如何在Python中将大型csv文件拆分为均匀大小的块？

Question

在一个基础我有下一个过程.

import csv
reader = csv.reader(open('huge_file.csv', 'rb'))

for line in reader:
    process_line(line)

看到这个相关的问题.我想每100行发送一次生产线,以实现批量分片.

实现相关答案的问题是csv对象是不可取消的,不能使用len.

>>> import csv
>>> reader = csv.reader(open('dataimport/tests/financial_sample.csv', 'rb'))
>>> len(reader)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: object of type '_csv.reader' has no len()
>>> reader[10:]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: '_csv.reader' object is unsubscriptable
>>> reader[10]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: '_csv.reader' object is unsubscriptable

我怎么解决这个问题？

Answer 1

只需将您的可reader订阅包装成一个list.显然这会破坏真正的大文件(请参阅下面的更新中的替代方案):

>>> reader = csv.reader(open('big.csv', 'rb'))
>>> lines = list(reader)
>>> print lines[:100]
...

进一步阅读:如何在Python中将列表拆分为大小均匀的块？

更新1(列表版本):另一种可能的方法是处理每个chuck,因为它在迭代过程中到达:

#!/usr/bin/env python

import csv
reader = csv.reader(open('4956984.csv', 'rb'))

chunk, chunksize = [], 100

def process_chunk(chuck):
    print len(chuck)
    # do something useful ...

for i, line in enumerate(reader):
    if (i % chunksize == 0 and i > 0):
        process_chunk(chunk)
        del chunk[:]  # or: chunk = []
    chunk.append(line)

# process the remainder
process_chunk(chunk)

更新2(生成器版本):我没有对它进行基准测试,但也许您可以通过使用块生成器来提高性能:

#!/usr/bin/env python

import csv
reader = csv.reader(open('4956984.csv', 'rb'))

def gen_chunks(reader, chunksize=100):
    """ 
    Chunk generator. Take a CSV `reader` and yield
    `chunksize` sized slices. 
    """
    chunk = []
    for i, line in enumerate(reader):
        if (i % chunksize == 0 and i > 0):
            yield chunk
            del chunk[:]  # or: chunk = []
        chunk.append(line)
    yield chunk

for chunk in gen_chunks(reader):
    print chunk # process chunk

# test gen_chunk on some dummy sequence:
for chunk in gen_chunks(range(10), chunksize=3):
    print chunk # process chunk

# => yields
# [0, 1, 2]
# [3, 4, 5]
# [6, 7, 8]
# [9]

Answer 2

我们可以使用 pandas 模块来处理这些大的 csv 文件。

df = pd.DataFrame()
temp = pd.read_csv('BIG_File.csv', iterator=True, chunksize=1000)
df = pd.concat(temp, ignore_index=True)