Box Plot in Python using Matplotlib

A Box Plot is also known as Whisker plot is created to display the summary of the set of data values having properties like minimum, first quartile, median, third quartile and maximum. In the box plot, a box is created from the first quartile to the third quartile, a vertical line is also there which goes through the box at the median. Here x-axis denotes the data to be plotted while the y-axis shows the frequency distribution.

Creating Box Plot

The matplotlib.pyplot module of matplotlib library provides boxplot() function with the help of which we can create box plots.

Syntax:

matplotlib.pyplot.boxplot(data, notch=None, vert=None, patch_artist=None, widths=None)

Parameters:

Attribute	Value
data	array or sequence of array to be plotted
notch	optional parameter accepts boolean values
vert	optional parameter accepts boolean values false and true for horizontal and vertical plot respectively
bootstrap	optional parameter accepts int specifies intervals around notched boxplots
usermedians	optional parameter accepts array or sequence of array dimension compatible with data
positions	optional parameter accepts array and sets the position of boxes
widths	optional parameter accepts array and sets the width of boxes
patch_artist	optional parameter having boolean values
labels	sequence of strings sets label for each dataset
meanline	optional having boolean value try to render meanline as full width of box
order	optional parameter sets the order of the boxplot

The data values given to the ax.boxplot() method can be a Numpy array or Python list or Tuple of arrays. Let us create the box plot by using numpy.random.normal() to create some random data, it takes mean, standard deviation, and the desired number of values as arguments.

Example:

Python3

# Import libraries
import matplotlib.pyplot as plt
import numpy as np
 
 
# Creating dataset
np.random.seed(10)
data = np.random.normal(100, 20, 200)
 
fig = plt.figure(figsize =(10, 7))
 
# Creating plot
plt.boxplot(data)
 
# show plot
plt.show()

Output:

box-plot-python

Customizing Box Plot

The matplotlib.pyplot.boxplot() provides endless customization possibilities to the box plot. The notch = True attribute creates the notch format to the box plot, patch_artist = True fills the boxplot with colors, we can set different colors to different boxes.The vert = 0 attribute creates horizontal box plot. labels takes same dimensions as the number data sets.

Example 1:

Python3

# Import libraries
import matplotlib.pyplot as plt
import numpy as np
 
 
# Creating dataset
np.random.seed(10)
 
data_1 = np.random.normal(100, 10, 200)
data_2 = np.random.normal(90, 20, 200)
data_3 = np.random.normal(80, 30, 200)
data_4 = np.random.normal(70, 40, 200)
data = [data_1, data_2, data_3, data_4]
 
fig = plt.figure(figsize =(10, 7))
 
# Creating axes instance
ax = fig.add_axes([0, 0, 1, 1])
 
# Creating plot
bp = ax.boxplot(data)
 
# show plot
plt.show()

Output:

box-plot-python

Example 2: Let’s try to modify the above plot with some of the customizations:

Python3

# Import libraries
import matplotlib.pyplot as plt
import numpy as np
 
# Creating dataset
np.random.seed(10)
data_1 = np.random.normal(100, 10, 200)
data_2 = np.random.normal(90, 20, 200)
data_3 = np.random.normal(80, 30, 200)
data_4 = np.random.normal(70, 40, 200)
data = [data_1, data_2, data_3, data_4]
 
fig = plt.figure(figsize =(10, 7))
ax = fig.add_subplot(111)
 
# Creating axes instance
bp = ax.boxplot(data, patch_artist = True,
                notch ='True', vert = 0)
 
colors = ['#0000FF', '#00FF00',
          '#FFFF00', '#FF00FF']
 
for patch, color in zip(bp['boxes'], colors):
    patch.set_facecolor(color)
 
# changing color and linewidth of
# whiskers
for whisker in bp['whiskers']:
    whisker.set(color ='#8B008B',
                linewidth = 1.5,
                linestyle =":")
 
# changing color and linewidth of
# caps
for cap in bp['caps']:
    cap.set(color ='#8B008B',
            linewidth = 2)
 
# changing color and linewidth of
# medians
for median in bp['medians']:
    median.set(color ='red',
               linewidth = 3)
 
# changing style of fliers
for flier in bp['fliers']:
    flier.set(marker ='D',
              color ='#e7298a',
              alpha = 0.5)
     
# x-axis labels
ax.set_yticklabels(['data_1', 'data_2',
                    'data_3', 'data_4'])
 
# Adding title
plt.title("Customized box plot")
 
# Removing top axes and right axes
# ticks
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
     
# show plot
plt.show()