# Maximizing Insights: Adjusting Bin Size in Matplotlib Histograms

Histograms are graphical representations of data that show the frequency distribution of a dataset. They provide information about how values are distributed across a range of values.

Histograms are commonly used in statistical analysis, and they are a fundamental tool for data visualization. In this article, we’ll discuss how to adjust the bin size in Matplotlib histograms to improve data visualization.

We’ll explore three methods that can be used to fine-tune the bin size. So, let’s dive in!

Method 1: Specify Number of Bins

The simplest way to adjust the bin size in a histogram is by specifying the number of bins.

Matplotlib’s hist() function allows you to set the number of bins using the ‘bins’ parameter. For example, if we have a dataset that ranges between 0 and 20, and we want to create a histogram with six bins, we can use the following code:

import matplotlib.pyplot as plt

## import numpy as np

data = np.random.normal(size=1000)

plt.hist(data, edgecolor=’black’, bins=6)

The ‘edgecolor’ parameter sets the color of the edges of each bar in the histogram, while the ‘bins’ parameter sets the number of bins. As we can see, the code above creates a histogram with six equal-width bins that span the range from 0 to 20.

Method 2: Specify Bin Boundaries

Another way to adjust the bin size is by specifying the bin boundaries. This gives us more control over the bin sizes and allows us to define bins of irregular widths.

To specify the boundaries, we can pass a list of bin edges to the ‘bins’ parameter. For example, if we want to create a histogram with five bins of different widths, we can use the following code:

plt.hist(data, edgecolor=’black’, bins=[0, 4, 8, 12, 16, 20])

In the code above, we pass a list of bin edges that define five bins of different widths.

The first bin ranges from 0 to 4, the second bin ranges from 4 to 8, and so on. This approach gives us more control over the bin widths and can be useful when the dataset has irregular distribution.

Method 3: Specify Bin Width

Finally, we can adjust the bin size by specifying the bin width. This can be done by creating an array of bin edges using the arange() function from NumPy. For example, if we want to create bins of width 2 that span from 0 to 20, we can use the following code:

plt.hist(data, edgecolor=’black’, bins=np.arange(0, 22, 2))

In the code above, we used the arange() function from NumPy to create an array of bin edges that range from 0 to 22 in increments of 2.

This creates bins of width 2 that start from 0 and end at 20. This approach gives us fine control over the bin size, but it can be difficult to choose an appropriate bin size when the dataset has an irregular distribution.

## Conclusion:

In this article, we discussed how to adjust the bin size in histograms using Matplotlib. We explored three methods that allow us to specify the bin size through the number of bins, the bin boundaries, and the bin width.

Each method has its advantages and disadvantages, and the right choice depends on the dataset’s distribution and the objective of the visualization. With this knowledge, you can create more informative and visually appealing histograms that enhance your data analysis.

Example 2: Specify Bin Boundaries

In Method 2, we learned how to adjust the bin size by specifying the bin boundaries. This method allows for greater flexibility in creating histograms.

Sometimes, the data may have a distribution that is not evenly spread out across the range, making it tricky to determine the optimal bin size. In this case, specifying bin boundaries can result in more informative histograms.

Let’s consider an example where we have a dataset of exam scores that range from 0 to 100, and we want to create a histogram that shows the distribution of the scores. Suppose we know that the passing grade is 50.

In this case, we may choose to create a histogram with the following bin boundaries:

plt.hist(scores, edgecolor=’black’, bins=[0, 50, 60, 70, 80, 90, 100])

Here, we have specified six bins, where the first bin includes scores that go from 0 to 50. The second bin includes scores that range from 50 to 60, and so on.

This approach allows us to highlight the distribution of the passing scores separately from the failing ones. Specifying bin boundaries can also be helpful when working with datasets containing outliers.

Outliers are data points that significantly depart from the expected or average value. For instance, suppose we have a dataset of housing prices where most prices are centered around \$200,000, but there are a few properties priced at \$3 million or above.

In this scenario, we may choose to set the bin boundaries to exclude the outliers. Example 3: Specify Bin Width

In Method 3, we learned how to adjust the bin size by specifying the bin width.

This method is useful when we want to have even bin sizes across the range of our dataset. Additionally, it allows us to easily visualize any specific range of the dataset.

Let’s consider an example where we have a dataset representing the height of students in a class. Suppose we want to create a histogram with a bin size of 5.

We can use the np.arange() function from NumPy to specify the bin width as follows:

plt.hist(student_height, edgecolor=’black’, bins=np.arange(140, 201, 5))

Here, we have specified bins with a width of 5 and a starting point of 140. This results in a histogram with 13 bins that show the distribution of the student heights.

We can see that most students are between 155 and 165 cm, with some outliers that are either shorter or taller. However, it is essential to be cautious when using this method.

If the bin width is too wide, we may lose important details like small peaks in the dataset. On the other hand, if the bin width is too narrow, we may create data fragments or miss significant trends in the data.

Thus, it is essential to consider the dataset’s nature and experiment with different bin widths to determine the most optimal one. Conclusion: