Time series data

A major use case for xarray is multi-dimensional time-series data. Accordingly, we’ve copied many of features that make working with time-series data in pandas such a joy to xarray. In most cases, we rely on pandas for the core functionality.

Creating datetime64 data

xarray uses the numpy dtypes datetime64[ns] and timedelta64[ns] to represent datetime data, which offer vectorized (if sometimes buggy) operations with numpy and smooth integration with pandas.

To convert to or create regular arrays of datetime64 data, we recommend using pandas.to_datetime() and pandas.date_range():

In [1]: pd.to_datetime(['2000-01-01', '2000-02-02'])
Out[1]: DatetimeIndex(['2000-01-01', '2000-02-02'], dtype='datetime64[ns]', freq=None)

In [2]: pd.date_range('2000-01-01', periods=365)
Out[2]: 
DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-03', '2000-01-04',
               '2000-01-05', '2000-01-06', '2000-01-07', '2000-01-08',
               '2000-01-09', '2000-01-10',
               ...
               '2000-12-21', '2000-12-22', '2000-12-23', '2000-12-24',
               '2000-12-25', '2000-12-26', '2000-12-27', '2000-12-28',
               '2000-12-29', '2000-12-30'],
              dtype='datetime64[ns]', length=365, freq='D')

Alternatively, you can supply arrays of Python datetime objects. These get converted automatically when used as arguments in xarray objects:

In [3]: import datetime

In [4]: xr.Dataset({'time': datetime.datetime(2000, 1, 1)})
Out[4]: 
<xarray.Dataset>
Dimensions:  ()
Data variables:
    time     datetime64[ns] 2000-01-01

When reading or writing netCDF files, xarray automatically decodes datetime and timedelta arrays using CF conventions (that is, by using a units attribute like 'days since 2000-01-01').

You can manual decode arrays in this form by passing a dataset to decode_cf():

In [5]: attrs = {'units': 'hours since 2000-01-01'}

In [6]: ds = xr.Dataset({'time': ('time', [0, 1, 2, 3], attrs)})

In [7]: xr.decode_cf(ds)
Out[7]: 
<xarray.Dataset>
Dimensions:  (time: 4)
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-01T01:00:00 ...
Data variables:
    *empty*

One unfortunate limitation of using datetime64[ns] is that it limits the native representation of dates to those that fall between the years 1678 and 2262. When a netCDF file contains dates outside of these bounds, dates will be returned as arrays of netcdftime.datetime objects.

Datetime indexing

xarray borrows powerful indexing machinery from pandas (see Indexing and selecting data).

This allows for several useful and suscinct forms of indexing, particularly for datetime64 data. For example, we support indexing with strings for single items and with the slice object:

In [8]: time = pd.date_range('2000-01-01', freq='H', periods=365 * 24)

In [9]: ds = xr.Dataset({'foo': ('time', np.arange(365 * 24)), 'time': time})

In [10]: ds.sel(time='2000-01')
Out[10]: 
<xarray.Dataset>
Dimensions:  (time: 744)
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-01T01:00:00 ...
Data variables:
    foo      (time) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ...

In [11]: ds.sel(time=slice('2000-06-01', '2000-06-10'))
Out[11]: 
<xarray.Dataset>
Dimensions:  (time: 240)
Coordinates:
  * time     (time) datetime64[ns] 2000-06-01 2000-06-01T01:00:00 ...
Data variables:
    foo      (time) int64 3648 3649 3650 3651 3652 3653 3654 3655 3656 3657 ...

You can also select a particular time by indexing with a datetime.time object:

In [12]: ds.sel(time=datetime.time(12))
Out[12]: 
<xarray.Dataset>
Dimensions:  (time: 365)
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01T12:00:00 2000-01-02T12:00:00 ...
Data variables:
    foo      (time) int64 12 36 60 84 108 132 156 180 204 228 252 276 300 ...

For more details, read the pandas documentation.

Datetime components

Similar to pandas, the components of datetime objects contained in a given DataArray can be quickly computed using a special .dt accessor.

In [13]: time = time = pd.date_range('2000-01-01', freq='6H', periods=365 * 4)

In [14]: ds = xr.Dataset({'foo': ('time', np.arange(365 * 24)), 'time': time})

ValueErrorTraceback (most recent call last)
<ipython-input-14-6d0a5b014145> in <module>()
----> 1 ds = xr.Dataset({'foo': ('time', np.arange(365 * 24)), 'time': time})

/srv/build/xarray/python-xarray-0.9.6/xarray/core/dataset.py in __init__(self, data_vars, coords, attrs, compat)
    351             coords = {}
    352         if data_vars is not None or coords is not None:
--> 353             self._set_init_vars_and_dims(data_vars, coords, compat)
    354         if attrs is not None:
    355             self.attrs = attrs

/srv/build/xarray/python-xarray-0.9.6/xarray/core/dataset.py in _set_init_vars_and_dims(self, data_vars, coords, compat)
    366 
    367         variables, coord_names, dims = merge_data_and_coords(
--> 368             data_vars, coords, compat=compat)
    369 
    370         self._variables = variables

/srv/build/xarray/python-xarray-0.9.6/xarray/core/merge.py in merge_data_and_coords(data, coords, compat, join)
    363     objs = [data, coords]
    364     explicit_coords = coords.keys()
--> 365     return merge_core(objs, compat, join, explicit_coords=explicit_coords)
    366 
    367 

/srv/build/xarray/python-xarray-0.9.6/xarray/core/merge.py in merge_core(objs, compat, join, priority_arg, explicit_coords, indexes)
    422     assert_unique_multiindex_level_names(variables)
    423 
--> 424     dims = calculate_dimensions(variables)
    425 
    426     for dim, size in dims.items():

/srv/build/xarray/python-xarray-0.9.6/xarray/core/dataset.py in calculate_dimensions(variables)
    108                 raise ValueError('conflicting sizes for dimension %r: '
    109                                  'length %s on %r and length %s on %r' %
--> 110                                  (dim, size, k, dims[dim], last_used[dim]))
    111     return dims
    112 

ValueError: conflicting sizes for dimension 'time': length 1460 on 'time' and length 8760 on 'foo'

In [15]: ds.time.dt.hour
Out[15]: 
<xarray.DataArray 'hour' (time: 8760)>
array([ 0,  1,  2, ..., 21, 22, 23])
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-01T01:00:00 ...

In [16]: ds.time.dt.dayofweek
Out[16]: 
<xarray.DataArray 'dayofweek' (time: 8760)>
array([5, 5, 5, ..., 5, 5, 5])
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-01T01:00:00 ...

The .dt accessor works on both coordinate dimensions as well as multi-dimensional data.

xarray also supports a notion of “virtual” or “derived” coordinates for datetime components implemented by pandas, including “year”, “month”, “day”, “hour”, “minute”, “second”, “dayofyear”, “week”, “dayofweek”, “weekday” and “quarter”:

In [17]: ds['time.month']
Out[17]: 
<xarray.DataArray 'month' (time: 8760)>
array([ 1,  1,  1, ..., 12, 12, 12])
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-01T01:00:00 ...

In [18]: ds['time.dayofyear']
Out[18]: 
<xarray.DataArray 'dayofyear' (time: 8760)>
array([  1,   1,   1, ..., 365, 365, 365])
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-01T01:00:00 ...

For use as a derived coordinate, xarray adds 'season' to the list of datetime components supported by pandas:

In [19]: ds['time.season']
Out[19]: 
<xarray.DataArray 'season' (time: 8760)>
array(['DJF', 'DJF', 'DJF', ..., 'DJF', 'DJF', 'DJF'],
      dtype='|S3')
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-01T01:00:00 ...

In [20]: ds['time'].dt.season
Out[20]: 
<xarray.DataArray 'season' (time: 8760)>
array(['DJF', 'DJF', 'DJF', ..., 'DJF', 'DJF', 'DJF'],
      dtype='|S3')
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-01T01:00:00 ...

The set of valid seasons consists of ‘DJF’, ‘MAM’, ‘JJA’ and ‘SON’, labeled by the first letters of the corresponding months.

You can use these shortcuts with both Datasets and DataArray coordinates.

Resampling and grouped operations

Datetime components couple particularly well with grouped operations (see GroupBy: split-apply-combine) for analyzing features that repeat over time. Here’s how to calculate the mean by time of day:

In [21]: ds.groupby('time.hour').mean()
Out[21]: 
<xarray.Dataset>
Dimensions:  (hour: 24)
Coordinates:
  * hour     (hour) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ...
Data variables:
    foo      (hour) float64 4.368e+03 4.369e+03 4.37e+03 4.371e+03 4.372e+03 ...

For upsampling or downsampling temporal resolutions, xarray offers a resample() method building on the core functionality offered by the pandas method of the same name. Resample uses essentially the same api as resample in pandas.

For example, we can downsample our dataset from hourly to 6-hourly:

In [22]: ds.resample('6H', dim='time', how='mean')
Out[22]: 
<xarray.Dataset>
Dimensions:  (time: 1460)
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-01T06:00:00 ...
Data variables:
    foo      (time) float64 2.5 8.5 14.5 20.5 26.5 32.5 38.5 44.5 50.5 56.5 ...

Resample also works for upsampling, in which case intervals without any values are marked by NaN:

In [23]: ds.resample('30Min', 'time')
Out[23]: 
<xarray.Dataset>
Dimensions:  (time: 17519)
Coordinates:
  * time     (time) datetime64[ns] 2000-01-01 2000-01-01T00:30:00 ...
Data variables:
    foo      (time) float64 0.0 nan 1.0 nan 2.0 nan 3.0 nan 4.0 nan 5.0 nan ...

Of course, all of these resampling and groupby operation work on both Dataset and DataArray objects with any number of additional dimensions.

For more examples of using grouped operations on a time dimension, see Toy weather data.