Convert Python Pandas Series to Datetime Values

Have you used #Python's `divmod()` function before? If not, `divmod()` takes two numbers as arguments and returns a tuple containing their quotient and remainder in a single operation

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?

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Generate a #Python #Pandas DatetimeIndex of datetime values, specifying the frequency, by passing freq and a code: pd.date_range(start='2026-03-01', end='2026-05-01', freq='8h') This returns a 184-element DatetimeIndex with evenly-spaced datetime values in that period.

Reading a CSV into #Python #Pandas, and want a column to be treated as datetime values? Use parse_dates: df = pd.read_csv(filename, parse_dates=['x', 'y']) Bonus: The PyArrow engine often (not always) parses columns that look like dates.

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How to use #Python and a defaultdict to return a custom message if the key does not exist in the dictionary 🐍🚀

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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.

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