Parsing Strings to Datetime in Python with strptime

If a #Python string contains an ISO-formatted date, skip strptime and use datetime.fromisoformat: import datetime as dt dt.datetime.fromisoformat('2026-04-12') # returns datetime at midnight dt.datetime.fromisoformat('2026-04-12 12:34:56') # returns datetime at 12:34:56

Having a tough time remembering #Python strptime vs. strftime? - The "p" (strptime) is for parsing, getting a datetime from a string - The "f" (strftime) is for formatting, getting a string from a datetime For help remembering the format codes, turn to https://buff.ly/oEdYGMI !

⚡ Python Tip: list.append(x) vs list.extend(x) 👉 append(): Adds one element [1,2].append([3,4]) → [1,2,[3,4]] 👉 extend(): Adds elements individually [1,2].extend([3,4]) → [1,2,3,4] 📌 Small difference. Big impact. #Python #CodingTips #LearnPython

Want to format a #Python datetime as a string? Use strftime: dt = datetime.datetime(2026, 4, 1, 13, 15, 17) dt.strftime('%Y-%m-%d') # '2026-04-01' dt.strftime('%Y-%m-%d %H:%M:%S') # '2026-04-01 13:15:17' dt.strftime('%y-%B-%e %H:%M:%S') # '26-April- 1 13:15:17'

Want to check if two #Python #Pandas datetimes are in the same month/quarter/year? Convert them both to periods, and compare with ==: date1.to_period('M') == date2.to_period('M') # same month? date1.to_period('Q') == date2.to_period('Q') # same quarter?

Here’s a tiny Python change that pays off fast. Python tip: use `@dataclass(slots=True)` for high-volume models. It removes per-instance `__dict__`, which usually means lower memory usage and slightly faster attribute access. Great for DTOs, parser outputs, event payloads, and cache objects where shape is fixed. Mini rule: if the object schema is stable, add `slots=True` by default. #Python #Performance #CodeQuality #SoftwareEngineering

Want to round #Python #Pandas datetimes? You have 3 options: - dt.floor — earlier - dt.ceil - later - dt.round — nearest For example: s.dt.floor('3h') # previous multiple-of-3 hour s.dt.ceil('15m') # next 15-minute block s.dt.round('1D') # nearest 1 day

Reading a CSV into #Python #Pandas, and parse_dates doesn't recognize the format? Pass the date_format keyword arg: df = pd.read_csv(filename, parse_dates=['x', 'y'], date_format='%Y-%m-%d %H:%M:%S') All strftime codes work!

Fun python 1/100 Ever wondered why these two give different outputs ? Its called Singleton in python …………….. ids = {id(True) for i in range(500)} print(ids) ids = {id(int("100")) for i in range(500)} print(ids) ……………… What you think would be the answer to each loop?

Generate a #Python #Pandas DatetimeIndex of datetime values with pd.date_range: pd.date_range(start='2026-03-01', end='2026-05-01', periods=10) This returns a 10-element series with evenly-spaced datetime values in that period.