Question

我想添加一个包含两列的pandas数据框：read_id和score

我使用以下代码：

    reads_array = []
    for x in Bio.SeqIO.parse("inp.fasta","fasta"):
             reads_array.append(x)

    columns = ["read_id","score"]
    df = pd.DataFrame(columns = columns)
    df = df.fillna(0)

    for x in reads_array:
                alignments=pairwise2.align.globalms("ACTTGAT",str(x.seq),2,-1,-.5,-.1)
                sorted_alignments = sorted(alignments, key=operator.itemgetter(2),reverse = True)
                read_id = x.name
                score = sorted_alignments[0][2]
                df['read_id'] = read_id
                df['score'] = score

但这不起作用。你能建议一种生成数据帧df的方法

Answer 1

df['read_id']和df['score']是系列。因此，如果您要迭代reads_array并计算某个值，请将其分配给df的列，请尝试以下操作：

for i, x in enumerate(reads_array):
    ...
    df.ix[i]['read_id'] = read_id
    df.ix[i]['score'] = score

Answer 2

在顶部确保你有

import numpy as np

然后用

替换您共享的代码

reads_array = []
for x in Bio.SeqIO.parse("inp.fastq", "fastq"):
    reads_array.append(x)

df = pd.DataFrame(np.zeros((len(reads_array), 2)), columns=["read_id", "score"])

for index, x in enumerate(reads_array): 
    alignments = pairwise2.align.globalms("ACTTGAT", str(x.seq), 2, -1, -.5, -.1)
    sorted_alignments = sorted(alignments, key=operator.itemgetter(2), reverse=True)
    read_id = x.name
    score = sorted_alignments[0][2]
    df.loc[index, 'read_id'] = read_id
    df.loc[index, 'score'] = score

原始代码的主要问题是两件事：

1）您的数据框有0行

2）df ['column_name']是指整个列，而不是单个单元格，因此当您执行df ['column_name'] = value时，该列中的所有单元格都将设置为该值

附加到熊猫数据框

2 个答案: