15.5 Data visualization
4 min read•june 24, 2024
Data visualization transforms raw numbers into meaningful graphics, making complex information easier to understand. It's a powerful tool for spotting trends, outliers, and patterns that might be missed in spreadsheets or tables.
Choosing the right visualization type is crucial. From simple bar charts to complex heatmaps, each serves a specific purpose. Python libraries like and make creating these visualizations a breeze, offering both flexibility and ease of use.
Introduction to Data Visualization
Role of data visualization
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- Represents data graphically enables effective communication of insights to technical and non-technical audiences
- Identifies patterns, trends, and outliers in data that may not be apparent from raw data or summary statistics
- Plays significant role in data analysis process
- Exploratory data analysis (EDA) helps understand distribution, relationships, and structure of data
- Model evaluation assesses performance and validity of machine learning models
- Storytelling conveys key messages and insights derived from data
- Enhances decision-making by providing clear and intuitive representation of complex data
- Enables stakeholders to grasp significance of findings quickly
- Facilitates data-driven decision-making by highlighting important aspects of data
Selection of visualization types
- Choosing appropriate visualization type depends on nature of data and purpose of analysis
- Univariate analysis (single variable)
- show distribution of continuous variable
- Bar charts display distribution of categorical variable
- represent summary statistics (median, quartiles, outliers) of continuous variable
- Bivariate analysis (relationship between two variables)
- Scatter plots visualize relationship between two continuous variables
- show trend or evolution of variable over time or another continuous variable
- Heatmaps represent correlation or interaction between two variables using color-coded matrices
- Multivariate analysis (relationship among multiple variables)
- Pair plots display pairwise relationships between multiple variables in grid of scatter plots
- Parallel coordinates represent multiple variables as parallel axes, with each data point as line connecting axes
- Radar charts compare multiple quantitative variables for different entities using polygon-shaped plot
- Geospatial analysis
- represent data values associated with geographical regions using color-coded polygons
- display data values as circles on map, with size of circle proportional to value
Creation with Python libraries
- Matplotlib
- Low-level library provides fine-grained control over visualization elements
- Syntax
import matplotlib.pyplot as plt - Basic workflow
- Create figure and axis
###fig,_ax_=_plt.[subplots](https://www.fiveableKeyTerm:Subplots)()_0### - Plot data on axis using appropriate functions (
,[ax.plot()](https://www.fiveableKeyTerm:ax.plot())
,[ax.scatter()](https://www.fiveableKeyTerm:ax.scatter())
)[ax.bar()](https://www.fiveableKeyTerm:ax.bar()) - Customize plot (labels, title, , etc.)
,[ax.set_xlabel()](https://www.fiveableKeyTerm:ax.set_xlabel())
,[ax.set_title()](https://www.fiveableKeyTerm:ax.set_title())[ax.legend()](https://www.fiveableKeyTerm:ax.legend()) - Display plot
[plt.show()](https://www.fiveableKeyTerm:plt.show())
- Create figure and axis
- Seaborn
- High-level library built on top of Matplotlib provides concise and aesthetically pleasing interface
- Syntax
import seaborn as sns - Provides various plot types and themes for statistical data visualization
- Distribution plots
,[sns.histplot()](https://www.fiveableKeyTerm:sns.histplot())
,[sns.kdeplot()](https://www.fiveableKeyTerm:sns.kdeplot())###sns.[boxplot](https://www.fiveableKeyTerm:Boxplot)()_0### - Categorical plots
,[sns.barplot()](https://www.fiveableKeyTerm:sns.barplot())
,[sns.countplot()](https://www.fiveableKeyTerm:sns.countplot())[sns.violinplot()](https://www.fiveableKeyTerm:sns.violinplot()) - Relationship plots
,[sns.scatterplot()](https://www.fiveableKeyTerm:sns.scatterplot())
,[sns.lineplot()](https://www.fiveableKeyTerm:sns.lineplot())###sns.[heatmap](https://www.fiveableKeyTerm:Heatmap)()_0###
- Distribution plots
- Automatically handles figure creation and axis labeling, making code more concise
- Supports built-in themes and color palettes for consistent and visually appealing plots
,[sns.set_style()](https://www.fiveableKeyTerm:sns.set_style())[sns.set_palette()](https://www.fiveableKeyTerm:sns.set_palette())
Best Practices and Considerations
Effective visual design principles
- Keep plot simple and uncluttered, focusing on key message
- Use appropriate colors and contrasts to enhance readability
- Avoid using too many colors or visually distracting elements
- Consider color blindness and ensure sufficient contrast between colors
- Choose appropriate scales and axis limits to avoid distorting data
- Use logarithmic scales when dealing with data spanning multiple orders of magnitude
- Start y-axis at zero when representing quantities or percentages
- Use clear and informative labels, titles, and legends
- Provide context and help audience understand plot without additional explanation
- Maintain consistency in style and formatting across multiple plots in project or presentation
- Optimize by minimizing non-data ink and maximizing data ink, as advocated by
Considerations for different data types and domains
- Time- data
- Use line plots to show trends and patterns over time
- Consider using moving averages or smoothing techniques to reduce noise and highlight overall trends
- Format x- appropriately based on time scale (dates, hours, minutes)
- Categorical data
- Use bar charts or heatmaps to compare values across categories
- Consider ordering categories based on meaningful criterion (alphabetical, frequency, magnitude)
- Geospatial data
- Use maps to represent data associated with geographical locations
- Choose appropriate map projections based on region and purpose of visualization
- Use color gradients or bubble sizes to encode data values on map
- Large datasets
- Use techniques like sampling, binning, or aggregation to reduce amount of data being visualized
- Employ interactive visualizations that allow zooming, panning, or filtering to explore data at different levels of detail
- Domain-specific conventions
- Be aware of conventions and best practices specific to your domain or industry
- Follow standard visualization techniques and color schemes commonly used in your field to ensure familiarity and ease of interpretation for target audience
Advanced Visualization Concepts
- : Choose appropriate visual elements (e.g., position, size, color) to represent data attributes effectively
- : Consider how human perception influences interpretation of visual elements in charts and graphs
- : Incorporate interactive features to allow users to explore and manipulate data visualizations dynamically
Key Terms to Review (49)
Ax.bar(): ax.bar() is a function in the Matplotlib library used to create bar charts, a type of data visualization that displays data using rectangular bars. It allows users to create customizable bar charts to effectively represent and compare categorical data.
Ax.legend(): ax.legend() is a method in the Matplotlib library that allows users to add a legend to a plot. A legend is a key that explains the meaning of the different elements (lines, points, etc.) displayed in the plot, making it easier for the viewer to interpret the data.
Ax.plot(): ax.plot() is a function in the Matplotlib library that is used to create line plots, which are a fundamental type of data visualization. It allows users to plot one or more sets of data points on a 2D coordinate system, with the x-axis representing the independent variable and the y-axis representing the dependent variable.
Ax.scatter(): ax.scatter() is a function in the Matplotlib library used to create a scatter plot, which is a type of data visualization that displays the relationship between two variables by plotting individual data points on a coordinate plane. It is a powerful tool for exploring and understanding the patterns and trends within a dataset.
Ax.set_title(): ax.set_title() is a method in the Matplotlib library that allows users to add a title to a specific plot or subplot within a figure. It is a crucial function in data visualization, as titles provide important context and labeling for the presented information.
Ax.set_xlabel(): ax.set_xlabel() is a method in the Matplotlib library used to set the label for the x-axis of a plot. It allows you to provide a descriptive text label that explains the data being displayed along the horizontal axis of a data visualization.
Axis Labels: Axis labels are the textual descriptions or titles assigned to the horizontal and vertical axes of a data visualization, providing clear identification of the variables or metrics being displayed. They serve as essential navigational aids, helping the viewer interpret the information presented in the chart or graph.
Bar chart: A bar chart is a visual representation of data using rectangular bars to compare different categories. The length of each bar is proportional to the value it represents, making it easy to see differences between categories at a glance. Bar charts can be displayed vertically or horizontally and are commonly used in data visualization to convey complex information clearly and effectively.
Box Plots: Box plots, also known as box-and-whisker plots, are a type of data visualization that provide a concise summary of the distribution of a dataset. They display the five-number summary of a dataset: the minimum value, the first quartile, the median, the third quartile, and the maximum value.
Boxplot: A boxplot, also known as a box-and-whisker plot, is a type of data visualization that provides a graphical summary of the distribution of a dataset. It displays the median, quartiles, and potential outliers of a dataset, allowing for a quick assessment of the dataset's central tendency, spread, and skewness.
Bubble Maps: Bubble maps are a type of data visualization that uses circles, or 'bubbles,' to represent data points. The size of each bubble corresponds to the magnitude or value of the data it represents, allowing for easy comparison and identification of patterns within the data.
Choropleth Maps: Choropleth maps are a type of thematic map that uses different shades or patterns of color to represent statistical data within predefined geographic areas, such as countries, states, or counties. These maps effectively visualize the spatial distribution and variation of a particular variable across a region.
Color Mapping: Color mapping is the process of assigning specific colors to represent data values in data visualization. It is a fundamental technique used to enhance the interpretability and clarity of visual representations, allowing viewers to quickly identify patterns, trends, and relationships within the data.
Colorbar: A colorbar, also known as a color scale or color legend, is a visual tool used in data visualization to provide a reference for interpreting the colors used in a plot or image. It is a crucial component that helps the viewer understand the relationship between the colors and the underlying data values being represented.
Correlation Matrix: A correlation matrix is a square matrix that displays the correlation coefficients between multiple variables. It is a powerful tool for understanding the relationships and patterns within a dataset, particularly in the context of data visualization.
Data Encoding: Data encoding is the process of converting information from one format or representation to another, often to facilitate efficient storage, transmission, or processing of digital data. It is a crucial concept in the context of data visualization, as the choice of data encoding can significantly impact the effectiveness and clarity of visual representations.
Data-to-Ink Ratio: The data-to-ink ratio is a concept in data visualization that refers to the proportion of a graphic dedicated to displaying actual data versus non-data elements such as gridlines, labels, and other visual embellishments. The goal is to maximize the amount of data presented while minimizing unnecessary visual clutter, allowing the viewer to focus on the most important information.
DataFrame: A DataFrame is a two-dimensional, labeled data structure in Python's Pandas library, similar to a spreadsheet or a SQL table. It is a fundamental data structure used in data science and data analysis tasks, providing a flexible and efficient way to store, manipulate, and analyze structured data.
Edward Tufte: Edward Tufte is a renowned American statistician, professor, and author who has made significant contributions to the field of data visualization. He is widely recognized for his pioneering work in developing principles and techniques for effectively presenting complex information through visual means.
Fig, ax = plt.subplots(): The 'fig, ax = plt.subplots()' function in Python's Matplotlib library is used to create a new figure and one or more axes (subplots) within that figure. It provides a convenient way to set up the plotting environment and manage the layout of multiple subplots in a single figure.
Heatmap: A heatmap is a data visualization technique that uses a color-coded system to represent the magnitude or frequency of values in a dataset. It is commonly used to explore and analyze patterns, trends, and relationships within large datasets, particularly in the context of exploratory data analysis and data visualization.
Histograms: A histogram is a graphical representation of the distribution of a dataset. It displays the frequency or count of data points falling within specified intervals or bins, providing a visual summary of the data's underlying distribution.
Interactivity: Interactivity refers to the ability of a user to actively engage with and manipulate digital content or systems, creating a dynamic and responsive experience. It is a fundamental aspect of data visualization, enabling users to explore and interact with visual representations of information.
Legend: A legend is a descriptive element in a data visualization that explains the meaning of the different visual elements, such as colors, symbols, or patterns, used to represent data. It serves as a key to interpreting the information presented in the visualization.
Line Plots: Line plots, also known as line graphs, are a type of data visualization that display information as a series of data points connected by straight line segments. They are commonly used to illustrate trends, patterns, and relationships over time or across different categories.
Matplotlib: Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. It provides a wide range of tools and functions for generating high-quality plots, graphs, and charts that can be used in various contexts, including data analysis, scientific research, and data-driven applications.
Pair Plot: A pair plot, also known as a scatterplot matrix, is a data visualization tool that displays the pairwise relationships between multiple variables in a dataset. It arranges a grid of individual scatterplots, each showing the relationship between two variables, allowing for the exploration of patterns, trends, and potential correlations within the data.
Plotly: Plotly is a powerful data visualization library that enables the creation of interactive and highly customizable plots, charts, and graphs. It is particularly useful for visualizing complex data sets and presenting them in an engaging and informative manner.
Plt.plot(): plt.plot() is a function in the Matplotlib library, a widely-used data visualization tool in Python. It is used to create 2D line plots, which are one of the most fundamental and commonly used types of data visualizations. The plt.plot() function allows users to plot data points and connect them with lines, enabling the effective display and analysis of numerical relationships.
Plt.show(): plt.show() is a function in the Matplotlib library, a popular data visualization tool in Python. This function is used to display the plot that has been created and configured using other Matplotlib functions. It is the final step in the plotting process, allowing the user to view and interact with the visualized data.
PNG: PNG (Portable Network Graphics) is a raster image format that supports lossless data compression, transparency, and a wide range of color depths. It is a popular choice for digital images, particularly those that require transparency or high-quality color representation, and is commonly used in data visualization applications.
Scatter Plot: A scatter plot is a type of data visualization that displays the relationship between two numerical variables by plotting individual data points on a two-dimensional graph. It allows for the identification of patterns, trends, and potential outliers in the data.
Seaborn: Seaborn is a powerful data visualization library built on top of Python's Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics that explore and present data in a clear and concise manner.
Series: A Series is a one-dimensional labeled data structure in the Pandas library, which is a fundamental data analysis tool in Python. It serves as the basic building block for more complex data structures and plays a crucial role in various aspects of data science, including exploratory data analysis and data visualization.
Sns.barplot(): sns.barplot() is a function in the Seaborn data visualization library that creates a bar plot to display the relationship between a categorical variable and a numerical variable. It is a powerful tool for visualizing and comparing data in a clear and intuitive way.
Sns.boxplot(): sns.boxplot() is a function in the Seaborn data visualization library that creates a box plot, a type of statistical graphic that displays the distribution of a dataset through its quartiles. It is a powerful tool for exploring and visualizing the spread and central tendency of data.
Sns.countplot(): sns.countplot() is a function in the Seaborn data visualization library that creates a bar plot to display the count or frequency of each category in a single categorical variable. It is a powerful tool for quickly visualizing the distribution of data within a dataset.
Sns.heatmap(): sns.heatmap() is a powerful data visualization function in the Seaborn library, which is a high-level data visualization tool built on top of Matplotlib. It is primarily used to create a visual representation of a 2-dimensional data matrix, where the individual values are represented as colors, providing a clear and intuitive way to analyze patterns and relationships within the data.
Sns.histplot(): sns.histplot() is a function in the Seaborn data visualization library that creates a histogram plot to visualize the distribution of a single numerical variable. It provides a powerful and customizable way to analyze and display the frequency and spread of data points within a dataset.
Sns.kdeplot(): sns.kdeplot() is a function in the Seaborn data visualization library that creates a kernel density estimation (KDE) plot. A KDE plot is a way to visualize the distribution of a continuous variable, providing a smooth estimate of the probability density function of the underlying data.
Sns.lineplot(): sns.lineplot() is a function in the Seaborn data visualization library that allows users to create line plots to visualize the relationship between two or more variables. It is a powerful tool for exploring trends, patterns, and changes over time in a dataset.
Sns.scatterplot(): sns.scatterplot() is a function in the Seaborn data visualization library that creates a scatter plot, which is a type of data visualization that displays the relationship between two numerical variables. It is a powerful tool for exploring and understanding the underlying patterns and relationships in a dataset.
Sns.set_palette(): sns.set_palette() is a function in the Seaborn data visualization library that allows users to set a default color palette for all subsequent plots. This function is particularly useful in creating a cohesive visual style across multiple plots in a data analysis or visualization project.
Sns.set_style(): sns.set_style() is a function in the Seaborn data visualization library that allows you to set the default visual style for all plots created in a Seaborn session. This function provides a convenient way to customize the overall aesthetic of your data visualizations, making it easier to achieve a consistent and visually appealing look across multiple plots.
Sns.violinplot(): sns.violinplot() is a function in the Seaborn data visualization library that creates a violin plot, which is a combination of a box plot and a kernel density estimate. It is used to display the distribution of a numerical variable or the relationship between a numerical variable and one or more categorical variables.
Subplots: Subplots are secondary plots within the main narrative of a story or visual representation, such as a data visualization. They serve to complement the primary plot, providing additional depth, complexity, and interconnected storylines that enhance the overall experience for the audience.
SVG: SVG (Scalable Vector Graphics) is a vector image format used for creating two-dimensional graphics that can be scaled to any size without losing quality. It is particularly useful for data visualization, as it allows for the creation of high-quality, responsive, and interactive graphics that can be easily integrated into web pages and applications.
Violin Plot: A violin plot is a data visualization technique that combines the features of a box plot and a kernel density plot. It provides a visual representation of the distribution of a dataset, allowing for the exploration of its shape, central tendency, and dispersion.
Visual Perception: Visual perception is the ability to interpret and make sense of the information received through the eyes. It involves the brain's processing of visual stimuli, allowing individuals to recognize, understand, and interact with their surrounding environment.