Difference between Histogram and Density Plot

Histograms and density plots are two powerful visualization tools used to represent data distributions, but they serve different purposes and offer unique advantages. A histogram is a bar chart that groups data into bins, showing the frequency or count of values within each bin. In contrast, a density plot uses a smooth curve to represent the data distribution. Instead of showing counts, it displays the proportion of data points within each range, providing a continuous and visually appealing estimate of the distribution, particularly useful for larger datasets.

In this article, will explain both histograms and density plots, highlighting their similarities, differences, applications, strengths, and weaknesses, along with how they each present data in distinct ways.

Histogram Definition

A histogram is a type of bar chart that represents the distribution of a dataset by dividing it into intervals, called bins. Each bin groups data points into a range of values, and the height of the bar indicates the frequency or count of data points falling within that range. It is a useful tool for showing the shape, spread, and central tendency of the data.

Histograms are commonly used in statistics and data analysis for visualizing the distribution of continuous variables. Unlike bar charts, the bars in histograms touch each other, emphasizing that the data is continuous.

This type of chart is particularly helpful for identifying patterns like skewness, symmetry, and the presence of outliers within the data.

Characteristics of a Histogram

Some of the key characteristics of histogram are:

• Bins (Intervals): Data is grouped into intervals, or bins, representing a range of values​(
• Bar Height (Frequency): The height of each bar reflects the frequency or count of data points within each bin​(
• Continuous Data: The bars touch each other, emphasizing that the data is continuous rather than categorical​(
• Distribution Shape: Reveals the data's shape (normal, skewed, uniform, etc.)​(
• Symmetry and Skewness: Helps identify whether the data is symmetrically distributed or skewed​(
• Outliers and Gaps: Highlights gaps and outliers in the dataset​

When to Use a Histogram

A histogram is best used in the following situations:

• Visualizing Data Distribution: When you want to understand how a dataset is distributed across different values, a histogram provides an effective view of the shape, spread, and central tendency of the data​.
• Identifying Patterns: It is ideal for spotting patterns such as normal distribution, skewness, uniformity, or multimodal data (multiple peaks)​.
• Comparing Frequency Counts: Use a histogram when you need to show the frequency or count of data points within specific ranges, which helps in identifying clusters, gaps, or outliers​
• Analyzing Continuous Data: Histograms work best for continuous data, where the values can take any number within a range, such as age, height, or temperature​

Density Plot Definition

A density plot is a graphical representation used to visualize the distribution of a continuous variable. It shows the estimated probability density function of the data, represented by a smooth curve. Unlike histograms, which display the frequency of data points in discrete bins, density plots provide a continuous and more fluid depiction of the distribution.

They are created using kernel density estimation (KDE), which smooths the data to show its underlying shape without the abrupt transitions seen in histograms.

Characteristics of a Density Plot

• Smooth Curve: Unlike histograms, density plots represent the data distribution as a smooth, continuous curve, offering a more fluid and refined depiction of the underlying distribution​(
• Density Representation: The y-axis represents the density (proportions) of the data rather than frequency counts. The area under the curve sums to 1, which reflects the probability density across the dataset​(
• Kernel Density Estimation (KDE): Density plots use kernel density estimation (KDE) to create a smooth curve by placing a kernel (e.g., Gaussian) over each data point. The bandwidth parameter controls the smoothness of the curve; smaller bandwidths produce more detailed curves, while larger ones result in smoother, more generalized curves​(
• Comparison of Multiple Distributions: Density plots are effective for comparing multiple datasets, as the overlapping curves make it easy to visually contrast their distributions​(
• Multimodal Distributions: They can reveal multiple peaks (modes) in the data that might not be as apparent in histograms​(
• No Bins: Density plots are free from the limitations of bin width selection (a challenge in histograms), providing a more flexible representation, especially for larger datasets​

When to Use Density Plot

A density plot is most useful in the following situations:

• Visualizing the Distribution of Continuous Data: When you want a smooth, continuous representation of how data is distributed across values, especially for larger datasets​(
• Comparing Multiple Distributions: Density plots are ideal for comparing two or more datasets, as overlapping curves provide an easy way to contrast different distributions​(
• Detecting Multimodal Distributions: If you suspect your data may have more than one peak (mode), a density plot will show multiple peaks more clearly than a histogram​(
• Avoiding Bin Selection Issues: Use a density plot when you want to avoid the sensitivity of histograms to bin size, which can distort the data's appearance​(
• Identifying Skewness and Outliers: Density plots are effective at revealing skewed distributions or detecting outliers in the data.

Key Difference between Histogram and Density Plot

Some of the key differences between histogram and density plot are listed in the following table:

Similarities between Histogram and Density Plot

Histograms and density plots share several similarities, despite their differences in representation:

• Data Visualization for Continuous Variables: Both histograms and density plots are used to visualize the distribution of continuous numerical data, allowing you to understand patterns like skewness, modality, and spread​.
• Show the Shape of the Data Distribution: Both plots provide insight into the general shape of the data's distribution, whether it is normal, skewed, or multimodal​
• Highlight Peaks and Modes: Histograms and density plots can both show the number of peaks (modes) in the data, helping identify whether the distribution is unimodal, bimodal, or multimodal

Conclusion

Density plots and histograms are methods of visualizing the distribution of values; they differ in the way of data representation and have certain advantages and disadvantages. Both histograms and density plots are easy to read and the first one is better for the data frequency and outliers identification while the second one provides a more precise view of data distribution.

Read More,

• Histogram
• Interpretations of Histogram
• Bar Graph
• Bar Graph and Histogram