如何使用python转置/枢转csv文件，而无需将整个文件加载到内存中？

Question

对于我的数据分析管道之一，我最终生成了很多单独的CSV文件。 我想转置它们，将它们连接起来，然后再次转置它们。 但是，数据量很大，因此将它们全部加载到内存中是不切实际的。

Answer 1

连接两个csv文件中的数据行（如果这就是您的意思），而又不将它们全部都加载到内存中是一个相对容易和快速的操作：只需从每个行中读取一行，将它们连接在一起，然后编写将其复制到输出文件，重复执行直到所有输入数据都用完为止。

从本质上讲，将数据转换为csv文件中的数据而不将整个内容读入内存将是一个非常慢的过程，因为它需要多次读取整个输入文件，每次都只从其中包含的一列中提取数据。 如果这是一个可接受的（或必要的）折衷方案，则基本上是使用内置的csv模块来完成的方法：

import csv

input_filename = 'input.csv'
output_filename = 'output.csv'

with open(output_filename, 'wb') as outputf:
    writer = csv.writer(outputf)
    with open(input_filename, 'rb') as inputf:
        # determine number of columns in input file by counting those in its first row
        # number of cols in input file determines number of rows in output file
        numcols = len(csv.reader(inputf).next())
        # read entire input file multiple times, extracting one column from each row
        for col_index in xrange(numcols):
            # write all of column data as a single row of the output file
            inputf.seek(0)  # rewind file for each pass
            writer.writerow(tuple(row[col_index] for row in csv.reader(inputf)))

Answer 2

当字段具有固定宽度时，这是一种可行的解决方案：

import sys
import os


def main():

    path_in = sys.argv[-1]
    path_out = os.path.basename(path_in)+'.transposed'

    with open(path_in) as fd_in:
        line = fd_in.readline()
        l = line.split()
        field_width = int(len(line)/len(l))

    file_size = os.path.getsize(path_in)
    cols2 = rows1 = line_count = int(file_size/len(line))
    rows2 = cols1 = len(l)

    with open(path_in) as fd_in, open(path_out, 'w') as fd_out:
        for row in range(rows2):
            for col in range(cols2-1):
                fd_in.seek(col*len(line)+row*field_width)
                fd_out.write('{} '.format(fd_in.read(field_width-1)))
            fd_in.seek((col+1)*len(line)+row*field_width)
            fd_out.write('{}\n'.format(fd_in.read(field_width-1)))

    return


if __name__ == '__main__':
    main()

如果字段没有固定的宽度，这是一种可行的解决方案：

import sys
import os


def main():

    path_in = sys.argv[-1]
    path_out = os.path.basename(path_in)+'.transposed'
    separator = ' '

    d_seek = {}
    with open(path_in) as fd_in:
        i = 0
        while True:
            tell = fd_in.tell()
            if fd_in.readline() == '':
                break
            d_seek[i] = tell
            i += 1
    cols2 = rows1 = i

    with open(path_in) as fd_in:
        line = fd_in.readline()
    rows2 = cols1 = len(line.split(separator))
    del line

    with open(path_in) as fd_in, open(path_out, 'w') as fd_out:
        for row2 in range(rows2):
            for row1 in range(rows1):
                fd_in.seek(d_seek[row1])
                j = 0
                s = ''
                while True:
                    char = fd_in.read(1)
                    j += 1
                    if char == separator or char == '\n':
                        break
                    s += char
                d_seek[row1] += len(s)+1
                if row1+1 < rows1:
                    fd_out.write('{} '.format(s))
                else:
                    fd_out.write('{}\n'.format(s))

    return


if __name__ == '__main__':
    main()

Answer 3

另一个简短的pythonic解决方案。 我用它来转置15,000,000 x 12,000的CSV。 它是快速而纯净的python。 您需要做的所有其他事情都是微不足道的，这绝对是最难的部分。

Github链接： https : //gist.github.com/arose13/facfb91b609d453f3ad840417faa503a

    def transpose_csv_out_of_core(csv_path, output_csv_path='transposed.csv', delimiter=','):
    """
    On my laptop it can transpose at ~375,000 lines a sec

    :param csv_path: 
    :param output_csv_path: 
    :param delimiter: 
    :return: 
    """
    import csv

    transposed_iterator = zip(*csv.reader(open(csv_path)))
    with open(output_csv_path, 'w') as out:
        for row in transposed_iterator:
            out.write(delimiter.join(row) + '\n')

Answer 4

以下代码模拟了从两个csv文件中读取。 第一个有两行

[1,2,1]
[3,4,1]

第二个

[7,8,2]
[9,10.2].

结果是两行

[1,2,1,7,8,2]
[3,4,1,9,10,2]

那是你想要的吗？

def source1():
    for i in [ [1,2, 1] ,[3,4, 1]] : yield i

def source2():
    for i in [ [7,8,2] ,[9,10,2]] : yield i

def join(*sources):
    while True:
        row = []
        for s in sources:
            row.extend(s.next())
        yield row

for row in join(source1(), source2()):
    print row

在您的情况下，您必须用csv文件迭代器替换对source1（）和source2（）的调用。

Answer 5

使用发电机，例如

from itertools import izip

file1 = open("test", "r")
file2 = open("test2", "r")

def lazy(file):
    for line in file:
        #do something with the line
        yield line

for lines in izip(lazy(file1), lazy(file2)):
    print lines

http://wiki.python.org/moin/Generators

编辑：您可以使用CSV模块来解析它，我也意识到文件对象的readlines（）方法不是惰性的，因此您必须在文件模式中使用for行。