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第一次在这里问一个问题，如果格式不好就道歉，请告诉我如何改进我的问题。
我正在寻求更好地理解pandas.read_csv（）函数的header和skiprows参数。
以下是我尝试在python中阅读的原始数据的示例：
MiniSonde 5 43656
"Log File Name : lwrhyp_deploy_20170104"
"Setup Date (MMDDYY) : 010417"
"Setup Time (HHMMSS) : 114539"
"Starting Date (MMDDYY) : 010417"
"Starting Time (HHMMSS) : 140000"
"Stopping Date (MMDDYY) : 123169"
"Stopping Time (HHMMSS) : 235959"
"Interval (HHMMSS) : 010000"
"Sensor warmup (HHMMSS) : 000100"
"Circltr warmup (HHMMSS) : 000030"
"Date","Time","","Temp","","SpCond","","Sal","","Dep25","","TDG","","TDG","","LDO%","","LDO","","IBatt",""
"MMDDYY","HHMMSS","","øC","","mS/cm","","ppt","","meters","","mmHg","","psia","","Sat","","mg/l","","Volts",""
01/04/17,14:00:00,"",7.97,"",.0691,"",.02,"",.75,"",735,"",14.22,"",52.7,"",6.15,"",11.4,""
01/04/17,15:00:00,"",7.9,"",.0692,"",.02,"",.76,"",736,"",14.23,"",52.8,"",6.17,"",11.4,""
01/04/17,16:00:00,"",7.89,"",.0694,"",.02,"",.77,"",736,"",14.23,"",52.3,"",6.12,"",11.4,""
01/04/17,17:00:00,"",7.88,"",.0699,"",.02,"",.78,"",735,"",14.21,"",51.8,"",6.06,"",11.4,""
01/04/17,18:00:00,"",7.85,"",.0699,"",.02,"",.78,"",733,"",14.18,"",51.3,"",6.01,"",11.4,""
01/04/17,19:00:00,"",7.83,"",.0706,"",.02,"",.78,"",731,"",14.14,"",51.3,"",6.01,"",11.4,""
01/04/17,20:00:00,"",7.81,"",.0706,"",.02,"",.79,"",730,"",14.12,"",51.1,"",5.99,"",11.4,""
01/04/17,21:00:00,"",7.81,"",.0699,"",.02,"",.79,"",730,"",14.11,"",50.8,"",5.95,"",11.4,""
01/04/17,22:00:00,"",7.76,"",.0702,"",.02,"",.8,"",729,"",14.1,"",50.5,"",5.92,"",11.3,""
01/04/17,23:00:00,"",7.76,"",.0704,"",.02,"",.8,"",729,"",14.09,"",50.5,"",5.93,"",11.3,""
01/05/17,00:00:00,"",7.76,"",.07,"",.02,"",.8,"",729,"",14.09,"",50.5,"",5.92,"",11.3,""
我正在尝试使用以＆＃34开头的行;日期＆＃34;或以＆＃34; MMDDYY＆＃34;开头的行作为我的标题行。当我在文本编辑器中打开原始数据时，对应于＆＃34;日期＆＃34;第14行是零索引python中的第13行。
我使用下面的代码认为它应该跳过前12行并开始读取第13行的数据：
test = pd.read_csv(filepath, skiprows=12, skip_blank_lines=True)
但是会产生错误：
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xf8 in position 0: invalid start byte
经过大量的摆弄，试验和错误的风格后，我发现下面的代码产生了我所追求的结果类型，但是我不明白它为什么会起作用：
test = pd.read_csv(filepath, skiprows=[14], header=11, skip_blank_lines=True)
我不明白read_csv是如何计算行数的。我是不正确的，标题行不在第11行，而是在第13行？该代码仅在skiprows = [14]时有效，为什么会这样？
另外，有没有办法防止原始数据中存在的空白列被读入数据帧？
答案 0 :(得分：0)
首先，skiprows没有按照您的想法行事。当您为列表提供输入时，它会在解析文件时跳过这些行。根据您的需要，只需使用header。
其次，pandas对文件行进行零索引。
第三，当你有skip_blank_lines=True时，在考虑＃header＃value之前，它似乎会重新索引你文件的行。因此，在您的示例中，它不会在标题之前（以及标题之后的空白行）索引空白行11和12。记住pandas对文件行进行零索引，我们可以看到标题上的header=11行如何：
line/ : content
0:MiniSonde 5 43656
1:"Log File Name : lwrhyp_deploy_20170104"
2:"Setup Date (MMDDYY) : 010417"
3:"Setup Time (HHMMSS) : 114539"
4:"Starting Date (MMDDYY) : 010417"
5:"Starting Time (HHMMSS) : 140000"
6:"Stopping Date (MMDDYY) : 123169"
7:"Stopping Time (HHMMSS) : 235959"
8:"Interval (HHMMSS) : 010000"
9:"Sensor warmup (HHMMSS) : 000100"
10:"Circltr warmup (HHMMSS) : 000030"
11:"Date","Time","","Temp","","SpCond","","Sal","","Dep25","","TDG","","TDG","","LDO%","","LDO","","IBatt",""
12:"MMDDYY","HHMMSS","","øC","","mS/cm","","ppt","","meters","","mmHg","","psia","","Sat","","mg/l","","Volts",""
13:01/04/17,14:00:00,"",7.97,"",.0691,"",.02,"",.75,"",735,"",14.22,"",52.7,"",6.15,"",11.4,""
14:01/04/17,15:00:00,"",7.9,"",.0692,"",.02,"",.76,"",736,"",14.23,"",52.8,"",6.17,"",11.4,""
15:01/04/17,16:00:00,"",7.89,"",.0694,"",.02,"",.77,"",736,"",14.23,"",52.3,"",6.12,"",11.4,""
16:01/04/17,17:00:00,"",7.88,"",.0699,"",.02,"",.78,"",735,"",14.21,"",51.8,"",6.06,"",11.4,""
17:01/04/17,18:00:00,"",7.85,"",.0699,"",.02,"",.78,"",733,"",14.18,"",51.3,"",6.01,"",11.4,""
18:01/04/17,19:00:00,"",7.83,"",.0706,"",.02,"",.78,"",731,"",14.14,"",51.3,"",6.01,"",11.4,""
19:01/04/17,20:00:00,"",7.81,"",.0706,"",.02,"",.79,"",730,"",14.12,"",51.1,"",5.99,"",11.4,""
20:01/04/17,21:00:00,"",7.81,"",.0699,"",.02,"",.79,"",730,"",14.11,"",50.8,"",5.95,"",11.4,""
21:01/04/17,22:00:00,"",7.76,"",.0702,"",.02,"",.8,"",729,"",14.1,"",50.5,"",5.92,"",11.3,""
22:01/04/17,23:00:00,"",7.76,"",.0704,"",.02,"",.8,"",729,"",14.09,"",50.5,"",5.93,"",11.3,""
23:01/05/17,00:00:00,"",7.76,"",.07,"",.02,"",.8,"",729,"",14.09,"",50.5,"",5.92,"",11.3,""