python
import numpy as np import pandas as pd import xarray as xr np.random.seed(123456) np.set_printoptions(threshold=10)
:pyxarray.DataArray
is xarray's implementation of a labeled, multi-dimensional array. It has several key properties:
values
: a :pynumpy.ndarray
holding 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
: anOrderedDict
to hold arbitrary metadata (attributes)
xarray uses dims
and coords
to enable its core metadata aware operations. Dimensions provide names that xarray 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: xarray makes no attempt to interpret them, and propagates them only in unambiguous cases (see FAQ, approach to metadata
).
The :py~xarray.DataArray
constructor takes:
data
: a multi-dimensional array of values (e.g., a numpy ndarray, :py~pandas.Series
, :py~pandas.DataFrame
or :py~pandas.Panel
)coords
: a list or dictionary of coordinates. If a list, it should be a list of tuples where the first element is the dimension name and the second element is the corresponding coordinate array_like object.dims
: a list of dimension names. If omitted andcoords
is a list of tuples, dimension names are taken fromcoords
.attrs
: a dictionary of attributes to add to the instancename
: a string that names the instance
python
data = np.random.rand(4, 3) locs = ['IA', 'IL', 'IN'] times = pd.date_range('2000-01-01', periods=4) foo = xr.DataArray(data, coords=[times, locs], dims=['time', 'space']) foo
Only data
is required; all of other arguments will be filled in with default values:
python
xr.DataArray(data)
As you can see, dimension names are always present in the xarray data model: if you do not provide them, defaults of the form dim_N
will be created. However, coordinates are always optional, and dimensions do not have automatic coordinate labels.
Note
This is different from pandas, where axes always have tick labels, which default to the integers [0, ..., n-1]
.
Prior to xarray v0.9, xarray copied this behavior: default coordinates for each dimension would be created if coordinates were not supplied explicitly. This is no longer the case.
Coordinates can be specified in the following ways:
- A list of values with length equal to the number of dimensions, providing coordinate labels for each dimension. Each value must be of one of the following forms:
- A :py
~xarray.DataArray
or :py~xarray.Variable
- A tuple of the form
(dims, data[, attrs])
, which is converted into arguments for :py~xarray.Variable
- A pandas object or scalar value, which is converted into a
DataArray
- A 1D array or list, which is interpreted as values for a one dimensional coordinate variable along the same dimension as it's name
- A :py
- A dictionary of
{coord_name: coord}
where values are of the same form as the list. Supplying coordinates as a dictionary allows other coordinates than those corresponding to dimensions (more on these later). If you supplycoords
as a dictionary, you must explicitly providedims
.
As a list of tuples:
python
xr.DataArray(data, coords=[('time', times), ('space', locs)])
As a dictionary:
python
- xr.DataArray(data, coords={'time': times, 'space': locs, 'const': 42,
'ranking': ('space', [1, 2, 3])},
dims=['time', 'space'])
As a dictionary with coords across multiple dimensions:
python
- xr.DataArray(data, coords={'time': times, 'space': locs, 'const': 42,
'ranking': (('time', 'space'), np.arange(12).reshape(4,3))},
dims=['time', 'space'])
If you create a DataArray
by supplying a pandas :py~pandas.Series
, :py~pandas.DataFrame
or :py~pandas.Panel
, 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 xr.DataArray(df)
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 modify values
inplace:
python
foo.values = 1.0 * foo.values
Note
The array values in a :py~xarray.DataArray
have a single (homogeneous) data type. To work with heterogeneous or structured data types in xarray, use coordinates, or put separate DataArray
objects in a single :py~xarray.Dataset
(see below).
Now fill in some of that missing metadata:
python
foo.name = 'foo' foo.attrs['units'] = 'meters' foo
The :py~xarray.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~xarray.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
For more details, see coordinates
below.
:pyxarray.Dataset
is xarray's multi-dimensional equivalent of a :py~pandas.DataFrame
. It is a dict-like container of labeled arrays (:py~xarray.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., arrays of numbers, datetime objects or strings)attrs
: anOrderedDict
to 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, xarray 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~xarray.Dataset
from scratch, supply dictionaries for any variables (data_vars
), coordinates (coords
) and attributes (attrs
).
data_vars
should be a dictionary with each key as the name of the variable and each value as one of:- A :py
~xarray.DataArray
or :py~xarray.Variable
- A tuple of the form
(dims, data[, attrs])
, which is converted into arguments for :py~xarray.Variable
- A pandas object, which is converted into a
DataArray
- A 1D array or list, which is interpreted as values for a one dimensional coordinate variable along the same dimension as it's name
- A :py
coords
should be a dictionary of the same form asdata_vars
.attrs
should be a dictionary.
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 = xr.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
Here we pass :pyxarray.DataArray
objects or a pandas object as values in the dictionary:
python
xr.Dataset({'bar': foo})
python
xr.Dataset({'bar': foo.to_pandas()})
Where a pandas object is supplied as a value, the names of its indexes are used as dimension names, and its data is aligned to any existing dimensions.
You can also create an dataset from:
- A :py
pandas.DataFrame
or :pypandas.Panel
along its columns and items respectively, by passing it into the :py~xarray.Dataset
directly - A :py
pandas.DataFrame
with :pyDataset.from_dataframe <xarray.Dataset.from_dataframe>
, which will additionally handle MultiIndexes Seepandas
- A netCDF file on disk with :py
~xarray.open_dataset
. Seeio
.
:py~xarray.Dataset
implements the Python mapping interface, with values given by :pyxarray.DataArray
objects:
python
'temperature' in ds ds['temperature']
Valid keys include each listed coordinate and data variable.
Data and coordinate variables are also contained separately in the :py~xarray.Dataset.data_vars
and :py~xarray.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
xarray 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.
Warning
We are changing the behavior of iterating over a Dataset the next major release of xarray, to only include data variables instead of both data variables and coordinates. In the meantime, prefer iterating over ds.data_vars
or ds.coords
.
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 = xr.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~xarray.Dataset.update
. Note that assigning a DataArray
or pandas 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~xarray.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), xarray 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 indexing with a list of names or using the :py~xarray.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~xarray.Dataset.assign
and :py~xarray.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~xarray.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 xarray, 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~xarray.Dataset.rename
method to rename dataset variables:
python
ds.rename({'temperature': 'temp', 'precipitation': 'precip'})
The related :py~xarray.Dataset.swap_dims
method allows you do 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, xarray does interpret and persist coordinates in operations that transform xarray objects. There are two types of coordinates in xarray:
- dimension coordinates are one dimensional coordinates with a name equal to their sole dimension (marked by
*
when printing a dataset or data array). They are used for label based indexing and alignment, like theindex
found on a pandas :py~pandas.DataFrame
or :py~pandas.Series
. Indeed, these "dimension" coordinates use a :pypandas.Index
internally to store their values. - non-dimension coordinates are variables that contain coordinate data, but are not a dimension coordinate. They can be multidimensional (see
examples.multidim
), and there is no relationship between the name of a non-dimension coordinate and the name(s) of its dimension(s). Non-dimension coordinates can be useful for indexing or plotting; otherwise, xarray does not make any direct use of the values associated with them. They are not used for alignment or automatic indexing, nor are they required to match when doing arithmetic (seecoordinates math
).
Note
xarray's terminology differs from the CF terminology, where the "dimension coordinates" are called "coordinate variables", and the "non-dimension coordinates" are called "auxiliary coordinate variables" (see 1295
for more details).
To entirely add or remove 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~xarray.Dataset.set_coords
and :py~xarray.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 xarray 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 = xr.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 xarray objects. In the future, we hope to write more helper functions so that you can easily make your functions act like xarray's built-in arithmetic.
To convert a coordinate (or any DataArray
) into an actual :pypandas.Index
, use the :py~xarray.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
Xarray supports labeling coordinate values with a :pypandas.MultiIndex
:
python
- midx = pd.MultiIndex.from_arrays([['R', 'R', 'V', 'V'], [.1, .2, .7, .9]],
names=('band', 'wn'))
mda = xr.DataArray(np.random.rand(4), coords={'spec': midx}, dims='spec') mda
For convenience multi-index levels are directly accessible as "virtual" or "derived" coordinates (marked by -
when printing a dataset or data array):
python
mda['band'] mda.wn
Indexing with multi-index levels is also possible using the sel
method (see multi-level indexing
).
Unlike other coordinates, "virtual" level coordinates are not stored in the coords
attribute of DataArray
and Dataset
objects (although they are shown when printing the coords
attribute). Consequently, most of the coordinates related methods don't apply for them. It also can't be used to replace one particular level.
Because in a DataArray
or Dataset
object each multi-index level is accessible as a "virtual" coordinate, its name must not conflict with the names of the other levels, coordinates and data variables of the same object. Even though Xarray set default names for multi-indexes with unnamed levels, it is recommended that you explicitly set the names of the levels.
Latitude and longitude are 2D arrays because the dataset uses projected coordinates.
reference_time
refers to the reference time at which the forecast was made, rather thantime
which is the valid time for which the forecast applies.↩