Indexing and selecting data

.. ipython:: python
    :suppress:

    import numpy as np
    import pandas as pd
    import xarray as xr

    np.random.seed(123456)

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 :py:class:`~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 :py:class:`~xarray.DataArray` objects as indexer. See :ref:`vectorized_indexing` for the details.

Positional indexing

Indexing a :py:class:`~xarray.DataArray` directly works (mostly) just like it does for numpy arrays, except that the returned object is always another DataArray:

.. ipython:: python

    da = xr.DataArray(
        np.random.rand(4, 3),
        [
            ("time", pd.date_range("2000-01-01", periods=4)),
            ("space", ["IA", "IL", "IN"]),
        ],
    )
    da[:2]
    da[0, 0]
    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 :ref:`vectorized_indexing`.

Xarray also supports label-based indexing, just like pandas. Because we use a :py:class:`pandas.Index` under the hood, label based indexing is very fast. To do label based indexing, use the :py:attr:`~xarray.DataArray.loc` attribute:

.. ipython:: python

    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, 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:

.. ipython:: python

    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:

Use the :py:meth:`~xarray.DataArray.sel` and :py:meth:`~xarray.DataArray.isel` convenience methods:

.. ipython:: python

    # index by integer array indices
    da.isel(space=0, time=slice(None, 2))

    # index by dimension coordinate labels
    da.sel(time=slice("2000-01-01", "2000-01-02"))

Use a dictionary as the argument for array positional or label based array indexing:

.. ipython:: python

    # index by integer array indices
    da[dict(space=0, time=slice(None, 2))]

    # 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, :py:class:`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 like da[space=0]

Nearest neighbor lookups

The label based selection methods :py:meth:`~xarray.Dataset.sel`, :py:meth:`~xarray.Dataset.reindex` and :py:meth:`~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':

.. ipython:: python

    da = xr.DataArray([1, 2, 3], [("x", [0, 1, 2])])
    da.sel(x=[1.1, 1.9], method="nearest")
    da.sel(x=0.1, method="backfill")
    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:

.. ipython:: python

    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:

.. ipython::
   :verbatim:

   In [1]: da.sel(x=slice(1, 3), method="nearest")
   NotImplementedError

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:

.. ipython:: python

    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:

.. ipython:: python

    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 :py:meth:`~xarray.Dataset.interp` and :py:meth:`~xarray.Dataset.interp_like`. See :ref:`interpolation <interp>` for the details.

Dataset indexing

We can also use these methods to index all variables in a dataset simultaneously, returning a new dataset:

.. ipython:: python

    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])
    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:

.. ipython:: python

    ds[dict(space=[0], time=[0])]
    ds.loc[dict(time="2000-01-01")]

Dropping labels and dimensions

The :py:meth:`~xarray.Dataset.drop_sel` method returns a new object with the listed index labels along a dimension dropped:

.. ipython:: python

    ds.drop_sel(space=["IN", "IL"])

drop_sel is both a Dataset and DataArray method.

Use :py:meth:`~xarray.Dataset.drop_dims` to drop a full dimension from a Dataset. Any variables with these dimensions are also dropped:

.. ipython:: python

    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 :py:meth:`~xarray.DataArray.where`:

.. ipython:: python

    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 :ref:`comput`:

.. ipython:: python

    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:

.. ipython:: python

    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 :py:meth:`~xarray.DataArray.isin`:

.. ipython:: python

    da = xr.DataArray([1, 2, 3, 4, 5], dims=["x"])
    da.isin([2, 4])

:py:meth:`~xarray.DataArray.isin` works particularly well with :py:meth:`~xarray.DataArray.where` to support indexing by arrays that are not already labels of an array:

.. ipython:: python

    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 :py:meth:`~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 :py:meth:`~xarray.DataArray` or :py:meth:`~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 :py:func:`numpy.ix_` helper:

.. ipython:: python

    da = xr.DataArray(
        np.arange(12).reshape((3, 4)),
        dims=["x", "y"],
        coords={"x": [0, 1, 2], "y": ["a", "b", "c", "d"]},
    )
    da
    da[[0, 2, 2], [1, 3]]

For more flexibility, you can supply :py:meth:`~xarray.DataArray` objects as indexers. Dimensions on resultant arrays are given by the ordered union of the indexers' dimensions:

.. ipython:: python

    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:

.. ipython:: python

    # 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 :py:meth:`~xarray.DataArray` as indexers, where the resultant array dimension is also determined by indexers' dimension:

.. ipython:: python

    ind = xr.DataArray([[0, 1], [0, 1]], dims=["a", "b"])
    da[ind]

Similar to how NumPy's advanced indexing works, vectorized indexing for xarray is based on our :ref:`broadcasting rules <compute.broadcasting>`. See :ref:`indexing.rules` for the complete specification.

Vectorized indexing also works with isel, loc, and sel:

.. ipython:: python

    ind = xr.DataArray([[0, 1], [0, 1]], dims=["a", "b"])
    da.isel(y=ind)  # same as da[:, ind]

    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

.. ipython:: python

    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":

.. ipython:: python

    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 :py:meth:`~xarray.Dataset.load`.

Note

If an indexer is a :py:meth:`~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 :py:meth:`~xarray.DataArray` you can use indexing with .loc :

.. ipython:: python

    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 :py:meth:`~xarray.where`:

.. ipython:: python

    # 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.

.. ipython:: python

    da = xr.DataArray(
        np.arange(12).reshape((3, 4)),
        dims=["x", "y"],
        coords={"x": [0, 1, 2], "y": ["a", "b", "c", "d"]},
    )
    da
    da[0] = -1  # assignment with broadcasting
    da

    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

    da[ind_x, ind_y] += 100  # increment is also possible
    da

Like numpy.ndarray, value assignment sometimes works differently from what one may expect.

.. ipython:: python

    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 for the details.

Note

Dask array does not support value assignment (see :ref:`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:

# DO NOT do this
da.isel(space=0) = 0

Instead, values can be assigned using dictionary-based indexing:

da[dict(space=0)] = 0

Assigning values with the chained indexing using .sel or .isel fails silently.

.. ipython:: python

    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 :py:class:`Dataset` at once:

.. ipython:: python

    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

    # by integer
    ds[dict(latitude=2, longitude=2)] = 1
    ds["u"]
    ds["v"]

    # by label
    ds.loc[dict(latitude=47.25, longitude=[11.25, 12])] = 100
    ds["u"]

    # dataset as new values
    new_dat = ds_org.loc[dict(latitude=48, longitude=[11.25, 12])]
    new_dat
    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 :py:class:`DataArray` or a :py:class:`Dataset` itself that contains all variables that also appear in the dataset to be modified.

More advanced indexing

The use of :py:meth:`~xarray.DataArray` objects as indexers enables very flexible indexing. The following is an example of the pointwise indexing:

.. ipython:: python

    da = xr.DataArray(np.arange(56).reshape((7, 8)), dims=["x", "y"])
    da
    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 :py:class:`~xarray.DataArray` with a coordinate,

.. ipython:: python

    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:

.. ipython:: python

    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 :py:meth:`~xarray.DataArray.reindex`:

.. ipython:: python

    da.reindex(space=["IA", "CA"])

The :py:meth:`~xarray.DataArray.reindex_like` method is a useful shortcut. To demonstrate, we will make a subset DataArray with new values:

.. ipython:: python

    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:

.. ipython:: python

    foo.reindex_like(baz)

The opposite operation asks us to reindex to a larger shape, so we fill in the missing values with NaN:

.. ipython:: python

    baz.reindex_like(foo)

The :py:func:`~xarray.align` function lets us perform more flexible database-like 'inner', 'outer', 'left' and 'right' joins:

.. ipython:: python

    xr.align(foo, baz, join="inner")
    xr.align(foo, baz, join="outer")

Both reindex_like and align work interchangeably between :py:class:`~xarray.DataArray` and :py:class:`~xarray.Dataset` objects, and with any number of matching dimension names:

.. ipython:: python

    ds
    ds.reindex_like(baz)
    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:

.. ipython:: python

    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:

.. ipython::
    :verbatim:

    In [62]: xr.align(da, da[:2])
    ValueError: arguments without labels along dimension 'x' cannot be aligned because they have different dimension sizes: {2, 3}

Underlying Indexes

Xarray uses the :py:class:`pandas.Index` internally to perform indexing operations. If you need to access the underlying indexes, they are available through the :py:attr:`~xarray.DataArray.indexes` attribute.

.. ipython:: python

    da = xr.DataArray(
        np.random.rand(4, 3),
        [
            ("time", pd.date_range("2000-01-01", periods=4)),
            ("space", ["IA", "IL", "IN"]),
        ],
    )
    da
    da.indexes
    da.indexes["time"]

Use :py:meth:`~xarray.DataArray.get_index` to get an index for a dimension, falling back to a default :py:class:`pandas.RangeIndex` if it has no coordinate labels:

.. ipython:: python

    da = xr.DataArray([1, 2, 3], dims="x")
    da
    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 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. However, you should still avoid assignment with chained indexing.

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:

.. ipython:: python

    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
    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:

.. ipython:: python

    mda.sel(x=[("a", 0), ("b", 1)])

Additionally, xarray supports dictionaries:

.. ipython:: python

    mda.sel(x={"one": "a", "two": 0})

For convenience, sel also accepts multi-index levels directly as keyword arguments:

.. ipython:: python

    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.

.. ipython:: python

    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.

(Only for label based indexing.) Look up positional indexes along each dimension from the corresponding :py:class:`pandas.Index`.
A full slice object : is inserted for each dimension without an indexer.
slice objects are converted into arrays, given by np.arange(*slice.indices(...)).
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'])).
For each variable in a Dataset or DataArray (the array and its coordinates):
1. Broadcast all relevant indexers based on their dimension names (see :ref:`compute.broadcasting` for full details).
2. Index the underling array by the broadcast indexers, using NumPy's advanced indexing rules.
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.

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indexing.rst

indexing.rst

Indexing and selecting data

Quick overview

Positional indexing

Indexing with dimension names

Nearest neighbor lookups

Dataset indexing

Dropping labels and dimensions

Masking with `where`

Selecting values with `isin`

Vectorized Indexing

Assigning values with indexing

More advanced indexing

Align and reindex

Missing coordinate labels

Underlying Indexes

Copies vs. Views

Multi-level indexing

Indexing rules

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File metadata and controls

Indexing and selecting data

Quick overview

Positional indexing

Indexing with dimension names

Nearest neighbor lookups

Dataset indexing

Dropping labels and dimensions

Masking with where

Selecting values with isin

Vectorized Indexing

Assigning values with indexing

More advanced indexing

Align and reindex

Missing coordinate labels

Underlying Indexes

Copies vs. Views

Multi-level indexing

Indexing rules

Masking with `where`

Selecting values with `isin`