https://docs.xarray.dev/en/stable/_static/dataset-diagram-logo.png

Xarray’s Data structures#

In this lesson, we cover the basics of Xarray data structures. Our learning goals are as follows. By the end of the lesson, we will be able to:

  • Understand the basic data structures (DataArray and Dataset objects) in Xarray


Introduction#

Multi-dimensional (a.k.a. N-dimensional, ND) arrays (sometimes called “tensors”) are an essential part of computational science. They are encountered in a wide range of fields, including physics, astronomy, geoscience, bioinformatics, engineering, finance, and deep learning. In Python, NumPy provides the fundamental data structure and API for working with raw ND arrays. However, real-world datasets are usually more than just raw numbers; they have labels which encode information about how the array values map to locations in space, time, etc.

Here is an example of how we might structure a dataset for a weather forecast:

https://docs.xarray.dev/en/stable/_images/dataset-diagram.png

You’ll notice multiple data variables (temperature, precipitation), coordinate variables (latitude, longitude), and dimensions (x, y, t). We’ll cover how these fit into Xarray’s data structures below.

Xarray doesn’t just keep track of labels on arrays – it uses them to provide a powerful and concise interface. For example:

  • Apply operations over dimensions by name: x.sum('time').

  • Select values by label (or logical location) instead of integer location: x.loc['2014-01-01'] or x.sel(time='2014-01-01').

  • Mathematical operations (e.g., x - y) vectorize across multiple dimensions (array broadcasting) based on dimension names, not shape.

  • Easily use the split-apply-combine paradigm with groupby: x.groupby('time.dayofyear').mean().

  • Database-like alignment based on coordinate labels that smoothly handles missing values: x, y = xr.align(x, y, join='outer').

  • Keep track of arbitrary metadata in the form of a Python dictionary: x.attrs.

The N-dimensional nature of xarray’s data structures makes it suitable for dealing with multi-dimensional scientific data, and its use of dimension names instead of axis labels (dim='time' instead of axis=0) makes such arrays much more manageable than the raw numpy ndarray: with xarray, you don’t need to keep track of the order of an array’s dimensions or insert dummy dimensions of size 1 to align arrays (e.g., using np.newaxis).

The immediate payoff of using xarray is that you’ll write less code. The long-term payoff is that you’ll understand what you were thinking when you come back to look at it weeks or months later.

Data structures#

Xarray provides two data structures: the DataArray and Dataset. The DataArray class attaches dimension names, coordinates and attributes to multi-dimensional arrays while Dataset combines multiple arrays.

Both classes are most commonly created by reading data. To learn how to create a DataArray or Dataset manually, see the Working with labeled data tutorial.

Xarray has a few small real-world tutorial datasets hosted in this GitHub repository https://github.com/pydata/xarray-data. We’ll use the xarray.tutorial.load_dataset convenience function to download and open the air_temperature (National Centers for Environmental Prediction) Dataset by name.

import numpy as np
import xarray as xr

Dataset#

Dataset objects are dictionary-like containers of DataArrays, mapping a variable name to each DataArray.

ds = xr.tutorial.load_dataset("air_temperature")
ds
<xarray.Dataset>
Dimensions:  (lat: 25, time: 2920, lon: 53)
Coordinates:
  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00
Data variables:
    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7
Attributes:
    Conventions:  COARDS
    title:        4x daily NMC reanalysis (1948)
    description:  Data is from NMC initialized reanalysis\n(4x/day).  These a...
    platform:     Model
    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...

We can access “layers” of the Dataset (individual DataArrays) with dictionary syntax

ds["air"]
<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>
array([[[241.2    , 242.5    , 243.5    , ..., 232.79999, 235.5    ,
         238.59999],
        [243.79999, 244.5    , 244.7    , ..., 232.79999, 235.29999,
         239.29999],
        [250.     , 249.79999, 248.89   , ..., 233.2    , 236.39   ,
         241.7    ],
        ...,
        [296.6    , 296.19998, 296.4    , ..., 295.4    , 295.1    ,
         294.69998],
        [295.9    , 296.19998, 296.79   , ..., 295.9    , 295.9    ,
         295.19998],
        [296.29   , 296.79   , 297.1    , ..., 296.9    , 296.79   ,
         296.6    ]],

       [[242.09999, 242.7    , 243.09999, ..., 232.     , 233.59999,
         235.79999],
        [243.59999, 244.09999, 244.2    , ..., 231.     , 232.5    ,
         235.7    ],
        [253.2    , 252.89   , 252.09999, ..., 230.79999, 233.39   ,
         238.5    ],
...
        [293.69   , 293.88998, 295.38998, ..., 295.09   , 294.69   ,
         294.29   ],
        [296.29   , 297.19   , 297.59   , ..., 295.29   , 295.09   ,
         294.38998],
        [297.79   , 298.38998, 298.49   , ..., 295.69   , 295.49   ,
         295.19   ]],

       [[245.09   , 244.29   , 243.29   , ..., 241.68999, 241.48999,
         241.79   ],
        [249.89   , 249.29   , 248.39   , ..., 239.59   , 240.29   ,
         241.68999],
        [262.99   , 262.19   , 261.38998, ..., 239.89   , 242.59   ,
         246.29   ],
        ...,
        [293.79   , 293.69   , 295.09   , ..., 295.29   , 295.09   ,
         294.69   ],
        [296.09   , 296.88998, 297.19   , ..., 295.69   , 295.69   ,
         295.19   ],
        [297.69   , 298.09   , 298.09   , ..., 296.49   , 296.19   ,
         295.69   ]]], dtype=float32)
Coordinates:
  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00
Attributes:
    long_name:     4xDaily Air temperature at sigma level 995
    units:         degK
    precision:     2
    GRIB_id:       11
    GRIB_name:     TMP
    var_desc:      Air temperature
    dataset:       NMC Reanalysis
    level_desc:    Surface
    statistic:     Individual Obs
    parent_stat:   Other
    actual_range:  [185.16 322.1 ]

We can save some typing by using the “attribute” or “dot” notation. This won’t work for variable names that clash with built-in method names (for example, mean).

ds.air
<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>
array([[[241.2    , 242.5    , 243.5    , ..., 232.79999, 235.5    ,
         238.59999],
        [243.79999, 244.5    , 244.7    , ..., 232.79999, 235.29999,
         239.29999],
        [250.     , 249.79999, 248.89   , ..., 233.2    , 236.39   ,
         241.7    ],
        ...,
        [296.6    , 296.19998, 296.4    , ..., 295.4    , 295.1    ,
         294.69998],
        [295.9    , 296.19998, 296.79   , ..., 295.9    , 295.9    ,
         295.19998],
        [296.29   , 296.79   , 297.1    , ..., 296.9    , 296.79   ,
         296.6    ]],

       [[242.09999, 242.7    , 243.09999, ..., 232.     , 233.59999,
         235.79999],
        [243.59999, 244.09999, 244.2    , ..., 231.     , 232.5    ,
         235.7    ],
        [253.2    , 252.89   , 252.09999, ..., 230.79999, 233.39   ,
         238.5    ],
...
        [293.69   , 293.88998, 295.38998, ..., 295.09   , 294.69   ,
         294.29   ],
        [296.29   , 297.19   , 297.59   , ..., 295.29   , 295.09   ,
         294.38998],
        [297.79   , 298.38998, 298.49   , ..., 295.69   , 295.49   ,
         295.19   ]],

       [[245.09   , 244.29   , 243.29   , ..., 241.68999, 241.48999,
         241.79   ],
        [249.89   , 249.29   , 248.39   , ..., 239.59   , 240.29   ,
         241.68999],
        [262.99   , 262.19   , 261.38998, ..., 239.89   , 242.59   ,
         246.29   ],
        ...,
        [293.79   , 293.69   , 295.09   , ..., 295.29   , 295.09   ,
         294.69   ],
        [296.09   , 296.88998, 297.19   , ..., 295.69   , 295.69   ,
         295.19   ],
        [297.69   , 298.09   , 298.09   , ..., 296.49   , 296.19   ,
         295.69   ]]], dtype=float32)
Coordinates:
  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00
Attributes:
    long_name:     4xDaily Air temperature at sigma level 995
    units:         degK
    precision:     2
    GRIB_id:       11
    GRIB_name:     TMP
    var_desc:      Air temperature
    dataset:       NMC Reanalysis
    level_desc:    Surface
    statistic:     Individual Obs
    parent_stat:   Other
    actual_range:  [185.16 322.1 ]

What is all this anyway? (String representations)#

Xarray has two representation types: "html" (which is only available in notebooks) and "text". To choose between them, use the display_style option.

So far, our notebook has automatically displayed the "html" representation (which we will continue using). The "html" representation is interactive, allowing you to collapse sections (left arrows) and view attributes and values for each value (right hand sheet icon and data symbol).

with xr.set_options(display_style="html"):
    display(ds)
<xarray.Dataset>
Dimensions:  (lat: 25, time: 2920, lon: 53)
Coordinates:
  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00
Data variables:
    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7
Attributes:
    Conventions:  COARDS
    title:        4x daily NMC reanalysis (1948)
    description:  Data is from NMC initialized reanalysis\n(4x/day).  These a...
    platform:     Model
    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...

The output consists of:

  • a summary of all dimensions of the Dataset (lat: 25, time: 2920, lon: 53): this tells us that the first dimension is named lat and has a size of 25, the second dimension is named time and has a size of 2920, and the third dimension is named lon and has a size of 53. Because we will access the dimensions by name, the order doesn’t matter.

  • an unordered list of coordinates or dimensions with coordinates with one item per line. Each item has a name, one or more dimensions in parentheses, a dtype and a preview of the values. Also, if it is a dimension coordinate, it will be marked with a *.

  • an alphabetically sorted list of dimensions without coordinates (if there are any)

  • an unordered list of attributes, or metadata

Compare that with the string representation, which is very similar except the dimensions are given a * prefix instead of bold and you cannot collapse or expand the outputs.

with xr.set_options(display_style="text"):
    display(ds)
<xarray.Dataset>
Dimensions:  (lat: 25, time: 2920, lon: 53)
Coordinates:
  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00
Data variables:
    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7
Attributes:
    Conventions:  COARDS
    title:        4x daily NMC reanalysis (1948)
    description:  Data is from NMC initialized reanalysis\n(4x/day).  These a...
    platform:     Model
    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...

To understand each of the components better, we’ll explore the “air” variable of our Dataset.

DataArray#

The DataArray class consists of an array (data) and its associated dimension names, labels, and attributes (metadata).

da = ds["air"]
da
<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>
array([[[241.2    , 242.5    , 243.5    , ..., 232.79999, 235.5    ,
         238.59999],
        [243.79999, 244.5    , 244.7    , ..., 232.79999, 235.29999,
         239.29999],
        [250.     , 249.79999, 248.89   , ..., 233.2    , 236.39   ,
         241.7    ],
        ...,
        [296.6    , 296.19998, 296.4    , ..., 295.4    , 295.1    ,
         294.69998],
        [295.9    , 296.19998, 296.79   , ..., 295.9    , 295.9    ,
         295.19998],
        [296.29   , 296.79   , 297.1    , ..., 296.9    , 296.79   ,
         296.6    ]],

       [[242.09999, 242.7    , 243.09999, ..., 232.     , 233.59999,
         235.79999],
        [243.59999, 244.09999, 244.2    , ..., 231.     , 232.5    ,
         235.7    ],
        [253.2    , 252.89   , 252.09999, ..., 230.79999, 233.39   ,
         238.5    ],
...
        [293.69   , 293.88998, 295.38998, ..., 295.09   , 294.69   ,
         294.29   ],
        [296.29   , 297.19   , 297.59   , ..., 295.29   , 295.09   ,
         294.38998],
        [297.79   , 298.38998, 298.49   , ..., 295.69   , 295.49   ,
         295.19   ]],

       [[245.09   , 244.29   , 243.29   , ..., 241.68999, 241.48999,
         241.79   ],
        [249.89   , 249.29   , 248.39   , ..., 239.59   , 240.29   ,
         241.68999],
        [262.99   , 262.19   , 261.38998, ..., 239.89   , 242.59   ,
         246.29   ],
        ...,
        [293.79   , 293.69   , 295.09   , ..., 295.29   , 295.09   ,
         294.69   ],
        [296.09   , 296.88998, 297.19   , ..., 295.69   , 295.69   ,
         295.19   ],
        [297.69   , 298.09   , 298.09   , ..., 296.49   , 296.19   ,
         295.69   ]]], dtype=float32)
Coordinates:
  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00
Attributes:
    long_name:     4xDaily Air temperature at sigma level 995
    units:         degK
    precision:     2
    GRIB_id:       11
    GRIB_name:     TMP
    var_desc:      Air temperature
    dataset:       NMC Reanalysis
    level_desc:    Surface
    statistic:     Individual Obs
    parent_stat:   Other
    actual_range:  [185.16 322.1 ]

String representations#

We can use the same two representations ("html", which is only available in notebooks, and "text") to display our DataArray.

with xr.set_options(display_style="html"):
    display(da)
<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>
array([[[241.2    , 242.5    , 243.5    , ..., 232.79999, 235.5    ,
         238.59999],
        [243.79999, 244.5    , 244.7    , ..., 232.79999, 235.29999,
         239.29999],
        [250.     , 249.79999, 248.89   , ..., 233.2    , 236.39   ,
         241.7    ],
        ...,
        [296.6    , 296.19998, 296.4    , ..., 295.4    , 295.1    ,
         294.69998],
        [295.9    , 296.19998, 296.79   , ..., 295.9    , 295.9    ,
         295.19998],
        [296.29   , 296.79   , 297.1    , ..., 296.9    , 296.79   ,
         296.6    ]],

       [[242.09999, 242.7    , 243.09999, ..., 232.     , 233.59999,
         235.79999],
        [243.59999, 244.09999, 244.2    , ..., 231.     , 232.5    ,
         235.7    ],
        [253.2    , 252.89   , 252.09999, ..., 230.79999, 233.39   ,
         238.5    ],
...
        [293.69   , 293.88998, 295.38998, ..., 295.09   , 294.69   ,
         294.29   ],
        [296.29   , 297.19   , 297.59   , ..., 295.29   , 295.09   ,
         294.38998],
        [297.79   , 298.38998, 298.49   , ..., 295.69   , 295.49   ,
         295.19   ]],

       [[245.09   , 244.29   , 243.29   , ..., 241.68999, 241.48999,
         241.79   ],
        [249.89   , 249.29   , 248.39   , ..., 239.59   , 240.29   ,
         241.68999],
        [262.99   , 262.19   , 261.38998, ..., 239.89   , 242.59   ,
         246.29   ],
        ...,
        [293.79   , 293.69   , 295.09   , ..., 295.29   , 295.09   ,
         294.69   ],
        [296.09   , 296.88998, 297.19   , ..., 295.69   , 295.69   ,
         295.19   ],
        [297.69   , 298.09   , 298.09   , ..., 296.49   , 296.19   ,
         295.69   ]]], dtype=float32)
Coordinates:
  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00
Attributes:
    long_name:     4xDaily Air temperature at sigma level 995
    units:         degK
    precision:     2
    GRIB_id:       11
    GRIB_name:     TMP
    var_desc:      Air temperature
    dataset:       NMC Reanalysis
    level_desc:    Surface
    statistic:     Individual Obs
    parent_stat:   Other
    actual_range:  [185.16 322.1 ]
with xr.set_options(display_style="text"):
    display(da)
<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>
array([[[241.2    , 242.5    , 243.5    , ..., 232.79999, 235.5    ,
         238.59999],
        [243.79999, 244.5    , 244.7    , ..., 232.79999, 235.29999,
         239.29999],
        [250.     , 249.79999, 248.89   , ..., 233.2    , 236.39   ,
         241.7    ],
        ...,
        [296.6    , 296.19998, 296.4    , ..., 295.4    , 295.1    ,
         294.69998],
        [295.9    , 296.19998, 296.79   , ..., 295.9    , 295.9    ,
         295.19998],
        [296.29   , 296.79   , 297.1    , ..., 296.9    , 296.79   ,
         296.6    ]],

       [[242.09999, 242.7    , 243.09999, ..., 232.     , 233.59999,
         235.79999],
        [243.59999, 244.09999, 244.2    , ..., 231.     , 232.5    ,
         235.7    ],
        [253.2    , 252.89   , 252.09999, ..., 230.79999, 233.39   ,
         238.5    ],
...
        [293.69   , 293.88998, 295.38998, ..., 295.09   , 294.69   ,
         294.29   ],
        [296.29   , 297.19   , 297.59   , ..., 295.29   , 295.09   ,
         294.38998],
        [297.79   , 298.38998, 298.49   , ..., 295.69   , 295.49   ,
         295.19   ]],

       [[245.09   , 244.29   , 243.29   , ..., 241.68999, 241.48999,
         241.79   ],
        [249.89   , 249.29   , 248.39   , ..., 239.59   , 240.29   ,
         241.68999],
        [262.99   , 262.19   , 261.38998, ..., 239.89   , 242.59   ,
         246.29   ],
        ...,
        [293.79   , 293.69   , 295.09   , ..., 295.29   , 295.09   ,
         294.69   ],
        [296.09   , 296.88998, 297.19   , ..., 295.69   , 295.69   ,
         295.19   ],
        [297.69   , 298.09   , 298.09   , ..., 296.49   , 296.19   ,
         295.69   ]]], dtype=float32)
Coordinates:
  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00
Attributes:
    long_name:     4xDaily Air temperature at sigma level 995
    units:         degK
    precision:     2
    GRIB_id:       11
    GRIB_name:     TMP
    var_desc:      Air temperature
    dataset:       NMC Reanalysis
    level_desc:    Surface
    statistic:     Individual Obs
    parent_stat:   Other
    actual_range:  [185.16 322.1 ]

In the string representation of a DataArray (versus a Dataset), we also see:

  • the DataArray name (‘air’)

  • a preview of the array data (collapsible in the "html" representation)

We can also access the data array directly:

ds.air.data  # (or equivalently, `da.data`)
array([[[241.2    , 242.5    , 243.5    , ..., 232.79999, 235.5    ,
         238.59999],
        [243.79999, 244.5    , 244.7    , ..., 232.79999, 235.29999,
         239.29999],
        [250.     , 249.79999, 248.89   , ..., 233.2    , 236.39   ,
         241.7    ],
        ...,
        [296.6    , 296.19998, 296.4    , ..., 295.4    , 295.1    ,
         294.69998],
        [295.9    , 296.19998, 296.79   , ..., 295.9    , 295.9    ,
         295.19998],
        [296.29   , 296.79   , 297.1    , ..., 296.9    , 296.79   ,
         296.6    ]],

       [[242.09999, 242.7    , 243.09999, ..., 232.     , 233.59999,
         235.79999],
        [243.59999, 244.09999, 244.2    , ..., 231.     , 232.5    ,
         235.7    ],
        [253.2    , 252.89   , 252.09999, ..., 230.79999, 233.39   ,
         238.5    ],
        ...,
        [296.4    , 295.9    , 296.19998, ..., 295.4    , 295.1    ,
         294.79   ],
        [296.19998, 296.69998, 296.79   , ..., 295.6    , 295.5    ,
         295.1    ],
        [296.29   , 297.19998, 297.4    , ..., 296.4    , 296.4    ,
         296.6    ]],

       [[242.29999, 242.2    , 242.29999, ..., 234.29999, 236.09999,
         238.7    ],
        [244.59999, 244.39   , 244.     , ..., 230.29999, 232.     ,
         235.7    ],
        [256.19998, 255.5    , 254.2    , ..., 231.2    , 233.2    ,
         238.2    ],
        ...,
        [295.6    , 295.4    , 295.4    , ..., 296.29   , 295.29   ,
         295.     ],
        [296.19998, 296.5    , 296.29   , ..., 296.4    , 296.     ,
         295.6    ],
        [296.4    , 296.29   , 296.4    , ..., 297.     , 297.     ,
         296.79   ]],

       ...,

       [[243.48999, 242.98999, 242.09   , ..., 244.18999, 244.48999,
         244.89   ],
        [249.09   , 248.98999, 248.59   , ..., 240.59   , 241.29   ,
         242.68999],
        [262.69   , 262.19   , 261.69   , ..., 239.39   , 241.68999,
         245.18999],
        ...,
        [294.79   , 295.29   , 297.49   , ..., 295.49   , 295.38998,
         294.69   ],
        [296.79   , 297.88998, 298.29   , ..., 295.49   , 295.49   ,
         294.79   ],
        [298.19   , 299.19   , 298.79   , ..., 296.09   , 295.79   ,
         295.79   ]],

       [[245.79   , 244.79   , 243.48999, ..., 243.29   , 243.98999,
         244.79   ],
        [249.89   , 249.29   , 248.48999, ..., 241.29   , 242.48999,
         244.29   ],
        [262.38998, 261.79   , 261.29   , ..., 240.48999, 243.09   ,
         246.89   ],
        ...,
        [293.69   , 293.88998, 295.38998, ..., 295.09   , 294.69   ,
         294.29   ],
        [296.29   , 297.19   , 297.59   , ..., 295.29   , 295.09   ,
         294.38998],
        [297.79   , 298.38998, 298.49   , ..., 295.69   , 295.49   ,
         295.19   ]],

       [[245.09   , 244.29   , 243.29   , ..., 241.68999, 241.48999,
         241.79   ],
        [249.89   , 249.29   , 248.39   , ..., 239.59   , 240.29   ,
         241.68999],
        [262.99   , 262.19   , 261.38998, ..., 239.89   , 242.59   ,
         246.29   ],
        ...,
        [293.79   , 293.69   , 295.09   , ..., 295.29   , 295.09   ,
         294.69   ],
        [296.09   , 296.88998, 297.19   , ..., 295.69   , 295.69   ,
         295.19   ],
        [297.69   , 298.09   , 298.09   , ..., 296.49   , 296.19   ,
         295.69   ]]], dtype=float32)

Named dimensions#

.dims are the named axes of your data. They may (dimension coordinates) or may not (dimensions without coordinates) have associated values. Names can be anything that fits into a Python set (i.e. calling hash() on it doesn’t raise an error), but to be useful they should be strings.

In this case we have 2 spatial dimensions (latitude and longitude are stored with shorthand names lat and lon) and one temporal dimension (time).

ds.air.dims
('time', 'lat', 'lon')

Coordinates#

.coords is a simple dict-like data container for mapping coordinate names to values. These values can be:

  • another DataArray object

  • a tuple of the form (dims, data, attrs) where attrs is optional. This is roughly equivalent to creating a new DataArray object with DataArray(dims=dims, data=data, attrs=attrs)

  • a 1-dimensional numpy array (or anything that can be coerced to one using numpy.array, such as a list) containing numbers, datetime objects, strings, etc. to label each point.

Here we see the actual timestamps and spatial positions of our air temperature data:

ds.air.coords
Coordinates:
  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00

The difference between the dimension labels (dimension coordinates) and normal coordinates is that for now it only is possible to use indexing operations (sel, reindex, etc.) with dimension coordinates. Also, while coordinates can have arbitrary dimensions, dimension coordinates have to be one-dimensional.

Attributes#

.attrs is a dictionary that can contain arbitrary Python objects (strings, lists, integers, dictionaries, etc.) containing information about your data. Your only limitation is that some attributes may not be writeable to certain file formats.

ds.air.attrs
{'long_name': '4xDaily Air temperature at sigma level 995',
 'units': 'degK',
 'precision': 2,
 'GRIB_id': 11,
 'GRIB_name': 'TMP',
 'var_desc': 'Air temperature',
 'dataset': 'NMC Reanalysis',
 'level_desc': 'Surface',
 'statistic': 'Individual Obs',
 'parent_stat': 'Other',
 'actual_range': array([185.16, 322.1 ], dtype=float32)}

To Pandas and back#

DataArray and Dataset objects are frequently created by converting from other libraries such as pandas or by reading from data storage formats such as NetCDF or zarr.

To convert from / to pandas, we can use the to_xarray methods on pandas objects or the to_pandas methods on xarray objects:

import pandas as pd
series = pd.Series(np.ones((10,)), index=list("abcdefghij"))
series
a    1.0
b    1.0
c    1.0
d    1.0
e    1.0
f    1.0
g    1.0
h    1.0
i    1.0
j    1.0
dtype: float64
arr = series.to_xarray()
arr
<xarray.DataArray (index: 10)>
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
Coordinates:
  * index    (index) object 'a' 'b' 'c' 'd' 'e' 'f' 'g' 'h' 'i' 'j'
arr.to_pandas()
index
a    1.0
b    1.0
c    1.0
d    1.0
e    1.0
f    1.0
g    1.0
h    1.0
i    1.0
j    1.0
dtype: float64

We can also control what pandas object is used by calling to_series / to_dataframe:

to_series: This will always convert DataArray objects to pandas.Series, using a MultiIndex for higher dimensions

ds.air.to_series()
time                 lat   lon  
2013-01-01 00:00:00  75.0  200.0    241.199997
                           202.5    242.500000
                           205.0    243.500000
                           207.5    244.000000
                           210.0    244.099991
                                       ...    
2014-12-31 18:00:00  15.0  320.0    297.389984
                           322.5    297.190002
                           325.0    296.489990
                           327.5    296.190002
                           330.0    295.690002
Name: air, Length: 3869000, dtype: float32

to_dataframe: This will always convert DataArray or Dataset objects to a pandas.DataFrame. Note that DataArray objects have to be named for this.

ds.air.to_dataframe()
air
time lat lon
2013-01-01 00:00:00 75.0 200.0 241.199997
202.5 242.500000
205.0 243.500000
207.5 244.000000
210.0 244.099991
... ... ... ...
2014-12-31 18:00:00 15.0 320.0 297.389984
322.5 297.190002
325.0 296.489990
327.5 296.190002
330.0 295.690002

3869000 rows × 1 columns

Since columns in a DataFrame need to have the same index, they are broadcasted.

ds.to_dataframe()
air
lat time lon
75.0 2013-01-01 00:00:00 200.0 241.199997
202.5 242.500000
205.0 243.500000
207.5 244.000000
210.0 244.099991
... ... ... ...
15.0 2014-12-31 18:00:00 320.0 297.389984
322.5 297.190002
325.0 296.489990
327.5 296.190002
330.0 295.690002

3869000 rows × 1 columns