Box plots

Matplotlib Basics

2 min read

Published Oct 5 2025, updated Aug 17 2026

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A box plot summarises a dataset’s distribution using five key statistics:

Minimum (lower whisker)
First quartile (Q1) — 25th percentile
Median (Q2) — 50th percentile
Third quartile (Q3) — 75th percentile
Maximum (upper whisker)

It may also show outliers (points beyond the whiskers).

Syntax:

plt.boxplot(x, notch=False, vert=True, patch_artist=False, labels=None,            widths=None, showmeans=False, meanline=False)

Parameters:

x = Data (list, array, or list of lists)
notch = Adds notch around median (confidence interval)
vert = Vertical (True) or horizontal (False) orientation
patch_artist = Fill the boxes with colour
labels = Labels for each dataset
widths = Width of boxes
showmeans = Show the mean value
meanline = Draw mean as a line instead of point
flierprops, boxprops, medianprops, whiskerprops = Customize element styles

Basic box plot

import matplotlib.pyplot as pltimport numpy as npdata = np.random.randn(100)plt.boxplot(data)plt.title("Basic Box Plot")plt.show()

Shows distribution, median line, quartiles, whiskers, and outliers.

Multiple Box Plots

Pass a list of datasets to compare side by side.

import matplotlib.pyplot as pltimport numpy as npdata1 = np.random.normal(0, 1, 100)data2 = np.random.normal(1, 2, 100)data3 = np.random.normal(2, 1.5, 100)plt.boxplot([data1, data2, data3], labels=['A', 'B', 'C'])plt.title("Multiple Box Plots")plt.show()

matplotlib box plot chart multiple example

Horizontal orientation

import matplotlib.pyplot as pltimport numpy as npdata1 = np.random.normal(0, 1, 100)data2 = np.random.normal(1, 2, 100)data3 = np.random.normal(2, 1.5, 100)plt.boxplot([data1, data2, data3], vert=False, labels=['A', 'B', 'C'])plt.title("Horizontal Box Plots")plt.show()

Great for long category names.

matplotlib box plot chart horizontal example

Notched Box Plots

Notches show a rough confidence interval around the median. If two notches don’t overlap, medians differ significantly.

import matplotlib.pyplot as pltimport numpy as npdata1 = np.random.normal(0, 1, 100)data2 = np.random.normal(1, 2, 100)data3 = np.random.normal(2, 1.5, 100)plt.boxplot([data1, data2, data3], notch=True, labels=['A', 'B', 'C'])plt.title("Notched Box Plots")plt.show()

matplotlib box plot chart notched example

Coloured (Filled) Boxes

Use patch_artist=True and set boxprops or facecolor.

import matplotlib.pyplot as pltimport numpy as npdata1 = np.random.normal(0, 1, 100)data2 = np.random.normal(1, 2, 100)data3 = np.random.normal(2, 1.5, 100)colors = ['#99ccff', '#ff9999', '#99ff99']plt.boxplot(    [data1, data2, data3],    patch_artist=True,    boxprops=dict(facecolor='lightblue', color='blue'),    medianprops=dict(color='red'),    labels=['A', 'B', 'C'])plt.title("Coloured Box Plots")plt.show()

matplotlib box plot chart coloured example

Or color each box differently:

import matplotlib.pyplot as pltimport numpy as npdata1 = np.random.normal(0, 1, 100)data2 = np.random.normal(1, 2, 100)data3 = np.random.normal(2, 1.5, 100)colors = ['#99ccff', '#ff9999', '#99ff99']boxes = plt.boxplot([data1, data2, data3], patch_artist=True)for patch, color in zip(boxes['boxes'], colors):    patch.set_facecolor(color)plt.show()

matplotlib box plot chart individual coloured example

Show mean and mean line

import matplotlib.pyplot as pltimport numpy as npdata1 = np.random.normal(0, 1, 100)data2 = np.random.normal(1, 2, 100)data3 = np.random.normal(2, 1.5, 100)plt.boxplot(    [data1, data2, data3],    showmeans=True,    meanline=True,    labels=['A', 'B', 'C'])plt.title("Box Plot with Mean Line")plt.show()

matplotlib box plot chart meanline example

Adjust box widths

import matplotlib.pyplot as pltimport numpy as npdata1 = np.random.normal(0, 1, 100)data2 = np.random.normal(1, 2, 100)data3 = np.random.normal(2, 1.5, 100)plt.boxplot([data1, data2, data3], widths=0.5, labels=['A', 'B', 'C'])plt.title("Custom Box Widths")plt.show()

matplotlib box plot chart box widths example

Customise outliers

import matplotlib.pyplot as pltimport numpy as npdata1 = np.random.normal(0, 1, 100)data2 = np.random.normal(1, 2, 100)data3 = np.random.normal(2, 1.5, 100)plt.boxplot(    [data1, data2, data3],    flierprops=dict(marker='o', markerfacecolor='red', markersize=6, linestyle='none'),    labels=['A', 'B', 'C'])plt.title("Custom Outlier Markers")plt.show()

matplotlib box plot chart custom outliers example

Display data points on top of box plot

Combine with plt.scatter() or plt.plot() for more detail.

import matplotlib.pyplot as pltimport numpy as npdata = np.random.randn(100)plt.boxplot(data)plt.scatter(np.random.normal(1, 0.04, size=len(data)), data, alpha=0.5)plt.title("Box Plot with Raw Data Overlay")plt.show()

matplotlib box plot chart data overlayed example

Advanced styling example

import matplotlib.pyplot as pltimport numpy as npdata1 = np.random.normal(0, 1, 100)data2 = np.random.normal(1, 2, 100)data3 = np.random.normal(2, 1.5, 100)plt.boxplot(    [data1, data2, data3],    patch_artist=True,    notch=True,    showmeans=True,    meanline=False,    boxprops=dict(facecolor='lightyellow', color='orange'),    whiskerprops=dict(color='orange', linewidth=2),    medianprops=dict(color='red', linewidth=2),    capprops=dict(color='orange'),    flierprops=dict(marker='o', color='black', alpha=0.5),    labels=['A', 'B', 'C'])plt.title("Advanced Styled Box Plot")plt.show()