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.
python
import numpy as np import pandas as pd import xarray as xr np.random.seed(123456)
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 :pypandas.to_datetime and :pypandas.date_range:
python
pd.to_datetime(['2000-01-01', '2000-02-02']) pd.date_range('2000-01-01', periods=365)
Alternatively, you can supply arrays of Python datetime objects. These get converted automatically when used as arguments in xarray objects:
python
import datetime xr.Dataset({'time': datetime.datetime(2000, 1, 1)})
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 :py~xarray.decode_cf:
python
attrs = {'units': 'hours since 2000-01-01'} ds = xr.Dataset({'time': ('time', [0, 1, 2, 3], attrs)}) xr.decode_cf(ds)
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.
xarray borrows powerful indexing machinery from pandas (see indexing).
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:
python
time = pd.date_range('2000-01-01', freq='H', periods=365 * 24) ds = xr.Dataset({'foo': ('time', np.arange(365 * 24)), 'time': time}) ds.sel(time='2000-01') ds.sel(time=slice('2000-06-01', '2000-06-10'))
You can also select a particular time by indexing with a :pydatetime.time object:
python
ds.sel(time=datetime.time(12))
For more details, read the pandas documentation.
Similar to pandas, the components of datetime objects contained in a given DataArray can be quickly computed using a special .dt accessor.
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time = time = pd.date_range('2000-01-01', freq='6H', periods=365 * 4) ds = xr.Dataset({'foo': ('time', np.arange(365 * 24)), 'time': time}) ds.time.dt.hour ds.time.dt.dayofweek
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":
python
ds['time.month'] ds['time.dayofyear']
For use as a derived coordinate, xarray adds 'season' to the list of datetime components supported by pandas:
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ds['time.season'] ds['time'].dt.season
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.
Datetime components couple particularly well with grouped operations (see groupby) for analyzing features that repeat over time. Here's how to calculate the mean by time of day:
python
ds.groupby('time.hour').mean()
For upsampling or downsampling temporal resolutions, xarray offers a :py~xarray.Dataset.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:
python
ds.resample('6H', dim='time', how='mean')
Resample also works for upsampling, in which case intervals without any values are marked by NaN:
python
ds.resample('30Min', 'time')
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.