【1.4】Pandas学习—遍历某列的所有行 - 好文

遍历某列的所有行
import pandas as pd df_pathway =
pd.read_excel('C:/Users/Administrator.USER-20160219OS/Desktop/代谢通路富集表.xlsx',sheetname='mbrole_enrich')
print(df_pathway.head(3),type(df_pathway))
print('-----------------------------------------------------------------------')
sid = df_pathway['Submitted IDs'] print(sid,type(sid))
print('-----------------------------------------------------------------------')
for i in df_pathway['Submitted IDs']: #遍历某列所有行 print(i,type(i)) # i类型是字符串
print(i.split(' ')) # 字符串转列表 print('------------------------------') values =
pd.DataFrame(i.split(' '),columns=['id']) # 列表转dataframe print(values)
运行结果如下：
ID Annotation Annotation Category Group \ 0 rno00564 Glycerophospholipid
metabolism KEGG pathways Pathways 1 rno00100 Steroid biosynthesis KEGG pathways
Pathways 2 rno00591 Linoleic acid metabolism KEGG pathways Pathways Database
set in set background in background \ 0 KEGG (Rattus norvegicus (rat)) 13 3
3069 46 1 KEGG (Rattus norvegicus (rat)) 13 3 3069 51 2 KEGG (Rattus norvegicus
(rat)) 13 2 3069 26 p-value -log(p-value) FDR correction Submitted IDs \ 0
0.000812 3.090444 0.00715 C00157 C00350 C04230 1 0.001100 2.958607 0.00715
C01561 C06085 C02530 2 0.005080 2.294136 0.02200 C00157 C14765 Matching IDs URL
0 C00157 C00350 C04230 https://www.genome.jp/kegg-bin/show_pathway?15... 1
C01561 C06085 C02530 https://www.genome.jp/kegg-bin/show_pathway?15... 2 C00157
C14765 https://www.genome.jp/kegg-bin/show_pathway?15... <class
'pandas.core.frame.DataFrame'>
----------------------------------------------------------------------- 0
C00157 C00350 C04230 1 C01561 C06085 C02530 2 C00157 C14765 3 C06427 C00157 4
C00350 5 C00350 6 C05790 C02191 7 C00836 8 C06007 9 C06427 10 C00157 11 C05499
12 C01561 C02191 C06427 C00157 C00350 C00836 C06007 Name: Submitted IDs, dtype:
object <class 'pandas.core.series.Series'>
----------------------------------------------------------------------- C00157
C00350 C04230 <class 'str'> ['C00157', 'C00350', 'C04230']
------------------------------ id 0 C00157 1 C00350 2 C04230 C01561 C06085
C02530 <class 'str'> ['C01561', 'C06085', 'C02530']
------------------------------ id 0 C01561 1 C06085 2 C02530 C00157 C14765
<class 'str'> ['C00157', 'C14765'] ------------------------------ id 0 C00157 1
C14765 C06427 C00157 <class 'str'> ['C06427', 'C00157']
------------------------------ id 0 C06427 1 C00157 C00350 <class 'str'>
['C00350'] ------------------------------ id 0 C00350 C00350 <class 'str'>
['C00350'] ------------------------------ id 0 C00350 C05790 C02191 <class
'str'> ['C05790', 'C02191'] ------------------------------ id 0 C05790 1 C02191
C00836 <class 'str'> ['C00836'] ------------------------------ id 0 C00836
C06007 <class 'str'> ['C06007'] ------------------------------ id 0 C06007
C06427 <class 'str'> ['C06427'] ------------------------------ id 0 C06427
C00157 <class 'str'> ['C00157'] ------------------------------ id 0 C00157
C05499 <class 'str'> ['C05499'] ------------------------------ id 0 C05499
C01561 C02191 C06427 C00157 C00350 C00836 C06007 <class 'str'> ['C01561',
'C02191', 'C06427', 'C00157', 'C00350', 'C00836', 'C06007']
------------------------------ id 0 C01561 1 C02191 2 C06427 3 C00157 4 C00350
5 C00836 6 C06007

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