Pandas boxplot()

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# create a dataframe
data = {'Math': [88, 74, 96, 85, 91],
        'Science': [92, 80, 75, 88, 90],
        'English': [79, 84, 87, 90, 93]}

df = pd.DataFrame(data)

# create a boxplot
boxplot = df.boxplot()
plt.show()

boxplot() Syntax

The syntax of the boxplot() method in Pandas is:

df.boxplot(column=None, by=None, ax=None, fontsize=None, rot=0, grid=True, figsize=None, layout=None, return_type=None,**kwargs)

boxplot() Arguments

The boxplot() method takes the following arguments:

• column (optional): specifies columns to plot
• by (optional): specifies columns to group by
• ax (optional): matplotlib axes object used to place the plot on specific axes or a subplot
• fontsize (optional): specifies font size for the axis labels
• rot (optional): specifies rotation of axis labels
• grid (optional): whether to display grid lines or not
• figsize (optional): specifies size of the figure to create
• layout (optional): specifies layout of the boxplots
• return_type (optional): specifies the type of object to return
• **kwargs (optional): additional keyword arguments

boxplot() Return Value

The boxplot() method in Pandas can return different types of objects based on the return_type parameter. The return_type parameter specifies the type of object that should be returned. The options are:

• 'axes': This is the default. When return_type='axes', the method returns a Matplotlib axes object or a NumPy array of axes objects if there are multiple subplots.

• 'dict': If return_type='dict', it returns a dictionary whose keys are the column names or group names (if by is specified) and whose values are dictionaries of Matplotlib lines representing the various parts of the box plot.

• 'both': When return_type='both', it returns a named tuple with two components: axes and lines, where axes is as described above and lines is a dictionary as in the 'dict' return type.

• None: If return_type=None, no object is returned. This might be used in situations where you only want to display the plot and do not need to interact with it programmatically afterward.

Example 1: Simple Box Plot

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# create a dataframe
data = {'Math': [88, 74, 96, 85, 91],
        'Science': [92, 80, 75, 88, 90],
        'English': [79, 84, 87, 90, 93]}

df = pd.DataFrame(data)

# create a boxplot
boxplot = df.boxplot(column=['Math'])
plt.show()

Output

In this example, we plotted a simple box plot for the Math column.

Example 2: Box Plot Grouped by Subject

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# create a dataframe
data = { 'Scores': [88, 74, 96, 91, 92, 80, 88, 90, 79, 87, 90, 93, ],
        'Subject': ['Maths', 'Maths', 'Maths', 'Maths', 'Science', 'Science', 'Science', 'Science', 'English', 'English', 'English', 'English']}

df = pd.DataFrame(data)

# create a boxplot grouped by subject
boxplot = df.boxplot(column=['Scores'], by='Subject')
plt.show()

Output

In this example, we used the by argument to group the Scores column by Subject before plotting the box plot.

Example 3: Customizing Box Plots

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# create a dataframe
data = {'Math': [88, 74, 96, 85, 91],
        'Science': [92, 80, 75, 88, 90],
        'English': [79, 84, 87, 90, 93]}

df = pd.DataFrame(data)

# create a boxplot grouped by subject
boxplot = df.boxplot(column=['Math'], grid=False, rot=45, fontsize=15, figsize=(8,6))
plt.show()

Output

In this example, we customized the box plot for the Math column.

Here,

• grid=False: means that the grid lines are not shown
• rot=45: rotates the label by 45 degrees
• fontsize=15: sets the font size of labels to 15
• figsize=(8,6): sets the size of the plot to 8x6 inches

Example4: Pandas boxplot() Return Type

import pandas as pd

# create a dataframe
data = {
    'A': [1, 2, 3, 4, 5],
    'B': [2, 3, 4, 5, 6],
    'C': [3, 4, 5, 6, 7]
}

df = pd.DataFrame(data)

# create a box plot of the data
# with dict return type
plot_dict = df.boxplot(return_type='dict')

print(plot_dict)

Output

{
'whiskers': [<matplotlib.lines.Line2D object at 0x117bf3710>, <matplotlib.lines.Line2D object at 0x117d936d0>, <matplotlib.lines.Line2D object at 0x117da7d10>, <matplotlib.lines.Line2D object at 0x117db4890>, <matplotlib.lines.Line2D object at 0x117dc0a90>, <matplotlib.lines.Line2D object at 0x117dc1610>],
 'caps': [<matplotlib.lines.Line2D object at 0x117da4310>, <matplotlib.lines.Line2D object at 0x117da5010>, <matplotlib.lines.Line2D object at 0x117db53d0>, <matplotlib.lines.Line2D object at 0x117db5f50>, <matplotlib.lines.Line2D object at 0x117dc21d0>, <matplotlib.lines.Line2D object at 0x117dc2d90>],
'boxes': [<matplotlib.lines.Line2D object at 0x117d64390>, <matplotlib.lines.Line2D object at 0x117da71d0>, <matplotlib.lines.Line2D object at 0x117db7f10>],
'medians': [<matplotlib.lines.Line2D object at 0x117da5bd0>, <matplotlib.lines.Line2D object at 0x117db6b10>, <matplotlib.lines.Line2D object at 0x117dc3850>],
'fliers': [<matplotlib.lines.Line2D object at 0x117da6250>, <matplotlib.lines.Line2D object at 0x117db7450>, <matplotlib.lines.Line2D object at 0x117dcc310>],
'means': []
}

In this example, we returned the box plot as a Python dictionary. This is useful when we want to interact with the box plot programmatically after creating it.