如何在条件下将csv文件合并到单个文件并将文件名添加为列？

Question

我的文件夹上有多个csv文件。列标题不同，但列数据相同。

括号内的数字是实际的列名。项目（67）67是列名

因此，忽略字符串Item，仅考虑int（）中的int并执行操作。

样本文件：https : //drive.google.com/open?id=1q7c1AqCRKRufSVh–
9o0W6rdz28QyBGa

说明：

驱动器上的文件应附加在一起。基于列名称的“启用条件”。如果条件上的整数与列名（列名（）内的整数）匹配，则应将其放在该列上。请检查预期的输出。
档案

File1: ID Item(67) Item (89) Item (91) Item (100)
       1    56      78        98        101     
       2    91      100       121       
File2: ID Item(96) Item (58) Item (99) Item (105)
       3  101      102        103       104
       4  112      113        117       119

健康）状况

d ={
    'File':['File1','File2'],
     'Price1':[67,67],
     'Price2':[89,67],
     'Price3':[91,67],
    'Price4':[100,91]
}
Condition=pd.DataFrame(data=d)
Condition

预期产量：

  File  ID   Price1 Price2 Price3 Price4
  File1  1    56      78    98     101     
  File1  2    91      100   121
  File2  3    101     102  104     103       
  File2  4     112      113  119      117

Answer 1

采用：

files = glob.glob('shelldemo/*.csv')

dfs = []
for fp in files:
    #if multiple columns with no ()  
    #df = pd.read_csv(fp, index_col=['S.no','id','number'])

    df = pd.read_csv(fp, index_col=['ID'])
    df['file'] = os.path.basename(fp).split('.')[0]
    df = df.set_index('file', append=True)
    df.columns = df.columns.str.extract('\((\d+)\)', expand=False).astype(int)
    dfs.append(df)


df1 = pd.concat(dfs, sort=False).reset_index()
print (df1)
   ID   file     58   67     89     91     96    100
0   1  file1    NaN   56   78.0   98.0    NaN  101.0
1   2  file1    NaN   91  100.0  121.0    NaN    NaN
2   3  file2  102.0  103    NaN    NaN  101.0  104.0
3   4  file2  113.0  117    NaN    NaN  112.0  119.0

print (df2)
    File  Price1  Price2  Price3  Price4
0  File1      67      89      91     100
1  File2      96      58     105      99

df2.columns = df2.columns.str.lower() 
df2['file'] = df2['file'].str.lower()

#merge data together by left join 
df = df1.merge(df2, on='file', how='left')
print (df)
   ID   file     58   67     89     91     96    100  price1  price2  price3  \
0   1  file1    NaN   56   78.0   98.0    NaN  101.0      67      89      91   
1   2  file1    NaN   91  100.0  121.0    NaN    NaN      67      89      91   
2   3  file2  102.0  103    NaN    NaN  101.0  104.0      96      58     105   
3   4  file2  113.0  117    NaN    NaN  112.0  119.0      96      58     105

   price4  
0     100  
1     100  
2      99  
3      99

#filter integers between ()
df1 = df.loc[:, df.columns.str.isnumeric().isnull()].copy()
#filter all columns with price
df2 = df.filter(regex='price').copy()

uniq_vals_df2 = df2.stack().dropna().drop_duplicates()
not_matched_vals = np.setdiff1d(uniq_vals_df2, df1.columns)
df1 = df1.join(pd.DataFrame(columns=not_matched_vals.tolist() + ['a']))

#replace columns by match values from df2
for c in df2.columns:
    df2[c] = df1.lookup(df1.index, df2[c].fillna('a'))
#join to original DataFrame    
df = df[['file','ID']].join(df2)

print (df)

    file  ID  price1  price2  price3  price4
0  file1   1    56.0    78.0    98.0   101.0
1  file1   2    91.0   100.0   121.0     NaN
2  file2   3   101.0   102.0     NaN     NaN
3  file2   4   112.0   113.0     NaN     NaN