# xarray.Variable

class `xarray.``Variable`(dims, data, attrs=None, encoding=None, fastpath=False)

  A netcdf-like variable consisting of dimensions, data and attributes which describe a single Array. A single Variable object is not fully described outside the context of its parent Dataset (if you want such a fully described object, use a DataArray instead).

  The main functional difference between Variables and numpy arrays is that numerical operations on Variables implement array broadcasting by dimension name. For example, adding an Variable with dimensions (‘time’,) to another Variable with dimensions (‘space’,) results in a new Variable with dimensions (‘time’, ‘space’). Furthermore, numpy reduce operations like `mean` or `sum` are overwritten to take a “dimension” argument instead of an “axis”.

  Variables are light-weight objects used as the building block for datasets. They are more primitive objects, so operations with them provide marginally higher performance than using DataArrays. However, manipulating data in the form of a Dataset or DataArray should almost always be preferred, because they can use more complete metadata in context of coordinate labels.

  `__init__`(dims, data, attrs=None, encoding=None, fastpath=False)

    Parameters:

    - dims (str or sequence of str) – Name(s) of the the data dimension(s). Must be either a string (only for 1D data) or a sequence of strings with length equal to the number of dimensions.
    - data (array_like) – Data array which supports numpy-like data access.
    - attrs (dict_like or None, optional) – Attributes to assign to the new variable. If None (default), an empty attribute dictionary is initialized.
    - encoding (dict_like or None, optional) – Dictionary specifying how to encode this array’s data into a serialized format like netCDF4. Currently used keys (for netCDF) include ‘_FillValue’, ‘scale_factor’, ‘add_offset’ and ‘dtype’. Well-behaved code to serialize a Variable should ignore unrecognized encoding items.

  Methods

  `__init__`(dims, data[, attrs, encoding, fastpath])

  param dims: | Name(s) of the the data dimension(s). Must be either a string (only

  `all`([dim, axis]) | Reduce this Variable’s data by applying all along some dimension(s).
  `any`([dim, axis]) | Reduce this Variable’s data by applying any along some dimension(s).
  `argmax`([dim, axis, skipna]) | Reduce this Variable’s data by applying argmax along some dimension(s).
  `argmin`([dim, axis, skipna]) | Reduce this Variable’s data by applying argmin along some dimension(s).
  `argsort`([axis, kind, order]) | Returns the indices that would sort this array.
  `astype`(dtype[, order, casting, subok, copy]) | Copy of the array, cast to a specified type.
  `broadcast_equals`(other[, equiv]) | True if two Variables have the values after being broadcast against each other; otherwise False.
  `chunk`([chunks, name, lock]) | Coerce this array’s data into a dask arrays with the given chunks.
  `clip`([min, max, out]) | Return an array whose values are limited to `[min, max]`.
  `coarsen`(windows, func[, boundary, side]) | Apply reduction function.
  `compute`(**kwargs) | Manually trigger loading of this variable’s data from disk or a remote source into memory and return a new variable.
  `concat`(variables[, dim, positions, shortcut]) | Concatenate variables along a new or existing dimension.
  `conj`() | Complex-conjugate all elements.
  `conjugate`() | Return the complex conjugate, element-wise.
  `copy`([deep, data]) | Returns a copy of this object.
  `count`([dim, axis]) | Reduce this Variable’s data by applying count along some dimension(s).
  `cumprod`([dim, axis, skipna]) | Apply cumprod along some dimension of Variable.
  `cumsum`([dim, axis, skipna]) | Apply cumsum along some dimension of Variable.
  `equals`(other[, equiv]) | True if two Variables have the same dimensions and values; otherwise False.
  `fillna`(value) |
  `get_axis_num`(dim, Iterable[Hashable]]) | Return axis number(s) corresponding to dimension(s) in this array.
  `identical`(other[, equiv]) | Like equals, but also checks attributes.
  `isel`(indexers, Any] = None, **indexers_kwargs) | Return a new array indexed along the specified dimension(s).
  `isnull`(*args, **kwargs) |
  `item`(*args) | Copy an element of an array to a standard Python scalar and return it.
  `load`(**kwargs) | Manually trigger loading of this variable’s data from disk or a remote source into memory and return this variable.
  `max`([dim, axis, skipna]) | Reduce this Variable’s data by applying max along some dimension(s).
  `mean`([dim, axis, skipna]) | Reduce this Variable’s data by applying mean along some dimension(s).
  `median`([dim, axis, skipna]) | Reduce this Variable’s data by applying median along some dimension(s).
  `min`([dim, axis, skipna]) | Reduce this Variable’s data by applying min along some dimension(s).
  `no_conflicts`(other[, equiv]) | True if the intersection of two Variable’s non-null data is equal; otherwise false.
  `notnull`(*args, **kwargs) |
  `pad_with_fill_value`([pad_widths, fill_value]) | Return a new Variable with paddings.
  `prod`([dim, axis, skipna]) | Reduce this Variable’s data by applying prod along some dimension(s).
  `quantile`(q[, dim, interpolation, keep_attrs]) | Compute the qth quantile of the data along the specified dimension.
  `rank`(dim[, pct]) | Ranks the data.
  `reduce`(func[, dim, axis, keep_attrs, …]) | Reduce this array by applying func along some dimension(s).
  `roll`([shifts]) | Return a new Variable with rolld data.
  `rolling_window`(dim, window, window_dim[, …]) | Make a rolling_window along dim and add a new_dim to the last place.
  `round`(*args, **kwargs) |
  `searchsorted`(v[, side, sorter]) | Find indices where elements of v should be inserted in a to maintain order.
  `set_dims`(dims[, shape]) | Return a new variable with given set of dimensions.
  `shift`([shifts, fill_value]) | Return a new Variable with shifted data.
  `squeeze`([dim]) | Return a new object with squeezed data.
  `stack`([dimensions]) | Stack any number of existing dimensions into a single new dimension.
  `std`([dim, axis, skipna]) | Reduce this Variable’s data by applying std along some dimension(s).
  `sum`([dim, axis, skipna]) | Reduce this Variable’s data by applying sum along some dimension(s).
  `to_base_variable`() | Return this variable as a base xarray.Variable
  `to_coord`() | to_coord has been deprecated.
  `to_dict`([data]) | Dictionary representation of variable.
  `to_index`() | Convert this variable to a pandas.Index
  `to_index_variable`() | Return this variable as an xarray.IndexVariable
  `to_variable`() | to_variable has been deprecated.
  `transpose`(*dims) | Return a new Variable object with transposed dimensions.
  `unstack`([dimensions]) | Unstack an existing dimension into multiple new dimensions.
  `var`([dim, axis, skipna]) | Reduce this Variable’s data by applying var along some dimension(s).
  `where`(cond[, other]) |

  Attributes

  `T` |
  `attrs` | Dictionary of local attributes on this variable.
  `chunks` | Block dimensions for this array’s data or None if it’s not a dask array.
  `data` |
  `dims` | Tuple of dimension names with which this variable is associated.
  `dtype` |
  `encoding` | Dictionary of encodings on this variable.
  `imag` |
  `nbytes` |
  `ndim` |
  `real` |
  `shape` |
  `size` |
  `sizes` | Ordered mapping from dimension names to lengths.
  `values` | The variable’s data as a numpy.ndarray