Data Visualization with Python Create an Impact with Meaningful Data Insights Using Interactive and Engaging Visuals
With so much data being continuously generated, developers with a knowledge of data analytics and data visualization are always in demand. With Data Visualization with Python, you'll learn how to use Python with NumPy, Pandas, Matplotlib, and Seaborn to create impactful data visualizations with...
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| Hlavní autoři: | , |
|---|---|
| Médium: | E-kniha |
| Jazyk: | angličtina |
| Vydáno: |
Birmingham
Packt Publishing, Limited
2019
Packt Publishing |
| Vydání: | 1 |
| Témata: | |
| ISBN: | 1789956463, 9781789956467 |
| On-line přístup: | Získat plný text |
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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: Loading a Sample Dataset and Calculating the Mean -- Activity 1: Using NumPy to Compute the Mean, Median, Variance, and Standard Deviation for the Given Numbers -- Basic NumPy Operations -- Activity 2: Indexing, Slicing, Splitting, and Iterating -- Advanced NumPy Operations -- Activity 3: Filtering, Sorting, Combining, and Reshaping -- pandas -- Advantages of pandas over NumPy -- Disadvantages of pandas -- Exercise 2: Loading a Sample Dataset and Calculating the Mean -- Activity 4: Using pandas to Compute the Mean, Median, and Variance for the Given Numbers -- Basic Operations of pandas -- Series -- Activity 5: Indexing, Slicing, and Iterating using pandas -- Advanced pandas Operations -- Activity 6: Filtering, Sorting, and Reshaping -- Summary -- Chapter 2: All You Need to Know About Plots -- Introduction -- Comparison Plots -- Line Chart -- Bar Chart -- Radar Chart -- Activity 7: Employee Skill Comparison -- Relation Plots -- Scatter Plot -- Bubble Plot -- Correlogram -- Heatmap -- Activity 8: Road Accidents Occurring over Two Decades -- Composition Plots -- Pie Chart -- Stacked Bar Chart -- Stacked Area Chart -- Activity 9: Smartphone Sales Units -- Venn Diagram -- Distribution Plots -- Histogram -- Density Plot -- Box Plot -- Violin Plot -- Activity 10: Frequency of Trains during Different Time Intervals -- Geo Plots -- Dot Map -- Choropleth Map -- Connection Map -- What Makes a Good Visualization?
- Bar Plots -- Activity 22: Movie Comparison Revisited -- Kernel Density Estimation -- Plotting Bivariate Distributions -- Visualizing Pairwise Relationships -- Violin Plots -- Activity 23: Comparing IQ Scores for Different Test Groups by Using a Violin Plot -- Multi-Plots in Seaborn -- FacetGrid -- Activity 24: Top 30 YouTube Channels -- Regression Plots -- Activity 25: Linear Regression -- Squarify -- Activity 26: Water Usage Revisited -- Summary -- Chapter 5: Plotting Geospatial Data -- Introduction -- The Design Principles of Geoplotlib -- Geospatial Visualizations -- Exercise 6: Visualizing Simple Geospatial Data -- Activity 27: Plotting Geospatial Data on a Map -- Exercise 7: Choropleth Plot with GeoJSON Data -- Tile Providers -- Exercise 8: Visually Comparing Different Tile Providers -- Custom Layers -- Activity 28: Working with Custom Layers -- Summary -- Chapter 6: Making Things Interactive with Bokeh -- Introduction -- Concepts of Bokeh -- Interfaces in Bokeh -- Output -- Bokeh Server -- Presentation -- Integrating -- Exercise 9: Plotting with Bokeh -- Exercise 10: Comparing the Plotting and Models Interfaces -- Adding Widgets -- Exercise 11: Basic Interactivity Widgets -- Activity 29: Extending Plots with Widgets -- Summary -- Chapter 7: Combining What We Have Learned -- Introduction -- Activity 30: Implementing Matplotlib and Seaborn on New York City Database -- Bokeh -- Activity 31: Visualizing Bokeh Stock Prices -- Geoplotlib -- Activity 32: Analyzing Airbnb Data with geoplotlib -- Summary -- Appendix -- Index
- Activity 11: Identifying the Ideal 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 -- Displaying Figures -- Saving Figures -- Exercise 3: Creating a Simple Visualization -- Basic Text and Legend Functions -- Labels -- Titles -- Text -- Annotations -- Legends -- Activity 12: Visualizing Stock Trends by Using a Line Plot -- Basic Plots -- Bar Chart -- Activity 13: Creating a Bar Plot for Movie Comparison -- Pie Chart -- Exercise 4: Creating a Pie Chart for Water Usage -- Stacked Bar Chart -- Activity 14: Creating a Stacked Bar Plot to Visualize Restaurant Performance -- Stacked Area Chart -- Activity 15: Comparing Smartphone Sales Units Using a Stacked Area Chart -- Histogram -- Box Plot -- Activity 16: Using a Histogram and a Box Plot to Visualize the Intelligence Quotient -- Scatter Plot -- Activity 17: Using a Scatter Plot to Visualize Correlation Between Various Animals -- Bubble Plot -- Layouts -- Subplots -- Tight Layout -- Radar Charts -- Exercise 5: Working on Radar Charts -- GridSpec -- Activity 18: Creating Scatter Plot with Marginal Histograms -- Images -- Basic Image Operations -- Activity 19: 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 -- Contexts -- Activity 20: Comparing IQ Scores for Different Test Groups by Using a Box Plot -- Color Palettes -- Categorical Color Palettes -- Sequential Color Palettes -- Diverging Color Palettes -- Activity 21: Using Heatmaps to Find Patterns in Flight Passengers' Data -- Interesting Plots in Seaborn