Python Pandas: 无法进行切片索引

Question

我正在尝试使用如下所示的 pandas 多索引数据框：

                   end ref|alt
chrom start
chr1  3000714  3000715     T|G
      3001065  3001066     G|T
      3001110  3001111     G|C
      3001131  3001132     G|A

我希望能够做到这一点：

df.loc[('chr1', slice(3000714, 3001110))]

失败并出现以下错误：

cannot do slice indexing on with these indexers [1204741] of

df.index.levels[1].dtype returns dtype('int64')，所以应该使用整数切片对吗？

此外，关于如何高效执行此操作的任何评论都将很有价值，因为数据框有 1200 万行，我需要使用这种切片查询来查询它 ~70百万次。

Answer 1

我认为您需要在末尾添加 ,: - 这意味着您需要对行进行切片，但需要所有列：

print (df.loc[('chr1', slice(3000714, 3001110)),:])
                   end ref|alt
chrom start                   
chr1  3000714  3000715     T|G
      3001065  3001066     G|T
      3001110  3001111     G|C

另一个解决方案是将 axis=0 添加到 loc:

print (df.loc(axis=0)[('chr1', slice(3000714, 3001110))])
                   end ref|alt
chrom start                   
chr1  3000714  3000715     T|G
      3001065  3001066     G|T
      3001110  3001111     G|C

但如果只需要3000714和3001110:

print (df.loc[('chr1', [3000714, 3001110]),:])
                   end ref|alt
chrom start                   
chr1  3000714  3000715     T|G
      3001110  3001111     G|C

idx = pd.IndexSlice
print (df.loc[idx['chr1', [3000714, 3001110]],:])
                   end ref|alt
chrom start                   
chr1  3000714  3000715     T|G
      3001110  3001111     G|C

时间:

In [21]: %timeit (df.loc[('chr1', slice(3000714, 3001110)),:])
1000 loops, best of 3: 757 µs per loop

In [22]: %timeit (df.loc(axis=0)[('chr1', slice(3000714, 3001110))])
1000 loops, best of 3: 743 µs per loop

In [23]: %timeit (df.loc[('chr1', [3000714, 3001110]),:])
1000 loops, best of 3: 824 µs per loop

In [24]: %timeit (df.loc[pd.IndexSlice['chr1', [3000714, 3001110]],:])
The slowest run took 5.35 times longer than the fastest. This could mean that an intermediate result is being cached.
1000 loops, best of 3: 826 µs per loop

Python Pandas: 无法进行切片索引

Python Pandas: cannot do slice indexing

python

pandas

dataframe

multi-index