an Interactive Temporal Visualization . 214 Summary. 215 Chapter 6: Interactive Visualization of Geographical Data 217 Introduction.218 Choropleth Maps . 218 Worldwide Choropleth Maps . 219 Exercise 45: Creating a Worldwide Choropleth Map. 220 Exercise 46: Tweaking a Worldwide Choropleth Map . 223 Exercise 47: Adding Animation to a Choropleth Map. 227
USA State Maps. 231 Exercise 48: Creating a USA State Choropleth Map. 232 Plots on Geographical Maps. 235 Scatter Plots. 235 Exercise 49: Creating a Scatter Plot on a Geographical Map. 235 Bubble Plots. 237 Exercise 50: Creating a Bubble Plot on a Geographical Map. 238 Line Plots on Geographical Maps. 244 Exercise 51: Creating Line Plots on a Geographical Map. 245 Activity 6: Creating a Choropleth Map to Represent Total Renewable Energy Production and Consumption across the World . 250 Summary. 255 Chapter 7: Avoiding Common Pitfalls to Create Interactive Visualizations_ 257 Introduction. 258 Data Formatting and Interpretation . 258 Avoiding Common Pitfalls while Dealing with Dirty Data. 259
Outliers. 259 Exercise 52: Visualizing Outliers in a Dataset with a Box Plot. 261 Exercise 53: Dealing with Outliers . 266 Missing Data . 269 Exercise 54: Dealing with Missing Values. 269 Duplicate Instances and/or Features. 275 Bad Feature Selection . 276 Activity 7: Determining Which Features to Visualize on a Scatter Plot. 276
Data Visualization . 279 Choosing a Visualization . 279 Common Pitfalls While Visualizing Data. 282 Exercise 55: Creating a Confusing Visualization. 283 Activity 8: Creating a Bar Graph for Improving a Visualization. 286 Cheat Sheet for the Visualization Process.288 Summary. 290 Appendix_293 Index 335
Interactive Data Visualization with Python - Second Edition With so much data being continuously generated, developers, who can present data as impactful and interesting visualizations, are always in demand. Interactive Data Visualization with Python, Second Edition sharpens your data exploration skills, tells you everything there is to know about interactive data visualization in Python. You'll begin by learning how to draw various plots with Matplotlib and Seaborn, the non-interactive data visualization libraries. You'll study different types of visualizations, compare them, and find out how to select a particular type of visualization to suit your requirements. After you get a hang of the various non-interactive visualization librarie you'll learn the principles of intuitive and persuasive data visualization, and use Boke and Plotły to transform your visuals into strong stories. You'll also gain insight into how interactive data and model visualization can optimize the performance of a regression model. By the end of the course, you'll have a new skill set that'll make you the go-to person for transforming data visualizations into engaging and interesting stories. Things you will learn: • Explore and apply different interactive data visualization techniques • Design data visualizations using interactive libraries • Manipulate plotting parameters and styles to create appealing plots • Use Matplotlib, Seaborn, Altair and Bokeh for drawing appealing plots • Customize data visualization for different audiences • Customize data visualization for different scenarios |
| adam_txt |
Table of Contents Preface ¡ Chapter 1 : Introduction to Visualization with Python - Basic and Customized Plotting_ լ Introduction. 2 Handling Data with pandas DataFrame. 3 Reading Data from Files.3 Exercise 1: Reading Data from Files. 3 Observing and Describing Data .4 Exercise 2: Observing and Describing Data.4 Selecting Columns from a DataFrame .8 Adding New Columns to a DataFrame.8 Exercise 3: Adding New Columns to the DataFrame.9 Applying Functions on DataFrame Columns. 10 Exercise 4: Applying Functions on DataFrame columns.11 Exercise 5: Applying Functions on Multiple Columns. 13 Deleting Columns from a DataFrame. 14 Exercise 6: Deleting Columns from a DataFrame .14 Writing a DataFrame to a
File.16 Exercise 7: Writing a DataFrame to a File . 16 Plotting with pandas and seaborn. 18 Creating Simple Plots to Visualize a Distribution of Variables.18 Exercise 8: Plotting and Analyzing a Histogram .19 Bar Plots. 25 Exercise 9: Creating a Bar Plot and Calculating the Mean Price Distribution .25
Exercise 10: Creating Bar Plots Grouped by a Specific Feature . 30 Tweaking Plot Parameters.31 Exercise 11 : Tweaking the Plot Parameters of a Grouped Bar Plot. 32 Annotations. 35 Exercise 12: Annotating a Bar Plot.36 Activity 1: Analyzing Different Scenarios and Generating the Appropriate Visualization .39 Summary.45 Chapter 2: Static Visualization ֊ Global Patterns and Summary Statistics_ 47 introduction.48 Creating Plots that Present Global Patterns in Data.48 Scatter Plots. 49 Exercise 13: Creating a Static Scatter Plot.50 Hexagonal Binning Plots. 51 Exercise 14: Creating a Static Hexagonal Binning Plot. 51 Contour
Plots. 53 Exercise 15: Creating a Static Contour Plot . 53 Line Plots. 54 Exercise 16: Creating a Static Line Plot . 55 Exercise 17: Presenting Data across Time with multiple Line Plots. 58 Heatmaps. 60 Exercise 18: Creating and Exploring a Static Heatmap . 60 The Concept of Linkage in Heatmaps. 66 Exercise 19: Creating Linkage in Static Heatmaps. ,. 66 Creating Plots That Present Summary Statistics of Your Data . 71 Histogram Revisited .71 Example 1: Histogram Revisited . 72
Box Plots . 73 Exercise 20: Creating and Exploring a Static Box Plot. 73 Violin Plots. 76 Exercise 21: Creating a Static Violin Plot.77 Activity 2: Design Static Visualization to Present Global Patterns and Summary Statistics . 78 Summary. 83 Chapter 3: From Static to Interactive Visualization_ 85 Introduction .86 Static versus Interactive Visualization . 88 Applications of Interactive Data Visualizations . 93 Getting Started with Interactive Data Visualizations. 95 Interactive Data Visualization with Bokeh . 98 Exercise 22: Preparing Our Dataset.99 Exercise 23: Creating the Base Static Plot for an Interactive Data Visualization. 104 Exercise 24:
Adding a Slider to the Static Plot. 107 Exercise 25: Adding a Hover Tool. 108 Interactive Data Visualization with Plotły Express . 113 Exercise 26: Creating an Interactive Scatter Plot. 113 Activity 3: Creating Different Interactive Visualizations Using Plotły Express. 117 Summary. 119 Chapter 4: Interactive Visualization of Data across Strata 121 Introduction. 122 Interactive Scatter Plots. 122 Exercise 27: Adding Zoom-In and Zoom-Out to a Static Scatter Plot . 124 Exercise 28: Adding Hover and Tooltip Functionality to a Scatter Plot. 127
Exercise 29: Exploring Select and Highlight Functionality on a Scatter Plot. 130 Exercise 30: Generating a Plot with Selection, Zoom, and Hover/Tooltip Functions. 133 Selection across Multiple Plots . 136 Exercise 31: Selection across Multiple Plots. 137 Selection Based on the Values of a Feature. 140 Exercise 32: Selection Based on the Values of a Feature. 141 Other Interactive Plots in altair . 143 Exercise 33: Adding a Zoom-In and Zoom-Out Feature and Calculating the Mean on a Static Bar Plot . 144 Exercise 34: An Alternative Shortcut for Representing the Mean on a Bar Plot. 150 Exercise 35: Adding a Zoom Feature on a Static Heatmap. 153 Exercise 36: Creating a Bar Plot and a Heatmap Next to Each Other. 157 Exercise 37: Dynamically Linkinga Bar Plot and a Heatmap . 160 Activity 4: Generate a Bar Plot and a Heatmap to Represent Content Rating Types in the Google Play Store Apps Dataset. 163
Summary.166 Chapter 5: Interactive Visualization of Data across Time 169 Introduction. 170 Temporal Data.170 Types of Temporal Data.171 Why Study Temporal Visualization? . 172 Understanding the Relation between Temporal Data and Time-Series Data . 174 Examples of Domains That Use Temporal Data . 175 Visualization of Temporal Data. 176 How Time-Series Data Is Manipulated and Visualized. 179
Date/Time Manipulation in pandas. 181 Building a DateTime Index. 182 Choosing the Right Aggregation Level for Temporal Data . 183 Exercise 38: Creating a Static Bar Plot and Calculating the Mean and Standard Deviation in Temporal Data . 185 Exercise 39: Calculating zscore to Find Outliers in Temporal Data . 190 Resampling in Temporal Data.194 Common Pitfalls of Upsampling and Downsampling. 194 Exercise 40: Upsampling and Downsampling in Temporal Data . 194 Using shift and tshift to Introduce a Lag in Time-Series Data. 199 Exercise 41: Using shift and tshift to Shift Time in Data. 199 Autocorrelation in Time Series. 201 Interactive Temporal Visualization. 203 Bokeh Basics. 204 Advantages of Using Bokeh. 204 Exercise 42: Adding Interactivity to Static Line Plots Using Bokeh. 206 Exercise 43: Changing the Line Color and Width on a Line Plot. 208 Exercise 44: Adding Box Annotations to Find Anomalies in a Dataset. 210
Interactivity in Bokeh . 212 Activity 5: Create an Interactive Temporal Visualization . 214 Summary. 215 Chapter 6: Interactive Visualization of Geographical Data 217 Introduction.218 Choropleth Maps . 218 Worldwide Choropleth Maps . 219 Exercise 45: Creating a Worldwide Choropleth Map. 220 Exercise 46: Tweaking a Worldwide Choropleth Map . 223 Exercise 47: Adding Animation to a Choropleth Map. 227
USA State Maps. 231 Exercise 48: Creating a USA State Choropleth Map. 232 Plots on Geographical Maps. 235 Scatter Plots. 235 Exercise 49: Creating a Scatter Plot on a Geographical Map. 235 Bubble Plots. 237 Exercise 50: Creating a Bubble Plot on a Geographical Map. 238 Line Plots on Geographical Maps. 244 Exercise 51: Creating Line Plots on a Geographical Map. 245 Activity 6: Creating a Choropleth Map to Represent Total Renewable Energy Production and Consumption across the World . 250 Summary. 255 Chapter 7: Avoiding Common Pitfalls to Create Interactive Visualizations_ 257 Introduction. 258 Data Formatting and Interpretation . 258 Avoiding Common Pitfalls while Dealing with Dirty Data. 259
Outliers. 259 Exercise 52: Visualizing Outliers in a Dataset with a Box Plot. 261 Exercise 53: Dealing with Outliers . 266 Missing Data . 269 Exercise 54: Dealing with Missing Values. 269 Duplicate Instances and/or Features. 275 Bad Feature Selection . 276 Activity 7: Determining Which Features to Visualize on a Scatter Plot. 276
Data Visualization . 279 Choosing a Visualization . 279 Common Pitfalls While Visualizing Data. 282 Exercise 55: Creating a Confusing Visualization. 283 Activity 8: Creating a Bar Graph for Improving a Visualization. 286 Cheat Sheet for the Visualization Process.288 Summary. 290 Appendix_293 Index 335
Interactive Data Visualization with Python - Second Edition With so much data being continuously generated, developers, who can present data as impactful and interesting visualizations, are always in demand. Interactive Data Visualization with Python, Second Edition sharpens your data exploration skills, tells you everything there is to know about interactive data visualization in Python. You'll begin by learning how to draw various plots with Matplotlib and Seaborn, the non-interactive data visualization libraries. You'll study different types of visualizations, compare them, and find out how to select a particular type of visualization to suit your requirements. After you get a hang of the various non-interactive visualization librarie you'll learn the principles of intuitive and persuasive data visualization, and use Boke and Plotły to transform your visuals into strong stories. You'll also gain insight into how interactive data and model visualization can optimize the performance of a regression model. By the end of the course, you'll have a new skill set that'll make you the go-to person for transforming data visualizations into engaging and interesting stories. Things you will learn: • Explore and apply different interactive data visualization techniques • Design data visualizations using interactive libraries • Manipulate plotting parameters and styles to create appealing plots • Use Matplotlib, Seaborn, Altair and Bokeh for drawing appealing plots • Customize data visualization for different audiences • Customize data visualization for different scenarios |
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| spelling | Belorkar, Abha Verfasser aut Interactive data visualization with Python present your data as an effective and compelling story Abha Belorkar, Sharath Chandra Guntuku, Shubhangi Hora, Anshu Kumar Second edition Birmingham Packt April 2020 vii, 332 Seiten Illustrationen, Diagramme, Karten txt rdacontent n rdamedia nc rdacarrier Visualisierung (DE-588)4188417-6 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 s Visualisierung (DE-588)4188417-6 s DE-604 Guntuku, Sharath Chandra Verfasser aut Hora, Shubhangi Verfasser aut Kumar, Anshu Verfasser aut Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032195556&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032195556&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
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| title | Interactive data visualization with Python present your data as an effective and compelling story |
| title_auth | Interactive data visualization with Python present your data as an effective and compelling story |
| title_exact_search | Interactive data visualization with Python present your data as an effective and compelling story |
| title_exact_search_txtP | Interactive data visualization with Python present your data as an effective and compelling story |
| title_full | Interactive data visualization with Python present your data as an effective and compelling story Abha Belorkar, Sharath Chandra Guntuku, Shubhangi Hora, Anshu Kumar |
| title_fullStr | Interactive data visualization with Python present your data as an effective and compelling story Abha Belorkar, Sharath Chandra Guntuku, Shubhangi Hora, Anshu Kumar |
| title_full_unstemmed | Interactive data visualization with Python present your data as an effective and compelling story Abha Belorkar, Sharath Chandra Guntuku, Shubhangi Hora, Anshu Kumar |
| title_short | Interactive data visualization with Python |
| title_sort | interactive data visualization with python present your data as an effective and compelling story |
| title_sub | present your data as an effective and compelling story |
| topic | Visualisierung (DE-588)4188417-6 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
| topic_facet | Visualisierung Python Programmiersprache |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032195556&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032195556&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT belorkarabha interactivedatavisualizationwithpythonpresentyourdataasaneffectiveandcompellingstory AT guntukusharathchandra interactivedatavisualizationwithpythonpresentyourdataasaneffectiveandcompellingstory AT horashubhangi interactivedatavisualizationwithpythonpresentyourdataasaneffectiveandcompellingstory AT kumaranshu interactivedatavisualizationwithpythonpresentyourdataasaneffectiveandcompellingstory |