Heatmap Example

Today at GetPlace we will take a closer look at heat maps and talk about their features.

What is a heatmap?

A heatmap is a graphical representation of data where individual values contained in a matrix are represented as colors. It's often used to visualize the distribution and intensity of data across two dimensions, such as time intervals or categories. Heatmaps are especially useful when dealing with large datasets, as they provide a way to quickly identify patterns, trends, and variations within the data.

In a heatmap, each cell of the matrix corresponds to a specific combination of values from the two dimensions being analyzed. The color of each cell is determined by a color scale that represents the magnitude of the data in that cell. Typically, warmer colors like red or yellow are used to represent higher values, while cooler colors like blue or green represent lower values. This color gradient allows for easy visual identification of areas with high or low activity, concentration, or values within the data.

Heatmaps are commonly used in various fields such as data analysis, statistics, biology, finance, and many others to provide insights into complex data relationships. They help researchers, analysts, and decision-makers identify trends, clusters, anomalies, and correlations that might be less obvious when looking at raw data alone.

2D-Density Plot

A 2D density plot, also known as a 2D kernel density plot, is a graphical representation used to visualize the distribution of data points in a two-dimensional space. It is a way to represent the density of data points in relation to their coordinates on a two-dimensional grid. This type of plot is particularly useful when you want to understand how data points are distributed across two continuous variables.

Here's how a 2D density plot works:

  1. Data Points: You have a set of data points, each associated with two continuous variables, often referred to as the x-axis and y-axis.
  2. Grid Creation: The 2D density plot creates a grid of cells over the range of x and y values in your dataset. Each cell in the grid represents a small region of the 2D space.
  3. Kernel Density Estimation: For each cell in the grid, a kernel density estimation is performed. This involves measuring the density of data points around that cell's coordinates. It's a way of estimating how many data points are clustered around that location.
  4. Color Mapping: The density of each cell is typically represented using colors. Cells with higher densities are assigned warmer colors (like red or yellow), while cells with lower densities are assigned cooler colors (like blue or green). The color intensity indicates the relative density in that region.
  5. Smooth Transition: The density estimation is often smoothed to create a visually appealing plot. This means that the density values from neighboring cells influence each other to provide a more gradual transition of colors and to reduce noise in the visualization.

The resulting plot provides insights into areas where data points are more concentrated or sparse. It can help you identify clusters, trends, and patterns in the data distribution. 2D density plots are particularly useful when dealing with large datasets or when you want to compare the distribution of two continuous variables.

Keep in mind that creating accurate density plots can involve some level of statistical knowledge, as choosing the appropriate kernel function and bandwidth can impact the interpretation of the plot.

When You Should Use A Heatmap?

A heatmap is an ideal choice when you need to visually represent the intensity or distribution of data across two dimensions. It finds utility in various scenarios, such as geographic mapping, user behavior analysis, and scientific data visualization. When dealing with spatial data, like population density or temperature variations, heatmaps offer a clear depiction of regional trends. In user experience research, they highlight hotspots on websites where users engage the most. For scientific data, heatmaps unveil patterns in gene expression or simulation outcomes. 

Businesses use heatmaps to showcase market trends, customer preferences, or risk assessment results. Additionally, heatmaps prove invaluable in machine learning by displaying variable correlations and guiding feature selection. In healthcare, they aid in visualizing medical imaging anomalies or temperature distributions. Overall, heatmaps serve as a versatile tool to simplify complex data analysis and effectively communicate patterns, making them an excellent choice whenever you want to understand, analyze, and present data distribution across two dimensions.

Example of Data Structure in Heatmap

let's consider an example of a data structure that could be used to create a heatmap. Imagine you are analyzing the sales performance of different products across various months. You want to visualize how the sales data is distributed across both products and time periods using a heatmap.

In this case, you could represent your data using a two-dimensional matrix, where the rows correspond to products and the columns correspond to months. Each cell in the matrix holds the sales data for a specific product in a specific month.

Here's how the data structure could look:

Data Format Example

In this example, the rows represent the different products (A, B, C), and the columns represent the months (Jan to Dec). The values in each cell represent the sales figures for the corresponding product in the given month. The data structure captures the relationships between products and months, allowing you to easily create a heatmap to visualize the sales distribution.

You could apply a color scale to the values in the matrix, where higher sales values are represented by warmer colors and lower sales values by cooler colors. This would allow you to quickly identify which products are performing well during certain months and where the sales are concentrated throughout the year.

Choosing the Right Color Pallette for Heatmaps

Selecting the right color palette for heatmaps is essential to ensure effective data visualization and interpretation. Begin by understanding the nature of your data and the message you want to convey. Consider the color perception of your audience, opting for colors that are easily distinguishable and avoiding hues that might cause confusion, particularly for those with color vision deficiencies. Prioritize contrast between colors to make high and low values clearly discernible.

Choose a palette that aligns with the number of categories or levels in your data. Multiple colors may be suitable for complex data, but avoid overwhelming the viewer with excessive color variations. Pay attention to color consistency across your visualizations for easy comparison.

If possible, use color-blind friendly palettes to ensure inclusivity. Most importantly, test your chosen palette with sample data to verify its effectiveness in representing data patterns, and assess how it appears in both color and grayscale.

Built-in color palettes in popular visualization libraries or tools like Matplotlib and ColorBrewer can be a helpful starting point. Balancing aesthetic appeal with functionality, the right color palette enhances your heatmap's ability to communicate insights accurately and intuitively.

Heatmap Legend

A Legend for Heatmap

A legend is vital for heatmaps as it provides a key to decode the color representation of data values. It clarifies the mapping between colors and corresponding values, enabling viewers to interpret the intensity of patterns accurately. Without a legend, the context of the heatmap becomes unclear, hindering its effectiveness in conveying insights. A well-designed legend enhances the heatmap's usability by guiding viewers in understanding the significance of color variations, fostering informed data analysis and decision-making.

Show Values In Cells

Displaying values in heatmap cells is crucial for providing precise information. It allows viewers to directly associate color shades with exact data values, enhancing data comprehension. This transparency minimizes ambiguity, supports accurate interpretation, and reinforces the heatmap's credibility as a reliable visualization tool.

Sort Levels By Value or Similarity

Sorting levels by similarity or value in heatmaps helps reveal underlying patterns and relationships in the data. Grouping similar elements together facilitates the identification of clusters and trends. Sorting by value arranges data in a logical order, emphasizing high or low values, aiding in immediate insights. Such organization enhances the heatmap's ability to visually communicate data relationships, making it easier for viewers to discern meaningful information and draw accurate conclusions.

Useful Tick Marks

When working with heatmaps, selecting useful tick marks for axes is crucial. Tick marks should align with data intervals, aiding in accurate interpretation. Choose tick positions that correspond to significant data points, such as months or categories. Ensure they provide clear reference points for viewers. If the data has a wide range, consider using logarithmic scales. Strive for a balance between providing enough tick marks for orientation without cluttering the visualization. Well-chosen tick marks enhance the heatmap's readability, facilitating proper data analysis and effective communication of insights.

Most Common Heatmaps Types

Clustered Heatmap

A clustered heatmap is a visualization technique that combines a heatmap with hierarchical clustering to reveal patterns and relationships within complex datasets. It's particularly useful when dealing with multivariate data where you want to identify groups of similar items or attributes.

In a clustered heatmap, rows and columns of the heatmap are rearranged based on their similarity, as determined by a clustering algorithm. This rearrangement organizes similar items or attributes into clusters, making patterns easier to discern. Typically, hierarchical clustering is used, where items or attributes are successively grouped into clusters based on their similarity, forming a hierarchical tree-like structure.

The heatmap's colors then represent the values of the data, showing the intensity of a particular variable or measurement. However, with clustering applied, you often observe blocks or rectangles of similar colors, indicating groups of items with similar behavior.

Clustered heatmaps are used in various fields, including genomics, biology, finance, and social sciences. In genomics, for example, they can show how gene expression profiles of different samples group together, suggesting functional relationships. In finance, they might reveal correlations between stock prices of different companies. By combining clustering with heatmaps, these visualizations provide a powerful tool to explore and understand intricate relationships within complex datasets.

A correlogram heatmap, often referred to as a correlation heatmap, is a visual representation that combines the concepts of a heatmap and a correlation matrix. It's used to display the correlations between multiple variables in a dataset. Correlation measures the strength and direction of the linear relationship between two variables, which is valuable for understanding how changes in one variable might relate to changes in another.

Correlogram Example


In a correlogram heatmap, the rows and columns of the heatmap correspond to the variables being analyzed. Each cell in the heatmap is colored based on the correlation coefficient between the variables represented by the respective row and column. Warm colors like red or orange typically represent positive correlations, while cool colors like blue indicate negative correlations. Neutral colors may denote low or no correlation.

This visualization technique helps identify patterns, dependencies, and relationships between variables. A strong positive correlation between two variables suggests that they tend to increase or decrease together, while a strong negative correlation indicates an inverse relationship.

Correlogram heatmaps are widely used in data analysis, including finance, economics, social sciences, and more. They provide an intuitive and visual way to quickly grasp the relationships between variables in large datasets, aiding decision-making, feature selection, and hypothesis generation. Additionally, they can serve as a starting point for more detailed analyses and investigations into the underlying factors influencing the data.

Related Data Plots

Bar Chart and Histogram

A bar chart and a histogram are both graphical representations used to display the distribution of data, but they have different applications and characteristics.

A bar chart is used to compare discrete categories or groups and the quantities associated with them. It consists of a series of bars, where each bar corresponds to a specific category, and the height or length of the bar represents the quantity or value of that category. Bar charts are effective for showing comparisons among different categories and are commonly used to visualize categorical data or data with distinct groups. For example, a bar chart can be used to display sales figures for different products or the number of students in different classes.

On the other hand, a histogram is used to visualize the distribution of continuous or numerical data. It divides the range of data into intervals or "bins" and then counts how many data points fall into each bin. The bins are represented as adjacent bars, and the height of each bar corresponds to the frequency or count of data points in that bin. Histograms provide insights into the shape of the data distribution, including information about the frequency of values within specific ranges. They are commonly used in fields like statistics and data analysis to examine the frequency distribution of a dataset, such as the distribution of ages in a population or exam scores in a class.

In summary, while both bar charts and histograms are used to represent data visually, bar charts are suited for comparing discrete categories with associated values, whereas histograms are used to display the distribution of continuous numerical data. The choice between these two types of visualizations depends on the nature of the data you want to present and the insights you want to convey.

Bar Chart

Grouped Bar Chart

A grouped bar chart is a type of data visualization that extends the basic bar chart to compare values across multiple categories and subcategories simultaneously. It provides a way to display and compare the quantities associated with different categories while also distinguishing subcategories within each main category.

In a grouped bar chart, multiple sets of bars are grouped together, with each group corresponding to a main category. Within each group, individual bars represent subcategories or specific attributes related to that main category. The bars within a group share the same baseline, making it easy to compare the values of subcategories within each main category.

This type of chart is particularly useful when you want to present a comprehensive view of data that involves multiple dimensions. For instance, if you're comparing sales figures for different products across various months or years, a grouped bar chart can show both the product-wise comparison and the temporal trends in a single visualization. Similarly, it's employed in scenarios like demographic data analysis, where you can show population distribution by gender and age groups for different regions.

Grouped bar charts aid in visualizing complex datasets, enabling viewers to identify trends, patterns, and relative comparisons with ease. However, as the number of subcategories or main categories increases, the chart might become crowded and harder to interpret. Careful design and labeling are essential to ensure the clarity and effectiveness of the visualization.

Scatter Plot

A scatter plot is a two-dimensional data visualization that uses individual data points to represent the relationship between two variables. Each point on the plot corresponds to a pair of values from the two variables, with one variable plotted on the x-axis and the other on the y-axis. 

Scatter plots are particularly useful for identifying patterns, trends, and correlations between variables. They visually depict the distribution of data points and can reveal if there's a positive, negative, or no correlation between the variables. Scatter plots are commonly employed in statistics, data analysis, and scientific research to gain insights into relationships within data.



A choropleth map is a geographical visualization that uses color shading to represent statistical data on a map. Different areas, such as countries, states, or regions, are shaded with colors or patterns to indicate variations in the data. The intensity of color corresponds to the data values, allowing viewers to quickly grasp spatial patterns and differences. Choropleth maps are valuable for visualizing geographical distributions of data, such as population density, income levels, or election results. They help identify trends, disparities, and regional variations, making complex data more accessible and insightful for decision-makers and researchers.

Visulatization Tools For Heatmaps

Several visualization tools offer features for creating heatmaps. Notable options include Matplotlib and Seaborn in Python, which provide customizable heatmap functions. R users can utilize ggplot2 for elegant heatmaps. Tableau, a popular data visualization software, offers user-friendly heatmap creation. JavaScript libraries like D3.js enable dynamic, interactive heatmaps for web applications. Additionally, tools like Excel and Google Sheets have built-in heatmap functionality for basic visualization needs. These tools simplify the process of generating informative heatmaps, catering to different skill levels and data complexities.

GetPlace also provides its heat maps for business intelligence. Our toolkit helps to conduct data analytics without the need for a professional understanding of every nuance - try it and see for yourself!

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