用python格式化日期确实不一致

Question

I have some really messed up dates that I'm trying to get into a consistent format %Y-%m-%d if it applies. Some of the dates lack the day, some of the dates are in the future or just plain impossible for those I'll just flag as incorrect. How might I tackle such inconsistencies with python?

sample dates:
4-Jul-97
8/31/02
20-May-95
5/12/92
Jun-13
8/4/98
90/1/90
3/10/77
7-Dec
nan
4/3/98
Aug-76
Mar-90
Sep, 2020
Apr-74
10/10/03
Dec-00

Answer 1

you can use the dateutil parser if you want

from dateutil.parser import parse
bad_dates = [...]
for d in bad_dates:
    try:
        print parse(d)
    except Exception, err:
        print 'couldn\'t parse', d, err

outputs

1997-07-04 00:00:00
2002-08-31 00:00:00
1995-05-20 00:00:00
1992-05-12 00:00:00
2015-06-13 00:00:00
1998-08-04 00:00:00
couldn't parse 90/1/90 day is out of range for month
1977-03-10 00:00:00
2015-12-07 00:00:00
couldn't parse nan unknown string format
1998-04-03 00:00:00
1976-08-30 00:00:00
1990-03-30 00:00:00
2020-09-30 00:00:00
1974-04-30 00:00:00
2003-10-10 00:00:00
couldn't parse Dec-00 day is out of range for month

if you would like to flag any that arent an easy parse you can check to see if they have 3 parts to parse and if they do try and parse it or else flag it like so

flagged, good = [],[]
splitters = ['-', ',', '/']
for d in bad_dates:
    try:
        a = None
        for s in splitters:
            if len(d.split(s)) == 3:
                a = parse(d)
                good.append(a)
        if not a:
            raise Exception
    except Exception, err:
        flagged.append(d)

Answer 2

Some of the values are ambiguous. You can get different result depending on priorities eg, if you want all dates to be treated consistently; you could specify a list of formats to try:

#!/usr/bin/env python
import re
import sys
from datetime import datetime

for line in sys.stdin:
    date_string = " ".join(re.findall(r'\w+', line)) # normalize delimiters
    for date_format in ["%d %b %y", "%m %d %y", "%b %y", "%d %b", "%b %Y"]:
        try:
            print(datetime.strptime(date_string, date_format).date())
            break
        except ValueError:
            pass
    else: # no break
        sys.stderr.write("failed to parse " + line)

Example:

$ python . <input.txt 
1997-07-04
2002-08-31
1995-05-20
1992-05-12
2013-06-01
1998-08-04
failed to parse 90/1/90
1977-03-10
1900-12-07
failed to parse nan
1998-04-03
1976-08-01
1990-03-01
2020-09-01
1974-04-01
2003-10-10
2000-12-01

You could use other criteria eg, you could maximize number of dates that are parsed successfully even if some dates are treated inconsistently instead ( dateutil , pandas solution might give solutions in this category).

Answer 3

pd.datetools.to_datetime will have a go at guessing for you, it seems to go ok with most of your your dates, although you might want to put in some additional rules?

df['sample'].map(lambda x : pd.datetools.to_datetime(x))
Out[52]: 
0     1997-07-04 00:00:00
1     2002-08-31 00:00:00
2     1995-05-20 00:00:00
3     1992-05-12 00:00:00
4     2015-06-13 00:00:00
5     1998-08-04 00:00:00
6                 90/1/90
7     1977-03-10 00:00:00
8     2015-12-07 00:00:00
9                     NaN
10    1998-04-03 00:00:00
11    1976-08-01 00:00:00
12    1990-03-01 00:00:00
13    2015-09-01 00:00:00
14    1974-04-01 00:00:00
15    2003-10-10 00:00:00
16                 Dec-00
Name: sample, dtype: object