API reference¶
Dataset¶
Creating a dataset¶
Dataset([variables, attributes]) | A netcdf-like data object consisting of variables and attributes which together form a self describing dataset. |
open_dataset(nc[, decode_cf, ...]) | Load a dataset from a file or file-like object. |
Attributes and underlying data¶
Dataset.coordinates | Dictionary of Coordinate objects used for label based indexing. |
Dataset.noncoordinates | Dictionary of DataArrays whose names do not match dimensions. |
Dataset.dimensions | Mapping from dimension names to lengths. |
Dataset.attrs | Dictionary of global attributes on this dataset |
Dataset contents¶
Datasets implement the mapping interface with keys given by variable names and values given by DataArray objects.
Dataset.__getitem__(key) | Access the given variable name in this dataset as a DataArray. |
Dataset.__setitem__(key, value) | Add an array to this dataset. |
Dataset.__delitem__(key) | Remove a variable from this dataset. |
Dataset.update(other[, inplace]) | Update this dataset’s variables and attributes with those from another dataset. |
Dataset.merge(other[, inplace, ...]) | Merge the variables of two datasets into a single new dataset. |
Dataset.concat(datasets[, dimension, ...]) | Concatenate datasets along a new or existing dimension. |
Dataset.copy([deep]) | Returns a copy of this dataset. |
Dataset.iteritems() | |
Dataset.itervalues() | |
Dataset.virtual_variables | A frozenset of variable names that don’t exist in this dataset but for which could be created on demand. |
Comparisons¶
Dataset.equals(other) | Two Datasets are equal if they have the same variables and all variables are equal. |
Dataset.identical(other) | Two Datasets are identical if they have the same variables and all variables are identical (with the same attributes), and they also have the same global attributes. |
Selecting¶
Dataset.indexed(**indexers) | Return a new dataset with each array indexed along the specified dimension(s). |
Dataset.labeled(**indexers) | Return a new dataset with each variable indexed by coordinate labels along the specified dimension(s). |
Dataset.reindex([copy]) | Conform this object onto a new set of coordinates or pandas.Index objects, filling in missing values with NaN. |
Dataset.reindex_like(other[, copy]) | Conform this object onto the coordinates of another object, filling in missing values with NaN. |
Dataset.rename(name_dict) | Returns a new object with renamed variables and dimensions. |
Dataset.select(*names) | Returns a new dataset that contains only the named variables and their coordinates. |
Dataset.unselect(*names) | Returns a new dataset without the named variables. |
Dataset.squeeze([dimension]) | Return a new dataset with squeezed data. |
Dataset.groupby(group[, squeeze]) | Group this dataset by unique values of the indicated group. |
IO / Conversion¶
Dataset.to_netcdf(filepath, **kwdargs) | Dump dataset contents to a location on disk using the netCDF4 package. |
Dataset.dumps(**kwargs) | Serialize dataset contents to a string. |
Dataset.dump_to_store(store) | Store dataset contents to a backends.*DataStore object. |
Dataset.to_dataframe() | Convert this dataset into a pandas.DataFrame. |
Dataset.from_dataframe(dataframe) | Convert a pandas.DataFrame into an xray.Dataset |
Dataset internals¶
These attributes and classes provide a low-level interface for working with Dataset variables. In general you should use the Dataset dictionary- like interface instead and working with DataArray objects:
Dataset.variables | Dictionary of Variable objects contained in this dataset. |
Variable(dims, data[, attributes, encoding]) | A netcdf-like variable consisting of dimensions, data and attributes which describe a single Array. |
Coordinate(*args, **kwargs) | Subclass of Variable which caches its data as a pandas.Index instead of a numpy.ndarray. |
Backends (experimental)¶
These backends provide a low-level interface for lazily loading data from external file-formats or protocols, and can be manually invoked to create arguments for the from_store and dump_to_store Dataset methods.
backends.NetCDF4DataStore(filename[, mode, ...]) | Store for reading and writing data via the Python-NetCDF4 library. |
backends.PydapDataStore(url) | Store for accessing OpenDAP datasets with pydap. |
backends.ScipyDataStore(filename_or_obj[, ...]) | Store for reading and writing data via scipy.io.netcdf. |
DataArray¶
DataArray | N-dimensional array with labeled coordinates and dimensions. |
Attributes and underlying data¶
DataArray.values | The variables’s data as a numpy.ndarray |
DataArray.as_index | The variable’s data as a pandas.Index. |
DataArray.coordinates | Dictionary of Coordinate objects used for label based indexing. |
DataArray.name | The name of the variable in dataset to which array operations are applied. |
DataArray.dataset | The dataset with which this DataArray is associated. |
DataArray.attrs | Dictionary storing arbitrary metadata with this array. |
DataArray.encoding | Dictionary of format-specific settings for how this array should be serialized. |
DataArray.variable |
NDArray attributes¶
DataArray.ndim | |
DataArray.shape | |
DataArray.size | |
DataArray.dtype |
Selecting¶
DataArray.__getitem__(key) | |
DataArray.__setitem__(key, value) | |
DataArray.loc | Attribute for location based indexing like pandas.. |
DataArray.indexed(**indexers) | Return a new DataArray whose dataset is given by indexing along the specified dimension(s). |
DataArray.labeled(**indexers) | Return a new DataArray whose dataset is given by selecting coordinate labels along the specified dimension(s). |
DataArray.reindex([copy]) | Conform this object onto a new set of coordinates or pandas.Index objects, filling in missing values with NaN. |
DataArray.reindex_like(other[, copy]) | Conform this object onto the coordinates of another object, filling in missing values with NaN. |
DataArray.rename(new_name_or_name_dict) | Returns a new DataArray with renamed variables. |
DataArray.select(*names) | Returns a new DataArray with only the named variables, as well as this DataArray’s array variable (and all associated coordinates). |
DataArray.unselect(*names) | Returns a new DataArray without the named variables. |
DataArray.squeeze([dimension]) | Return a new DataArray object with squeezed data. |
Group operations¶
DataArray.groupby(group[, squeeze]) | Group this dataset by unique values of the indicated group. |
DataArray.concat(arrays[, dimension, ...]) | Stack arrays along a new or existing dimension to form a new DataArray. |
Computations¶
DataArray.transpose(*dimensions) | Return a new DataArray object with transposed dimensions. |
DataArray.T | |
DataArray.reduce(func[, dimension, axis]) | Reduce this array by applying func along some dimension(s). |
DataArray.get_axis_num(dimension) | Return axis number(s) corresponding to dimension(s) in this array. |
DataArray.all([dimension, axis]) | Reduce this DataArray’s data’ by applying numpy.all along some dimension(s). |
DataArray.any([dimension, axis]) | Reduce this DataArray’s data’ by applying numpy.any along some dimension(s). |
DataArray.argmax([dimension, axis]) | Reduce this DataArray’s data’ by applying numpy.argmax along some dimension(s). |
DataArray.argmin([dimension, axis]) | Reduce this DataArray’s data’ by applying numpy.argmin along some dimension(s). |
DataArray.max([dimension, axis]) | Reduce this DataArray’s data’ by applying numpy.max along some dimension(s). |
DataArray.min([dimension, axis]) | Reduce this DataArray’s data’ by applying numpy.min along some dimension(s). |
DataArray.mean([dimension, axis]) | Reduce this DataArray’s data’ by applying numpy.mean along some dimension(s). |
DataArray.std([dimension, axis]) | Reduce this DataArray’s data’ by applying numpy.std along some dimension(s). |
DataArray.sum([dimension, axis]) | Reduce this DataArray’s data’ by applying numpy.sum along some dimension(s). |
DataArray.var([dimension, axis]) | Reduce this DataArray’s data’ by applying numpy.var along some dimension(s). |
Comparisons¶
DataArray.equals(other) | True if two DataArrays have the same dimensions, coordinates and values; otherwise False. |
DataArray.identical(other) | Like equals, but also checks DataArray names and attributes, and attributes on their coordinates. |
IO / Conversion¶
DataArray.to_dataframe() | Convert this array into a pandas.DataFrame. |
DataArray.to_series() | Convert this array into a pandas.Series. |
DataArray.from_series(series) | Convert a pandas.Series into an xray.DatasetArray |
DataArray.copy([deep]) | Returns a copy of this array. |