python_字符串处理&正则

python_字符串处理&正则

String Manipulation
String Object Methods
val = 'a,b, guido'
val.split(',')
pieces = [x.strip() for x in val.split(',')]
pieces
first, second, third = pieces
first + '::' + second + '::' + third
'::'.join(pieces)
'guido' in val
val.index(',')
val.find(':')
val.index(':')
val.count(',')
val.replace(',', '::')
val.replace(',', '')
Regular Expressions
import re
text = "foo bar\t baz \tqux"
re.split('\s+', text)
regex = re.compile('\s+')
regex.split(text)
regex.findall(text)
text = """Dave dave@google.com
Steve steve@gmail.com
Rob rob@gmail.com
Ryan ryan@yahoo.com
"""
pattern = r'[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,4}'

# re.IGNORECASE makes the regex case-insensitive
regex = re.compile(pattern, flags=re.IGNORECASE)
regex.findall(text)
m = regex.search(text)
m
text[m.start():m.end()]
print(regex.match(text))
print(regex.sub('REDACTED', text))
pattern = r'([A-Z0-9._%+-]+)@([A-Z0-9.-]+)\.([A-Z]{2,4})'
regex = re.compile(pattern, flags=re.IGNORECASE)
m = regex.match('wesm@bright.net')
m.groups()
regex.findall(text)
print(regex.sub(r'Username: \1, Domain: \2, Suffix: \3', text))
Vectorized String Functions in pandas
data = {'Dave': 'dave@google.com', 'Steve': 'steve@gmail.com',
'Rob': 'rob@gmail.com', 'Wes': np.nan}
data = pd.Series(data)
data
data.isnull()
data.str.contains('gmail')
pattern
data.str.findall(pattern, flags=re.IGNORECASE)
matches = data.str.match(pattern, flags=re.IGNORECASE)
matches
matches.str.get(1)
matches.str[0]
data.str[:5]
pd.options.display.max_rows =