Repeat rows based on item count for each row and assign values for repeated rows

To have a more interesting example, with a source row containing 0 somewhere else than in the last column, I created df as:

     Item  Room1  Room2  Room3  Room4
0    Ball      1      1      1      0
1     Bat      1      1      1      1
2  Wicket      1      1      1      0
3    Xxxx      0      1      1      1

The first step is to define a function to process each row:

def rowProc(row):
    n = 0
    res = []
    for idx, val in row[row > 0].items():
        outRow = row.copy()
        if n > 0:
            outRow[idx] = 0
        res.append(outRow)
        n += 1
    return pd.DataFrame(res)

An important project detail is that the source row comes here from a bit "changed" DataFrame, namely Item column will be set as the index. So the only processed columns are "further" (Room...) columns.

For the current row it generates a DataFrame containing:

• as many rows as how many ones contains the source row,
• the first output row is an exact copy of the source row (like in your expected result),
• further rows have consecutive ones set to 0.

Then run:

result = pd.concat(df.set_index('Item').apply(rowProc, axis=1).tolist())
result.index.name = 'Item'
result.reset_index(inplace=True)

The result is:

      Item  Room1  Room2  Room3  Room4
0     Ball      1      1      1      0
1     Ball      1      0      1      0
2     Ball      1      1      0      0
3      Bat      1      1      1      1
4      Bat      1      0      1      1
5      Bat      1      1      0      1
6      Bat      1      1      1      0
7   Wicket      1      1      1      0
8   Wicket      1      0      1      0
9   Wicket      1      1      0      0
10    Xxxx      0      1      1      1
11    Xxxx      0      1      0      1
12    Xxxx      0      1      1      0