Question

我有一个关于合并4个数据框的查询例如，我有4个数据框，如下所示：

print(df1)

SET I      Violations
Rule 1            1
Rule 2            1
Rule 3            6

print(df2)

SET II       Violations
Rule 1            2
Rule 2            3
Rule 3            6

print(df3)

SET III       Violations
Rule 1            2
Rule 2            4
Rule 3            8


print(df4)

SET IV       Violations
Rule 1            2
Rule 2            5
Rule 3            8

我的预期输出：

SET I      Violations
Rule 1            1
Rule 2            1
Rule 3            6


SET II       Violations
Rule 1            2
Rule 2            3
Rule 3            6

SET III       Violations
Rule 1            2
Rule 2            4
Rule 3            8

SET IV       Violations
Rule 1            2
Rule 2            5
Rule 3            8

我现在得到的输出：

a）

SET I   SET II    SET III   SET IV  Violations
Rule 1                                     1
Rule 2                                     1
Rule 3                                     6 
        Rule 1                             2
        Rule 2                             3
        Rule 3                             6
         Rule 1                    2
                 Rule 2                    4
                 Rule 3                    8
                       Rule 1          2
                           Rule 2          5
                           Rule 3          8

command_used：

pandas.concat([df1,df2,df3,df4],axis=0,ignore_index=True)

b）

Rule 1  1   Rule 1    2    Rule 1  2      Rule 1  2 
Rule 2  1   Rule 2    3    Rule 2  4      Rule 2  5
Rule 3  6   Rule 3    6    Rule 3  8      Rule 3  8

command_used：

pandas.concat([df1,df2,df3,df4],axis=1,ignore_index=True)

请帮助我以上

Answer 1

如果您需要列标题作为最后一个DataFrame中的行值以及各组之间的空白行，则可以尝试transposing，reset_index，然后移回然后移至{{3 }}，然后再使用DataFrame.append：

s = pd.Series([''] * df1.shape[1], name='blank')

df_new = pd.concat([df.T.reset_index().T.append(s) for df in [df1, df2, df3, df4]], ignore_index=True)
df_new.columns = ['SET', 'VIOLATIONS']
print(df_new)

        SET  VIOLATIONS
0     SET I  Violations
1    Rule 1           1
2    Rule 2           1
3    Rule 3           6
4                      
5    SET II  Violations
6    Rule 1           2
7    Rule 2           3
8    Rule 3           6
9                      
10  SET III  Violations
11   Rule 1           2
12   Rule 2           4
13   Rule 3           8
14                     
15   SET IV  Violations
16   Rule 1           2
17   Rule 2           5
18   Rule 3           8
19

否则使用：

df_new = pd.concat([df.set_axis(range(len(s)), axis=1, inplace=False).append(s)
                    for df in [df1, df2, df3, df4]], ignore_index=True)
df_new.columns = ['SET', 'VIOLATIONS']
print(df_new)

       SET VIOLATIONS
0   Rule 1          1
1   Rule 2          1
2   Rule 3          6
3                    
4   Rule 1          2
5   Rule 2          3
6   Rule 3          6
7                    
8   Rule 1          2
9   Rule 2          4
10  Rule 3          8
11                   
12  Rule 1          2
13  Rule 2          5
14  Rule 3          8
15

编辑

要输出到没有索引的excel工作簿，请使用pandas.concat：

df_new.to_excel('./workbookname.xls', index=False)

     SET  VIOLATIONS
       0           1
  Rule 1           1
  Rule 2           1
  Rule 3           6

  SET II  Violations
  Rule 1           2
  Rule 2           3
  Rule 3           6

 SET III  Violations
  Rule 1           2
  Rule 2           4
  Rule 3           8

  SET IV  Violations
  Rule 1           2
  Rule 2           5
  Rule 3           8

编辑2：

要添加sum个违规，列表理解不再是可行的选择。这次，我们将使用for循环，将结果附加到列表中，并在末尾concat进行

df_new = []

for df in [df1, df2, df3, df4]:
    total = pd.Series(['Total', df.iloc[:, 1].sum()], name='sum')
    blank = pd.Series(['', ''], name='blank')
    df_new.append(df.T.reset_index().T.append(total).append(blank))

df_new = pd.concat(df_new, ignore_index=True).rename(columns={0:'SET', 1:'VIOLATION'})
df_new.to_excel('./workbookname.xls', index=False)

Answer 2

获得此输出的原因是每个SET列的列名都不同。在使用pd.concat之前，您必须协调这些名称。另外，在pd.concat中，您使用了错误的axis。

dfs = [df1, df2, df3, df4]

for d in dfs:
    d.columns = [col[:3] if 'SET' in col else col for col in d.columns]

df_all = pd.concat(dfs, ignore_index=True)

哪种产量：

print(df_all)
       SET  Violations
0   Rule 1           1
1   Rule 2           1
2   Rule 3           6
3   Rule 1           2
4   Rule 2           3
5   Rule 3           6
6   Rule 1           2
7   Rule 2           4
8   Rule 3           8
9   Rule 1           2
10  Rule 2           5
11  Rule 3           8

垂直合并数据框

2 个答案:

编辑

编辑2：