# Indexing and selecting data Xarray offers extremely flexible indexing routines that combine the best features of NumPy and pandas for data selection. The most basic way to access elements of a [`DataArray`](../generated/xarray.DataArray.html.md#xarray.DataArray) object is to use Python’s `[]` syntax, such as `array[i, j]`, where `i` and `j` are both integers. As xarray objects can store coordinates corresponding to each dimension of an array, label-based indexing similar to `pandas.DataFrame.loc` is also possible. In label-based indexing, the element position `i` is automatically looked-up from the coordinate values. Dimensions of xarray objects have names, so you can also lookup the dimensions by name, instead of remembering their positional order. ## Quick overview In total, xarray supports four different kinds of indexing, as described below and summarized in this table: | Dimension lookup | Index lookup | `DataArray` syntax | `Dataset` syntax | |--------------------|----------------|-----------------------------------------------------------|-----------------------------------------------------------| | Positional | By integer | `da[:, 0]` | *not available* | | Positional | By label | `da.loc[:, 'IA']` | *not available* | | By name | By integer | `da.isel(space=0)` or
`da[dict(space=0)]` | `ds.isel(space=0)` or
`ds[dict(space=0)]` | | By name | By label | `da.sel(space='IA')` or
`da.loc[dict(space='IA')]` | `ds.sel(space='IA')` or
`ds.loc[dict(space='IA')]` | More advanced indexing is also possible for all the methods by supplying [`DataArray`](../generated/xarray.DataArray.html.md#xarray.DataArray) objects as indexer. See [Vectorized Indexing](#vectorized-indexing) for the details. ## Positional indexing Indexing a [`DataArray`](../generated/xarray.DataArray.html.md#xarray.DataArray) directly works (mostly) just like it does for numpy arrays, except that the returned object is always another DataArray: ```ipython3 da = xr.DataArray( np.random.rand(4, 3), [ ("time", pd.date_range("2000-01-01", periods=4)), ("space", ["IA", "IL", "IN"]), ], ) da[:2] ``` ```ipython3 da[0, 0] ``` ```ipython3 da[:, [2, 1]] ``` Attributes are persisted in all indexing operations. #### WARNING Positional indexing deviates from the NumPy when indexing with multiple arrays like `da[[0, 1], [0, 1]]`, as described in [Vectorized Indexing](#vectorized-indexing). Xarray also supports label-based indexing, just like pandas. Because we use a [`pandas.Index`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.html#pandas.Index) under the hood, label based indexing is very fast. To do label based indexing, use the [`loc`](../generated/xarray.DataArray.loc.html.md#xarray.DataArray.loc) attribute: ```ipython3 da.loc["2000-01-01":"2000-01-02", "IA"] ``` In this example, the selected is a subpart of the array in the range ‘2000-01-01’:’2000-01-02’ along the first coordinate `time` and with ‘IA’ value from the second coordinate `space`. You can perform any of the [label indexing operations supported by pandas](https://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-label), including indexing with individual, slices and lists/arrays of labels, as well as indexing with boolean arrays. Like pandas, label based indexing in xarray is *inclusive* of both the start and stop bounds. Setting values with label based indexing is also supported: ```ipython3 da.loc["2000-01-01", ["IL", "IN"]] = -10 da ``` ## Indexing with dimension names With the dimension names, we do not have to rely on dimension order and can use them explicitly to slice data. There are two ways to do this: 1. Use the [`sel()`](../generated/xarray.DataArray.sel.html.md#xarray.DataArray.sel) and [`isel()`](../generated/xarray.DataArray.isel.html.md#xarray.DataArray.isel) convenience methods: ```ipython3 # index by integer array indices da.isel(space=0, time=slice(None, 2)) ``` ```ipython3 # index by dimension coordinate labels da.sel(time=slice("2000-01-01", "2000-01-02")) ``` 1. Use a dictionary as the argument for array positional or label based array indexing: ```ipython3 # index by integer array indices da[dict(space=0, time=slice(None, 2))] ``` ```ipython3 # index by dimension coordinate labels da.loc[dict(time=slice("2000-01-01", "2000-01-02"))] ``` The arguments to these methods can be any objects that could index the array along the dimension given by the keyword, e.g., labels for an individual value, `Python slice` objects or 1-dimensional arrays. #### NOTE We would love to be able to do indexing with labeled dimension names inside brackets, but unfortunately, [Python does not yet support indexing with keyword arguments](https://legacy.python.org/dev/peps/pep-0472/) like `da[space=0]` ## Nearest neighbor lookups The label based selection methods [`sel()`](../generated/xarray.Dataset.sel.html.md#xarray.Dataset.sel), [`reindex()`](../generated/xarray.Dataset.reindex.html.md#xarray.Dataset.reindex) and [`reindex_like()`](../generated/xarray.Dataset.reindex_like.html.md#xarray.Dataset.reindex_like) all support `method` and `tolerance` keyword argument. The method parameter allows for enabling nearest neighbor (inexact) lookups by use of the methods `'pad'`, `'backfill'` or `'nearest'`: ```ipython3 da = xr.DataArray([1, 2, 3], [("x", [0, 1, 2])]) da.sel(x=[1.1, 1.9], method="nearest") ``` ```ipython3 da.sel(x=0.1, method="backfill") ``` ```ipython3 da.reindex(x=[0.5, 1, 1.5, 2, 2.5], method="pad") ``` Tolerance limits the maximum distance for valid matches with an inexact lookup: ```ipython3 da.reindex(x=[1.1, 1.5], method="nearest", tolerance=0.2) ``` The method parameter is not yet supported if any of the arguments to `.sel()` is a `slice` object: ```ipython3 da.sel(x=slice(1, 3), method="nearest") ``` ```ipythontb NotImplementedError: cannot use ``method`` argument if any indexers are slice objects ``` However, you don’t need to use `method` to do inexact slicing. Slicing already returns all values inside the range (inclusive), as long as the index labels are monotonic increasing: ```ipython3 da.sel(x=slice(0.9, 3.1)) ``` Indexing axes with monotonic decreasing labels also works, as long as the `slice` or `.loc` arguments are also decreasing: ```ipython3 reversed_da = da[::-1] reversed_da.loc[3.1:0.9] ``` #### NOTE If you want to interpolate along coordinates rather than looking up the nearest neighbors, use [`interp()`](../generated/xarray.Dataset.interp.html.md#xarray.Dataset.interp) and [`interp_like()`](../generated/xarray.Dataset.interp_like.html.md#xarray.Dataset.interp_like). See [interpolation](interpolation.html.md#interp) for the details. ## Dataset indexing We can also use these methods to index all variables in a dataset simultaneously, returning a new dataset: ```ipython3 da = xr.DataArray( np.random.rand(4, 3), [ ("time", pd.date_range("2000-01-01", periods=4)), ("space", ["IA", "IL", "IN"]), ], ) ds = da.to_dataset(name="foo") ds.isel(space=[0], time=[0]) ``` ```ipython3 ds.sel(time="2000-01-01") ``` Positional indexing on a dataset is not supported because the ordering of dimensions in a dataset is somewhat ambiguous (it can vary between different arrays). However, you can do normal indexing with dimension names: ```ipython3 ds[dict(space=[0], time=[0])] ``` ```ipython3 ds.loc[dict(time="2000-01-01")] ``` ## Dropping labels and dimensions The [`drop_sel()`](../generated/xarray.Dataset.drop_sel.html.md#xarray.Dataset.drop_sel) method returns a new object with the listed index labels along a dimension dropped: ```ipython3 ds.drop_sel(space=["IN", "IL"]) ``` `drop_sel` is both a `Dataset` and `DataArray` method. Use [`drop_dims()`](../generated/xarray.Dataset.drop_dims.html.md#xarray.Dataset.drop_dims) to drop a full dimension from a Dataset. Any variables with these dimensions are also dropped: ```ipython3 ds.drop_dims("time") ``` ## Masking with `where` Indexing methods on xarray objects generally return a subset of the original data. However, it is sometimes useful to select an object with the same shape as the original data, but with some elements masked. To do this type of selection in xarray, use [`where()`](../generated/xarray.DataArray.where.html.md#xarray.DataArray.where): ```ipython3 da = xr.DataArray(np.arange(16).reshape(4, 4), dims=["x", "y"]) da.where(da.x + da.y < 4) ``` This is particularly useful for ragged indexing of multi-dimensional data, e.g., to apply a 2D mask to an image. Note that `where` follows all the usual xarray broadcasting and alignment rules for binary operations (e.g., `+`) between the object being indexed and the condition, as described in [Computation](computation.html.md#compute): ```ipython3 da.where(da.y < 2) ``` By default `where` maintains the original size of the data. For cases where the selected data size is much smaller than the original data, use of the option `drop=True` clips coordinate elements that are fully masked: ```ipython3 da.where(da.y < 2, drop=True) ``` ## Selecting values with `isin` To check whether elements of an xarray object contain a single object, you can compare with the equality operator `==` (e.g., `arr == 3`). To check multiple values, use [`isin()`](../generated/xarray.DataArray.isin.html.md#xarray.DataArray.isin): ```ipython3 da = xr.DataArray([1, 2, 3, 4, 5], dims=["x"]) da.isin([2, 4]) ``` [`isin()`](../generated/xarray.DataArray.isin.html.md#xarray.DataArray.isin) works particularly well with [`where()`](../generated/xarray.DataArray.where.html.md#xarray.DataArray.where) to support indexing by arrays that are not already labels of an array: ```ipython3 lookup = xr.DataArray([-1, -2, -3, -4, -5], dims=["x"]) da.where(lookup.isin([-2, -4]), drop=True) ``` However, some caution is in order: when done repeatedly, this type of indexing is significantly slower than using [`sel()`](../generated/xarray.DataArray.sel.html.md#xarray.DataArray.sel). ## Vectorized Indexing Like numpy and pandas, xarray supports indexing many array elements at once in a vectorized manner. If you only provide integers, slices, or unlabeled arrays (array without dimension names, such as `np.ndarray`, `list`, but not [`DataArray()`](../generated/xarray.DataArray.html.md#xarray.DataArray) or [`Variable()`](../generated/xarray.Variable.html.md#xarray.Variable)) indexing can be understood as orthogonally. Each indexer component selects independently along the corresponding dimension, similar to how vector indexing works in Fortran or MATLAB, or after using the [`numpy.ix_()`](https://numpy.org/doc/stable/reference/generated/numpy.ix_.html#numpy.ix_) helper: ```ipython3 da = xr.DataArray( np.arange(12).reshape((3, 4)), dims=["x", "y"], coords={"x": [0, 1, 2], "y": ["a", "b", "c", "d"]}, ) da ``` ```ipython3 da[[0, 2, 2], [1, 3]] ``` For more flexibility, you can supply [`DataArray()`](../generated/xarray.DataArray.html.md#xarray.DataArray) objects as indexers. Dimensions on resultant arrays are given by the ordered union of the indexers’ dimensions: ```ipython3 ind_x = xr.DataArray([0, 1], dims=["x"]) ind_y = xr.DataArray([0, 1], dims=["y"]) da[ind_x, ind_y] # orthogonal indexing ``` Slices or sequences/arrays without named-dimensions are treated as if they have the same dimension which is indexed along: ```ipython3 # Because [0, 1] is used to index along dimension 'x', # it is assumed to have dimension 'x' da[[0, 1], ind_x] ``` Furthermore, you can use multi-dimensional [`DataArray()`](../generated/xarray.DataArray.html.md#xarray.DataArray) as indexers, where the resultant array dimension is also determined by indexers’ dimension: ```ipython3 ind = xr.DataArray([[0, 1], [0, 1]], dims=["a", "b"]) da[ind] ``` Similar to how [NumPy’s advanced indexing](https://numpy.org/doc/stable/user/basics.indexing.html#advanced-indexing) works, vectorized indexing for xarray is based on our [broadcasting rules](computation.html.md#compute-broadcasting). See [Indexing rules](#indexing-rules) for the complete specification. Vectorized indexing also works with `isel`, `loc`, and `sel`: ```ipython3 ind = xr.DataArray([[0, 1], [0, 1]], dims=["a", "b"]) da.isel(y=ind) # same as da[:, ind] ``` ```ipython3 ind = xr.DataArray([["a", "b"], ["b", "a"]], dims=["a", "b"]) da.loc[:, ind] # same as da.sel(y=ind) ``` These methods may also be applied to `Dataset` objects ```ipython3 ds = da.to_dataset(name="bar") ds.isel(x=xr.DataArray([0, 1, 2], dims=["points"])) ``` Vectorized indexing may be used to extract information from the nearest grid cells of interest, for example, the nearest climate model grid cells to a collection specified weather station latitudes and longitudes. To trigger vectorized indexing behavior you will need to provide the selection dimensions with a new shared output dimension name. In the example below, the selections of the closest latitude and longitude are renamed to an output dimension named “points”: ```ipython3 ds = xr.tutorial.open_dataset("air_temperature") # Define target latitude and longitude (where weather stations might be) target_lon = xr.DataArray([200, 201, 202, 205], dims="points") target_lat = xr.DataArray([31, 41, 42, 42], dims="points") # Retrieve data at the grid cells nearest to the target latitudes and longitudes da = ds["air"].sel(lon=target_lon, lat=target_lat, method="nearest") da ``` #### TIP If you are lazily loading your data from disk, not every form of vectorized indexing is supported (or if supported, may not be supported efficiently). You may find increased performance by loading your data into memory first, e.g., with [`load()`](../generated/xarray.Dataset.load.html.md#xarray.Dataset.load). #### NOTE If an indexer is a [`DataArray()`](../generated/xarray.DataArray.html.md#xarray.DataArray), its coordinates should not conflict with the selected subpart of the target array (except for the explicitly indexed dimensions with `.loc`/`.sel`). Otherwise, `IndexError` will be raised. ## Assigning values with indexing To select and assign values to a portion of a [`DataArray()`](../generated/xarray.DataArray.html.md#xarray.DataArray) you can use indexing with `.loc` : ```ipython3 ds = xr.tutorial.open_dataset("air_temperature") # add an empty 2D dataarray ds["empty"] = xr.full_like(ds.air.mean("time"), fill_value=0) # modify one grid point using loc() ds["empty"].loc[dict(lon=260, lat=30)] = 100 # modify a 2D region using loc() lc = ds.coords["lon"] la = ds.coords["lat"] ds["empty"].loc[ dict(lon=lc[(lc > 220) & (lc < 260)], lat=la[(la > 20) & (la < 60)]) ] = 100 ``` or [`where()`](../generated/xarray.where.html.md#xarray.where): ```ipython3 # modify one grid point using xr.where() ds["empty"] = xr.where( (ds.coords["lat"] == 20) & (ds.coords["lon"] == 260), 100, ds["empty"] ) # or modify a 2D region using xr.where() mask = ( (ds.coords["lat"] > 20) & (ds.coords["lat"] < 60) & (ds.coords["lon"] > 220) & (ds.coords["lon"] < 260) ) ds["empty"] = xr.where(mask, 100, ds["empty"]) ``` Vectorized indexing can also be used to assign values to xarray object. ```ipython3 da = xr.DataArray( np.arange(12).reshape((3, 4)), dims=["x", "y"], coords={"x": [0, 1, 2], "y": ["a", "b", "c", "d"]}, ) da ``` ```ipython3 da[0] = -1 # assignment with broadcasting da ``` ```ipython3 ind_x = xr.DataArray([0, 1], dims=["x"]) ind_y = xr.DataArray([0, 1], dims=["y"]) da[ind_x, ind_y] = -2 # assign -2 to (ix, iy) = (0, 0) and (1, 1) da ``` ```ipython3 da[ind_x, ind_y] += 100 # increment is also possible da ``` Like `numpy.ndarray`, value assignment sometimes works differently from what one may expect. ```ipython3 da = xr.DataArray([0, 1, 2, 3], dims=["x"]) ind = xr.DataArray([0, 0, 0], dims=["x"]) da[ind] -= 1 da ``` Where the 0th element will be subtracted 1 only once. This is because `v[0] = v[0] - 1` is called three times, rather than `v[0] = v[0] - 1 - 1 - 1`. See [Assigning values to indexed arrays](https://numpy.org/doc/stable/user/basics.indexing.html#assigning-values-to-indexed-arrays) for the details. #### NOTE Dask array does not support value assignment (see [Parallel Computing with Dask](dask.html.md#dask) for the details). #### NOTE Coordinates in both the left- and right-hand-side arrays should not conflict with each other. Otherwise, `IndexError` will be raised. #### WARNING Do not try to assign values when using any of the indexing methods `isel` or `sel`: ```python # DO NOT do this da.isel(space=0) = 0 ``` Instead, values can be assigned using dictionary-based indexing: ```python da[dict(space=0)] = 0 ``` Assigning values with the chained indexing using `.sel` or `.isel` fails silently. See the cell below: ```ipython3 da = xr.DataArray([0, 1, 2, 3], dims=["x"]) # DO NOT do this da.isel(x=[0, 1, 2])[1] = -1 da ``` You can also assign values to all variables of a `Dataset` at once: ```ipython3 ds_org = xr.tutorial.open_dataset("eraint_uvz").isel( latitude=slice(56, 59), longitude=slice(255, 258), level=0 ) # set all values to 0 ds = xr.zeros_like(ds_org) ds ``` ```ipython3 # by integer ds[dict(latitude=2, longitude=2)] = 1 ds["u"] ``` ```ipython3 ds["v"] ``` ```ipython3 # by label ds.loc[dict(latitude=47.25, longitude=[11.25, 12])] = 100 ds["u"] ``` ```ipython3 # dataset as new values new_dat = ds_org.loc[dict(latitude=48, longitude=[11.25, 12])] new_dat ``` ```ipython3 ds.loc[dict(latitude=47.25, longitude=[11.25, 12])] = new_dat ds["u"] ``` The dimensions can differ between the variables in the dataset, but all variables need to have at least the dimensions specified in the indexer dictionary. The new values must be either a scalar, a `DataArray` or a `Dataset` itself that contains all variables that also appear in the dataset to be modified. ## More advanced indexing The use of [`DataArray()`](../generated/xarray.DataArray.html.md#xarray.DataArray) objects as indexers enables very flexible indexing. The following is an example of the pointwise indexing: ```ipython3 da = xr.DataArray(np.arange(56).reshape((7, 8)), dims=["x", "y"]) da ``` ```ipython3 da.isel(x=xr.DataArray([0, 1, 6], dims="z"), y=xr.DataArray([0, 1, 0], dims="z")) ``` where three elements at `(ix, iy) = ((0, 0), (1, 1), (6, 0))` are selected and mapped along a new dimension `z`. If you want to add a coordinate to the new dimension `z`, you can supply a [`DataArray`](../generated/xarray.DataArray.html.md#xarray.DataArray) with a coordinate, ```ipython3 da.isel( x=xr.DataArray([0, 1, 6], dims="z", coords={"z": ["a", "b", "c"]}), y=xr.DataArray([0, 1, 0], dims="z"), ) ``` Analogously, label-based pointwise-indexing is also possible by the `.sel` method: ```ipython3 da = xr.DataArray( np.random.rand(4, 3), [ ("time", pd.date_range("2000-01-01", periods=4)), ("space", ["IA", "IL", "IN"]), ], ) times = xr.DataArray( pd.to_datetime(["2000-01-03", "2000-01-02", "2000-01-01"]), dims="new_time" ) da.sel(space=xr.DataArray(["IA", "IL", "IN"], dims=["new_time"]), time=times) ``` ## Align and reindex Xarray’s `reindex`, `reindex_like` and `align` impose a `DataArray` or `Dataset` onto a new set of coordinates corresponding to dimensions. The original values are subset to the index labels still found in the new labels, and values corresponding to new labels not found in the original object are in-filled with `NaN`. Xarray operations that combine multiple objects generally automatically align their arguments to share the same indexes. However, manual alignment can be useful for greater control and for increased performance. To reindex a particular dimension, use [`reindex()`](../generated/xarray.DataArray.reindex.html.md#xarray.DataArray.reindex): ```ipython3 da.reindex(space=["IA", "CA"]) ``` The [`reindex_like()`](../generated/xarray.DataArray.reindex_like.html.md#xarray.DataArray.reindex_like) method is a useful shortcut. To demonstrate, we will make a subset DataArray with new values: ```ipython3 foo = da.rename("foo") baz = (10 * da[:2, :2]).rename("baz") baz ``` Reindexing `foo` with `baz` selects out the first two values along each dimension: ```ipython3 foo.reindex_like(baz) ``` The opposite operation asks us to reindex to a larger shape, so we fill in the missing values with `NaN`: ```ipython3 baz.reindex_like(foo) ``` The [`align()`](../generated/xarray.align.html.md#xarray.align) function lets us perform more flexible database-like `'inner'`, `'outer'`, `'left'` and `'right'` joins: ```ipython3 xr.align(foo, baz, join="inner") ``` ```ipython3 xr.align(foo, baz, join="outer") ``` Both `reindex_like` and `align` work interchangeably between [`DataArray`](../generated/xarray.DataArray.html.md#xarray.DataArray) and [`Dataset`](../generated/xarray.Dataset.html.md#xarray.Dataset) objects, and with any number of matching dimension names: ```ipython3 ds ``` ```ipython3 ds.reindex_like(baz) ``` ```ipython3 other = xr.DataArray(["a", "b", "c"], dims="other") # this is a no-op, because there are no shared dimension names ds.reindex_like(other) ``` ## Missing coordinate labels Coordinate labels for each dimension are optional (as of xarray v0.9). Label based indexing with `.sel` and `.loc` uses standard positional, integer-based indexing as a fallback for dimensions without a coordinate label: ```ipython3 da = xr.DataArray([1, 2, 3], dims="x") da.sel(x=[0, -1]) ``` Alignment between xarray objects where one or both do not have coordinate labels succeeds only if all dimensions of the same name have the same length. Otherwise, it raises an informative error: ```ipython3 xr.align(da, da[:2]) ``` ```ipythontb AlignmentError: cannot reindex or align along dimension 'x' because of conflicting dimension sizes: {2, 3} ``` ## Underlying Indexes Xarray uses the [`pandas.Index`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.html#pandas.Index) internally to perform indexing operations. If you need to access the underlying indexes, they are available through the [`indexes`](../generated/xarray.DataArray.indexes.html.md#xarray.DataArray.indexes) attribute. ```ipython3 da = xr.DataArray( np.random.rand(4, 3), [ ("time", pd.date_range("2000-01-01", periods=4)), ("space", ["IA", "IL", "IN"]), ], ) da ``` ```ipython3 da.indexes ``` ```ipython3 da.indexes["time"] ``` Use [`get_index()`](../generated/xarray.DataArray.get_index.html.md#xarray.DataArray.get_index) to get an index for a dimension, falling back to a default [`pandas.RangeIndex`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.RangeIndex.html#pandas.RangeIndex) if it has no coordinate labels: ```ipython3 da = xr.DataArray([1, 2, 3], dims="x") da ``` ```ipython3 da.get_index("x") ``` ## Copies vs. Views Whether array indexing returns a view or a copy of the underlying data depends on the nature of the labels. For positional (integer) indexing, xarray follows the same [rules](https://numpy.org/doc/stable/user/basics.copies.html) as NumPy: - Positional indexing with only integers and slices returns a view. - Positional indexing with arrays or lists returns a copy. The rules for label based indexing are more complex: - Label-based indexing with only slices returns a view. - Label-based indexing with arrays returns a copy. - Label-based indexing with scalars returns a view or a copy, depending upon if the corresponding positional indexer can be represented as an integer or a slice object. The exact rules are determined by pandas. Whether data is a copy or a view is more predictable in xarray than in pandas, so unlike pandas, xarray does not produce [SettingWithCopy warnings](https://pandas.pydata.org/pandas-docs/stable/indexing.html#returning-a-view-versus-a-copy). However, you should still avoid assignment with chained indexing. Note that other operations (such as [`values()`](../generated/xarray.DataArray.values.html.md#xarray.DataArray.values)) may also return views rather than copies. ## Multi-level indexing Just like pandas, advanced indexing on multi-level indexes is possible with `loc` and `sel`. You can slice a multi-index by providing multiple indexers, i.e., a tuple of slices, labels, list of labels, or any selector allowed by pandas: ```ipython3 midx = pd.MultiIndex.from_product([list("abc"), [0, 1]], names=("one", "two")) mda = xr.DataArray(np.random.rand(6, 3), [("x", midx), ("y", range(3))]) mda ``` ```ipython3 mda.sel(x=(list("ab"), [0])) ``` You can also select multiple elements by providing a list of labels or tuples or a slice of tuples: ```ipython3 mda.sel(x=[("a", 0), ("b", 1)]) ``` Additionally, xarray supports dictionaries: ```ipython3 mda.sel(x={"one": "a", "two": 0}) ``` For convenience, `sel` also accepts multi-index levels directly as keyword arguments: ```ipython3 mda.sel(one="a", two=0) ``` Note that using `sel` it is not possible to mix a dimension indexer with level indexers for that dimension (e.g., `mda.sel(x={'one': 'a'}, two=0)` will raise a `ValueError`). Like pandas, xarray handles partial selection on multi-index (level drop). As shown below, it also renames the dimension / coordinate when the multi-index is reduced to a single index. ```ipython3 mda.loc[{"one": "a"}, ...] ``` Unlike pandas, xarray does not guess whether you provide index levels or dimensions when using `loc` in some ambiguous cases. For example, for `mda.loc[{'one': 'a', 'two': 0}]` and `mda.loc['a', 0]` xarray always interprets (‘one’, ‘two’) and (‘a’, 0) as the names and labels of the 1st and 2nd dimension, respectively. You must specify all dimensions or use the ellipsis in the `loc` specifier, e.g. in the example above, `mda.loc[{'one': 'a', 'two': 0}, :]` or `mda.loc[('a', 0), ...]`. ## Indexing rules Here we describe the full rules xarray uses for vectorized indexing. Note that this is for the purposes of explanation: for the sake of efficiency and to support various backends, the actual implementation is different. 1. (Only for label based indexing.) Look up positional indexes along each dimension from the corresponding [`pandas.Index`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.html#pandas.Index). 2. A full slice object `:` is inserted for each dimension without an indexer. 3. `slice` objects are converted into arrays, given by `np.arange(*slice.indices(...))`. 4. Assume dimension names for array indexers without dimensions, such as `np.ndarray` and `list`, from the dimensions to be indexed along. For example, `v.isel(x=[0, 1])` is understood as `v.isel(x=xr.DataArray([0, 1], dims=['x']))`. 5. For each variable in a `Dataset` or `DataArray` (the array and its coordinates): 1. Broadcast all relevant indexers based on their dimension names (see [Broadcasting by dimension name](computation.html.md#compute-broadcasting) for full details). 2. Index the underling array by the broadcast indexers, using NumPy’s advanced indexing rules. 6. If any indexer DataArray has coordinates and no coordinate with the same name exists, attach them to the indexed object. #### NOTE Only 1-dimensional boolean arrays can be used as indexers.