xarray

name	xarray
description	N-dimensional labeled arrays for geoscience data. Read/write NetCDF, work with climate and oceanographic datasets, perform multi-dimensional analysis with labeled coordinates. Use when Claude needs to: (1) Read/write NetCDF or Zarr files, (2) Work with multidimensional arrays with labeled dimensions, (3) Analyze climate, ocean, or atmosphere data, (4) Compute temporal aggregations (daily/monthly/annual means), (5) Perform area-weighted statistics, (6) Process large datasets with Dask, (7) Apply CF conventions to scientific data.
version	1.0.0
author	Geoscience Skills
license	MIT
tags	["NetCDF","Climate Data","Multi-Dimensional Arrays","Dask","Zarr","Geoscience"]
dependencies	["xarray>=2023.0","numpy","pandas","netcdf4"]
complements	["verde","pyvista","pooch"]
workflow_role	data-loading

xarray - Multi-Dimensional Geoscience Data

Quick Reference

import xarray as xr

# Read
ds = xr.open_dataset('data.nc')

# Access data
temp = ds['temperature']         # DataArray
values = temp.values             # numpy array
df = ds.to_dataframe()           # pandas DataFrame

# Structure info
print(ds)                        # Overview
print(ds.dims)                   # Dimensions
print(ds.data_vars)              # Variables

# Write
ds.to_netcdf('output.nc')

Key Classes

Class	Purpose
Dataset	Collection of aligned DataArrays (like NetCDF file)
DataArray	Single variable with labeled dimensions
Coordinates	Dimension labels (time, lat, lon)

Essential Operations

Select Data

# By coordinate value
temp_jan = ds['temperature'].sel(time='2020-01-15')
temp_region = ds['temperature'].sel(lat=slice(-30, 30), lon=slice(-60, 60))

# Nearest value
temp_point = ds['temperature'].sel(lat=35.5, lon=-120.3, method='nearest')

# By index
temp_first = ds['temperature'].isel(time=)