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