Xarray for multidimensional gridded data

In the previous set of lectures, we saw how Pandas provided a way to keep track of additional “metadata” surrounding tabular datasets, including “indexes” for each row and labels for each column. These features, together with Pandas’ many useful routines for all kinds of data munging and analysis, have made Pandas one of the most popular python packages in the world.

However, not all Earth science datasets easily fit into the “tabular” model (i.e. rows and columns) imposed by Pandas. In particular, we often deal with multidimensional data. By multidimensional data (also often called N-dimensional), I mean data with many independent dimensions or axes. For example, we might represent Earth’s surface temperature \(T\) as a three dimensional variable

\[ T(x, y, t) \]

where \(x\) is longitude, \(y\) is latitude, and \(t\) is time.

The point of xarray is to provide pandas-level convenience for working with this type of data.

xarray data model

Learning Goals for Xarray

Because of the importance of xarray for data analysis in geoscience, we are going to spend a long time on it. The goals of this section include the following.

Lesson 1: Xarray Fundamentals

Dataset Creation

  1. Describe the core xarray data structures, the DataArray and the Dataset, and the components that make them up, including: Data Variables, Dimensions, Coordinates, Indexes, and Attributes

  2. Create xarray DataArrays and DataSets out of raw numpy arrays

  3. Create xarray objects with and without indexes

  4. Load xarray datasets from netCDF files and openDAP servers

  5. View and set attributes

Basic Indexing and Interpolation

  1. Select data by position using .isel with values or slices

  2. Select data by label using .sel with values or slices

  3. Select timeseries data by date/time with values or slices

  4. Use nearest-neighbor lookups with .sel

  5. Mask data with .where

  6. Interpolate data in one and several dimensions

Basic Computation

  1. Do basic arithmetic with DataArrays and Datasets

  2. Use numpy universal function on DataArrays and Datasets, or use corresponding built-in xarray methods

  3. Combine multiple xarray objects in arithmetic operations and understand how they are broadcasted / aligned

  4. Perform aggregation (reduction) along one or multiple dimensions of a DataArray or Dataset

Basic Plotting

  1. Use built-in xarray plotting for 1D and 2D DataArrays

  2. Customize plots with options

Lesson 2: Advanced Usage

Xarray’s groupby, resample, and rolling

  1. Split xarray objects into groups using groupby

  2. Apply reduction operations to groups (e.g. mean)

  3. Apply non-reducing functions to groups (e.g. standardize)

  4. Use groupby with time coordinates (e.g. to create climatologies)

  5. Use artimetic between GroupBy objects and regular DataArrays / Datasets

  6. Use groupby_bins to aggregate data in bins

  7. Use resample on time dimensions

  8. Use rolling to apply rolling aggregations

Merging Combining Datasets

  1. Concatentate DataArrays and Datasets along a new or existing dimension

  2. Merge multiple datasets with different variables

  3. Add a new data variable to an existing Dataset

Reshaping Data

  1. Transpose dimension order

  2. Swap coordinates

  3. Expand and squeeze dimensions

  4. Convert between DataArray and Dataset

  5. Use stack and unstack to transform data

Advanced Computations

  1. Use differentiate to take derivatives of data

  2. Use apply_ufunc to apply custom or specialized operations to data

Plotting

  1. Show multiple line plots over a dimension using the hue keyword

  2. Create multiple 2D plots using faceting