Working with labeled data#

Learning goals:

Use different forms of indexing to select data based on position and coordinates
Select datetime ranges

Scientific data is inherently labeled. For example, time series data includes timestamps that label individual periods or points in time, spatial data has coordinates (e.g. longitude, latitude, elevation), and model or laboratory experiments are often identified by unique identifiers. In this notebook we’ll see that labeled dimensions make code much easier to understand!

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

We’ll start by comparing common indexing operations with a numpy array and equivalent xarray DataArray:

# axis0: x, axis1: y
np_array = np.arange(10).reshape(2, 5)
np_array

array([[0, 1, 2, 3, 4],
       [5, 6, 7, 8, 9]])

da = xr.DataArray(np_array, dims=("x", "y"))
da

<xarray.DataArray (x: 2, y: 5)>
array([[0, 1, 2, 3, 4],
       [5, 6, 7, 8, 9]])
Dimensions without coordinates: x, y

Position-based indexing#

Indexing#

Recall that indexing is selecting a value from an array based on its position

np_array[0, 3]

da.isel(x=0, y=3)  # or da[{"x": 0, "y": 3}]

<xarray.DataArray ()>
array(3)

Slicing#

And slicing retrieves a range of values

np_array[:2, 1:]

array([[1, 2, 3, 4],
       [6, 7, 8, 9]])

da.isel(x=slice(None, 2), y=slice(1, None))

<xarray.DataArray (x: 2, y: 4)>
array([[1, 2, 3, 4],
       [6, 7, 8, 9]])
Dimensions without coordinates: x, y

Label-based indexing#

Remembering the axis order can be challenging even with 2D arrays (is np_array[0,3] the first row and third column or first column and third row? or did I store these samples by row or by column when I saved the data?!). The difficulty is compounded with added dimensions. Xarray objects eliminate much of the mental overhead by adding coordinate labels:

To select data by coordinate labels instead of integer indices we can use the same syntax, using sel instead of isel:

this will require us to specify exact coordinate values. If we don’t have those, we can use the method parameter (see Dataset.sel for documentation):

We can also select multiple values:

If instead of selecting data we want to drop it, we can use drop_sel:

Working with labeled data

Contents

Working with labeled data#

Position-based indexing#

Indexing#

Slicing#

Label-based indexing#

Exercises#