Xarray

12. Xarray#

12.1. Overview#

Xarray is a powerful Python library designed for working with multi-dimensional labeled datasets, often used in fields such as climate science, oceanography, and remote sensing. It provides a high-level interface for manipulating and analyzing datasets that can be thought of as extensions of NumPy arrays. Xarray is particularly useful for geospatial data because it supports labeled axes (dimensions), coordinates, and metadata, making it easier to work with datasets that vary across time, space, and other dimensions.

12.2. Learning Objectives#

By the end of this lecture, you should be able to:

Understand the basic concepts and data structures in Xarray, including DataArray and Dataset.
Load and inspect multi-dimensional geospatial datasets using Xarray.
Perform basic operations on Xarray objects, such as selection, indexing, and arithmetic operations.
Use Xarray to efficiently work with large geospatial datasets, including time series and raster data.
Apply Xarray to common geospatial analysis tasks, such as calculating statistics, regridding, and visualization.

12.3. What is Xarray?#

Xarray extends the capabilities of NumPy by providing a data structure for labeled, multi-dimensional arrays. The two main data structures in Xarray are:

DataArray: A labeled, multi-dimensional array, which includes dimensions, coordinates, and attributes.
Dataset: A collection of DataArray objects that share the same dimensions.

Xarray is particularly useful for working with datasets where dimensions have meaningful labels (e.g., time, latitude, longitude) and where metadata is important.

12.4. Installing Xarray#

Before we start, ensure that Xarray is installed. You can install it via pip:

# %pip install xarray pooch

12.5. Importing Libraries#

import matplotlib.pyplot as plt
import numpy as np
import xarray as xr

xr.set_options(keep_attrs=True, display_expand_data=False)
np.set_printoptions(threshold=10, edgeitems=2)

12.10. Visualization with Xarray#

Xarray integrates well with Matplotlib and other visualization libraries, making it easy to create plots directly from DataArray and Dataset objects.

# Plot the mean temperature
mean_temperature.plot()
plt.show()

../../_images/01e415b0bd171b159030ff9c9e1255958d0667c759d342dde8fccf6a1b041d0d.png

# Plot a time series for a specific location
temperature.sel(lat=40.0, lon=260.0).plot()
plt.show()

../../_images/0bbd51a9b82b90b0364633d6de754f5b8e0f09081b869b545105410d47f6f056.png

12.12. Why Use Xarray?#

Xarray is valuable for handling multi-dimensional data, especially in scientific applications. It provides metadata, dimension names, and coordinate labels, making it much easier to understand and manipulate data compared to raw NumPy arrays.

12.12.1. Without Xarray (Using NumPy)#

Here’s how a task might look without Xarray, using NumPy arrays:

lat = ds.air.lat.data
lon = ds.air.lon.data
temp = ds.air.data

plt.figure()
plt.pcolormesh(lon, lat, temp[0, :, :])

<matplotlib.collections.QuadMesh at 0x7ff28ab00380>

../../_images/6f22a45aceea1603970dffd90e49785bd6c7ac2c0cd389750d9db81af6266002.png

While this approach works, it’s not clear what 0 refers to (in this case, it’s the first time step).

12.12.2. With Xarray#

With Xarray, you can use more intuitive and readable indexing:

ds.air.isel(time=0).plot(x="lon")

<matplotlib.collections.QuadMesh at 0x7ff27e672690>

../../_images/5175a33104c01e3f7b4a1fd979100d3b328f5c7833965e5db963af9b0a365c71.png

This example selects the first time step and plots it using labeled axes (lat and lon), which is much clearer.

12.14. High-Level Computations with Xarray#

Xarray offers several high-level operations that make common computations straightforward, such as groupby, resample, rolling, and weighted.

12.14.1. Example: GroupBy Operation#

You can calculate statistics such as the seasonal mean of the dataset:

seasonal_mean = ds.groupby("time.season").mean()
seasonal_mean.air.plot(col="season")

<xarray.plot.facetgrid.FacetGrid at 0x7ff27d2b3f20>

../../_images/15302f7427fd458a1917f31397518b085ec0f7e70ac29e62d8124c110dc259e6.png

12.15. Computation with Weights#

Xarray allows for weighted computations, useful in geospatial contexts where grid cells vary in size. For example, you can weight the mean of the dataset by cell area.

Example: Weighting by cell area

cell_area = xr.ones_like(ds.air.lon)  # Placeholder for actual area calculation
weighted_mean = ds.weighted(cell_area).mean(dim=["lon", "lat"])
weighted_mean.air.plot()

[<matplotlib.lines.Line2D at 0x7ff27d0329f0>]

../../_images/10f99c0bb3e851ba6c198a19a8a162f5219073f80a132f5276dddd258bfbf267.png

12.17. Summary#

Xarray is a powerful library for working with multi-dimensional geospatial data. It simplifies data handling by offering labeled dimensions and coordinates, enabling intuitive operations and making analysis more transparent. Xarray’s ability to work seamlessly with NumPy, Dask, and Pandas makes it an essential tool for geospatial and scientific analysis. With Xarray, you can efficiently manage and analyze large, complex datasets, making it a valuable asset for researchers and developers alike.

Xarray

Contents

12. Xarray#

12.1. Overview#

12.2. Learning Objectives#

12.3. What is Xarray?#

12.4. Installing Xarray#

12.5. Importing Libraries#

12.6. Xarray Data Structures#

12.6.1. Loading a Dataset#

12.7. Working with DataArrays#

12.7.1. DataArray Components#

12.8. Indexing and Selecting Data#

12.9. Performing Operations on DataArrays#

12.10. Visualization with Xarray#

12.11. Working with Datasets#

12.12. Why Use Xarray?#

12.12.1. Without Xarray (Using NumPy)#

12.12.2. With Xarray#

12.13. Advanced Indexing: Label vs. Position-Based Indexing#

12.13.1. Label-based Indexing#

12.13.2. Position-based Indexing#

12.14. High-Level Computations with Xarray#

12.14.1. Example: GroupBy Operation#

12.15. Computation with Weights#

12.16. Reading and Writing Files#

12.16.1. Writing to netCDF#

12.16.2. Reading from netCDF#

12.17. Summary#