1. 程式人生 > >【1.4】Pandas學習—遍歷某列的所有行

【1.4】Pandas學習—遍歷某列的所有行

遍歷某列的所有行

import pandas as pd

df_pathway = pd.read_excel('C:/Users/Administrator.USER-20160219OS/Desktop/代謝通路富集表.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']: #遍歷某列所有行
    print(i,type(i)) # i型別是字串
    print(i.split(' ')) # 字串轉列表
    print('------------------------------')
    values = pd.DataFrame(i.split(' '),columns=['id']) # 列表轉dataframe
    print(values)

執行結果如下:

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