# Reading and writing files

xarray supports direct serialization and IO to several file formats, from simple Pickle files to the more flexible netCDF format (recommended).

## netCDF

The recommended way to store xarray data structures is netCDF, which is a binary file format for self-described datasets that originated in the geosciences. xarray is based on the netCDF data model, so netCDF files on disk directly correspond to `Dataset` objects (more accurately, a group in a netCDF file directly corresponds to a to `Dataset` object. See Groups for more.)

NetCDF is supported on almost all platforms, and parsers exist for the vast majority of scientific programming languages. Recent versions of netCDF are based on the even more widely used HDF5 file-format.

Tip

If you aren’t familiar with this data format, the netCDF FAQ is a good place to start.

Reading and writing netCDF files with xarray requires scipy or the netCDF4-Python library to be installed (the later is required to read/write netCDF V4 files and use the compression options described below).

We can save a Dataset to disk using the `Dataset.to_netcdf()` method:

    In [1]: ds = xr.Dataset({'foo': (('x', 'y'), np.random.rand(4, 5))},
       ...:                 coords={'x': [10, 20, 30, 40],
       ...:                         'y': pd.date_range('2000-01-01', periods=5),
       ...:                         'z': ('x', list('abcd'))})
       ...:

    In [2]: ds.to_netcdf('saved_on_disk.nc')

By default, the file is saved as netCDF4 (assuming netCDF4-Python is installed). You can control the format and engine used to write the file with the `format` and `engine` arguments.

Tip

Using the h5netcdf package by passing `engine='h5netcdf'` to `open_dataset()` can sometimes be quicker than the default `engine='netcdf4'` that uses the netCDF4 package.

We can load netCDF files to create a new Dataset using `open_dataset()`:

    In [3]: ds_disk = xr.open_dataset('saved_on_disk.nc')

    In [4]: ds_disk
    Out[4]:
    <xarray.Dataset>
    Dimensions:  (x: 4, y: 5)
    Coordinates:
      * x        (x) int64 10 20 30 40
      * y        (y) datetime64[ns] 2000-01-01 2000-01-02 ... 2000-01-04 2000-01-05
        z        (x) object ...
    Data variables:
        foo      (x, y) float64 ...

Similarly, a DataArray can be saved to disk using the `DataArray.to_netcdf()` method, and loaded from disk using the `open_dataarray()` function. As netCDF files correspond to `Dataset` objects, these functions internally convert the `DataArray` to a `Dataset` before saving, and then convert back when loading, ensuring that the `DataArray` that is loaded is always exactly the same as the one that was saved.

A dataset can also be loaded or written to a specific group within a netCDF file. To load from a group, pass a `group` keyword argument to the `open_dataset` function. The group can be specified as a path-like string, e.g., to access subgroup ‘bar’ within group ‘foo’ pass ‘/foo/bar’ as the `group` argument. When writing multiple groups in one file, pass `mode='a'` to `to_netcdf` to ensure that each call does not delete the file.

Data is always loaded lazily from netCDF files. You can manipulate, slice and subset Dataset and DataArray objects, and no array values are loaded into memory until you try to perform some sort of actual computation. For an example of how these lazy arrays work, see the OPeNDAP section below.

It is important to note that when you modify values of a Dataset, even one linked to files on disk, only the in-memory copy you are manipulating in xarray is modified: the original file on disk is never touched.

Tip

xarray’s lazy loading of remote or on-disk datasets is often but not always desirable. Before performing computationally intense operations, it is often a good idea to load a Dataset (or DataArray) entirely into memory by invoking the `Dataset.load()` method.

Datasets have a `Dataset.close()` method to close the associated netCDF file. However, it’s often cleaner to use a `with` statement:

    # this automatically closes the dataset after use
    In [5]: with xr.open_dataset('saved_on_disk.nc') as ds:
       ...:     print(ds.keys())
       ...:
    KeysView(<xarray.Dataset>
    Dimensions:  (x: 4, y: 5)
    Coordinates:
      * x        (x) int64 10 20 30 40
      * y        (y) datetime64[ns] 2000-01-01 2000-01-02 ... 2000-01-04 2000-01-05
        z        (x) object ...
    Data variables:
        foo      (x, y) float64 ...)

Although xarray provides reasonable support for incremental reads of files on disk, it does not support incremental writes, which can be a useful strategy for dealing with datasets too big to fit into memory. Instead, xarray integrates with dask.array (see Parallel computing with Dask), which provides a fully featured engine for streaming computation.

It is possible to append or overwrite netCDF variables using the `mode='a'` argument. When using this option, all variables in the dataset will be written to the original netCDF file, regardless if they exist in the original dataset.

### Groups

NetCDF groups are not supported as part of the `Dataset` data model. Instead, groups can be loaded individually as Dataset objects. To do so, pass a `group` keyword argument to the `open_dataset()` function. The group can be specified as a path-like string, e.g., to access subgroup `'bar'` within group `'foo'` pass `'/foo/bar'` as the `group` argument. In a similar way, the `group` keyword argument can be given to the `Dataset.to_netcdf()` method to write to a group in a netCDF file. When writing multiple groups in one file, pass `mode='a'` to `Dataset.to_netcdf()` to ensure that each call does not delete the file.

### Reading encoded data

NetCDF files follow some conventions for encoding datetime arrays (as numbers with a “units” attribute) and for packing and unpacking data (as described by the “scale_factor” and “add_offset” attributes). If the argument `decode_cf=True` (default) is given to `open_dataset()`, xarray will attempt to automatically decode the values in the netCDF objects according to CF conventions. Sometimes this will fail, for example, if a variable has an invalid “units” or “calendar” attribute. For these cases, you can turn this decoding off manually.

You can view this encoding information (among others) in the `DataArray.encoding` and `DataArray.encoding` attributes:

    In [6]: ds_disk['y'].encoding
    Out[6]:
    {'zlib': False,
     'shuffle': False,
     'complevel': 0,
     'fletcher32': False,
     'contiguous': True,
     'chunksizes': None,
     'source': 'saved_on_disk.nc',
     'original_shape': (5,),
     'dtype': dtype('int64'),
     'units': 'days since 2000-01-01 00:00:00',
     'calendar': 'proleptic_gregorian'}

    In [7]: ds_disk.encoding
    Out[7]:
    {'unlimited_dims': set(),
     'source': 'saved_on_disk.nc'}

Note that all operations that manipulate variables other than indexing will remove encoding information.

#### Reading multi-file datasets

NetCDF files are often encountered in collections, e.g., with different files corresponding to different model runs or one file per timestamp. xarray can straightforwardly combine such files into a single Dataset by making use of `concat()`, `merge()`, `combine_nested()` and `combine_by_coords()`. For details on the difference between these functions see Combining data.

Xarray includes support for manipulating datasets that don’t fit into memory with dask. If you have dask installed, you can open multiple files simultaneously in parallel using `open_mfdataset()`:

    xr.open_mfdataset('my/files/*.nc', parallel=True)

This function automatically concatenates and merges multiple files into a single xarray dataset. It is the recommended way to open multiple files with xarray. For more details on parallel reading, see Combining along multiple dimensions, Reading and writing data and a blog post by Stephan Hoyer. `open_mfdataset()` takes many kwargs that allow you to control its behaviour (for e.g. `parallel`, `combine`, `compat`, `join`, `concat_dim`). See its docstring for more details.

Note

A common use-case involves a dataset distributed across a large number of files with each file containing a large number of variables. Commonly a few of these variables need to be concatenated along a dimension (say `"time"`), while the rest are equal across the datasets (ignoring floating point differences). The following command with suitable modifications (such as `parallel=True`) works well with such datasets:

    xr.open_mfdataset('my/files/*.nc', concat_dim="time",
                      data_vars='minimal', coords='minimal', compat='override')

This command concatenates variables along the `"time"` dimension, but only those that already contain the `"time"` dimension (`data_vars='minimal', coords='minimal'`). Variables that lack the `"time"` dimension are taken from the first dataset (`compat='override'`).

Sometimes multi-file datasets are not conveniently organized for easy use of `open_mfdataset()`. One can use the `preprocess` argument to provide a function that takes a dataset and returns a modified Dataset. `open_mfdataset()` will call `preprocess` on every dataset (corresponding to each file) prior to combining them.

If `open_mfdataset()` does not meet your needs, other approaches are possible. The general pattern for parallel reading of multiple files using dask, modifying those datasets and then combining into a single `Dataset` is:

    def modify(ds):
        # modify ds here
        return ds

    # this is basically what open_mfdataset does
    open_kwargs = dict(decode_cf=True, decode_times=False)
    open_tasks = [dask.delayed(xr.open_dataset)(f, **open_kwargs) for f in file_names]
    tasks = [dask.delayed(modify)(task) for task in open_tasks]
    datasets = dask.compute(tasks)  # get a list of xarray.Datasets
    combined = xr.combine_nested(datasets)  # or some combination of concat, merge

As an example, here’s how we could approximate `MFDataset` from the netCDF4 library:

    from glob import glob
    import xarray as xr

    def read_netcdfs(files, dim):
        # glob expands paths with * to a list of files, like the unix shell
        paths = sorted(glob(files))
        datasets = [xr.open_dataset(p) for p in paths]
        combined = xr.concat(dataset, dim)
        return combined

    combined = read_netcdfs('/all/my/files/*.nc', dim='time')

This function will work in many cases, but it’s not very robust. First, it never closes files, which means it will fail one you need to load more than a few thousands file. Second, it assumes that you want all the data from each file and that it can all fit into memory. In many situations, you only need a small subset or an aggregated summary of the data from each file.

Here’s a slightly more sophisticated example of how to remedy these deficiencies:

    def read_netcdfs(files, dim, transform_func=None):
        def process_one_path(path):
            # use a context manager, to ensure the file gets closed after use
            with xr.open_dataset(path) as ds:
                # transform_func should do some sort of selection or
                # aggregation
                if transform_func is not None:
                    ds = transform_func(ds)
                # load all data from the transformed dataset, to ensure we can
                # use it after closing each original file
                ds.load()
                return ds

        paths = sorted(glob(files))
        datasets = [process_one_path(p) for p in paths]
        combined = xr.concat(datasets, dim)
        return combined

    # here we suppose we only care about the combined mean of each file;
    # you might also use indexing operations like .sel to subset datasets
    combined = read_netcdfs('/all/my/files/*.nc', dim='time',
                            transform_func=lambda ds: ds.mean())

This pattern works well and is very robust. We’ve used similar code to process tens of thousands of files constituting 100s of GB of data.

### Writing encoded data

Conversely, you can customize how xarray writes netCDF files on disk by providing explicit encodings for each dataset variable. The `encoding` argument takes a dictionary with variable names as keys and variable specific encodings as values. These encodings are saved as attributes on the netCDF variables on disk, which allows xarray to faithfully read encoded data back into memory.

It is important to note that using encodings is entirely optional: if you do not supply any of these encoding options, xarray will write data to disk using a default encoding, or the options in the `encoding` attribute, if set. This works perfectly fine in most cases, but encoding can be useful for additional control, especially for enabling compression.

In the file on disk, these encodings as saved as attributes on each variable, which allow xarray and other CF-compliant tools for working with netCDF files to correctly read the data.

#### Scaling and type conversions

These encoding options work on any version of the netCDF file format:

- `dtype`: Any valid NumPy dtype or string convertable to a dtype, e.g., `'int16'` or `'float32'`. This controls the type of the data written on disk.
- `_FillValue`: Values of `NaN` in xarray variables are remapped to this value when saved on disk. This is important when converting floating point with missing values to integers on disk, because `NaN` is not a valid value for integer dtypes. As a default, variables with float types are attributed a `_FillValue` of `NaN` in the output file, unless explicitly disabled with an encoding `{'_FillValue': None}`.
- `scale_factor` and `add_offset`: Used to convert from encoded data on disk to to the decoded data in memory, according to the formula `decoded = scale_factor * encoded + add_offset`.

These parameters can be fruitfully combined to compress discretized data on disk. For example, to save the variable `foo` with a precision of 0.1 in 16-bit integers while converting `NaN` to `-9999`, we would use `encoding={'foo': {'dtype': 'int16', 'scale_factor': 0.1, '_FillValue': -9999}}`. Compression and decompression with such discretization is extremely fast.

#### String encoding

xarray can write unicode strings to netCDF files in two ways:

- As variable length strings. This is only supported on netCDF4 (HDF5) files.
- By encoding strings into bytes, and writing encoded bytes as a character array. The default encoding is UTF-8.

By default, we use variable length strings for compatible files and fall-back to using encoded character arrays. Character arrays can be selected even for netCDF4 files by setting the `dtype` field in `encoding` to `S1` (corresponding to NumPy’s single-character bytes dtype).

If character arrays are used:

- The string encoding that was used is stored on disk in the `_Encoding` attribute, which matches an ad-hoc convention adopted by the netCDF4-Python library. At the time of this writing (October 2017), a standard convention for indicating string encoding for character arrays in netCDF files was still under discussion. Technically, you can use any string encoding recognized by Python if you feel the need to deviate from UTF-8, by setting the `_Encoding` field in `encoding`. But we don’t recommend it.
- The character dimension name can be specifed by the `char_dim_name` field of a variable’s `encoding`. If this is not specified the default name for the character dimension is `'string%s' % data.shape[-1]`. When decoding character arrays from existing files, the `char_dim_name` is added to the variables `encoding` to preserve if encoding happens, but the field can be edited by the user.

Warning

Missing values in bytes or unicode string arrays (represented by `NaN` in xarray) are currently written to disk as empty strings `''`. This means missing values will not be restored when data is loaded from disk. This behavior is likely to change in the future (GH1647). Unfortunately, explicitly setting a `_FillValue` for string arrays to handle missing values doesn’t work yet either, though we also hope to fix this in the future.

#### Chunk based compression

`zlib`, `complevel`, `fletcher32`, `continguous` and `chunksizes` can be used for enabling netCDF4/HDF5’s chunk based compression, as described in the documentation for createVariable for netCDF4-Python. This only works for netCDF4 files and thus requires using `format='netCDF4'` and either `engine='netcdf4'` or `engine='h5netcdf'`.

Chunk based gzip compression can yield impressive space savings, especially for sparse data, but it comes with significant performance overhead. HDF5 libraries can only read complete chunks back into memory, and maximum decompression speed is in the range of 50-100 MB/s. Worse, HDF5’s compression and decompression currently cannot be parallelized with dask. For these reasons, we recommend trying discretization based compression (described above) first.

#### Time units

The `units` and `calendar` attributes control how xarray serializes `datetime64` and `timedelta64` arrays to datasets on disk as numeric values. The `units` encoding should be a string like `'days since 1900-01-01'` for `datetime64` data or a string like `'days'` for `timedelta64` data. `calendar` should be one of the calendar types supported by netCDF4-python: ‘standard’, ‘gregorian’, ‘proleptic_gregorian’ ‘noleap’, ‘365_day’, ‘360_day’, ‘julian’, ‘all_leap’, ‘366_day’.

By default, xarray uses the `'proleptic_gregorian'` calendar and units of the smallest time difference between values, with a reference time of the first time value.

#### Coordinates

You can control the `coordinates` attribute written to disk by specifying `DataArray.encoding["coordinates"]`. If not specified, xarray automatically sets `DataArray.encoding["coordinates"]` to a space-delimited list of names of coordinate variables that share dimensions with the `DataArray` being written. This allows perfect roundtripping of xarray datasets but may not be desirable. When an xarray `Dataset` contains non-dimensional coordinates that do not share dimensions with any of the variables, these coordinate variable names are saved under a “global” `"coordinates"` attribute. This is not CF-compliant but again facilitates roundtripping of xarray datasets.

### Invalid netCDF files

The library `h5netcdf` allows writing some dtypes (booleans, complex, …) that aren’t allowed in netCDF4 (see h5netcdf documentation). This feature is availabe through `DataArray.to_netcdf()` and `Dataset.to_netcdf()` when used with `engine="h5netcdf"` and currently raises a warning unless `invalid_netcdf=True` is set:

    # Writing complex valued data
    In [8]: da = xr.DataArray([1.+1.j, 2.+2.j, 3.+3.j])

    In [9]: da.to_netcdf("complex.nc", engine="h5netcdf", invalid_netcdf=True)

    # Reading it back
    In [10]: xr.open_dataarray("complex.nc", engine="h5netcdf")
    Out[10]:
    <xarray.DataArray (dim_0: 3)>
    array([1.+1.j, 2.+2.j, 3.+3.j])
    Dimensions without coordinates: dim_0

Warning

Note that this produces a file that is likely to be not readable by other netCDF libraries!

## Iris

The Iris tool allows easy reading of common meteorological and climate model formats (including GRIB and UK MetOffice PP files) into `Cube` objects which are in many ways very similar to `DataArray` objects, while enforcing a CF-compliant data model. If iris is installed xarray can convert a `DataArray` into a `Cube` using `DataArray.to_iris()`:

    In [11]: da = xr.DataArray(np.random.rand(4, 5), dims=['x', 'y'],
       ....:                   coords=dict(x=[10, 20, 30, 40],
       ....:                               y=pd.date_range('2000-01-01', periods=5)))
       ....:

    In [12]: cube = da.to_iris()
    ---------------------------------------------------------------------------
    ModuleNotFoundError                       Traceback (most recent call last)
    <ipython-input-12-cca90e1ed038> in <module>
    ----> 1 cube = da.to_iris()

    /build/python-xarray-kTVyut/python-xarray-0.15.0/xarray/core/dataarray.py in to_iris(self)
       2473         from ..convert import to_iris
       2474
    -> 2475         return to_iris(self)
       2476
       2477     @classmethod

    /build/python-xarray-kTVyut/python-xarray-0.15.0/xarray/convert.py in to_iris(dataarray)
        176     """
        177     # Iris not a hard dependency
    --> 178     import iris
        179     from iris.fileformats.netcdf import parse_cell_methods
        180

    ModuleNotFoundError: No module named 'iris'

    In [13]: cube
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-13-41d6431c1ce8> in <module>
    ----> 1 cube

    NameError: name 'cube' is not defined

Conversely, we can create a new `DataArray` object from a `Cube` using `DataArray.from_iris()`:

    In [14]: da_cube = xr.DataArray.from_iris(cube)
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-14-f9603d06ba9e> in <module>
    ----> 1 da_cube = xr.DataArray.from_iris(cube)

    NameError: name 'cube' is not defined

    In [15]: da_cube
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-15-0e8985178ccc> in <module>
    ----> 1 da_cube

    NameError: name 'da_cube' is not defined

## OPeNDAP

xarray includes support for OPeNDAP (via the netCDF4 library or Pydap), which lets us access large datasets over HTTP.

For example, we can open a connection to GBs of weather data produced by the PRISM project, and hosted by IRI at Columbia:

    In [16]: remote_data = xr.open_dataset(
       ....:     'http://iridl.ldeo.columbia.edu/SOURCES/.OSU/.PRISM/.monthly/dods',
       ....:     decode_times=False)
       ....:

    In [17]: remote_data
    Out[17]:
    <xarray.Dataset>
    Dimensions:  (T: 1422, X: 1405, Y: 621)
    Coordinates:
      * X        (X) float32 -125.0 -124.958 -124.917 -124.875 -124.833 -124.792 -124.75 ...
      * T        (T) float32 -779.5 -778.5 -777.5 -776.5 -775.5 -774.5 -773.5 -772.5 -771.5 ...
      * Y        (Y) float32 49.9167 49.875 49.8333 49.7917 49.75 49.7083 49.6667 49.625 ...
    Data variables:
        ppt      (T, Y, X) float64 ...
        tdmean   (T, Y, X) float64 ...
        tmax     (T, Y, X) float64 ...
        tmin     (T, Y, X) float64 ...
    Attributes:
        Conventions: IRIDL
        expires: 1375315200

Note

Like many real-world datasets, this dataset does not entirely follow CF conventions. Unexpected formats will usually cause xarray’s automatic decoding to fail. The way to work around this is to either set `decode_cf=False` in `open_dataset` to turn off all use of CF conventions, or by only disabling the troublesome parser. In this case, we set `decode_times=False` because the time axis here provides the calendar attribute in a format that xarray does not expect (the integer `360` instead of a string like `'360_day'`).

We can select and slice this data any number of times, and nothing is loaded over the network until we look at particular values:

    In [18]: tmax = remote_data['tmax'][:500, ::3, ::3]

    In [19]: tmax
    Out[19]:
    <xarray.DataArray 'tmax' (T: 500, Y: 207, X: 469)>
    [48541500 values with dtype=float64]
    Coordinates:
      * Y        (Y) float32 49.9167 49.7917 49.6667 49.5417 49.4167 49.2917 ...
      * X        (X) float32 -125.0 -124.875 -124.75 -124.625 -124.5 -124.375 ...
      * T        (T) float32 -779.5 -778.5 -777.5 -776.5 -775.5 -774.5 -773.5 ...
    Attributes:
        pointwidth: 120
        standard_name: air_temperature
        units: Celsius_scale
        expires: 1443657600

    # the data is downloaded automatically when we make the plot
    In [20]: tmax[0].plot()

Some servers require authentication before we can access the data. For this purpose we can explicitly create a `backends.PydapDataStore` and pass in a Requests session object. For example for HTTP Basic authentication:

    import xarray as xr
    import requests

    session = requests.Session()
    session.auth = ('username', 'password')

    store = xr.backends.PydapDataStore.open('http://example.com/data',
                                            session=session)
    ds = xr.open_dataset(store)

Pydap’s cas module has functions that generate custom sessions for servers that use CAS single sign-on. For example, to connect to servers that require NASA’s URS authentication:

    import xarray as xr
    from pydata.cas.urs import setup_session

    ds_url = 'https://gpm1.gesdisc.eosdis.nasa.gov/opendap/hyrax/example.nc'

    session = setup_session('username', 'password', check_url=ds_url)
    store = xr.backends.PydapDataStore.open(ds_url, session=session)

    ds = xr.open_dataset(store)

## Pickle

The simplest way to serialize an xarray object is to use Python’s built-in pickle module:

    In [21]: import pickle

    # use the highest protocol (-1) because it is way faster than the default
    # text based pickle format
    In [22]: pkl = pickle.dumps(ds, protocol=-1)

    In [23]: pickle.loads(pkl)
    Out[23]:
    <xarray.Dataset>
    Dimensions:  (x: 4, y: 5)
    Coordinates:
      * x        (x) int64 10 20 30 40
      * y        (y) datetime64[ns] 2000-01-01 2000-01-02 ... 2000-01-04 2000-01-05
        z        (x) object ...
    Data variables:
        foo      (x, y) float64 ...

Pickling is important because it doesn’t require any external libraries and lets you use xarray objects with Python modules like `multiprocessing` or Dask. However, pickling is not recommended for long-term storage.

Restoring a pickle requires that the internal structure of the types for the pickled data remain unchanged. Because the internal design of xarray is still being refined, we make no guarantees (at this point) that objects pickled with this version of xarray will work in future versions.

Note

When pickling an object opened from a NetCDF file, the pickle file will contain a reference to the file on disk. If you want to store the actual array values, load it into memory first with `Dataset.load()` or `Dataset.compute()`.

## Dictionary

We can convert a `Dataset` (or a `DataArray`) to a dict using `Dataset.to_dict()`:

    In [24]: d = ds.to_dict()

    In [25]: d
    Out[25]:
    {'coords': {'x': {'dims': ('x',), 'attrs': {}, 'data': [10, 20, 30, 40]},
      'y': {'dims': ('y',),
       'attrs': {},
       'data': [datetime.datetime(2000, 1, 1, 0, 0),
        datetime.datetime(2000, 1, 2, 0, 0),
        datetime.datetime(2000, 1, 3, 0, 0),
        datetime.datetime(2000, 1, 4, 0, 0),
        datetime.datetime(2000, 1, 5, 0, 0)]},
      'z': {'dims': ('x',), 'attrs': {}, 'data': ['a', 'b', 'c', 'd']}},
     'attrs': {},
     'dims': {'x': 4, 'y': 5},
     'data_vars': {'foo': {'dims': ('x', 'y'),
       'attrs': {},
       'data': [[0.12696983303810094,
         0.966717838482003,
         0.26047600586578334,
         0.8972365243645735,
         0.37674971618967135],
        [0.33622174433445307,
         0.45137647047539964,
         0.8402550832613813,
         0.12310214428849964,
         0.5430262020470384],
        [0.37301222522143085,
         0.4479968246859435,
         0.12944067971751294,
         0.8598787065799693,
         0.8203883631195572],
        [0.35205353914802473,
         0.2288873043216132,
         0.7767837505077176,
         0.5947835894851238,
         0.1375535565632705]]}}}

We can create a new xarray object from a dict using `Dataset.from_dict()`:

    In [26]: ds_dict = xr.Dataset.from_dict(d)

    In [27]: ds_dict
    Out[27]:
    <xarray.Dataset>
    Dimensions:  (x: 4, y: 5)
    Coordinates:
      * x        (x) int64 10 20 30 40
      * y        (y) datetime64[ns] 2000-01-01 2000-01-02 ... 2000-01-04 2000-01-05
        z        (x) <U1 'a' 'b' 'c' 'd'
    Data variables:
        foo      (x, y) float64 0.127 0.9667 0.2605 0.8972 ... 0.7768 0.5948 0.1376

Dictionary support allows for flexible use of xarray objects. It doesn’t require external libraries and dicts can easily be pickled, or converted to json, or geojson. All the values are converted to lists, so dicts might be quite large.

To export just the dataset schema, without the data itself, use the `data=False` option:

    In [28]: ds.to_dict(data=False)
    Out[28]:
    {'coords': {'x': {'dims': ('x',),
       'attrs': {},
       'dtype': 'int64',
       'shape': (4,)},
      'y': {'dims': ('y',), 'attrs': {}, 'dtype': 'datetime64[ns]', 'shape': (5,)},
      'z': {'dims': ('x',), 'attrs': {}, 'dtype': 'object', 'shape': (4,)}},
     'attrs': {},
     'dims': {'x': 4, 'y': 5},
     'data_vars': {'foo': {'dims': ('x', 'y'),
       'attrs': {},
       'dtype': 'float64',
       'shape': (4, 5)}}}

This can be useful for generating indices of dataset contents to expose to search indices or other automated data discovery tools.

## Rasterio

GeoTIFFs and other gridded raster datasets can be opened using rasterio, if rasterio is installed. Here is an example of how to use `open_rasterio()` to read one of rasterio’s test files:

    In [29]: rio = xr.open_rasterio('RGB.byte.tif')

    In [30]: rio
    Out[30]:
    <xarray.DataArray (band: 3, y: 718, x: 791)>
    [1703814 values with dtype=uint8]
    Coordinates:
      * band     (band) int64 1 2 3
      * y        (y) float64 2.827e+06 2.826e+06 2.826e+06 2.826e+06 2.826e+06 ...
      * x        (x) float64 1.021e+05 1.024e+05 1.027e+05 1.03e+05 1.033e+05 ...
    Attributes:
        res:        (300.0379266750948, 300.041782729805)
        transform:  (300.0379266750948, 0.0, 101985.0, 0.0, -300.041782729805, 28...
        is_tiled:   0
        crs:        +init=epsg:32618

The `x` and `y` coordinates are generated out of the file’s metadata (`bounds`, `width`, `height`), and they can be understood as cartesian coordinates defined in the file’s projection provided by the `crs` attribute. `crs` is a PROJ4 string which can be parsed by e.g. pyproj or rasterio. See Parsing rasterio geocoordinates for an example of how to convert these to longitudes and latitudes.

Warning

This feature has been added in xarray v0.9.6 and should still be considered as being experimental. Please report any bug you may find on xarray’s github repository.

## Zarr

Zarr is a Python package providing an implementation of chunked, compressed, N-dimensional arrays. Zarr has the ability to store arrays in a range of ways, including in memory, in files, and in cloud-based object storage such as Amazon S3 and Google Cloud Storage. Xarray’s Zarr backend allows xarray to leverage these capabilities.

Warning

Zarr support is still an experimental feature. Please report any bugs or unexepected behavior via github issues.

Xarray can’t open just any zarr dataset, because xarray requires special metadata (attributes) describing the dataset dimensions and coordinates. At this time, xarray can only open zarr datasets that have been written by xarray. To write a dataset with zarr, we use the `Dataset.to_zarr` method. To write to a local directory, we pass a path to a directory

    In [31]: ds = xr.Dataset({'foo': (('x', 'y'), np.random.rand(4, 5))},
       ....:                 coords={'x': [10, 20, 30, 40],
       ....:                         'y': pd.date_range('2000-01-01', periods=5),
       ....:                         'z': ('x', list('abcd'))})
       ....:

    In [32]: ds.to_zarr('path/to/directory.zarr')
    Out[32]: <xarray.backends.zarr.ZarrStore at 0x7f8a9e1fdd60>

(The suffix `.zarr` is optional–just a reminder that a zarr store lives there.) If the directory does not exist, it will be created. If a zarr store is already present at that path, an error will be raised, preventing it from being overwritten. To override this behavior and overwrite an existing store, add `mode='w'` when invoking `to_zarr`.

It is also possible to append to an existing store. For that, set `append_dim` to the name of the dimension along which to append. `mode` can be omitted as it will internally be set to `'a'`.

    In [33]: ds1 = xr.Dataset({'foo': (('x', 'y', 't'), np.random.rand(4, 5, 2))},
       ....:                  coords={'x': [10, 20, 30, 40],
       ....:                          'y': [1,2,3,4,5],
       ....:                          't': pd.date_range('2001-01-01', periods=2)})
       ....:

    In [34]: ds1.to_zarr('path/to/directory.zarr')
    Out[34]: <xarray.backends.zarr.ZarrStore at 0x7f8a9e1ff540>

    In [35]: ds2 = xr.Dataset({'foo': (('x', 'y', 't'), np.random.rand(4, 5, 2))},
       ....:                  coords={'x': [10, 20, 30, 40],
       ....:                          'y': [1,2,3,4,5],
       ....:                          't': pd.date_range('2001-01-03', periods=2)})
       ....:

    In [36]: ds2.to_zarr('path/to/directory.zarr', append_dim='t')
    Out[36]: <xarray.backends.zarr.ZarrStore at 0x7f8a9e1ff2c0>

To store variable length strings use `dtype=object`.

To read back a zarr dataset that has been created this way, we use the `open_zarr()` method:

    In [37]: ds_zarr = xr.open_zarr('path/to/directory.zarr')

    In [38]: ds_zarr
    Out[38]:
    <xarray.Dataset>
    Dimensions:  (t: 4, x: 4, y: 5)
    Coordinates:
      * t        (t) datetime64[ns] 2001-01-01 2001-01-02 2001-01-03 2001-01-04
      * x        (x) int64 10 20 30 40
      * y        (y) int64 1 2 3 4 5
    Data variables:
        foo      (x, y, t) float64 dask.array<chunksize=(4, 5, 2), meta=np.ndarray>

### Cloud Storage Buckets

It is possible to read and write xarray datasets directly from / to cloud storage buckets using zarr. This example uses the gcsfs package to provide a `MutableMapping` interface to Google Cloud Storage, which we can then pass to xarray:

    import gcsfs
    fs = gcsfs.GCSFileSystem(project='<project-name>', token=None)
    gcsmap = gcsfs.mapping.GCSMap('<bucket-name>', gcs=fs, check=True, create=False)
    # write to the bucket
    ds.to_zarr(store=gcsmap)
    # read it back
    ds_gcs = xr.open_zarr(gcsmap)

### Zarr Compressors and Filters

There are many different options for compression and filtering possible with zarr. These are described in the zarr documentation. These options can be passed to the `to_zarr` method as variable encoding. For example:

    In [39]: import zarr

    In [40]: compressor = zarr.Blosc(cname='zstd', clevel=3, shuffle=2)

    In [41]: ds.to_zarr('foo.zarr', encoding={'foo': {'compressor': compressor}})
    Out[41]: <xarray.backends.zarr.ZarrStore at 0x7f8a9e1fdd10>

Note

Not all native zarr compression and filtering options have been tested with xarray.

### Consolidated Metadata

Xarray needs to read all of the zarr metadata when it opens a dataset. In some storage mediums, such as with cloud object storage (e.g. amazon S3), this can introduce significant overhead, because two separate HTTP calls to the object store must be made for each variable in the dataset. With version 2.3, zarr will support a feature called consolidated metadata, which allows all metadata for the entire dataset to be stored with a single key (by default called `.zmetadata`). This can drastically speed up opening the store. (For more information on this feature, consult the zarr docs.)

If you have zarr version 2.3 or greater, xarray can write and read stores with consolidated metadata. To write consolidated metadata, pass the `consolidated=True` option to the `Dataset.to_zarr` method:

    ds.to_zarr('foo.zarr', consolidated=True)

To read a consolidated store, pass the `consolidated=True` option to `open_zarr()`:

    ds = xr.open_zarr('foo.zarr', consolidated=True)

Xarray can’t perform consolidation on pre-existing zarr datasets. This should be done directly from zarr, as described in the zarr docs.

## GRIB format via cfgrib

xarray supports reading GRIB files via ECMWF cfgrib python driver and ecCodes C-library, if they are installed. To open a GRIB file supply `engine='cfgrib'` to `open_dataset()`:

    In [42]: ds_grib = xr.open_dataset('example.grib', engine='cfgrib')

We recommend installing ecCodes via conda:

    conda install -c conda-forge eccodes
    pip install cfgrib

## Formats supported by PyNIO

xarray can also read GRIB, HDF4 and other file formats supported by PyNIO, if PyNIO is installed. To use PyNIO to read such files, supply `engine='pynio'` to `open_dataset()`.

We recommend installing PyNIO via conda:

    conda install -c conda-forge pynio

## Formats supported by PseudoNetCDF

xarray can also read CAMx, BPCH, ARL PACKED BIT, and many other file formats supported by PseudoNetCDF, if PseudoNetCDF is installed. PseudoNetCDF can also provide Climate Forecasting Conventions to CMAQ files. In addition, PseudoNetCDF can automatically register custom readers that subclass PseudoNetCDF.PseudoNetCDFFile. PseudoNetCDF can identify readers heuristically, or format can be specified via a key in backend_kwargs.

To use PseudoNetCDF to read such files, supply `engine='pseudonetcdf'` to `open_dataset()`.

Add `backend_kwargs={'format': '<format name>'}` where <format name> options are listed on the PseudoNetCDF page.

## CSV and other formats supported by Pandas

For more options (tabular formats and CSV files in particular), consider exporting your objects to pandas and using its broad range of IO tools. For CSV files, one might also consider xarray_extras.