IIUC和你的输入数据框有默认的范围索引，那么你可以通过从每个索引中减去1.5，如果'是'，并通过新索引重新排序数据框来实现：

df1.set_index(df1.index-np.where(df1['Match'] =='Yes', 1.5, 0)).sort_index().reset_index(drop=True)

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输出量：

Activity                     Parent       Start      Finish WBS Level Match
0                 Construction                             01/02/2022  01/06/2022         1    No
1                   Foundation                             01/03/2023  01/06/2023         1   Yes
2                   Level Site               Construction  01/02/2022  01/02/2022         2    No
3               Lay Foundation    Construction>Foundation  01/03/2022  01/04/2022         3    No
4  Prepare land for foundation    Construction>Foundation  01/05/2022  01/06/2022         3    No
5             Install Footings          Building Envelope  01/07/2022  01/07/2022         2   Yes
6            Building Envelope                             01/07/2023  01/16/2023         1    No
7                      Pouring                              01/08/202  01/09/2023         1    No
8              Pour Foundation  Building Envelope>Pouring  01/08/2022  01/09/2022         3    No
9                 Installation                             01/09/2022  01/14/2022         1    No

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这里有一个使用索引而不是使用.shift()交换所需行的解决方案（因为我不清楚如何在groupby()中执行此操作）。可能无法很好地扩展，但应该在较小的数据集上完成这项工作。

df1 = df1.reset_index(drop=True)  # ensure index is unique

# Loop through only the indices of rows to be shifted, to avoid looping through every row
shift_indices = df1[df1['Match'] == 'Yes'].index
for shift_idx in shift_indices:
    # No need to shift if at the top
    if shift_idx == 0:
        continue
    above_idx = shift_idx - 1
    above_row = df1.loc[above_idx].copy()  # copy as otherwise this row will change during the shift
    # If the row above is also a match, then no need to swap it
    if above_row['Match'] != 'Yes':
        shift_row = df1.loc[shift_idx]
        df1.loc[above_idx] = shift_row
        df1.loc[shift_idx] = above_row

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mask = df1['Match'] == 'Yes'
df1.loc[mask, 'Activity':'Match'], df1.loc[mask.shift(-1, fill_value=False), 'Activity':'Match'] = df1.loc[mask.shift(-1, fill_value=False), 'Activity':'Match'].values, df1.loc[mask, 'Activity':'Match'].values

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这里有另一种方法，它不是很快，但仍然可以完成工作：

for i, row in df1.iterrows():
    if row['Match'] == 'Yes':
        print(i)
        df1.iloc[i], df1.iloc[i-1] =  df1.iloc[i-1].copy(), df1.iloc[i].copy()

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