在python中读取scipy / numpy中的csv文件

Question

我在使用python读取由制表符分隔的csv文件时遇到问题。我使用以下功能：

def csv2array(filename, skiprows=0, delimiter='\t', raw_header=False, missing=None, with_header=True):
    """
    Parse a file name into an array. Return the array and additional header lines. By default,
    parse the header lines into dictionaries, assuming the parameters are numeric,
    using 'parse_header'.
    """
    f = open(filename, 'r')
    skipped_rows = []
    for n in range(skiprows):
        header_line = f.readline().strip()
        if raw_header:
            skipped_rows.append(header_line)
        else:
            skipped_rows.append(parse_header(header_line))
    f.close()
    if missing:
        data = genfromtxt(filename, dtype=None, names=with_header,
                          deletechars='', skiprows=skiprows, missing=missing)
    else:
    if delimiter != '\t':
        data = genfromtxt(filename, dtype=None, names=with_header, delimiter=delimiter,
                  deletechars='', skiprows=skiprows)
    else:
        data = genfromtxt(filename, dtype=None, names=with_header,
                  deletechars='', skiprows=skiprows)        
    if data.ndim == 0:
    data = array([data.item()])
    return (data, skipped_rows)

问题是genfromtxt抱怨我的文件，例如错误：

Line #27100 (got 12 columns instead of 16)

我不确定这些错误来自何处。有任何想法吗？

这是导致问题的示例文件：

#Gene   120-1   120-3   120-4   30-1    30-3    30-4    C-1 C-2 C-5 genesymbol  genedesc
ENSMUSG00000000001  7.32    9.5 7.76    7.24    11.35   8.83    6.67    11.35   7.12    Gnai3   guanine nucleotide binding protein alpha
ENSMUSG00000000003  0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Pbsn    probasin

有没有更好的方法来编写通用的csv2array函数？谢谢。

Answer 1

查看python CSV模块：http :
//docs.python.org/library/csv.html

import csv
reader = csv.reader(open("myfile.csv", "rb"), 
                    delimiter='\t', quoting=csv.QUOTE_NONE)

header = []
records = []
fields = 16

if thereIsAHeader: header = reader.next()

for row, record in enumerate(reader):
    if len(record) != fields:
        print "Skipping malformed record %i, contains %i fields (%i expected)" %
            (record, len(record), fields)
    else:
        records.append(record)

# do numpy stuff.