嵌套for循环：列表未获得所需的输出

Question

我想做的是估计每个肽的得分，即行

我的代码如下：

import csv, math

def train_data(fname):
        #load csv training files
        peptide= []
        allele= []
        score = []
        with open (fname) as train:
                reader = csv.DictReader(train, delimiter='\t')
                for row in reader:
                        peptide.append(row['peptide'])
                        allele.append(row['allele'])
                        score.append(row['score'])

        return [peptide, allele, score]

def ff():
        peptide, allele, score = train_data('sample.txt')
        p={'A':(0.074+0.077)/2, 'R':(0.052+0.053)/2, 'N':(0.045+0.044)/2, 'D':(0.054+0.051)/2, 'C':(0.025+0.022)/2, 'Q':(0.034+0.035)/2, 'E':(0.054+0.056)/2, 'G':(0.074+0.074)/2, 'H':(0.026+0.025)/2, 'I':(0.068+0.064)/2, 'L':(0.099+0.096)/2, 'K':(0.058+0.058)/2, 'M':(0.025+0.024)/2, 'F':(0.047+0.048)/2, 'P':(0.039+0.041)/2, 'S':(0.057+0.059)/2, 'T':(0.051+0.053)/2, 'W':(0.013+0.014)/2, 'Y':(0.032+0.033)/2, 'V':(0.073+0.072)/2}
        for i in range(len(peptide)):
#                peptide[i]=list(peptide[i])
                peptide.append(peptide[i])
                for j in range(len(peptide[i])):
                        print(peptide[2][j])
                        #est_score+=p[peptide[i][j]]
                print ('---')
        print(peptide[2][1])

if __name__=='__main__':

        ff()

当我运行这个代码时，我得到的输出是所有的肽值，即循环中打印stmt的肽[I][j]，但我只想得到肽[2][j]值。 在循环外也可以正常工作。打印（肽[2][1]）使o/p非常精细，即值'A'

我的csv文件如下：

peptide score   allele  
AAAGAEAGKATTEEQ 0.190842    DRB1_0101
AAAGAEAGKATTEEQ 0.006301    DRB1_0301
AAAGAEAGKATTEEQ 0.066851    DRB1_0401
AAAGAEAGKATTEEQ 0.006344    DRB1_0405
AAAGAEAGKATTEEQ 0.035130    DRB1_0701
AAAGAEAGKATTEEQ 0.006288    DRB1_0802
AAAGAEAGKATTEEQ 0.176268    DRB1_0901
AAAGAEAGKATTEEQ 0.042555    DRB1_1101
AAAGAEAGKATTEEQ 0.114855    DRB1_1302
AAAGAEAGKATTEEQ 0.006377    DRB1_1501
AAAGAEAGKATTEEQ 0.006296    DRB3_0101
AAAGAEAGKATTEEQ 0.006313    DRB4_0101
AAAGAEAGKATTEEQ 0.070413    DRB5_0101

What I want to do is to estimate the score of each peptide i.e. row not all the rows together using: est_score+=p[peptide[i][j]]

Answer 1

对我来说，它只是打印peptide[2][j]，但它打印了很多次，这就是你想要的吗？你知道吗

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A

Python2和Python3都给了我同样的结果。你知道吗

Answer 2

import csv, math

p={'A':(0.074+0.077)/2, 'R':(0.052+0.053)/2, 'N':(0.045+0.044)/2, 'D':(0.054+0.051)/2, 'C':(0.025+0.022)/2, 'Q':(0.034+0.035)/2, 'E':(0.054+0.056)/2, 'G':(0.074+0.074)/2, 'H':(0.026+0.025)/2, 'I':(0.068+0.064)/2, 'L':(0.099+0.096)/2, 'K':(0.058+0.058)/2, 'M':(0.025+0.024)/2, 'F':(0.047+0.048)/2, 'P':(0.039+0.041)/2, 'S':(0.057+0.059)/2, 'T':(0.051+0.053)/2, 'W':(0.013+0.014)/2, 'Y':(0.032+0.033)/2, 'V':(0.073+0.072)/2}

def train_data(fname):
        #load csv training files
        peptide= []
        allele= []
        score = []
        with open (fname) as train:
                reader = csv.DictReader(train, delimiter='\t')
                for row in reader:
                        peptide.append(row['peptide'])
                        allele.append(row['allele'])
                        score.append(row['score'])

        return [peptide, allele, score]

def ff():
        peptide, allele, score = train_data('peptide.txt')
        for i in range(len(peptide)):
                est_score = 0
                for char in peptide[i]:
                    est_score += p[char]
                print("est_score: " + str(est_score), "\t: read_score: " + str(score[i]) )
                print (' -')
        print(peptide[2][1])

if __name__=='__main__':

        ff()

est评分总是相同的，因为在您提供的文件中，每一行的肽都是相同的。这张照片：

est_score: 0.9625000000000001   : read_score: 0.190842
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est_score: 0.9625000000000001   : read_score: 0.006301
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est_score: 0.9625000000000001   : read_score: 0.066851
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est_score: 0.9625000000000001   : read_score: 0.006344
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est_score: 0.9625000000000001   : read_score: 0.035130
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est_score: 0.9625000000000001   : read_score: 0.006288
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est_score: 0.9625000000000001   : read_score: 0.176268
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est_score: 0.9625000000000001   : read_score: 0.042555
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est_score: 0.9625000000000001   : read_score: 0.114855
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est_score: 0.9625000000000001   : read_score: 0.006377
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est_score: 0.9625000000000001   : read_score: 0.006296
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est_score: 0.9625000000000001   : read_score: 0.006313
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est_score: 0.9625000000000001   : read_score: 0.070413
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A