Convert awkwardly formatted excel data into tabular format using python

I have an Excel spreadsheet containing records for each day of the month. Unfortunately, the dataset has been formatted in an awkward way, making it difficult to analyse. I would like to restructure the data into a tabular format with columns for the date, venues, and the corresponding quantity under each heading. I've attached images of the current format as well as the desired output.

Current Format

             0    1       2    3       4    5       6    7       8    9
0   01/01/2023  NaN     NaN  NaN     NaN  NaN     NaN  NaN     NaN  NaN
1       Venue1  QTY  Venue2  QTY  Venue3  QTY  Venue4  QTY  Venue5  QTY
2            A    0       A    0       A    1       A    0       A    0
3            B   17       B    3       B   11       B    3       B    0
4            C    0       C    0       C    1       C    0       C    0
5            D    0       D    0       D   29       D    0       D    0
6            E    0       E    0       E    0       E    0       E    0
7            F    0       F    0       F    0       F    0       F    0
8            G    0       G    0       G    0       G    0       G    0
9            H    0       H    0       H    0       H    0       H    0
10  02/01/2023  NaN     NaN  NaN     NaN  NaN     NaN  NaN     NaN  NaN
11      Venue1  QTY  Venue2  QTY  Venue3  QTY  Venue4  QTY  Venue5  QTY
12           A    0       A    0       A    1       A    0       A    0
13           B   11       B    3       B    0       B    6       B    2
14           C    0       C    0       C    0       C    0       C    0
15           D   20       D    0       D   28       D    0       D   24
16           E    0       E    0       E    0       E    0       E    0
17           F    0       F    0       F    0       F    0       F    0
18           G    0       G    0       G    0       G    0       G    0
19           H    0       H    0       H    0       H    0       H    0

<br>

Required Format

I've tried manipulating in pandas, but i'm not sure how to go about it exactly to get the the desired result. Any suggestions or sample code would be greatly appreciated. Thank You!

Here is one way to do it with pandas reshaping :

tmp = pd.read_excel("file.xlsx", header=None)

m = pd.to_datetime(tmp[0], errors="coerce").notnull()

blocks = {n: g.set_axis(g.iloc[0], axis=1).iloc[1:]
          for n,g in tmp.loc[~m].groupby(tmp[0].where(m).ffill())}
df = (
    pd.concat(blocks, names=["Date"])
        .assign(Venues= lambda x: x["Venue1"])
        .pipe(lambda x: x.set_axis(
            [f"{col}" if i%2 == 0 else f"QTY_{x.columns[i-1]}"
             for i, col in enumerate(x.columns)], axis=1))
        .filter(regex="QTY.+|Venues").set_index("Venues", append=True)
     .rename(lambda x: x.split("_")[1], axis=1).droplevel(1).unstack(1)
     .stack(0).reset_index(names=["Date", "Venues"]).rename_axis(columns=None)
)

Output :

print(df)

         Date  Venues  A   B  C   D  E  F  G  H
0  01/01/2023  Venue1  0  17  0   0  0  0  0  0
1  01/01/2023  Venue2  0   3  0   0  0  0  0  0
2  01/01/2023  Venue3  1  11  1  29  0  0  0  0
3  01/01/2023  Venue4  0   3  0   0  0  0  0  0
4  01/01/2023  Venue5  0   0  0   0  0  0  0  0
5  02/01/2023  Venue1  0  11  0  20  0  0  0  0
6  02/01/2023  Venue2  0   3  0   0  0  0  0  0
7  02/01/2023  Venue3  1   0  0  28  0  0  0  0
8  02/01/2023  Venue4  0   6  0   0  0  0  0  0
9  02/01/2023  Venue5  0   2  0  24  0  0  0  0