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# Box Plot in Python using Matplotlib

Last Updated on July 18, 2024 by Abhishek Sharma

Box plots, also known as box-and-whisker plots, are a standardized way of displaying the distribution of data based on a five-number summary: minimum, first quartile (Q1), median, third quartile (Q3), and maximum. They are particularly useful for identifying outliers and understanding the spread and skewness of the data. In this article, we will explore how to create and interpret box plots in Python using the Matplotlib library.

## Getting Started with Matplotlib

Matplotlib is a powerful plotting library in Python that provides a wide range of functionalities for creating static, animated, and interactive visualizations. To get started, you need to install Matplotlib if you haven’t already:

``pip install matplotlib``

Creating a Basic Box Plot
To create a basic box plot, we need some sample data. Let’s start by generating a random dataset and then plotting it using Matplotlib.

``````import matplotlib.pyplot as plt
import numpy as np

# Generate random data
np.random.seed(10)
data = np.random.normal(100, 20, 200)

# Create a box plot
plt.boxplot(data)
plt.title('Box Plot of Random Data')
plt.ylabel('Values')
plt.show()``````

In this example:

• np.random.normal(100, 20, 200) generates 200 data points from a normal distribution with a mean of 100 and a standard deviation of 20.
• plt.boxplot(data) creates the box plot.
• plt.title and plt.ylabel are used to set the title and y-axis label of the plot.

Customizing the Box Plot
Matplotlib allows you to customize various aspects of the box plot, such as the color, orientation, and appearance of the whiskers and outliers.

Changing Box Plot Colors
You can change the colors of the different components of the box plot using the boxprops, whiskerprops, capprops, medianprops, and flierprops parameters.

``````plt.boxplot(data,
boxprops=dict(color="blue"),
whiskerprops=dict(color="red"),
capprops=dict(color="green"),
medianprops=dict(color="orange"),
flierprops=dict(markerfacecolor='purple', marker='o'))
plt.title('Customized Box Plot')
plt.ylabel('Values')
plt.show()``````

Horizontal Box Plot
To create a horizontal box plot, you can use the vert parameter.

``````plt.boxplot(data, vert=False)
plt.title('Horizontal Box Plot')
plt.xlabel('Values')
plt.show()``````

Box Plot with Multiple Data Sets
Box plots can also be used to compare multiple data sets side by side. Let’s create box plots for three different datasets.

``````# Generate multiple datasets
data1 = np.random.normal(100, 20, 200)
data2 = np.random.normal(90, 15, 200)
data3 = np.random.normal(110, 25, 200)

# Create a box plot for multiple datasets
data = [data1, data2, data3]
plt.boxplot(data, labels=['Dataset 1', 'Dataset 2', 'Dataset 3'])
plt.title('Box Plot for Multiple Datasets')
plt.ylabel('Values')
plt.show()``````

In this example:

• We generate three different datasets: data1, data2, and data3.
• We pass these datasets as a list to plt.boxplot and use the labels parameter to label each dataset.

Adding Notch to the Box Plot
Adding a notch to the box plot helps in visualizing the confidence interval around the median. You can do this using the notch parameter.

``````plt.boxplot(data, notch=True, labels=['Dataset 1', 'Dataset 2', 'Dataset 3'])
plt.title('Box Plot with Notches')
plt.ylabel('Values')
plt.show()``````

Interpreting the Box Plot
Here’s how to interpret the different components of a box plot:

• Box: The box represents the interquartile range (IQR), which contains the middle 50% of the data. The bottom of the box is the first quartile (Q1), and the top of the box is the third quartile (Q3).
• Whiskers: The whiskers extend from the box to the minimum and maximum values within 1.5 times the IQR from the Q1 and Q3, respectively.
• Median Line: The line inside the box represents the median (Q2) of the data.
• Outliers: Data points outside the whiskers are considered outliers and are plotted as individual points.

Conclusion
Box plots are a powerful tool for visualizing the distribution of data and identifying outliers. Matplotlib makes it easy to create and customize box plots to suit your needs. Whether you’re comparing multiple datasets or looking for insights into a single dataset, box plots provide a clear and concise way to understand your data.

## FAQs on Box Plots in Python using Matplotlib

Below are some FAQs on Box Plots in Python using Matplotlib:

1. What is a box plot?
A box plot, also known as a box-and-whisker plot, is a graphical representation of the distribution of a dataset. It displays the data’s minimum, first quartile (Q1), median, third quartile (Q3), and maximum values. Box plots are useful for identifying outliers and understanding the spread and skewness of the data.

2. How do I create a box plot in Python using Matplotlib?
To create a basic box plot using Matplotlib, you can use the following code:

``````import matplotlib.pyplot as plt
import numpy as np

# Generate random data
np.random.seed(10)
data = np.random.normal(100, 20, 200)

# Create a box plot
plt.boxplot(data)
plt.title('Box Plot of Random Data')
plt.ylabel('Values')
plt.show()``````

3. How can I customize the colors of the box plot?
You can customize the colors of different components of the box plot using the boxprops, whiskerprops, capprops, medianprops, and flierprops parameters. Here’s an example:

``````plt.boxplot(data,
boxprops=dict(color="blue"),
whiskerprops=dict(color="red"),
capprops=dict(color="green"),
medianprops=dict(color="orange"),
flierprops=dict(markerfacecolor='purple', marker='o'))
plt.title('Customized Box Plot')
plt.ylabel('Values')
plt.show()``````

4. How do I create a horizontal box plot?
To create a horizontal box plot, you can set the vert parameter to False:

``````plt.boxplot(data, vert=False)
plt.title('Horizontal Box Plot')
plt.xlabel('Values')
plt.show()``````

5. Can I create box plots for multiple datasets?
Yes, you can create box plots for multiple datasets by passing a list of datasets to plt.boxplot. Here’s an example:

``````# Generate multiple datasets
data1 = np.random.normal(100, 20, 200)
data2 = np.random.normal(90, 15, 200)
data3 = np.random.normal(110, 25, 200)

# Create a box plot for multiple datasets
data = [data1, data2, data3]
plt.boxplot(data, labels=['Dataset 1', 'Dataset 2', 'Dataset 3'])
plt.title('Box Plot for Multiple Datasets')
plt.ylabel('Values')
plt.show()``````