[英]How to use regex re.compile Match() or findall() in list comprehension
I am trying to use regex in list comprehension without needing to use the pandas extract()
functions.我正在尝试在列表理解中使用正则表达式,而无需使用 pandas
extract()
函数。
I want to use regex because my code might need to change where I need to use more complex pattern matching.我想使用正则表达式,因为我的代码可能需要更改我需要使用更复杂模式匹配的位置。 A kind user here suggested I use the
str
accessor functions but again it mainly works because the current pattern is simple enough.这里的一位好心用户建议我使用
str
访问器函数,但它再次主要起作用,因为当前模式足够简单。
As of now, I need to return pandas rows that either contain nan
or whose values under ODFS_FILE_CREATE_DATETIME
are not 10 string numbers ie: does not match the current format: 2020012514
.截至目前,我需要返回 pandas 行,这些行要么包含
nan
,要么ODFS_FILE_CREATE_DATETIME
下的值不是 10 个字符串数字,即:与当前格式不匹配: 2020012514
。 To this intent I tried to bypass the str
method and use regex.为此,我试图绕过
str
方法并使用正则表达式。 However this doesn't do anything.然而,这并没有做任何事情。 It puts everything into my list of tuples even though I told it to only put values that only contain
nan
or where the bool(regex.search())
is not true:即使我告诉它只放置仅包含
nan
或bool(regex.search())
不正确的值,它也会将所有内容放入我的元组列表中:
def process_csv_formatting(csv):
odfscsv_df = pd.read_csv(csv, header=None,names=['ODFS_LOG_FILENAME', 'ODFS_FILE_CREATE_DATETIME', 'LOT', 'TESTER', 'WAFER_SCRIBE'], dtype={'ODFS_FILE_CREATE_DATETIME': str})
odfscsv_df['CSV_FILENAME'] = csv.name
odfscdate_re = re.compile(r"\d{10}")
errortup = [(odfsname, "Bad_ODFS_FILE_CREATE_DATETIME= " + str(cdatetime), csv.name) for odfsname,cdatetime in zip(odfscsv_df['ODFS_LOG_FILENAME'], odfscsv_df['ODFS_FILE_CREATE_DATETIME']) if not odfscdate_re.search(str(cdatetime))]
emptypdf = pd.DataFrame(columns=['ODFS_LOG_FILENAME', 'ODFS_FILE_CREATE_DATETIME', 'LOT', 'TESTER', 'WAFER_SCRIBE'])
#print([tuple(x) for x in odfscsv_df[odfscsv_df.isna().any(1) | odfscdate_re.search(str(odfscsv_df['ODFS_FILE_CREATE_DATETIME'])) ].values])
m1 = odfscsv_df.isna().any(1)
m1 = odfscsv_df.isna().any(1)
s = odfscsv_df['ODFS_FILE_CREATE_DATETIME']
m2 = ~s.astype(str).str.isnumeric()
m2 = bool(odfscdate_re.search(str(s)))
m4 = not m2
print(m4)
m3 = s.astype(str).str.len().ne(10)
#print([tuple(x) for x in odfscsv_df[m1 | m2 | m3].values])
print([tuple(x) for x in odfscsv_df[m1 | ~bool(odfscdate_re.search(str(s)))].values])
if len(errortup) != 0:
#print(errortup) #put this in log file statement somehow
#print(errortup[0][2])
return emptypdf
else:
return odfscsv_df
If you want to use re
module.如果你想使用
re
模块。 You need to use it with map
.您需要将它与
map
一起使用。 For 10-digit strings, use this pattern r"^\d{10}$"
对于 10 位字符串,使用此模式
r"^\d{10}$"
import re
odfscdate_re = re.compile(r"^\d{10}$")
m1 = odfscsv_df.isna().any(1)
m2 = odfscsv_df['ODFS_FILE_CREATE_DATETIME'].map(lambda x:
odfscdate_re.search(str(x)) == None)
[tuple(x) for x in odfscsv_df[m1 | m2].values]
Note : depend on your requirement, I think you may also use match
instead of search
.注意:取决于您的要求,我认为您也可以使用
match
而不是search
。
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