# xarray.Dataset.convert_calendar

#### Dataset.convert_calendar(calendar, dim='time', align_on=None, missing=None, use_cftime=None)[[source]](https://github.com/pydata/xarray/blob/main/xarray/core/dataset.py#L10168-L10289)

Convert the Dataset to another calendar.

Only converts the individual timestamps, does not modify any data except
in dropping invalid/surplus dates or inserting missing dates.

If the source and target calendars are either no_leap, all_leap or a
standard type, only the type of the time array is modified.
When converting to a leap year from a non-leap year, the 29th of February
is removed from the array. In the other direction the 29th of February
will be missing in the output, unless missing is specified,
in which case that value is inserted.

For conversions involving 360_day calendars, see Notes.

This method is safe to use with sub-daily data as it doesn’t touch the
time part of the timestamps.

* **Parameters:**
  * **calendar** ([`str`](https://docs.python.org/3/builtins/stdtypes.html#str)) – The target calendar name.
  * **dim** (`Hashable`, *default*: `"time"`) – Name of the time coordinate.
  * **align_on** (`{None, 'date', 'year'}`, *optional*) – Must be specified when either source or target is a 360_day calendar,
    ignored otherwise. See Notes.
  * **missing** (`Any` or [`None`](https://docs.python.org/3/builtins/constants.html#None), *optional*) – By default, i.e. if the value is None, this method will simply attempt
    to convert the dates in the source calendar to the same dates in the
    target calendar, and drop any of those that are not possible to
    represent.  If a value is provided, a new time coordinate will be
    created in the target calendar with the same frequency as the original
    time coordinate; for any dates that are not present in the source, the
    data will be filled with this value.  Note that using this mode requires
    that the source data have an inferable frequency; for more information
    see [`xarray.infer_freq()`](xarray.infer_freq.html.md#xarray.infer_freq).  For certain frequency, source, and
    target calendar combinations, this could result in many missing values, see notes.
  * **use_cftime** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool) or [`None`](https://docs.python.org/3/builtins/constants.html#None), *optional*) – Whether to use cftime objects in the output, only used if calendar
    is one of {“proleptic_gregorian”, “gregorian” or “standard”}.
    If True, the new time axis uses cftime objects.
    If None (default), it uses [`numpy.datetime64`](https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64) values if the
    date range permits it, and [`cftime.datetime`](https://unidata.github.io/cftime/api.html#cftime.datetime) objects if not.
    If False, it uses [`numpy.datetime64`](https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64)  or fails.
* **Returns:**
  [`Dataset`](xarray.Dataset.html.md#xarray.Dataset) – Copy of the dataarray with the time coordinate converted to the
  target calendar. If ‘missing’ was None (default), invalid dates in
  the new calendar are dropped, but missing dates are not inserted.
  If missing was given, the new data is reindexed to have a time axis
  with the same frequency as the source, but in the new calendar; any
  missing datapoints are filled with missing.

### Notes

Passing a value to missing is only usable if the source’s time coordinate as an
inferable frequencies (see [`infer_freq()`](xarray.infer_freq.html.md#xarray.infer_freq)) and is only appropriate
if the target coordinate, generated from this frequency, has dates equivalent to the
source. It is usually **not** appropriate to use this mode with:

- Period-end frequencies : ‘A’, ‘Y’, ‘Q’ or ‘M’, in opposition to ‘AS’ ‘YS’, ‘QS’ and ‘MS’
- Sub-monthly frequencies that do not divide a day evenly
  : or ‘mH’ where 24 % m != 0).

If one of the source or target calendars is “360_day”, align_on must
be specified and two options are offered.

- “year”
  : The dates are translated according to their relative position in the year,
    ignoring their original month and day information, meaning that the
    missing/surplus days are added/removed at regular intervals.
    <br/>
    From a 360_day to a standard calendar, the output will be missing the
    following dates (day of year in parentheses):
    <br/>
    To a leap year:
    : January 31st (31), March 31st (91), June 1st (153), July 31st (213),
      September 31st (275) and November 30th (335).
    <br/>
    To a non-leap year:
    : February 6th (36), April 19th (109), July 2nd (183),
      September 12th (255), November 25th (329).
    <br/>
    From a standard calendar to a “360_day”, the following dates in the
    source array will be dropped:
    <br/>
    From a leap year:
    : January 31st (31), April 1st (92), June 1st (153), August 1st (214),
      September 31st (275), December 1st (336)
    <br/>
    From a non-leap year:
    : February 6th (37), April 20th (110), July 2nd (183),
      September 13th (256), November 25th (329)
    <br/>
    This option is best used on daily and subdaily data.
- “date”
  : The month/day information is conserved and invalid dates are dropped
    from the output. This means that when converting from a “360_day” to a
    standard calendar, all 31st (Jan, March, May, July, August, October and
    December) will be missing as there is no equivalent dates in the
    “360_day” calendar and the 29th (on non-leap years) and 30th of February
    will be dropped as there are no equivalent dates in a standard calendar.
    <br/>
    This option is best used with data on a frequency coarser than daily.