Convert Python 3D dict into a dataframe

I’ve read excel file using pd.read_excel and i also have specified from which sheets i want them using Regex.

def gettingSheetName(_directory,_regex):
    '''reads the file using regex as a pattern for Sheet name'''
    xl = pd.ExcelFile(_directory)
    regex = re.compile(_regex)
    sheets = [n for n in xl.sheet_names if regex.match(n)]
    if sheets:
        return pd.read_excel(xl, sheet_name=sheets,index_col=False)

Therefor in return i get a Dictionary with 3 Dimensions.

{'SheetName':      Col1                 Col2
0             27                someText
1             35                someText
..           ...                         ...
171         4444                someText
172         6666                someText

i’ve tried pd.DataFrame.from_dict(Dic) to convert this to Pandas Data frame. and i got this error

If using all scalar values, you must pass an index

i also tried this code that i found pd.DataFrame(list(Sach.items()), columns=['Col1', 'Col2']) but the resualt looks like this

    Col1       col2
0   Sheetname  Col1 Col2 0....

Sheet name goes to value and after that Columns and finally the values that i’m assuming are starting with the first index which was 0
I’ve search everywhere and all i could find was for 2D Dic which doesn’t consider the Sheet name.
although i used this for loop instead of other popular methods which does the job, my Question is is there any way that i can use a Pandas function to achieve this? or possibly can i Not take the Sheetname from the beginning?

for key in Dic:
     Dic=Dic[key]

EDIT 1:

so after using return pd.concat(pd.read_excel(xl, sheet_name=sheets,index_col=False)) and then pd.DataFrame.from_dict(Sach) i got this Data Frame which is almost what i wanted but i only need the Cols and Values

                 Col1    Col2
Sheetname   0    Val1    Val2
            1

>Solution :

Use concat with DataFrame.reset_index:

return pd.concat(pd.read_excel(xl,sheet_name=sheets,index_col=False)).reset_index(drop=True)

d = {'SheetName':  pd.DataFrame({'Col1':[27,25], 'Col2':list('ab')}),
     'SheetName1':  pd.DataFrame({'Col1':[270,205], 'Col2':list('th')})} 
print (d)
{'SheetName':    Col1 Col2
0    27    a
1    25    b, 'SheetName1':    Col1 Col2
0   270    t
1   205    h}

By default get MultiIndex:

df = pd.concat(d)
print (df)
              Col1 Col2
SheetName  0    27    a
           1    25    b
SheetName1 0   270    t
           1   205    h

For convert it to column use:

df = pd.concat(d).droplevel(1).rename_axis('sheets').reset_index()
print (df)
       sheets  Col1 Col2
0   SheetName    27    a
1   SheetName    25    b
2  SheetName1   270    t
3  SheetName1   205    h

For remove MultiIndex use:

df = pd.concat(d).reset_index(drop=True)
print (df)
   Col1 Col2
0    27    a
1    25    b
2   270    t
3   205    h