如何在条件中将一列值替换为另一列值[重复]

Question

我有一个这样的熊猫数据框

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df['similarity']>0.99，然后是df['RT (min)'] = df['patch']。 例如，df 应该是这样的：

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

RT (min) 中的 48,49,50 行替换为补丁中的 48,49,50 行

我试过了

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

当我得到错误时

InvalidIndexError: Reindexing only valid with uniquely valued Index objects

我不知道怎么解决。

Answer 1

类似这样的：

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

举个例子：

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

创建一个掩码并用于分配来自patch的值

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

Answer 2

pd.Series.mask

您可以按如下方式分配：

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