💿Data Visualization Unit 18 – Data Viz with Python Libraries

Key Concepts and Terminology

• Data visualization involves representing data graphically to facilitate understanding and communication
• Visualizations can be static (images) or interactive (allowing user exploration)
• Types of data include numerical (quantitative), categorical (qualitative), temporal, and geospatial
• Visual encoding refers to mapping data attributes to visual properties (position, size, color, shape)
• Marks are the basic graphical elements used to represent data points (points, lines, bars)
• Channels are the visual variables used to encode data (x-position, y-position, color, size, shape)
  • Position channels (x, y) are most effective for encoding quantitative data
  • Color and shape are better suited for encoding categorical data
• Scales map data values to visual properties and control the mapping between data and visual elements
• Axes provide reference points and labels for the scales used in a visualization

Python Libraries for Data Visualization

• Matplotlib is a fundamental plotting library providing fine-grained control over visualizations
  • Offers a MATLAB-like interface for creating static, animated, and interactive visualizations
  • Supports a wide range of plot types (line plots, scatter plots, bar plots, histograms)
• Seaborn is a statistical data visualization library built on top of Matplotlib
  • Provides a high-level interface for creating informative and attractive statistical graphics
  • Offers built-in themes and color palettes for enhancing the aesthetics of plots
• Plotly is a web-based plotting library for creating interactive and publication-quality figures
  • Enables the creation of interactive plots with hover effects, zooming, and panning
  • Supports various plot types (line charts, scatter plots, heatmaps, 3D plots)
• Bokeh is a powerful library for creating interactive visualizations in modern web browsers
  • Allows building sophisticated and customizable interactive plots and dashboards
  • Provides tools for handling large and streaming datasets efficiently
• Altair is a declarative statistical visualization library based on Vega and Vega-Lite
  • Uses a grammar of graphics approach, where visualizations are specified through a JSON syntax
  • Enables the creation of a wide range of statistical charts (scatter plots, line charts, bar charts)

Data Preparation and Cleaning

• Data preparation involves transforming raw data into a suitable format for visualization
• Common data preparation tasks include handling missing values, filtering, aggregating, and reshaping data
• Pandas is a powerful data manipulation library in Python for data preparation and cleaning
  • Provides data structures (DataFrame, Series) for efficient data handling and analysis
  • Offers functions for reading data from various sources (CSV, Excel, databases)
• Data cleaning involves identifying and correcting errors, inconsistencies, and outliers in the data
  • Handling missing values by removing rows/columns or imputing values (mean, median, mode)
  • Dealing with outliers by removing them or transforming the data (log transformation)
• Data transformation techniques include scaling, normalization, and encoding categorical variables
  • Scaling data to a specific range (e.g., 0 to 1) to ensure comparability across variables
  • Encoding categorical variables as numerical values or one-hot encoding for analysis and visualization
• Aggregating and summarizing data helps in understanding patterns and trends
  • Grouping data by categories and calculating summary statistics (mean, sum, count)
  • Pivoting and reshaping data to facilitate analysis and visualization

Basic Plotting Techniques

• Line plots display the relationship between two continuous variables
  • Useful for showing trends, patterns, and changes over time
  • Can be created using

    plt.plot()

    in Matplotlib or

    sns.lineplot()

    in Seaborn
• Scatter plots represent individual data points on a two-dimensional plane
  • Reveal relationships, clusters, and outliers in the data
  • Can be created using

    plt.scatter()

    in Matplotlib or

    sns.scatterplot()

    in Seaborn
• Bar plots compare categorical variables using rectangular bars
  • Height of each bar represents the value of the corresponding category
  • Can be created using

    plt.bar()

    in Matplotlib or

    sns.barplot()

    in Seaborn
• Histograms visualize the distribution of a single continuous variable
  • Divide the data into bins and show the frequency or count of values in each bin
  • Can be created using

    plt.hist()

    in Matplotlib or

    sns.histplot()

    in Seaborn
• Box plots summarize the distribution of a continuous variable across categories
  • Display the median, quartiles, and outliers of the data
  • Can be created using

    plt.boxplot()

    in Matplotlib or

    sns.boxplot()

    in Seaborn
• Heatmaps represent data using color-coded matrices
  • Useful for visualizing relationships between two variables or comparing multiple variables
  • Can be created using

    plt.imshow()

    in Matplotlib or

    sns.heatmap()

    in Seaborn

Advanced Visualization Methods

• Subplots allow displaying multiple plots in a single figure
  • Useful for comparing different variables or showcasing different aspects of the data
  • Can be created using

    plt.subplots()

    in Matplotlib or

    fig, ax = plt.subplots()

    for more control
• Faceting creates multiple subplots based on categorical variables
  • Enables the comparison of relationships across different subsets of the data
  • Can be achieved using

    sns.FacetGrid()

    or

    sns.catplot()

    in Seaborn
• Multiple plots can be combined to create complex visualizations
  • Overlaying plots to show multiple variables or compare different datasets
  • Arranging plots in a grid or using subplots to create dashboard-like visualizations
• 3D plots add an extra dimension to the visualization
  • Useful for representing data with three continuous variables
  • Can be created using

    ax = plt.axes(projection='3d')

    in Matplotlib
• Geographic plots visualize data on maps
  • Choropleth maps shade regions based on a variable's value
  • Scatter plots can be overlaid on maps to show the distribution of data points
  • Libraries like Plotly and Folium provide interactive mapping capabilities

Customization and Styling

• Matplotlib provides a wide range of customization options for fine-tuning the appearance of plots
  • Changing plot properties such as colors, line styles, marker styles, and sizes
  • Modifying axis labels, titles, legends, and tick labels
• Seaborn offers built-in themes and color palettes for quick and consistent styling
  • Setting the plot style using

    sns.set_style()

    (darkgrid, whitegrid, dark, white, ticks)
  • Choosing color palettes using

    sns.color_palette()

    or

    sns.set_palette()

• Plot aesthetics can be enhanced by adjusting various elements
  • Adding grid lines to improve readability (

    plt.grid()

    or

    ax.grid()

    )
  • Setting axis limits and scales (

    plt.xlim()

    ,

    plt.ylim()

    ,

    plt.xscale()

    ,

    plt.yscale()

    )
  • Rotating tick labels for better visibility (

    plt.xticks(rotation=45)

    )
• Annotations and text can be added to highlight specific data points or provide additional information
  • Adding text using

    plt.text()

    or

    ax.text()

  • Drawing arrows or lines to emphasize specific points or trends
• Saving plots in various file formats is essential for sharing and publishing
  • Using

    plt.savefig()

    to save plots as PNG, JPEG, PDF, or SVG files
  • Adjusting figure size, resolution, and transparency when saving plots

Interactive Visualizations

• Interactive plots allow users to explore and interact with the data
  • Zooming, panning, and selecting data points for more information
  • Updating the plot dynamically based on user input or controls
• Plotly provides a range of interactive plotting capabilities
  • Creating interactive plots with hover tooltips, zooming, and panning
  • Building interactive dashboards and web applications using Plotly and Dash
• Bokeh enables the creation of interactive visualizations in web browsers
  • Designing interactive plots with tools for zooming, panning, and selecting data points
  • Linking multiple plots to create interactive dashboards
• Jupyter notebooks support interactive plotting with inline plots
  • Displaying interactive plots within the notebook environment
  • Enabling interactive features like zooming, panning, and selecting data points
• Interactive widgets can be used to control plot parameters and update visualizations dynamically
  • Using libraries like ipywidgets to create sliders, drop-down menus, and text inputs
  • Linking widgets to plot functions to update the visualization based on user input

Best Practices and Common Pitfalls

• Choose the appropriate plot type based on the data and the message you want to convey
  • Use line plots for continuous data over time, scatter plots for relationships, and bar plots for comparisons
  • Consider the data type (numerical, categorical) and the number of variables when selecting a plot type
• Keep the plot simple and focused on the main message
  • Avoid clutter and unnecessary elements that distract from the data
  • Use clear and concise labels, titles, and legends to guide the viewer's understanding
• Use color effectively to enhance the plot's readability and aesthetic appeal
  • Employ a consistent color scheme throughout the visualization
  • Consider color blindness and ensure sufficient contrast between colors
• Pay attention to the scale and aspect ratio of the plot
  • Choose appropriate scales (linear, logarithmic) based on the data range and distribution
  • Maintain a suitable aspect ratio to avoid distorting the data or creating misleading visuals
• Handle missing data and outliers appropriately
  • Decide whether to remove or impute missing values based on the data and analysis requirements
  • Identify and handle outliers to prevent them from skewing the visualization
• Optimize the plot for the intended audience and medium
  • Consider the technical background and domain knowledge of the target audience
  • Adjust the plot size, font sizes, and resolution based on the presentation medium (paper, screen)
• Test and iterate on the visualization to ensure clarity and effectiveness
  • Gather feedback from others to identify areas for improvement
  • Refine the plot based on feedback and insights gained during the iteration process