In addition to the standard datetime methods, Pandas provides several additional datetime-specific methods. Here are some of the commonly used ones:
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dt.floor(freq): Rounds down the datetime to the specified frequency. -
dt.ceil(freq): Rounds up the datetime to the specified frequency. -
dt.to_period(freq): Converts the datetime to a Period object with the specified frequency. -
dt.to_pydatetime(): Converts the datetime to a Python datetime object. -
dt.strftime(format): Formats the datetime as a string using the specified format string. -
dt.round(freq): Rounds the datetime to the specified frequency. -
dt.tz_localize(tz, ambiguous, nonexistent): Localizes a datetime to a specified timezone. -
dt.tz_convert(tz): Converts the timezone of a datetime to a new timezone. -
dt.day_name(): Returns the name of the day of the week. -
dt.month_name(): Returns the name of the month. -
dt.is_month_start: Returns a boolean indicating whether the datetime is the start of a month. -
dt.is_month_end: Returns a boolean indicating whether the datetime is the end of a month. -
dt.is_year_start: Returns a boolean indicating whether the datetime is the start of a year. -
dt.is_year_end: Returns a boolean indicating whether the datetime is the end of a year.
These additional methods can be very useful for working with datetime data in Pandas, as they provide additional flexibility and functionality beyond the standard datetime methods.