大熊猫将相同系列附加到每列

您可以使用double concat：

print (pd.concat([dm] * df.shape[1], axis=1, keys=df.columns))
           A         B         C
m0  0.823788  0.823788  0.823788
m1  0.615354  0.615354  0.615354
m2  0.606740  0.606740  0.606740
m3  0.386629  0.386629  0.386629
m4  0.637147  0.637147  0.637147

print (pd.concat([df, pd.concat([dm] * df.shape[1], axis=1, keys=df.columns)]))
           A         B         C
p0  0.789966  0.699837  0.981560
p1  0.415609  0.469310  0.961144
p2  0.920938  0.476615  0.422665
p3  0.323782  0.805231  0.030874
p4  0.761674  0.361134  0.788632
m0  0.823788  0.823788  0.823788
m1  0.615354  0.615354  0.615354
m2  0.606740  0.606740  0.606740
m3  0.386629  0.386629  0.386629
m4  0.637147  0.637147  0.637147

要创建附加文件df，可以使用numpy repeat和numpy.newaxis：

x = dm.values

print (pd.DataFrame(np.repeat(x[:, np.newaxis], df.shape[1], 1), 
                    columns=df.columns,
                    index=dm.index))
           A         B         C
m0  0.399837  0.399837  0.399837
m1  0.890191  0.890191  0.890191
m2  0.580747  0.580747  0.580747
m3  0.354032  0.354032  0.354032
m4  0.329108  0.329108  0.329108

print(pd.concat([df, pd.DataFrame(np.repeat(x[:, np.newaxis], df.shape[1], 1), 
                    columns=df.columns,
                    index=dm.index)]))

           A         B         C
p0  0.087337  0.375891  0.736282
p1  0.777897  0.932047  0.960629
p2  0.945546  0.062647  0.734047
p3  0.247740  0.582076  0.232282
p4  0.078683  0.869736  0.038905
m0  0.399837  0.399837  0.399837
m1  0.890191  0.890191  0.890191
m2  0.580747  0.580747  0.580747
m3  0.354032  0.354032  0.354032
m4  0.329108  0.329108  0.329108    

编辑1：

为创造新的另一种解决方案df有numpy.tile：

dm2 = pd.DataFrame(np.tile(dm.values[:, None], (1, df.shape[1])), dm.index, df.columns)
df.append(dm2)