Python的大熊貓：不能做切片索引

Question

我想用大熊貓多指標數據框，看起來像這樣的工作：

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

我希望能夠做到這一點：

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

失敗並出現以下錯誤：

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

df.index.levels[1].dtype回報dtype('int64')，所以應該使用整數片對嗎？

另外，任何有關如何做到這一點有關的意見將是有價值的，因爲數據幀有1200萬行，我需要查詢它與這種切片查詢〜7000萬次。

Answer 1

我想你需要添加,:到底 - 這意味着你需要切片行，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 

另一種解決方案是增加axis=0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 

個

時序：

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