I have 4 pandas Series objects, they are different in size and also they have different indexes. I want to create a barplot or boxplot to show how median values of these Series differ.
e.g one of my Series is:
another:
I can't set seaborn.boxplot or seaborn.barplot to visualize something like this: 
Use concat with DataFrame.stack and Series.reset_index for DataFrame and then plot:
s1 = pd.Series([1,2,3])
s2 = pd.Series([20,1,3,6,90], index=list('abcde'))
s3 = pd.Series([4,5,2.6], index=list('ABC'))
s4 = pd.Series([7,20.8], index=list('XY'))
df = (pd.concat([s1, s2, s3, s4], axis=1, keys=('a','b','c','d'))
.stack()
.rename_axis(('a','b'))
.reset_index(name='c'))
print (df)
a b c
0 0 a 1.0
1 1 a 2.0
2 2 a 3.0
3 A c 4.0
4 B c 5.0
5 C c 2.6
6 X d 7.0
7 Y d 20.8
8 a b 20.0
9 b b 1.0
10 c b 3.0
11 d b 6.0
12 e b 90.0
sns.barplot(data=df, x='b', y='c')
Similar idea with DataFrame.melt and remove missing values by DataFrame.dropna:
s1 = pd.Series([1,2,3])
s2 = pd.Series([20,1,3,6,90], index=list('abcde'))
s3 = pd.Series([4,5,2.6], index=list('ABC'))
s4 = pd.Series([7,20.8], index=list('XY'))
df = pd.concat([s1, s2, s3, s4], axis=1, keys=('a','b','c','d')).melt().dropna()
print (df)
variable value
0 a 1.0
1 a 2.0
2 a 3.0
21 b 20.0
22 b 1.0
23 b 3.0
24 b 6.0
25 b 90.0
29 c 4.0
30 c 5.0
31 c 2.6
45 d 7.0
46 d 20.8
sns.barplot(data=df, x='variable', y='value')
