Boxplot of Multiple Columns of a Pandas Dataframe on the Same Figure (seaborn)

The seaborn equivalent of

df.boxplot()

is

sns.boxplot(x="variable", y="value", data=pd.melt(df))

or just

sns.boxplot(data=df)

which will plot any column of numeric values, without converting the DataFrame from a wide to long format, using seaborn v0.11.1. This will create a single figure, with a separate boxplot for each column.

Complete example with melt:

import numpy as np; np.random.seed(42)
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.DataFrame(data = np.random.random(size=(4,4)), columns = ['A','B','C','D'])

sns.boxplot(x="variable", y="value", data=pd.melt(df))

plt.show()

This works because pd.melt converts a wide-form dataframe

          A         B         C         D
0  0.374540  0.950714  0.731994  0.598658
1  0.156019  0.155995  0.058084  0.866176
2  0.601115  0.708073  0.020584  0.969910
3  0.832443  0.212339  0.181825  0.183405

to long-form

   variable     value
0         A  0.374540
1         A  0.156019
2         A  0.601115
3         A  0.832443
4         B  0.950714
5         B  0.155995
6         B  0.708073
7         B  0.212339
8         C  0.731994
9         C  0.058084
10        C  0.020584
11        C  0.181825
12        D  0.598658
13        D  0.866176
14        D  0.969910
15        D  0.183405

plt.boxplot([df1,df2],   boxprops=dict(color='red'), labels=['title 1','title 2'])

You could use the built-in pandas method df.plot(kind='box') as suggested in this question.
I realize this answer will not help you if you have to use seaborn, but it may be useful for people with simpler requirements.

import numpy as np; np.random.seed(42)
import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame(data = np.random.random(size=(4,4)), columns = ['A','B','C','D'])

df.plot(kind='box')
plt.show()