Method strftime

pandas/core/arrays/datetimelike.py:1787–1837

Convert to Index using specified date_format. Return an Index of formatted strings specified by date_format, which supports the same string format as the python standard library. Details of the string format can be found in `python string format doc <https:/

(self, date_format: str)

    """

    def strftime(self, date_format: str) -> npt.NDArray[np.object_]:
        """
        Convert to Index using specified date_format.

        Return an Index of formatted strings specified by date_format, which
        supports the same string format as the python standard library. Details
        of the string format can be found in `python string format
        doc <https://docs.python.org/3/library/datetime.html
        #strftime-and-strptime-behavior>`__.

        Formats supported by the C `strftime` API but not by the python string format
        doc (such as `"%R"`, `"%r"`) are not officially supported and should be
        preferably replaced with their supported equivalents (such as `"%H:%M"`,
        `"%I:%M:%S %p"`).

        Note that `PeriodIndex` support additional directives, detailed in
        `Period.strftime`.

        Parameters
        ----------
        date_format : str
            Date format string (e.g. "%%Y-%%m-%%d").

        Returns
        -------
        ndarray[object]
            NumPy ndarray of formatted strings.

        See Also
        --------
        to_datetime : Convert the given argument to datetime.
        DatetimeIndex.normalize : Return DatetimeIndex with times to midnight.
        DatetimeIndex.round : Round the DatetimeIndex to the specified freq.
        DatetimeIndex.floor : Floor the DatetimeIndex to the specified freq.
        Timestamp.strftime : Format a single Timestamp.
        Period.strftime : Format a single Period.

        Examples
        --------
        >>> rng = pd.date_range(pd.Timestamp("2018-03-10 09:00"), periods=3, freq="s")
        >>> rng.strftime("%B %d, %Y, %r")
        Index(['March 10, 2018, 09:00:00 AM', 'March 10, 2018, 09:00:01 AM',
               'March 10, 2018, 09:00:02 AM'],
              dtype='str')
        """
        result = self._format_native_types(date_format=date_format, na_rep=np.nan)
        if using_string_dtype():
            from pandas import StringDtype

            return pd_array(result, dtype=StringDtype(na_value=np.nan))  # type: ignore[return-value]
        return result.astype(object, copy=False)


class TimelikeOps(DatetimeLikeArrayMixin):