我有一个像这样的CSV文件:
Header1,Header2,Header3,Header4
AA,12,ABCS,A1
BDDV,34,ABCS,BB2
ABCS,5666,gf,KK0
其中列只能包含字母/单词,或只包含数字或两者。我有这样的多个文件,每个列中的列不一定相同。我希望得到列中只包含字母而没有数字的每个元素的计数。
我想要的输出是
Header1- [('AA', 1),('BDDV',1),('ABCS',1)] Header3- [('ABCS', 2),('gf', 1)]
虽然这两列都有ABCS'但我想为每列分别计算它们。
我可以通过硬编码列号来获得计数,如下所示:
import csv
import collections
count_number = collections.Counter()
with open('filename.csv') as input_file:
r = csv.reader(input_file, delimiter=',')
headers = next(r)
for row in r:
count_number[row[1]] += 1
print count_number.most_common()
但我对如何针对列进行操作感到困惑。
答案 0 :(得分:1)
这可以使用每个标头Counter
:
#!/usr/bin/env python
from collections import Counter, defaultdict
import csv
header_counter = defaultdict(Counter)
with open('filename.csv') as input_file:
r = csv.reader(input_file, delimiter=',')
# read headers
headers = next(r)
for row in r:
# count values for each row to add in header context
row_val = sum([w.isdigit() for w in row])
# zip each row with headers to know where to count
for header, val in zip(headers, row):
# count only non-digits
if not any(map(str.isdigit, val)):
header_counter[header].update({val: row_val})
for k, v in header_counter.iteritems():
print k, v
输出:
Header3 Counter({'ABCS': 2, 'gf': 1})
Header1 Counter({'AA': 1, 'BDDV': 1, 'ABCS': 1})
答案 1 :(得分:1)
仅限部分解决方案(您仍需要在CSV读取器的第二次迭代中过滤带有数字的列)。
import csv
import collections
with open('filename.csv') as input_file:
r = csv.reader(input_file, delimiter=',')
headers = next(r)
count_number = [collections.Counter() for I in Len(headers)]
for row in r:
for i, val in enumerate(row):
count_number[i][val] += 1
print [cr.most_common() for cr in count_number]