有没有更快的方法在两个数组中找到匹配的特性（Python）？

Question

我尝试遍历一个文件中的每个特性（每行1个），并根据第二个文件中该行的一列查找所有匹配的特性。我有这个解决方案，在小文件上做我想要的，但在大文件上非常慢（我的文件有20000000行）。Here's a sample of the two input files.

我的（慢）代码：

FEATUREFILE = 'S2_STARRseq_rep1_vsControl_peaks.bed'
CONSERVATIONFILEDIR = './conservation/'
with open(str(FEATUREFILE),'r') as peakFile, open('featureConservation.td',"w+") as outfile:
for line in peakFile.readlines():
    chrom = line.split('\t')[0]
    startPos = int(line.split('\t')[1])
    endPos = int(line.split('\t')[2])
    peakName = line.split('\t')[3]
    enrichVal = float(line.split('\t')[4])

    #Reject negative peak starts, if they exist (sometimes this can happen w/ MACS)
    if startPos > 0:
        with open(str(CONSERVATIONFILEDIR) + str(chrom)+'.bed','r') as conservationFile:
            cumulConserv = 0.
            n = 0
            for conservLine in conservationFile.readlines():
                position = int(conservLine.split('\t')[1])
                conservScore = float(conservLine.split('\t')[3])
                if position >= startPos and position <= endPos:
                    cumulConserv += conservScore
                    n+=1
        featureConservation = cumulConserv/(n)
        outfile.write(str(chrom) + '\t' + str(startPos) + '\t' + str(endPos) + '\t' + str(peakName) + '\t' + str(enrichVal) + '\t' + str(featureConservation) + '\n')

Answer 1

对于我来说，最好的解决方案似乎是为熊猫重写上面的代码。以下是在一些非常大的文件上对我有效的方法：

from __future__ import division
import pandas as pd

FEATUREFILE = 'S2_STARRseq_rep1_vsControl_peaks.bed'
CONSERVATIONFILEDIR = './conservation/'

peakDF = pd.read_csv(str(FEATUREFILE), sep = '\t', header=None, names=['chrom','start','end','name','enrichmentVal'])
#Reject negative peak starts, if they exist (sometimes this can happen w/ MACS)
peakDF.drop(peakDF[peakDF.start <= 0].index, inplace=True)
peakDF.reset_index(inplace=True)
peakDF.drop('index', axis=1, inplace=True)
peakDF['conservation'] = 1.0 #placeholder

chromNames = peakDF.chrom.unique()

for chromosome in chromNames: 
    chromSubset = peakDF[peakDF.chrom == str(chromosome)]
    chromDF = pd.read_csv(str(CONSERVATIONFILEDIR) + str(chromosome)+'.bed', sep='\t', header=None, names=['chrom','start','end','conserveScore'])

for i in xrange(0,len(chromSubset.index)):
    x = chromDF[chromDF.start >= chromSubset['start'][chromSubset.index[i]]]
    featureSubset = x[x.start < chromSubset['end'][chromSubset.index[i]]]
    x=None
    featureConservation = float(sum(featureSubset.conserveScore)/(chromSubset['end'][chromSubset.index[i]]-chromSubset['start'][chromSubset.index[i]]))
    peakDF.set_value(chromSubset.index[i],'conservation',featureConservation)
    featureSubset=None

 peakDF.to_csv("featureConservation.td", sep = '\t')