我有一个函数findFullPath，它接受一个文件的字符串，并使用完整路径列表查找该文件的完整路径。例如;
>>> i = 4000
>>> serisuid = candidates.iloc[i].seriesuid
>>> fullPath = findFullPath(serisuid,fullPaths)
>>> print(serisuid)
>>> print(fullPath)
1.3.6.1.4.1.14519.5.2.1.6279.6001.100684836163890911914061745866
/home/msmith/luna16/subset1/1.3.6.1.4.1.14519.5.2.1.6279.6001.100684836163890911914061745866.raw
我正在尝试将此函数应用于完整列候选[“seriesuid”]并使用类似下面的内容返回带有完整路径的新列，但到目前为止它不成功;
>>> candidates["seriesuidFullPaths"] = candidates[["seriesuid"]].apply(findFullPath,args=(fullPaths,),axis=1)
[编辑]
抱歉有点含糊不清。所以我的功能是
def findFullPath(seriesuid,fullPaths):
fullPath = [s.replace(".mhd",".raw") for s in fullPaths if serisuid in s][0]
return fullPath
在我通过顶部给出的案例代码完美地工作，但是当我在系列中应用它时会产生不正确的完整文件路径。此外，我得到了一个复制错误;
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
但我认为我正在编辑实际的数据框，所以我有点困惑。
[实施例]
>>> candidates.head()
seriesuid coordX \
0 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222365663678666836860 -56.08
1 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222365663678666836860 53.21
2 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222365663678666836860 103.66
3 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222365663678666836860 -33.66
4 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222365663678666836860 -32.25
coordY coordZ class
0 -67.85 -311.92 0
1 -244.41 -245.17 0
2 -121.80 -286.62 0
3 -72.75 -308.41 0
4 -85.36 -362.51 0
我刚刚更新了fullPaths以仅包含.raw个文件。
>>> fullPaths = [path for path in fullPaths if ".raw" in path]
>>> fullPaths[:5]
['/home/msmith/luna16/subset4/1.3.6.1.4.1.14519.5.2.1.6279.6001.142154819868944114554521645782.raw',
'/home/msmith/luna16/subset4/1.3.6.1.4.1.14519.5.2.1.6279.6001.211071908915618528829547301883.raw',
'/home/msmith/luna16/subset4/1.3.6.1.4.1.14519.5.2.1.6279.6001.390009458146468860187238398197.raw',
'/home/msmith/luna16/subset4/1.3.6.1.4.1.14519.5.2.1.6279.6001.463214953282361219537913355115.raw',
'/home/msmith/luna16/subset4/1.3.6.1.4.1.14519.5.2.1.6279.6001.252358625003143649770119512644.raw']
我想用相关的.raw文件路径替换候选中的每个seriesuid。希望这可以解决它。
答案 0 :(得分：1)
此错误：
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
通常，当您在DataFrame的切片上应用函数时会发生这种情况。
你摆脱这个错误的一种方法是：
candidates = df.loc[<Your condition>].copy()
其中df是您最初创建的源DataFrame。
然后，如果它是正确的，它应该工作：
candidates["seriesuidFullPaths"] = candidates[["seriesuid"]].apply(findFullPath,args=(fullPaths,),axis=1)
答案 1 :(得分：1)
您可以尝试从列表DataFrame创建新p fullPath，将seriesuid提取到新列seriesuid，然后merge将其提取{ {1}}列DataFrame的候选人。
我更改了列表seriesuid中的第一个和最后一个项目以进行测试：
fullPathprint candidates
seriesuid coordX coordY coordZ \
0 9.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222... -56.08 -67.85 -311.92
1 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222... 53.21 -244.41 -245.17
2 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222... 103.66 -121.80 -286.62
3 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222... -33.66 -72.75 -308.41
4 1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222... -32.25 -85.36 -362.51
class
0 0
1 0
2 0
3 0
4 0
fullPath = ['/home/msmith/luna16/subset4/9.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222365663678666836860.raw',
'/home/msmith/luna16/subset4/1.3.6.1.4.1.14519.5.2.1.6279.6001.211071908915618528829547301883.raw',
'/home/msmith/luna16/subset4/1.3.6.1.4.1.14519.5.2.1.6279.6001.390009458146468860187238398197.raw',
'/home/msmith/luna16/subset4/1.3.6.1.4.1.14519.5.2.1.6279.6001.463214953282361219537913355115.raw',
'/home/msmith/luna16/subset4/9.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222365663678666836860.raw']
p = pd.DataFrame(fullPath, columns=['paths'])
p["paths"] = p["paths"].str.replace(".raw","")
p['seriesuid'] = p['paths'].str.split('/').str[5]
print p
paths \
0 /home/msmith/luna16/subset4/9.3.6.1.4.1.14519....
1 /home/msmith/luna16/subset4/1.3.6.1.4.1.14519....
2 /home/msmith/luna16/subset4/1.3.6.1.4.1.14519....
3 /home/msmith/luna16/subset4/1.3.6.1.4.1.14519....
4 /home/msmith/luna16/subset4/9.3.6.1.4.1.14519....
seriesuid
0 9.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222...
1 1.3.6.1.4.1.14519.5.2.1.6279.6001.211071908915...
2 1.3.6.1.4.1.14519.5.2.1.6279.6001.390009458146...
3 1.3.6.1.4.1.14519.5.2.1.6279.6001.463214953282...
4 9.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222...
如果列表print pd.merge(candidates, p, on=['seriesuid'])
seriesuid coordX coordY coordZ \
0 9.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222... -56.08 -67.85 -311.92
1 9.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222... -56.08 -67.85 -311.92
class paths
0 0 /home/msmith/luna16/subset4/9.3.6.1.4.1.14519....
1 0 /home/msmith/luna16/subset4/9.3.6.1.4.1.14519....
中的目录长度不同，您可以使用：
fullPathfullPath = ['/home/msmith/luna16/subset4/9.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222365663678666836860.raw',
'/home/msmith/luna16/subset4/1.3.6.1.4.1.14519.5.2.1.6279.6001.211071908915618528829547301883.raw',
'/msmith/luna16/subset4/1.3.6.1.4.1.14519.5.2.1.6279.6001.390009458146468860187238398197.raw',
'/home/msmith/luna16/subset4/1.3.6.1.4.1.14519.5.2.1.6279.6001.463214953282361219537913355115.raw',
'/home/msmith/luna16/subset4/9.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222365663678666836860.raw']
p = pd.DataFrame(fullPath, columns=['paths'])
#replace .raw to empty string
p["paths"] = p["paths"].str.replace(".raw","")
#find last string splitted by / and get it to column seriesuid
p[['tmp','seriesuid']] = p['paths'].str.rsplit('/', expand=True, n=1)
#drop unnecessary column tmp
p = p.drop(['tmp'], axis=1)
print p
paths \
0 /home/msmith/luna16/subset4/9.3.6.1.4.1.14519....
1 /home/msmith/luna16/subset4/1.3.6.1.4.1.14519....
2 /msmith/luna16/subset4/1.3.6.1.4.1.14519.5.2.1...
3 /home/msmith/luna16/subset4/1.3.6.1.4.1.14519....
4 /home/msmith/luna16/subset4/9.3.6.1.4.1.14519....
seriesuid
0 9.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222...
1 1.3.6.1.4.1.14519.5.2.1.6279.6001.211071908915...
2 1.3.6.1.4.1.14519.5.2.1.6279.6001.390009458146...
3 1.3.6.1.4.1.14519.5.2.1.6279.6001.463214953282...
4 9.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222...