问题描述
Category Sector 1 Blanks Sector 2 Sector 3 Sector 4 Sector 5 Sector 6 Other Sector 7
Blank 0 1 0 0 0 0 0 0 0
3D 1 0 0 0 0 1 1 0 0
3DPrinting 0 0 1 0 0 1 1 0 0
3DTechnology 0 0 0 1 0 1 1 0 0
B2B 0 0 0 0 1 0 0 0 1
B2B Express Delivery 0 0 0 0 0 0 0 0 1
K-12 Education 0 0 0 0 0 0 0 1 0
M2M 0 0 0 0 0 0 0 1 0
P2P Money Transfer 0 0 0 0 1 0 0 0 1
cols = list(df.columns.difference(['Category']))
cols
df = pd.melt(df,id_vars=['Category'],value_vars=cols)
df = df[~(df.value == 0)]
df = df.drop('value',axis = 1)
df = df.rename(columns = {"variable" : "Sectors"})
df.head()
哪个可以完成这项工作,我可以根据需要重塑数据框,如下所示:
Category Sectors
3D Sector 1
3DPrinting Sector 2
3DTechnology Sector 3
B2B Sector 4
B3B Express Delivery Sector 7
....
....
不知何故,我不满意,正在寻找上述多行代码的替代方案。
有没有可能?
解决方法
尝试使用 melt
:
(df.melt('Category',var_name='Sectors')
.query('value>0 & Sectors != "Blanks"')
)
输出:
Category Sectors value
1 3D Sector 1 1
20 3DPrinting Sector 2 1
30 3DTechnology Sector 3 1
40 B2B Sector 4 1
44 P2P Money Transfer Sector 4 1
46 3D Sector 5 1
47 3DPrinting Sector 5 1
48 3DTechnology Sector 5 1
55 3D Sector 6 1
56 3DPrinting Sector 6 1
57 3DTechnology Sector 6 1
69 K-12 Education Other 1
70 M2M Other 1
76 B2B Sector 7 1
77 B2B Express Delivery Sector 7 1
80 P2P Money Transfer Sector 7 1