Convert Datetime Strings to Python Objects with Strptime

Use Python’s datetime.strptime with the format string “%b %d %Y %I:%M%p” to convert “Jun 1 2005 1:33PM” into a python datetime object. For a list, apply a list comprehension or use pandas.to_datetime for vectorized conversion; for flexible, locale-tolerant parsing use dateutil.parser.parse as a fallback.

Contents

• Convert with datetime.strptime
• Common format string explained
• Parse a list: list comprehension and pandas
• Flexible parsing with dateutil
• Edge cases: AM/PM, locale, whitespace, timezones
• Debugging ValueError and import issues
• Sources
• Conclusion

Convert with python datetime.strptime

The most direct and strict way to convert the string “Jun 1 2005 1:33PM” to a datetime object is datetime.strptime from the standard library. Use the format that matches the input exactly:

from datetime import datetime

s = "Jun 1 2005 1:33PM"
dt = datetime.strptime(s, "%b %d %Y %I:%M%p")
print(dt) # 2005-06-01 13:33:00

Notes:

• If you wrote import datetime instead of from datetime import datetime, call datetime.datetime.strptime(...).
• datetime.strptime is strict: the format string must match the input (including AM/PM tokens and separators).
• See the Python docs for the full list of strptime/strftime directives: https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior

Common format string for “Jun 1 2005 1:33PM”

The matching format is:

• Format: "%b %d %Y %I:%M%p"

What each token means:

• %b — abbreviated month name (e.g., “Jun”) — depends on the locale
• %d — day of month as a decimal (01-31). Single-digit days like “1” are accepted
• %Y — 4-digit year (2005)
• %I — hour (12-hour clock) (01-12)
• %M — minute (00-59)
• %p — locale’s AM/PM (e.g., “AM” or “PM”)

Why %I and %p? Because the input uses a 12-hour clock with an AM/PM suffix (1:33PM), so %H (24-hour) would be wrong.

Converting strings to python datetime: list, pandas, performance

For a Python list you can map or use a list comprehension:

from datetime import datetime

strings = ["Jun 1 2005 1:33PM", "Aug 28 1999 12:00AM"]
datetimes = [datetime.strptime(s, "%b %d %Y %I:%M%p") for s in strings]

If you have large arrays (Pandas Series), pandas.to_datetime is convenient and fast when you give the format:

import pandas as pd

s = pd.Series(["Jun 1 2005 1:33PM", "Aug 28 1999 12:00AM"])
dt = pd.to_datetime(s, format="%b %d %Y %I:%M%p")

• Passing format speeds up parsing because pandas won’t try to infer many different patterns. See pandas.to_datetime docs: https://pandas.pydata.org/docs/reference/api/pandas.to_datetime.html
• If some rows are malformed, use errors="coerce" to get NaT for bad values.

Using dateutil.parser.parse for flexible parsing

What if formats vary or you don’t want to write a format string? dateutil’s parser is forgiving:

from dateutil import parser

s = "Jun 1 2005 1:33PM"
dt = parser.parse(s)

• dateutil.parser.parse auto-detects the structure (great for mixed inputs). Documentation: https://dateutil.readthedocs.io/en/stable/parser.html
• Trade-off: it’s more flexible but slower than a fixed strptime and may be too permissive for some use cases.
• A common pattern: try strict strptime, and on ValueError fall back to dateutil for messy inputs.

Edge cases: AM/PM, locale, whitespace, timezones

AM/PM and 12 o’clock:

• 12:00AM → 00:00 (midnight)
• 12:00PM → 12:00 (noon)
  strptime handles these correctly when you use %I:%M%p.

Locale concerns:

• %b and %p rely on the current locale. If your strings are English but the environment locale is different, parsing can fail. dateutil is often less sensitive to locale; otherwise set the locale explicitly or map month abbreviations yourself.

Hidden whitespace and extra spaces:

• Strings sometimes have multiple spaces (e.g., "Jun 1 2005 …"). Collapse whitespace first:

import re
s = re.sub(r"\s+", " ", s).strip()
dt = datetime.strptime(s, "%b %d %Y %I:%M%p")

Timezones (naive vs aware):

• The parsed objects above are naive (no timezone). To attach/convert timezones, use zoneinfo (Python 3.9+) or dateutil.tz:

from datetime import datetime
from zoneinfo import ZoneInfo

dt = datetime.strptime("Jun 1 2005 1:33PM", "%b %d %Y %I:%M%p")
# attach timezone (assume the string is UTC)
dt_utc = dt.replace(tzinfo=ZoneInfo("UTC"))
# convert to another zone
dt_ny = dt_utc.astimezone(ZoneInfo("America/New_York"))

Docs for zoneinfo: https://docs.python.org/3/library/zoneinfo.html

Be careful: replace(tzinfo=...) just labels the time as being in that zone; if you need to convert from one zone to another, use astimezone.

Debugging ValueError and import issues

Common errors and fixes

• ValueError: time data ‘…’ does not match format ‘…’

• Fix: compare repr(s) to see hidden characters, check for extra spaces, and verify your format tokens. Try collapsing whitespace or print the string characters to spot non-breaking spaces.

• AttributeError: module ‘datetime’ has no attribute ‘strptime’

• Cause: you did import datetime and then called datetime.strptime(...). Fix either:

• from datetime import datetime and call datetime.strptime(...), or

• import datetime and call datetime.datetime.strptime(...).

• Mixed formats in a list

• Strategy: strict pass first (fast), fallback to dateutil for exceptions; or use pandas.to_datetime(..., errors="coerce") then inspect NaT values.

Quick debugging helper:

from datetime import datetime
from dateutil import parser
import re

def parse_with_fallback(s):
 s = re.sub(r"\s+", " ", s).strip()
 try:
 return datetime.strptime(s, "%b %d %Y %I:%M%p")
 except ValueError:
 return parser.parse(s) # slower, but flexible

Sources

• Python datetime strftime/strptime behavior: https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior
• python-dateutil parser documentation: https://dateutil.readthedocs.io/en/stable/parser.html
• pandas.to_datetime documentation: https://pandas.pydata.org/docs/reference/api/pandas.to_datetime.html
• zoneinfo (timezone support in stdlib): https://docs.python.org/3/library/zoneinfo.html

Conclusion

To convert “Jun 1 2005 1:33PM” into a python datetime, the normal approach is datetime.strptime(s, "%b %d %Y %I:%M%p"). For many strings use a list comprehension or pandas.to_datetime with the same format for speed; if inputs vary, fall back to dateutil.parser.parse. That pattern — strict + fast first, flexible fallback second — covers most real-world needs when working with python datetime conversion.