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Weather and climate data

.. ipython:: python
   :suppress:

    import xarray as xr

xarray can leverage metadata that follows the Climate and Forecast (CF) conventions if present. Examples include automatic labelling of plots with descriptive names and units if proper metadata is present (see :ref:`plotting`) and support for non-standard calendars used in climate science through the cftime module (see :ref:`CFTimeIndex`). There are also a number of geosciences-focused projects that build on xarray (see :ref:`related-projects`).

CF-compliant coordinate variables

MetPy adds a metpy accessor that allows accessing coordinates with appropriate CF metadata using generic names x, y, vertical and time. There is also a cartopy_crs attribute that provides projection information, parsed from the appropriate CF metadata, as a Cartopy projection object. See their documentation for more information.

Non-standard calendars and dates outside the Timestamp-valid range

Through the standalone cftime library and a custom subclass of :py:class:`pandas.Index`, xarray supports a subset of the indexing functionality enabled through the standard :py:class:`pandas.DatetimeIndex` for dates from non-standard calendars commonly used in climate science or dates using a standard calendar, but outside the Timestamp-valid range (approximately between years 1678 and 2262).

Note

As of xarray version 0.11, by default, :py:class:`cftime.datetime` objects will be used to represent times (either in indexes, as a :py:class:`~xarray.CFTimeIndex`, or in data arrays with dtype object) if any of the following are true:

  • The dates are from a non-standard calendar
  • Any dates are outside the Timestamp-valid range.

Otherwise pandas-compatible dates from a standard calendar will be represented with the np.datetime64[ns] data type, enabling the use of a :py:class:`pandas.DatetimeIndex` or arrays with dtype np.datetime64[ns] and their full set of associated features.

For example, you can create a DataArray indexed by a time coordinate with dates from a no-leap calendar and a :py:class:`~xarray.CFTimeIndex` will automatically be used:

.. ipython:: python

   from itertools import product
   from cftime import DatetimeNoLeap
   dates = [DatetimeNoLeap(year, month, 1) for year, month in
            product(range(1, 3), range(1, 13))]
   da = xr.DataArray(np.arange(24), coords=[dates], dims=['time'], name='foo')

xarray also includes a :py:func:`~xarray.cftime_range` function, which enables creating a :py:class:`~xarray.CFTimeIndex` with regularly-spaced dates. For instance, we can create the same dates and DataArray we created above using:

.. ipython:: python

   dates = xr.cftime_range(start='0001', periods=24, freq='MS', calendar='noleap')
   da = xr.DataArray(np.arange(24), coords=[dates], dims=['time'], name='foo')

With :py:meth:`~xarray.CFTimeIndex.strftime` we can also easily generate formatted strings from the datetime values of a :py:class:`~xarray.CFTimeIndex` directly or through the :py:meth:`~xarray.DataArray.dt` accessor for a :py:class:`~xarray.DataArray` using the same formatting as the standard datetime.strftime convention .

.. ipython:: python

    dates.strftime('%c')
    da['time'].dt.strftime('%Y%m%d')

For data indexed by a :py:class:`~xarray.CFTimeIndex` xarray currently supports:

.. ipython:: python

   da.sel(time='0001')
   da.sel(time=slice('0001-05', '0002-02'))
  • Access of basic datetime components via the dt accessor (in this case just "year", "month", "day", "hour", "minute", "second", "microsecond", "season", "dayofyear", and "dayofweek"):
.. ipython:: python

   da.time.dt.year
   da.time.dt.month
   da.time.dt.season
   da.time.dt.dayofyear
   da.time.dt.dayofweek
  • Group-by operations based on datetime accessor attributes (e.g. by month of the year):
.. ipython:: python

   da.groupby('time.month').sum()


.. ipython:: python

   da.interp(time=[DatetimeNoLeap(1, 1, 15), DatetimeNoLeap(1, 2, 15)])
  • Interpolation using datetime strings:
.. ipython:: python

   da.interp(time=['0001-01-15', '0001-02-15'])
  • Differentiation:
.. ipython:: python

   da.differentiate('time')
  • Serialization:
.. ipython:: python

   da.to_netcdf('example-no-leap.nc')
   xr.open_dataset('example-no-leap.nc')


.. ipython:: python
    :suppress:

    import os
    os.remove('example-no-leap.nc')


.. ipython:: python

    da.resample(time='81T', closed='right', label='right', base=3).mean()

Note

For some use-cases it may still be useful to convert from a :py:class:`~xarray.CFTimeIndex` to a :py:class:`pandas.DatetimeIndex`, despite the difference in calendar types. The recommended way of doing this is to use the built-in :py:meth:`~xarray.CFTimeIndex.to_datetimeindex` method:

.. ipython:: python
   :okwarning:

    modern_times = xr.cftime_range('2000', periods=24, freq='MS', calendar='noleap')
    da = xr.DataArray(range(24), [('time', modern_times)])
    da
    datetimeindex = da.indexes['time'].to_datetimeindex()
    da['time'] = datetimeindex

However in this case one should use caution to only perform operations which do not depend on differences between dates (e.g. differentiation, interpolation, or upsampling with resample), as these could introduce subtle and silent errors due to the difference in calendar types between the dates encoded in your data and the dates stored in memory.