xarray-for-multidimensional-data

# Xarray for Multidimensional Data

Master **Xarray**, the powerful library for working with labeled multidimensional arrays in scientific Python. Learn how to efficiently handle complex datasets with multiple dimensions, coordinates, and metadata - from climate data and satellite imagery to experimental measurements and simulations.

**Official Documentation**: https://docs.xarray.dev/

**GitHub**: https://github.com/pydata/xarray

## Quick Reference Card

### Installation & Setup
```bash
# Using pixi (recommended for scientific projects)
pixi add xarray netcdf4 dask

# Using pip
pip install xarray[complete]

# Optional dependencies for specific formats
pixi add zarr h5netcdf scipy bottleneck

# Geospatial extensions (for raster data, CRS handling, reprojection)
pixi add rioxarray xesmf

# DataTree is built into Xarray (no separate installation needed)
```

### Essential Xarray Concepts
```python
import xarray as xr
import numpy as np

# DataArray: Single labeled array
temperature = xr.DataArray(
    data=np.random.randn(3, 4),
    dims=["time", "location"],
    coords={
        "time": ["2024-01-01", "2024-01-02", "2024-01-03"],
        "location": ["A", "B", "C", "D"]
    },
    name="temperature"
)

# Dataset: Collection of DataArrays
ds = xr.Dataset({
    "temperature": temperature,
    "pressure": (["time", "location"], np.random.randn(3, 4))
})
```

### Essential Operations
```python
# Selection by label
ds.sel(time="2024-01-01")
ds.sel(location="A")

# Selection by index
ds.isel(time=0)

# Slicing
ds.sel(time=slice("2024-01-01", "2024-01-02"))

# Aggregation
ds.mean(dim="time")
ds.sum(dim="location")

# Computation
ds["temperature"] + 273.15  # Celsius to Kelvin
ds.groupby("time.month").mean()

# I/O operations
ds.to_netcdf("data.nc")
ds = xr.open_dataset("data.nc")
```

### Quick Decision Tree

```
Working with multidimensional scientific data?
├─ YES → Use Xarray for labeled dimensions
└─ NO → NumPy/Pandas sufficient

Need to track coordinates and metadata