python - Performing regex extraction with apply() on pandas dataframe column uses result of first row for all rows -

i have column of time zone strings in pandas dateframe each string entry variation on following:

'local time zone (america/chicago (cst) offset -21600)' 

i trying extract numeric offset (listed in seconds) @ end of string apply call:

df['minuteoffset'] = df.timezone.apply(lambda x: int(re.match('.*?offset (-?[0-9]*)\\)', a).group(1))) 

however, when @ data frame, see first value -21600 carried forward rows though other rows have other values. how can correctly extract regex on per row basis generate new column , why above failing?

i way:

in [85]: in [82]: df out[85]:    id                                               timezone 0   1  local time zone (america/chicago (cst) offset -21600) 1   2    local time zone (kiev/ukraine (eest) offset +10800)  in [86]: df['minuteoffset'] = df.timezone.str.replace(r'.*offset\s+([\+\-\d+]+)\)', r'\1').astype(int)/60  in [87]: df out[87]:    id                                               timezone  minuteoffset 0   1  local time zone (america/chicago (cst) offset -21600)        -360.0 1   2    local time zone (kiev/ukraine (eest) offset +10800)         180.0