Reuven Lerner’s Post

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!

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 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?

Want to take a #Python #Pandas series of strings, and get datetime values? Use pd.to_datetime: pd.to_datetime(df['x']) Notice: It's not a method! It's a top-level pd function. Specify a non-standard "format" with a strftime string: pd.to_datetime(df['x'], format='%d/%m/%Y')

Want to find which time period contains a #Python #Pandas datetime? Use to_period: pd.Timestamp.now().to_period('1D') pd.Timestamp.now().to_period('6W') This returns a Pandas "Period" object, useful (among other things) for checking if two dates are within the same period.

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 !

Want to set the time on a #Python #Pandas series of datetimes to midnight? Use dt.normalize: df['x'].dt.normalize() You get back series of datetime values, but all times are 00:00:00.

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.

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

#Python includes a handy built-in function called `exec()` that can execute arbitrary code Never expose your APIs to this or the related `eval()` function since that could allow people to harm your computer!