如何从 python 中的字符串中提取月份和年份？

Question

Input text :

text = "Wipro Limited | Hyderabad, IN                Dec 2017 – Present
Project Analyst 

Infosys | Delhi, IN                Apr 2017 – Nov 2017 
Software Developer 

HCL Technologies | Hyderabad, IN                Jun 2016 – Mar 2017 
Software Engineer  
"

I have written a code for this but it displays in list for each extracted word and unable to do anything of it.

regex = re.compile('(?P<month>[a-zA-Z]+)\s+(?P<year>\d{4})\s+\–\s+(?P<month1>[a-zA-Z]+)\s+(?P<year1>\d{4})')
mat = re.findall(regex, text)
mat

Check out the code: https://regex101.com/r/mMlgYp/1 . I want the output like below to preview the dates and make difference of it then calculate total experience: Here Present or Till date should consider current month and year.

import time
Present = time.strftime("%m-%Y")
Present 
# output: '05-2020'

#Desired output
Extracted dates: 
[('Dec 2017 - Present'),
 ('Apr 2017 - Nov 2017'),
 ('Jun 2016 - Mar 2017')]# and so on ...should display all the search results 

First experience: 1.9 years 
second experience: 8 months
third experience: 7 months
# and so on ...should display all the search results 
Total experience: 3.4 years

Please help me with this I'm new to programming lang and NLP, regex stuff.

Answer 1

You probably ultimately want this in a dataframe since you tagged it pandas (see Andrej's answer ), but either way, you can parse dates from the string with the interpolated:

fr"(?i)((?:{months}) *\d{{4}}) *(?:-|–) *(present|(?:{months}) *\d{{4}})"

Where {months} is an alternating group of all possible month names and abbreviations.

import calendar
import re
from datetime import datetime
from dateutil.relativedelta import relativedelta

text = """Wipro Limited | Hyderabad, IN                Dec 2017 – Present
Project Analyst 

Infosys | Delhi, IN                Apr 2017 – Nov 2017 
Software Developer 

HCL Technologies | Hyderabad, IN                Jun 2016 – Mar 2017 
Software Engineer  
"""

def parse_date(x, fmts=("%b %Y", "%B %Y")):
    for fmt in fmts:
        try:
            return datetime.strptime(x, fmt)
        except ValueError:
            pass

months = "|".join(calendar.month_abbr[1:] + calendar.month_name[1:])
pattern = fr"(?i)((?:{months}) *\d{{4}}) *(?:-|–) *(present|(?:{months}) *\d{{4}})"
total_experience = None

for start, end in re.findall(pattern, text):
    if end.lower() == "present":
        today = datetime.today()
        end = f"{calendar.month_abbr[today.month]} {today.year}"

    duration = relativedelta(parse_date(end), parse_date(start))

    if total_experience:
        total_experience += duration
    else: 
        total_experience = duration

    print(f"{start}-{end} ({duration.years} years, {duration.months} months)")

if total_experience:
    print(f"total experience:  {total_experience.years} years, {total_experience.months} months")
else:
    print("couldn't parse text")

Output:

Dec 2017-May 2020 (2 years, 5 months)
Apr 2017-Nov 2017 (0 years, 7 months)
Jun 2016-Mar 2017 (0 years, 9 months)
total experience:  3 years, 9 months

Answer 2

import re
import numpy as np
import pandas as pd

text = '''Wipro Limited | Hyderabad, IN                Dec 2017 – Present
Project Analyst

Infosys | Delhi, IN                Apr 2017 – Nov 2017
Software Developer

HCL Technologies | Hyderabad, IN                Jun 2016 – Mar 2017
Software Engineer
'''

def pretty_format(monthts):
    return f'{monthts/12:.1f} years' if monthts > 11 else f'{monthts:.1f} months'

data = []
for employer, d1, d2 in re.findall(r'(.*?)\s*\|.*([A-Z][a-z]{2} [12]\d{3}) – (?:([A-Z][a-z]{2} [12]\d{3})|Present)', text):
    data.append({'Employer': employer, 'Begin': d1, 'End': d2 or np.nan})

df = pd.DataFrame(data)
df['Begin'] = pd.to_datetime(df['Begin'])
df['End'] = pd.to_datetime(df['End'])

df['Experience'] = ((df['End'].fillna(pd.to_datetime('now')) - df['Begin']) / np.timedelta64(1, 'M')).apply(pretty_format)
print(df)

total = np.sum(df['End'].fillna(pd.to_datetime('now')) - df['Begin']) / np.timedelta64(1, 'M')
print()
print(f'Total experience = {pretty_format(total)}')

Prints:

           Employer      Begin        End  Experience
0     Wipro Limited 2017-12-01        NaT   2.5 years
1           Infosys 2017-04-01 2017-11-01  7.0 months
2  HCL Technologies 2016-06-01 2017-03-01  9.0 months

Total experience = 3.8 years