我正在尝试在Python中构建一些代码,将列中的多个值分隔成单独的行,并根据时间戳的同一天聚合Active-Ticket
的列,是否可以使用任何内部库或我需要安装外部库吗?
我的示例文件(目前,Active-Tickets列为空):
Input.csv
Timestamp,CaseID,Active-Tickets
14FEB2017:10:55:23,K456 G578 T213,
13FEB2017:10:56:12,F891 A63,
14FEB2017:11:59:14,T427 T31212 F900000,
15FEB2017:03:55:23,K456 G578 T213,
14FEB2017:05:56:12,F891 A63,
我想要实现的目标:
Output.csv
Timestamp,CaseID,Active-Tickets
14FEB2017:10:55:23,K456,8 (because there are 8 cases happened on the same day)
14FEB2017:10:55:23,G578,8
14FEB2017:10:55:23,T213,8
13FEB2017:10:56:12,F891,2 (because there are 2 cases happened on the same day)
13FEB2017:10:56:12,A63,2
14FEB2017:11:59:14,T427,8
14FEB2017:11:59:14,T31212,8
14FEB2017:11:59:14,F900000,8
15FEB2017:03:55:23,K456,3 (because there are 3 cases happened on the same day)
15FEB2017:03:55:23,G578,3
15FEB2017:03:55:23,T213,3
14FEB2017:05:56:12,F891,8
14FEB2017:05:56:12,A63,8
我的想法是:
- 的值
获取列Timestamp
检查日期是否相同,
将按空格分隔的所有CaseID存储到基于日期的列表中
计算每个日期列表中的元素数量
- 醇>
将计算元素的值返回到
Active-Tickets
。
但问题是,数据量不小,假设一天中最少有50个案例,那么我认为我的方式是不可能的。
答案 0 :(得分:1)
以下是使用itertools.chain.from_iterable()
执行此操作的一种方法。它只会将计数保留在内存中,因此可能适用于您的情况。它会两次读取csv
文件。一旦得到计数,一次写入输出,但只使用迭代器进行读取,所以应该保持内存需求下降。
<强>代码:强>
import csv
import itertools as it
from collections import Counter
# read through file and get counts per date
with open('test.csv', 'rU') as f:
reader = csv.reader(f)
header = next(reader)
dates = it.chain.from_iterable(
[date for _ in ids.split()]
for date, ids in ((x[0].split(':')[0], x[1]) for x in reader))
counts = Counter(dates)
# read through file again, and output as individual records with counts
with open('test.csv', 'rU') as f:
reader = csv.reader(f)
header = next(reader)
records = it.chain.from_iterable(
[(l[0], d) for d in l[1].split()] for l in reader)
new_lines = (l + (str(counts[l[0].split(':')[0]]), ) for l in records)
with open('test2.csv', 'wb') as f_out:
writer = csv.writer(f_out)
writer.writerow(header)
writer.writerows(new_lines)
<强>结果:强>
Timestamp,CaseID,Active-Tickets
14FEB2017:10:55:23,K456,8
14FEB2017:10:55:23,G578,8
14FEB2017:10:55:23,T213,8
13FEB2017:10:56:12,F891,2
13FEB2017:10:56:12,A63,2
14FEB2017:11:59:14,T427,8
14FEB2017:11:59:14,T31212,8
14FEB2017:11:59:14,F900000,8
15FEB2017:03:55:23,K456,3
15FEB2017:03:55:23,G578,3
15FEB2017:03:55:23,T213,3
14FEB2017:05:56:12,F891,8
14FEB2017:05:56:12,A63,8
计数器在2.6
collections.Counter
已经被移植到python 2.5+(Here)