Python 如何在条件中用另一个列值替换一个列值

我有一个像这样的数据框

df.head(10)
7   RT (min)    Area (Ab*s) Quality patch   similarity
8   10.167      23278313    64      NaN     NaN
9   10.167      23278313    47      NaN     NaN
10  10.167      23278313    38      NaN     NaN
28  10.333      3407159     49      10.167  0.983935
29  10.333      3407159     22      10.167  0.983935
30  10.333      3407159     16      10.167  0.983935
48  10.390      3299202     38      10.333  0.994514
49  10.390      3299202     35      10.333  0.994514
50  10.390      3299202     32      10.333  0.994514
68  10.516      2015786     50      10.390  0.988018

我想要当

df['similarity']>0.99时，然后
df['RT（min）]=df['patch']
。
例如，df应如下所示：

7   RT (min)    Area (Ab*s) Quality patch   similarity
8   10.167      23278313    64      NaN     NaN
9   10.167      23278313    47      NaN     NaN
10  10.167      23278313    38      NaN     NaN
28  10.333      3407159     49      10.167  0.983935
29  10.333      3407159     22      10.167  0.983935
30  10.333      3407159     16      10.167  0.983935
48  10.333      3299202     38      10.333  0.994514
49  10.333      3299202     35      10.333  0.994514
50  10.333      3299202     32      10.333  0.994514
68  10.516      2015786     50      10.390  0.988018

mask = df['similarity'] > 0.99
df.loc[mask, 'RT'] = df['patch']

RT（min）中的48,49,50行替换为补片中的48,49,50行

我试过了

p = df[df['similarity']>0.99].index.tolist()
df['RT (min)'][p] =df['patch'][p]

而我得到了错误

InvalidIndexError: Reindexing only valid with uniquely valued Index objects

我不知道怎么算出来。
类似这样的：

7   RT (min)    Area (Ab*s) Quality patch   similarity
8   10.167      23278313    64      NaN     NaN
9   10.167      23278313    47      NaN     NaN
10  10.167      23278313    38      NaN     NaN
28  10.333      3407159     49      10.167  0.983935
29  10.333      3407159     22      10.167  0.983935
30  10.333      3407159     16      10.167  0.983935
48  10.333      3299202     38      10.333  0.994514
49  10.333      3299202     35      10.333  0.994514
50  10.333      3299202     32      10.333  0.994514
68  10.516      2015786     50      10.390  0.988018

mask = df['similarity'] > 0.99
df.loc[mask, 'RT'] = df['patch']

例如：

df = pd.DataFrame({"RT":[10.1,10.2,10.4],"patch":[float("NaN"),10.3,10.3],"similarity":[float("NaN"),0.9,0.998]})

制作：

    RT  patch   similarity
0   10.1    NaN NaN
1   10.2    10.3    0.900
2   10.4    10.3    0.998

创建一个遮罩，并用于从
patch

mask = df['similarity'] > 0.99
df.loc[mask, 'RT'] = df['patch']

结果:

RT  patch   similarity
0   10.1    NaN NaN
1   10.2    10.3    0.900
2   10.3    10.3    0.998

您可以按如下方式分配：

df['RT'] = df['RT'].mask(df['similarity'] > 0.99, df['patch'])

你完全解决了我的问题，谢谢你的详细介绍answer@X.tang很好-请将答案标记为已接受？