python - 忽略 pandas.read_csv() 中破坏 header= keywords 的坏数据行

Question

我有一系列非常困惑的 *.csv 文件正在由 pandas 读取。一个 csv 示例是:

Instrument 35392
"Log File Name : station"
"Setup Date (MMDDYY) : 031114"
"Setup Time (HHMMSS) : 073648"
"Starting Date (MMDDYY) : 031114"
"Starting Time (HHMMSS) : 090000"
"Stopping Date (MMDDYY) : 031115"
"Stopping Time (HHMMSS) : 235959"
"Interval (HHMMSS) : 010000"
"Sensor warmup (HHMMSS) : 000200"
"Circltr warmup (HHMMSS) : 000200" 


"Date","Time","","Temp","","SpCond","","Sal","","IBatt",""
"MMDDYY","HHMMSS","","øC","","mS/cm","","ppt","","Volts",""

"Random message here 031114 073721 to 031114 083200"
03/11/14,09:00:00,"",15.85,"",1.408,"",.74,"",6.2,""
03/11/14,10:00:00,"",15.99,"",1.96,"",1.05,"",6.3,""
03/11/14,11:00:00,"",14.2,"",40.8,"",26.12,"",6.2,""
03/11/14,12:00:01,"",14.2,"",41.7,"",26.77,"",6.2,""
03/11/14,13:00:00,"",14.5,"",41.3,"",26.52,"",6.2,""
03/11/14,14:00:00,"",14.96,"",41,"",26.29,"",6.2,""
"message 3"
"message 4"**

我一直在使用此代码导入 *csv 文件，处理双标题，拉出空列，然后删除包含错误数据的违规行:

DF = pd.read_csv(BADFILE,parse_dates={'Datetime_(ascii)': [0,1]}, sep=",", \
             header=[10,11],na_values=['','na', 'nan nan'], \
             skiprows=[10], encoding='cp1252')

DF = DF.dropna(how="all", axis=1)
DF = DF.dropna(thresh=2)
droplist = ['message', 'Random']
DF = DF[~DF['Datetime_(ascii)'].str.contains('|'.join(droplist))]

DF.head()

Datetime_(ascii)    (Temp, øC)  (SpCond, mS/cm) (Sal, ppt)  (IBatt, Volts)
0   03/11/14 09:00:00   15.85   1.408   0.74    6.2
1   03/11/14 10:00:00   15.99   1.960   1.05    6.3
2   03/11/14 11:00:00   14.20   40.800  26.12   6.2
3   03/11/14 12:00:01   14.20   41.700  26.77   6.2
4   03/11/14 13:00:00   14.50   41.300  26.52   6.2

这工作得很好，直到我有一个文件在标题后有一个错误的 1 行行:“这里随机消息 031114 073721 到 031114 083200”

我收到的错误是:

    *C:\Users\USER\AppData\Local\Continuum\Anaconda3\lib\site-
    packages\pandas\io\parsers.py in _do_date_conversions(self, names, data)
   1554             data, names = _process_date_conversion(
   1555                 data, self._date_conv, self.parse_dates, self.index_col,
    -> 1556                 self.index_names, names, 
    keep_date_col=self.keep_date_col)
   1557 
   1558         return names, data
    C:\Users\USER\AppData\Local\Continuum\Anaconda3\lib\site-
    packages\pandas\io\parsers.py in _process_date_conversion(data_dict, 
    converter, parse_spec, index_col, index_names, columns, keep_date_col)
   2975     if not keep_date_col:
   2976         for c in list(date_cols):
    -> 2977             data_dict.pop(c)
   2978             new_cols.remove(c)
   2979 
   KeyError: ('Time', 'HHMMSS')*

如果我删除该行，代码就可以正常工作。同样，如果我删除 header= 行，代码就可以正常工作。但是，我希望能够保留它，因为我正在阅读数百个这样的文件。

困难:我不想在调用pandas.read_csv()之前打开每个文件，因为这些文件可能相当大 - 因此我不想多次读取和保存!另外，我更喜欢真正的 pandas/pythonic 解决方案，该解决方案不涉及首先将文件作为 stringIO 缓冲区打开以删除有问题的行。

Answer 1

这是一种方法，利用 skip_rows 接受可调用函数这一事实。该函数仅接收正在考虑的行索引，这是该参数的内置限制。

因此，可调用函数skip_test()首先检查当前索引是否在要跳过的已知索引集中。如果不匹配，则它打开实际文件并检查相应的行以查看其内容是否匹配。

skip_test() 函数有点 hacky，因为它确实检查实际文件，尽管它只检查直到它正在评估的当前行索引。它还假设坏行始终以相同的字符串开头(在示例中为 "foo")，但这似乎是给定 OP 的安全假设。

# example data
""" foo.csv
uid,a,b,c
0,1,2,3
skip me
1,11,22,33
foo
2,111,222,333 
"""

import pandas as pd

def skip_test(r, fn, fail_on, known):
    if r in known: # we know we always want to skip these
        return True
    # check if row index matches problem line in file
    # for efficiency, quit after we pass row index in file
    f = open(fn, "r")
    data = f.read()
    for i, line in enumerate(data.splitlines()):
        if (i == r) & line.startswith(fail_on):
            return True
        elif i > r:
            break
    return False

fname = "foo.csv"
fail_str = "foo"
known_skip = [2]
pd.read_csv(fname, sep=",", header=0, 
            skiprows=lambda x: skip_test(x, fname, fail_str, known_skip))
# output
   uid    a    b    c
0    0    1    2    3
1    1   11   22   33
2    2  111  222  333

如果您确切地知道随机消息出现时将出现在哪一行，那么这会快得多，因为您可以告诉它不要检查文件内容以查找超过潜在违规行的任何索引。