python - 跳过CSV文件的顶部行 [英] python - skip top lines of CSV file
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问题描述
我有一个从第三方应用程序生成的csv文件的集合。在每个文件的顶部它有一个标题,空白行,关于内容的X行,空行,然后剩下的是实际的csv。
由于行数是可变的,我可以跳过X行。我现在通过使用拆分和获取列数跳过线,但我相信有一个更好的方法。
我可以使用csvreader或熊猫?
#当前代码
for line in greport.data.splitlines():
#行使用字段
fields = line.rstrip()。rstrip(',')。split(',')
如果len(fields) 5:
continue
else:
< process file>
#
#示例文件
报表标题
服务器名称:all
组名称:all
客户端名称:all
保存集名称: all
状态:all
备份类型:all
级别:all
组开始时间:从11/11/14 6:00:00 PM到11/12/14 5 :59:00 PM
客户名称,保存集名称,保存集ID,组开始时间,保存类型,级别,状态
server1,All ,, 11/11/14 6: 00:00 PM,save,skip,succeeded,
server2,All ,, 11/11/14 6:00:00,save,skip,succeeded,
server3,All ,, 11/12 / 14 12:00:00 AM,save,skip,succeeded,
server4,ASR:\,3630378478,11 / 11/14 11:00:00 PM,save,1,succeeded,
是的,你可以通过实现相同的效果。 csv
:
import csv
with open 'r')as f:
reader = csv.reader(f)
对于阅读器中的行:
如果len(row) 5:
continue
#process the data
I have a collection of csv files generated from a third party application. At the top of each file it has a title, blank line, X lines about the content, blank line, and then the rest is the actual csv.
Since the number of lines is variable I can just skip X lines. I am currently skipping the lines by using a split and getting the number of columns, but I'm sure there is a better way.
can I do this with csvreader or pandas?
# current code
for line in greport.data.splitlines():
# split up the line to work with the fields
fields = line.rstrip().rstrip(',').split(',')
if len(fields) < 5:
continue
else:
<process file>
#
# sample file
Title of report
Server Name: all
Group Name: all
Client Name: all
Save Set Name: all
Status: all
Backup Type: all
Level: all
Group Start Time: from 11/11/14 6:00:00 PM to 11/12/14 5:59:00 PM
Client Name,Save Set Name,Save Set ID,Group Start Time,Save Type,Level,Status
server1,All,,11/11/14 6:00:00 PM,save,skip,succeeded,
server2,All,,11/11/14 6:00:00 PM,save,skip,succeeded,
server3,All,,11/12/14 12:00:00 AM,save,skip,succeeded,
server4,ASR:\,3630378478,11/11/14 11:00:00 PM,save,1,succeeded,
解决方案 Yes, you can achieve the same effect with csv
:
import csv
with open('data', 'r') as f:
reader = csv.reader(f)
for row in reader:
if len(row) < 5:
continue
#process the data
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