Adding a legend to a matplotlib boxplot with multiple plots on same axes

Just as a complement to @ImportanceOfBeingErnest's response, if you are plotting in a for loop like this:

for data in datas:
    ax.boxplot(data, positions=[1,4], notch=True, widths=0.35, 
             patch_artist=True, boxprops=dict(facecolor="C0"))

You cannot save the plots as variables. So in that case, create legend labels list legends, append the plots into another list elements and use list comprehension to put a legend for each of them:

labels = ['A', 'B']
colors = ['blue', 'red']
elements = []

for dIdx, data in enumerate(datas):
    elements.append(ax.boxplot(data, positions=[1,4], notch=True,\
    widths=0.35, patch_artist=True, boxprops=dict(facecolor=colors[dIdx])))

ax.legend([element["boxes"][0] for element in elements], 
    [labels[idx] for idx,_ in enumerate(datas)])

The boxplot returns a dictionary of artists

result : dict
A dictionary mapping each component of the boxplot to a list of the matplotlib.lines.Line2D instances created. That dictionary has the following keys (assuming vertical boxplots):

• boxes: the main body of the boxplot showing the quartiles and the median’s confidence intervals if enabled.
• [...]

Using the boxes, you can get the legend artists as

ax.legend([bp1["boxes"][0], bp2["boxes"][0]], ['A', 'B'], loc='upper right')

Complete example:

import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)

data1=np.random.randn(40,2)
data2=np.random.randn(30,2)

fig, ax = plt.subplots()
bp1 = ax.boxplot(data1, positions=[1,4], notch=True, widths=0.35, 
                 patch_artist=True, boxprops=dict(facecolor="C0"))
bp2 = ax.boxplot(data2, positions=[2,5], notch=True, widths=0.35, 
                 patch_artist=True, boxprops=dict(facecolor="C2"))

ax.legend([bp1["boxes"][0], bp2["boxes"][0]], ['A', 'B'], loc='upper right')

ax.set_xlim(0,6)
plt.show()