matlab histogram y axis percentage

Charlotte

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27-02-2025

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Histograms are a fundamental tool for visualizing the distribution of data. In MATLAB, creating a histogram is straightforward. However, sometimes you need the Y-axis to represent the percentage of data falling into each bin, rather than the raw counts. This article will guide you through several methods to achieve this, catering to different levels of customization.

Understanding Histogram Basics in MATLAB

Before diving into percentage representation, let's briefly review how to create a basic histogram in MATLAB. The primary function is histogram().

data = randn(1000,1); % Example data: 1000 random numbers from a normal distribution
histogram(data);

This code generates a histogram with the default number of bins, displaying the frequency (count) of data points in each bin on the Y-axis.

Method 1: Normalizing the Histogram Data

The simplest approach involves normalizing the counts to obtain percentages. We can access the bin counts using the Values property of the histogram object and then calculate the percentages.

data = randn(1000,1);
h = histogram(data);
counts = h.Values;
total = sum(counts);
percentages = counts / total * 100;
h.Values = percentages;
ylabel('Percentage');

This code first creates the histogram, extracts the bin counts, calculates the percentage for each bin, updates the histogram's Values property, and finally labels the Y-axis appropriately. This method directly modifies the histogram object for a clean solution.

Method 2: Using the Normalization Property

MATLAB's histogram function offers a more direct method using the Normalization property. This avoids manual calculations.

data = randn(1000,1);
histogram(data,'Normalization','probability');
ylabel('Percentage');

Setting Normalization to 'probability' directly normalizes the counts to probabilities (values between 0 and 1). Multiplying by 100 converts these probabilities to percentages. This is often the preferred and most efficient method. Note that you can also use 'probability' for a normalized probability density function in the Y axis.

Method 3: Advanced Customization with bar

For more granular control over the appearance and annotation of your histogram, consider using the bar function instead. This provides flexibility for adding titles, legends, and other visual elements.

data = randn(1000,1);
[counts,edges] = histcounts(data);
percentages = counts / sum(counts) * 100;
binwidth = edges(2) - edges(1);
bar(edges(1:end-1) + binwidth/2, percentages, 'BarWidth', 1);
xlabel('Data Values');
ylabel('Percentage');
title('Histogram with Percentage Y-Axis');

This method uses histcounts to get the counts and bin edges separately. Then, it calculates the percentages and utilizes the bar function for plotting. The BarWidth property ensures the bars fill the entire bin width.

Handling Empty Bins and Zero Percentages

If your data results in empty bins, you might encounter warnings or unexpected results when calculating percentages. You might add a check for zero counts to avoid division by zero errors. For example:

data = randn(1000,1);
[counts,edges] = histcounts(data);
percentages = counts ./ sum(counts) .* 100; %Use element-wise operations to avoid zero division errors
percentages(isnan(percentages)) = 0; %Handle NaN values resulting from empty bins.
bar(edges(1:end-1) + binwidth/2, percentages, 'BarWidth', 1);
xlabel('Data Values');
ylabel('Percentage');
title('Histogram with Percentage Y-Axis');

Choosing the Right Method

The best method depends on your specific needs. Method 2 (using the Normalization property) is generally the quickest and easiest for a simple percentage histogram. Method 1 offers intermediate control and is useful if you need to perform additional calculations on the counts before plotting. Method 3 provides the most control over the visualization but requires more coding. Always remember to clearly label your axes for easy interpretation. This ensures that your MATLAB histogram accurately and clearly displays percentage data.