[英]Seaborn plot with pandas Series objects
將concat
與DataFrame.stack
和Series.reset_index
用於 DataFrame 和 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')
與DataFrame.melt
DataFrame.dropna
缺失值:
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')
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