Python:Matplotlib .boxplot()

The matplotlib’s .boxplot() method is a powerful data visualization function in matplotlib’s pyplot module that creates box-and-whisker plots to display the statistical summary of a dataset. This method displays the distribution of data through quartiles, showing the median, first quartile (Q1), third quartile (Q3), and potential outliers in a compact visual format.

Syntax

matplotlib.pyplot.boxplot(x, notch=None, sym=None, vert=None, ...)

Note: The ellipses (...) indicate that there are many additional optional parameters available, such as widths, patch_artist, showmeans, boxprops, and others. These parameters provide detailed control over the style, layout, and display of the boxplot.

Parameters:

• x: The input data (array-like or sequence of arrays). Can be a 1D array for a single boxplot or a sequence of arrays for multiple boxplots.
• notch: Boolean, optional. If True, a notched boxplot is created to indicate confidence intervals around the median.
• sym: String, optional. Default symbol for outlier points. An empty string hides the outliers.
• vert: Boolean, optional. If True (default), plots boxes vertically. If False, plots horizontally.

Return value:

The method returns a dictionary containing the matplotlib artists used in the boxplot. The dictionary includes keys for ‘boxes’, ‘medians’, ‘whiskers’, ‘caps’, ‘fliers’, and ‘means’.

Example 1: Creating a Basic Boxplot using matplotlib.pyplot.boxplot()

This example demonstrates how to create a simple boxplot using randomly generated data:

import matplotlib.pyplot as plt
import numpy as np

# Set random seed for reproducibility
np.random.seed(42)

# Generate sample data
data = np.random.normal(100, 15, 200)

# Create the boxplot
plt.figure(figsize=(8, 6))
plt.boxplot(data)
plt.title('Basic Boxplot Example')
plt.ylabel('Values')
plt.show()

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The output of this code is:

The code generates a dataset with 200 values following a normal distribution with a mean of 100 and a standard deviation of 15. The resulting boxplot displays the median as a horizontal line, the box representing the interquartile range (IQR), whiskers extending to the most extreme non-outlier data points, and any outliers as individual points.

Example 2: Multiple Dataset Comparison using the matplotlib.pyplot.boxplot() method

This example shows how to create boxplots for multiple datasets to compare their distributions:

import matplotlib.pyplot as plt
import numpy as np

# Set random seed for reproducibility
np.random.seed(42)

# Generate multiple datasets with different characteristics
dataset1 = np.random.normal(80, 10, 100)  # Lower mean, smaller spread
dataset2 = np.random.normal(100, 20, 100)  # Higher mean, larger spread
dataset3 = np.random.exponential(25, 100)  # Exponential distribution
dataset4 = np.random.uniform(50, 150, 100)  # Uniform distribution

# Combine datasets
data = [dataset1, dataset2, dataset3, dataset4]

# Create multiple boxplots
plt.figure(figsize=(10, 6))
box_plot = plt.boxplot(data, labels=['Normal (80,10)', 'Normal (100,20)',
                                   'Exponential (25)', 'Uniform (50,150)'])
plt.title('Comparison of Different Distributions')
plt.ylabel('Values')
plt.xlabel('Distribution Type')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

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The output of this code is:

This example creates four different datasets with distinct statistical properties and displays them side by side. The boxplots make it easy to compare the medians, spreads, and presence of outliers across the different distributions.

Example 3: Customized Sales Performance Analysis on Boxplot

This example demonstrates a real-world scenario analyzing quarterly sales performance across different product categories:

import matplotlib.pyplot as plt
import numpy as np

# Set random seed for reproducibility
np.random.seed(42)

# Simulate quarterly sales data (in thousands)
electronics = np.random.normal(150, 25, 50)  # Electronics sales
clothing = np.random.normal(120, 30, 50)     # Clothing sales
home_goods = np.random.normal(100, 20, 50)   # Home goods sales
sports = np.random.normal(80, 15, 50)        # Sports equipment sales

# Add some outliers to make it more realistic
electronics = np.append(electronics, [220, 250])  # High-performance months
clothing = np.append(clothing, [200, 40])         # Seasonal variations
home_goods = np.append(home_goods, [180])         # Holiday boost
sports = np.append(sports, [150, 30])             # Seasonal impact

# Combine all sales data
sales_data = [electronics, clothing, home_goods, sports]
categories = ['Electronics', 'Clothing', 'Home Goods', 'Sports']

# Create customized boxplot
plt.figure(figsize=(12, 8))
box_plot = plt.boxplot(sales_data,
                      labels=categories,
                      patch_artist=True,  # Fill with colors
                      notch=True,         # Show confidence intervals
                      showmeans=True)     # Show mean values

# Customize colors for each category
colors = ['lightblue', 'lightgreen', 'lightcoral', 'lightyellow']
for patch, color in zip(box_plot['boxes'], colors):
  patch.set_facecolor(color)

# Customize the plot appearance
plt.title('Quarterly Sales Performance Analysis by Product Category',
          fontsize=16, fontweight='bold')
plt.ylabel('Sales (in thousands USD)', fontsize=12)
plt.xlabel('Product Categories', fontsize=12)
plt.grid(axis='y', alpha=0.3)
plt.tight_layout()
plt.show()

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The output of this code is:

This example simulates a business scenario where sales data is analyzed across different product categories. The customized boxplot uses colors to distinguish categories, shows confidence intervals through notches, and displays mean values alongside medians. This visualization helps identify which product categories perform best and have the most consistent sales patterns.

Frequently Asked Questions

1. What is a box plot in Matplotlib?

A box plot displays data distribution through five statistics: minimum, Q1, median, Q3, and maximum, with outliers shown as individual points.

2. What is the difference between Seaborn Boxplot and Matplotlib Boxplot?

Seaborn’s boxplot offers better default styling and easier categorical data handling, while Matplotlib’s boxplot provides more low-level control and customization options.

3. How to plot a boxplot in a Python DataFrame?

Pass DataFrame columns to plt.boxplot([df['col1'], df['col2']]) or use pandas’ built-in df.boxplot() method.