I'm running below code to clean text
import pandas as pd
def not_regex(pattern):
return r"((?!{}).)".format(pattern)
tmp = pd.DataFrame(['No one has a European accent either @',
'That the kid reminds me of Kevin'])
tmp[0].str.replace(not_regex('(\\b[-/]\\b|[a-zA-Z0-9])'), ' ')
Then it returns a warning
<ipython-input-8-ef8a43f91dbd>:9: FutureWarning: The default value of regex will change from True to False in a future version.
tmp[0].str.replace(not_regex('(\\b[-/]\\b|[a-zA-Z0-9])'), ' ')
Could you please elaborate on the reason of this warning?
See Pandas 1.2.0 release notes:
The default value of regex for
Series.str.replace()will change from True to False in a future release. In addition, single character regular expressions will not be treated as literal strings when regex=True is set (GH24804)
I.e., use regular expressions explicitly now:
dframe['colname'] = dframe['colname'].str.replace(r'\D+', regex=True)
I have like
df.Experience.head(5)
0 24 years experience
1 12 years experience
2 9 years experience
3 12 years experience
4 20 years experience
Name: Experience, dtype: object
I use like
df['Experience']=df['Experience'].str.replace(r'\D+','', regex=True).astype(int)
I get like
df.Experience.head(5)
0 24
1 12
2 9
3 12
4 20
Name: Experience, dtype: int64