I am using the example from the docs (with a slightly modified data structure):

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

data = pd.DataFrame({"peaches":[1,4,7,5,8,2,5,7], "oranges":[2,7,9,1,9,2,7,3], "tomato":[3,8,5,4,5,6,7,1]})
labels = ['peaches', 'oranges', 'tomatoes']
colors = ['peachpuff', 'orange', 'tomato']

fig, ax = plt.subplots()
ax.set_ylabel('fruit weight (g)')

bplot = ax.boxplot(data,
                   patch_artist=True,  # fill with color
                   tick_labels=labels)  # will be used to label x-ticks

plt.show()

What I need to do is color the top part of each box (I.E. median to Q3) with one shade of blue, and the bottom part (median to Q2) with a deeper shade of blue (ignore the colors given in the example).

I think this is achieved by iterating through and adding a rectangle using ax.add.patch(plt.Rectangle).

But as a relative beginner in this area I can't work out how to iterate through the data and the boxplot. Please can anyone assist?

I am using the example from the docs (with a slightly modified data structure):

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

data = pd.DataFrame({"peaches":[1,4,7,5,8,2,5,7], "oranges":[2,7,9,1,9,2,7,3], "tomato":[3,8,5,4,5,6,7,1]})
labels = ['peaches', 'oranges', 'tomatoes']
colors = ['peachpuff', 'orange', 'tomato']

fig, ax = plt.subplots()
ax.set_ylabel('fruit weight (g)')

bplot = ax.boxplot(data,
                   patch_artist=True,  # fill with color
                   tick_labels=labels)  # will be used to label x-ticks

plt.show()

What I need to do is color the top part of each box (I.E. median to Q3) with one shade of blue, and the bottom part (median to Q2) with a deeper shade of blue (ignore the colors given in the example).

I think this is achieved by iterating through and adding a rectangle using ax.add.patch(plt.Rectangle).

But as a relative beginner in this area I can't work out how to iterate through the data and the boxplot. Please can anyone assist?

• python
• matplotlib
• boxplot

1 Answer 1

You can try something like the following:

• Create a boxplot of known x axis coordinates, and width

• Find the quartile ranges of the boxplot (gives you the y coordinates and height of the boxplot)

• Add a rectangle of dimensions of the boxplot

import matplotlib.pyplot as plt
import matplotlib.patches as patches


# Create Dataset
data = pd.DataFrame({"peaches":[1,4,7,5,8,2,5,7], "oranges":[2,7,9,1,9,2,7,3], "tomato":[3,8,5,4,5,6,7,1]})
labels = ['peaches', 'oranges', 'tomatoes']

# Colors for the boxplot
color = ['skyblue', 'blue']

#x axis position for the boxplot
positions = [2, 5, 8]

#Width of the boxplots
width = [0.2] * data.columns.size

fig, ax = plt.subplots(1)

bplot = ax.boxplot(data,
                    patch_artist=False,  
                    tick_labels=labels, widths=width, positions=positions) 


# Add color to boxplots


for i, col in enumerate(data.columns):

  
  q75, q50, q25 = np.percentile(data[col], [75, 50 ,25])
  iqr = q75 - q25
  median = q50
  
  # Create a rectangle patch - offset by -width[i]/2 to overlay inside border
  rect1 = patches.Rectangle((positions[i] -width[i]/2 , median), width[i], q25-median, facecolor=color[0], alpha=0.5)
  rect2 = patches.Rectangle((positions[i] -width[i]/2, median), width[i], q75-median, facecolor=color[1], alpha=0.5)

  # Add the rectangle to the axes
  ax.add_patch(rect2)
  ax.add_patch(rect1)

  ax.set_xlim(np.min(positions) - np.min(positions)/2 , np.max(positions) + np.min(positions)/2)


# Display the plot
plt.ylabel('fruit weight (g)')
plt.show()