python
import numpy as np import pandas as pd import xray np.random.seed(123456) np.set_printoptions(threshold=10)
:pyxray.DataArray is xray's implementation of a labeled, multi-dimensional array. It has several key properties:
values: a :pynumpy.ndarrayholding the array's valuesdims: dimension names for each axis (e.g.,('x', 'y', 'z'))coords: a dict-like container of arrays (coordinates) that label each point (e.g., 1-dimensional arrays of numbers, datetime objects or strings)attrs: anOrderedDictto hold arbitrary metadata (attributes)
xray uses dims and coords to enable its core metadata aware operations. Dimensions provide names that xray uses instead of the axis argument found in many numpy functions. Coordinates enable fast label based indexing and alignment, building on the functionality of the index found on a pandas :py~pandas.DataFrame or :py~pandas.Series.
DataArray objects also can have a name and can hold arbitrary metadata in the form of their attrs property (an ordered dictionary). Names and attributes are strictly for users and user-written code: xray makes no attempt to interpret them, and propagates them only in unambiguous cases (see FAQ, approach to metadata).
The :py~xray.DataArray constructor takes a multi-dimensional array of values (e.g., a numpy ndarray), a list or dictionary of coordinates label and a list of dimension names:
python
data = np.random.rand(4, 3) locs = ['IA', 'IL', 'IN'] times = pd.date_range('2000-01-01', periods=4) foo = xray.DataArray(data, coords=[times, locs], dims=['time', 'space']) foo
All of these arguments (except for data) are optional, and will be filled in with default values:
python
xray.DataArray(data)
As you can see, dimensions and coordinate arrays corresponding to each dimension are always present. This behavior is similar to pandas, which fills in index values in the same way.
The data array constructor also supports supplying coords as a list of (dim, ticks[, attrs]) pairs with length equal to the number of dimensions:
python
xray.DataArray(data, coords=[('time', times), ('space', locs)])
Yet another option is to supply coords in the form of a dictionary where the values are scaler values, 1D arrays or tuples (in the same form as the dataarray constructor). This form lets you supply other coordinates than those corresponding to dimensions (more on these later):
python
- xray.DataArray(data, coords={'time': times, 'space': locs, 'const': 42,
'ranking': ('space', [1, 2, 3])},
dims=['time', 'space'])
You can also create a DataArray by supplying a pandas :py~pandas.Series, :py~pandas.DataFrame or :py~pandas.Panel, in which case any non-specified arguments in the DataArray constructor will be filled in from the pandas object:
python
df = pd.DataFrame({'x': [0, 1], 'y': [2, 3]}, index=['a', 'b']) df.index.name = 'abc' df.columns.name = 'xyz' df xray.DataArray(df)
xray does not (yet!) support labeling coordinate values with a :pypandas.MultiIndex (see 164). However, the alternate from_series constructor will automatically unpack any hierarchical indexes it encounters by expanding the series into a multi-dimensional array, as described in pandas.
Let's take a look at the important properties on our array:
python
foo.values foo.dims foo.coords foo.attrs print(foo.name)
You can even modify values inplace:
python
foo.values = 1.0 * foo.values
Note
The array values in a :py~xray.DataArray have a single (homogeneous) data type. To work with heterogeneous or structured data types in xray, use coordinates, or put separate DataArray objects in a single :py~xray.Dataset (see below).
Now fill in some of that missing metadata:
python
foo.name = 'foo' foo.attrs['units'] = 'meters' foo
The :py~xray.DataArray.rename method is another option, returning a new data array:
python
foo.rename('bar')
The coords property is dict like. Individual coordinates can be accessed from the coordinates by name, or even by indexing the data array itself:
python
foo.coords['time'] foo['time']
These are also :py~xray.DataArray objects, which contain tick-labels for each dimension.
Coordinates can also be set or removed by using the dictionary like syntax:
python
foo['ranking'] = ('space', [1, 2, 3]) foo.coords del foo['ranking'] foo.coords
:pyxray.Dataset is xray's multi-dimensional equivalent of a :py~pandas.DataFrame. It is a dict-like container of labeled arrays (:py~xray.DataArray objects) with aligned dimensions. It is designed as an in-memory representation of the data model from the netCDF file format.
In addition to the dict-like interface of the dataset itself, which can be used to access any variable in a dataset, datasets have four key properties:
dims: a dictionary mapping from dimension names to the fixed length of each dimension (e.g.,{'x': 6, 'y': 6, 'time': 8})data_vars: a dict-like container of DataArrays corresponding to variablescoords: another dict-like container of DataArrays intended to label points used indata_vars(e.g., 1-dimensional arrays of numbers, datetime objects or strings)attrs: anOrderedDictto hold arbitrary metadata
The distinction between whether a variables falls in data or coordinates (borrowed from CF conventions) is mostly semantic, and you can probably get away with ignoring it if you like: dictionary like access on a dataset will supply variables found in either category. However, xray does make use of the distinction for indexing and computations. Coordinates indicate constant/fixed/independent quantities, unlike the varying/measured/dependent quantities that belong in data.
Here is an example of how we might structure a dataset for a weather forecast:
In this example, it would be natural to call temperature and precipitation "data variables" and all the other arrays "coordinate variables" because they label the points along the dimensions. (see1 for more background on this example).
To make an :py~xray.Dataset from scratch, supply dictionaries for any variables, coordinates and attributes you would like to insert into the dataset.
For the vars and coords arguments, keys should be the name of the variable and values should be scalars, 1d arrays or tuples of the form (dims, data[, attrs]) sufficient to label each array:
dimsshould be a sequence of strings.datashould be a numpy.ndarray (or array-like object) that has a dimensionality equal to the length ofdims.attrsis an arbitrary Python dictionary for storing metadata associated with a particular array.
Let's create some fake data for the example we show above:
python
temp = 15 + 8 * np.random.randn(2, 2, 3) precip = 10 * np.random.rand(2, 2, 3) lon = [[-99.83, -99.32], [-99.79, -99.23]] lat = [[42.25, 42.21], [42.63, 42.59]]
# for real use cases, its good practice to supply array attributes such as # units, but we won't bother here for the sake of brevity ds = xray.Dataset({'temperature': (['x', 'y', 'time'], temp), 'precipitation': (['x', 'y', 'time'], precip)}, coords={'lon': (['x', 'y'], lon), 'lat': (['x', 'y'], lat), 'time': pd.date_range('2014-09-06', periods=3), 'reference_time': pd.Timestamp('2014-09-05')}) ds
Notice that we did not explicitly include coordinates for the "x" or "y" dimensions, so they were filled in array of ascending integers of the proper length.
We can also pass :pyxray.DataArray objects as values in the dictionary instead of tuples:
python
xray.Dataset({'bar': foo})
You can also create an dataset from a :pypandas.DataFrame with :pyDataset.from_dataframe <xray.Dataset.from_dataframe> or from a netCDF file on disk with :py~xray.open_dataset. See pandas and io.
:py~xray.Dataset implements the Python dictionary interface, with values given by :pyxray.DataArray objects:
python
'temperature' in ds
ds.keys()
ds['temperature']
The valid keys include each listed coordinate and data variable.
Data and coordinate variables are also contained separately in the :py~xray.Dataset.data_vars and :py~xray.Dataset.coords dictionary-like attributes:
python
ds.data_vars ds.coords
Finally, like data arrays, datasets also store arbitrary metadata in the form of `attributes`:
python
ds.attrs
ds.attrs['title'] = 'example attribute' ds
xray does not enforce any restrictions on attributes, but serialization to some file formats may fail if you use objects that are not strings, numbers or :pynumpy.ndarray objects.
As a useful shortcut, you can use attribute style access for reading (but not setting) variables and attributes:
python
ds.temperature
This is particularly useful in an exploratory context, because you can tab-complete these variable names with tools like IPython.
We can update a dataset in-place using Python's standard dictionary syntax. For example, to create this example dataset from scratch, we could have written:
python
ds = xray.Dataset() ds['temperature'] = (('x', 'y', 'time'), temp) ds['precipitation'] = (('x', 'y', 'time'), precip) ds.coords['lat'] = (('x', 'y'), lat) ds.coords['lon'] = (('x', 'y'), lon) ds.coords['time'] = pd.date_range('2014-09-06', periods=3) ds.coords['reference_time'] = pd.Timestamp('2014-09-05')
To change the variables in a Dataset, you can use all the standard dictionary methods, including values, items, __delitem__, get and :py~xray.Dataset.update. Note that assigning a DataArray object to a Dataset variable using __setitem__ or update will automatically align<update> the array(s) to the original dataset's indexes.
You can copy a Dataset by calling the :py~xray.Dataset.copy method. By default, the copy is shallow, so only the container will be copied: the arrays in the Dataset will still be stored in the same underlying :pynumpy.ndarray objects. You can copy all data by calling ds.copy(deep=True).
In addition to dictionary-like methods (described above), xray has additional methods (like pandas) for transforming datasets into new objects.
For removing variables, you can select and drop an explicit list of variables by using the by indexing with a list of names or using the :py~xray.Dataset.drop methods to return a new Dataset. These operations keep around coordinates:
python
list(ds[['temperature']]) list(ds[['x']]) list(ds.drop('temperature'))
If a dimension name is given as an argument to drop, it also drops all variables that use that dimension:
python
list(ds.drop('time'))
As an alternate to dictionary-like modifications, you can use :py~xray.Dataset.assign and :py~xray.Dataset.assign_coords. These methods return a new dataset with additional (or replaced) or values:
python
ds.assign(temperature2 = 2 * ds.temperature)
There is also the :py~xray.Dataset.pipe method that allows you to use a method call with an external function (e.g., ds.pipe(func)) instead of simply calling it (e.g., func(ds)). This allows you to write pipelines for transforming you data (using "method chaining") instead of writing hard to follow nested function calls:
python
# these lines are equivalent, but with pipe we can make the logic flow # entirely from left to right plt.plot((2 * ds.temperature.sel(x=0)).mean('y')) (ds.temperature .sel(x=0) .pipe(lambda x: 2 * x) .mean('y') .pipe(plt.plot))
Both pipe and assign replicate the pandas methods of the same names (:pyDataFrame.pipe <pandas.DataFrame.pipe> and :pyDataFrame.assign <pandas.DataFrame.assign>).
With xray, there is no performance penalty for creating new datasets, even if variables are lazily loaded from a file on disk. Creating new objects instead of mutating existing objects often results in easier to understand code, so we encourage using this approach.
Another useful option is the :py~xray.Dataset.rename method to rename dataset variables:
python
ds.rename({'temperature': 'temp', 'precipitation': 'precip'})
Finally, you can use :py~xray.Dataset.swap_dims to swap dimension and non-dimension variables:
python
ds.coords['day'] = ('time', [6, 7, 8]) ds.swap_dims({'time': 'day'})
Coordinates are ancillary variables stored for DataArray and Dataset objects in the coords attribute:
python
ds.coords
Unlike attributes, xray does interpret and persist coordinates in operations that transform xray objects.
One dimensional coordinates with a name equal to their sole dimension (marked by * when printing a dataset or data array) take on a special meaning in xray. They are used for label based indexing and alignment, like the index found on a pandas :py~pandas.DataFrame or :py~pandas.Series. Indeed, these "dimension" coordinates use a :pypandas.Index internally to store their values.
Other than for indexing, xray does not make any direct use of the values associated with coordinates. Coordinates with names not matching a dimension are not used for alignment or indexing, nor are they required to match when doing arithmetic (see coordinates math).
To entirely add or removing coordinate arrays, you can use dictionary like syntax, as shown above.
To convert back and forth between data and coordinates, you can use the :py~xray.Dataset.set_coords and :py~xray.Dataset.reset_coords methods:
python
ds.reset_coords() ds.set_coords(['temperature', 'precipitation']) ds['temperature'].reset_coords(drop=True)
Notice that these operations skip coordinates with names given by dimensions, as used for indexing. This mostly because we are not entirely sure how to design the interface around the fact that xray cannot store a coordinate and variable with the name but different values in the same dictionary. But we do recognize that supporting something like this would be useful.
Coordinates objects also have a few useful methods, mostly for converting them into dataset objects:
python
ds.coords.to_dataset()
The merge method is particularly interesting, because it implements the same logic used for merging coordinates in arithmetic operations (see comput):
python
alt = xray.Dataset(coords={'z': [10], 'lat': 0, 'lon': 0}) ds.coords.merge(alt.coords)
The coords.merge method may be useful if you want to implement your own binary operations that act on xray objects. In the future, we hope to write more helper functions so that you can easily make your functions act like xray's built-in arithmetic.
To convert a coordinate (or any DataArray) into an actual :pypandas.Index, use the :py~xray.DataArray.to_index method:
python
ds['time'].to_index()
A useful shortcut is the indexes property (on both DataArray and Dataset), which lazily constructs a dictionary whose keys are given by each dimension and whose the values are Index objects:
python
ds.indexes
To convert from a Dataset to a DataArray, use :py~xray.Dataset.to_array:
python
arr = ds.to_array() arr
This method broadcasts all data variables in the dataset against each other, then concatenates them along a new dimension into a new array while preserving coordinates.
To convert back from a DataArray to a Dataset, use :py~xray.DataArray.to_dataset:
python
arr.to_dataset(dim='variable')
The broadcasting behavior of to_array means that the resulting array includes the union of data variable dimensions:
python
ds2 = xray.Dataset({'a': 0, 'b': ('x', [3, 4, 5])})
# the input dataset has 4 elements ds2
# the resulting array has 6 elements ds2.to_array()
Otherwise, the result could not be represented as an orthogonal array.
If you use to_dataset without supplying the dim argument, the DataArray will be converted into a Dataset of one variable:
python
arr.to_dataset(name='combined')
Latitude and longitude are 2D arrays because the dataset uses projected coordinates.
reference_timerefers to the reference time at which the forecast was made, rather thantimewhich is the valid time for which the forecast applies.↩