Update pandas DataFrame with .str.replace() vs .replace()

Question

I have a column in my pandas Dataframe df that contains a string with some trailing hex-encoded NULLs (\\x00). At least I think that it\'s that. When I tried to replace them wit

Answer 1

The former looks for exact matches, the latter looks for matches in any part of the string, which is why the latter works for you.

The str methods are synonymous with the standard string equivalents but are vectorised

Answer 2

You did not specify a regex or require an exact match, hence str.replace worked

str.replace(old, new[, count])

Return a copy of the string with all occurrences of substring old replaced by new. If the optional argument count is given, only the first count occurrences are replaced.

DataFrame.replace(to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad', axis=None)

parameter: to_replace : str, regex, list, dict, Series, numeric, or None

str or regex: str: string exactly matching to_replace will be replaced with value regex: regexs matching to_replace will be replaced with value

They're not actually in the string: you have unescaped control characters, which Python displays using the hexadecimal notation:

remove all non-word characters in the following way:

re.sub(r'[^\w]', '', '\x00\x00\x00\x08\x01\x008\xe6\x7f')