API reference

This page provides an auto-generated summary of xarray’s API. For more details and examples, refer to the relevant chapters in the main part of the documentation.

See also: What parts of xarray are considered public API?

Top-level functions

apply_ufunc(func, *args[, input_core_dims, ...])	Apply a vectorized function for unlabeled arrays on xarray objects.
align(*objects[, join, copy, indexes, ...])	Given any number of Dataset and/or DataArray objects, returns new objects with aligned indexes and dimension sizes.
broadcast(*args[, exclude])	Explicitly broadcast any number of DataArray or Dataset objects against one another.
concat(objs, dim[, data_vars, coords, ...])	Concatenate xarray objects along a new or existing dimension.
merge(objects[, compat, join, fill_value, ...])	Merge any number of xarray objects into a single Dataset as variables.
combine_by_coords([data_objects, compat, ...])	Attempt to auto-magically combine the given datasets (or data arrays) into one by using dimension coordinates.
combine_nested(datasets, concat_dim[, ...])	Explicitly combine an N-dimensional grid of datasets into one by using a succession of concat and merge operations along each dimension of the grid.
where(cond, x, y[, keep_attrs])	Return elements from x or y depending on cond.
infer_freq(index)	Infer the most likely frequency given the input index.
full_like(other, fill_value[, dtype])	Return a new object with the same shape and type as a given object.
zeros_like(other[, dtype])	Return a new object of zeros with the same shape and type as a given dataarray or dataset.
ones_like(other[, dtype])	Return a new object of ones with the same shape and type as a given dataarray or dataset.
cov(da_a, da_b[, dim, ddof])	Compute covariance between two DataArray objects along a shared dimension.
corr(da_a, da_b[, dim])	Compute the Pearson correlation coefficient between two DataArray objects along a shared dimension.
cross(a, b, *, dim)	Compute the cross product of two (arrays of) vectors.
dot(*arrays[, dims])	Generalized dot product for xarray objects.
polyval(coord, coeffs[, degree_dim])	Evaluate a polynomial at specific values
map_blocks(func, obj[, args, kwargs, template])	Apply a function to each block of a DataArray or Dataset.
show_versions([file])	print the versions of xarray and its dependencies
set_options(**kwargs)	Set options for xarray in a controlled context.
get_options()	Get options for xarray.
unify_chunks(*objects)	Given any number of Dataset and/or DataArray objects, returns new objects with unified chunk size along all chunked dimensions.

Dataset

Creating a dataset

Dataset([data_vars, coords, attrs])	A multi-dimensional, in memory, array database.
decode_cf(obj[, concat_characters, ...])	Decode the given Dataset or Datastore according to CF conventions into a new Dataset.

Attributes

Dataset.dims	Mapping from dimension names to lengths.
Dataset.sizes	Mapping from dimension names to lengths.
Dataset.dtypes	Mapping from data variable names to dtypes.
Dataset.data_vars	Dictionary of DataArray objects corresponding to data variables
Dataset.coords	Dictionary of xarray.DataArray objects corresponding to coordinate variables
Dataset.attrs	Dictionary of global attributes on this dataset
Dataset.encoding	Dictionary of global encoding attributes on this dataset
Dataset.indexes	Mapping of pandas.Index objects used for label based indexing.
Dataset.chunks	Mapping from dimension names to block lengths for this dataset's data, or None if the underlying data is not a dask array.
Dataset.chunksizes	Mapping from dimension names to block lengths for this dataset's data, or None if the underlying data is not a dask array.
Dataset.nbytes	Total bytes consumed by the data arrays of all variables in this dataset.

Dictionary interface

Datasets implement the mapping interface with keys given by variable names and values given by DataArray objects.

Dataset.__getitem__(key)	Access variables or coordinates of this dataset as a DataArray or a subset of variables or a indexed dataset.
Dataset.__setitem__(key, value)	Add an array to this dataset.
Dataset.__delitem__(key)	Remove a variable from this dataset.
Dataset.update(other)	Update this dataset's variables with those from another dataset.
Dataset.get(k[,d])
Dataset.items()
Dataset.keys()
Dataset.values()

Dataset contents

Dataset.copy([deep, data])	Returns a copy of this dataset.
Dataset.assign([variables])	Assign new data variables to a Dataset, returning a new object with all the original variables in addition to the new ones.
Dataset.assign_coords([coords])	Assign new coordinates to this object.
Dataset.assign_attrs(*args, **kwargs)	Assign new attrs to this object.
Dataset.pipe(func, *args, **kwargs)	Apply func(self, *args, **kwargs)
Dataset.merge(other[, overwrite_vars, ...])	Merge the arrays of two datasets into a single dataset.
Dataset.rename([name_dict])	Returns a new object with renamed variables, coordinates and dimensions.
Dataset.rename_vars([name_dict])	Returns a new object with renamed variables including coordinates
Dataset.rename_dims([dims_dict])	Returns a new object with renamed dimensions only.
Dataset.swap_dims([dims_dict])	Returns a new object with swapped dimensions.
Dataset.expand_dims([dim, axis])	Return a new object with an additional axis (or axes) inserted at the corresponding position in the array shape.
Dataset.drop_vars(names, *[, errors])	Drop variables from this dataset.
Dataset.drop_indexes(coord_names, *[, errors])	Drop the indexes assigned to the given coordinates.
Dataset.drop_duplicates(dim[, keep])	Returns a new Dataset with duplicate dimension values removed.
Dataset.drop_dims(drop_dims, *[, errors])	Drop dimensions and associated variables from this dataset.
Dataset.set_coords(names)	Given names of one or more variables, set them as coordinates
Dataset.reset_coords([names, drop])	Given names of coordinates, reset them to become variables
Dataset.reset_encoding()	Return a new Dataset without encoding on the dataset or any of its variables/coords.
Dataset.convert_calendar(calendar[, dim, ...])	Convert the Dataset to another calendar.
Dataset.interp_calendar(target[, dim])	Interpolates the Dataset to another calendar based on decimal year measure.
Dataset.get_index(key)	Get an index for a dimension, with fall-back to a default RangeIndex

Comparisons

Dataset.equals(other)	Two Datasets are equal if they have matching variables and coordinates, all of which are equal.
Dataset.identical(other)	Like equals, but also checks all dataset attributes and the attributes on all variables and coordinates.
Dataset.broadcast_equals(other)	Two Datasets are broadcast equal if they are equal after broadcasting all variables against each other.

Indexing

Dataset.loc	Attribute for location based indexing.
Dataset.isel([indexers, drop, missing_dims])	Returns a new dataset with each array indexed along the specified dimension(s).
Dataset.sel([indexers, method, tolerance, drop])	Returns a new dataset with each array indexed by tick labels along the specified dimension(s).
Dataset.drop_sel([labels, errors])	Drop index labels from this dataset.
Dataset.drop_isel([indexers])	Drop index positions from this Dataset.
Dataset.head([indexers])	Returns a new dataset with the first n values of each array for the specified dimension(s).
Dataset.tail([indexers])	Returns a new dataset with the last n values of each array for the specified dimension(s).
Dataset.thin([indexers])	Returns a new dataset with each array indexed along every n-th value for the specified dimension(s)
Dataset.squeeze([dim, drop, axis])	Return a new object with squeezed data.
Dataset.interp([coords, method, ...])	Interpolate a Dataset onto new coordinates
Dataset.interp_like(other[, method, ...])	Interpolate this object onto the coordinates of another object, filling the out of range values with NaN.
Dataset.reindex([indexers, method, ...])	Conform this object onto a new set of indexes, filling in missing values with fill_value.
Dataset.reindex_like(other[, method, ...])	Conform this object onto the indexes of another object, filling in missing values with fill_value.
Dataset.set_index([indexes, append])	Set Dataset (multi-)indexes using one or more existing coordinates or variables.
Dataset.reset_index(dims_or_levels[, drop])	Reset the specified index(es) or multi-index level(s).
Dataset.set_xindex(coord_names[, index_cls])	Set a new, Xarray-compatible index from one or more existing coordinate(s).
Dataset.reorder_levels([dim_order])	Rearrange index levels using input order.
Dataset.query([queries, parser, engine, ...])	Return a new dataset with each array indexed along the specified dimension(s), where the indexers are given as strings containing Python expressions to be evaluated against the data variables in the dataset.

Missing value handling

Dataset.isnull([keep_attrs])	Test each value in the array for whether it is a missing value.
Dataset.notnull([keep_attrs])	Test each value in the array for whether it is not a missing value.
Dataset.combine_first(other)	Combine two Datasets, default to data_vars of self.
Dataset.count([dim, keep_attrs])	Reduce this Dataset's data by applying count along some dimension(s).
Dataset.dropna(dim[, how, thresh, subset])	Returns a new dataset with dropped labels for missing values along the provided dimension.
Dataset.fillna(value)	Fill missing values in this object.
Dataset.ffill(dim[, limit])	Fill NaN values by propagating values forward
Dataset.bfill(dim[, limit])	Fill NaN values by propagating values backward
Dataset.interpolate_na([dim, method, limit, ...])	Fill in NaNs by interpolating according to different methods.
Dataset.where(cond[, other, drop])	Filter elements from this object according to a condition.
Dataset.isin(test_elements)	Tests each value in the array for whether it is in test elements.

Computation

Dataset.map(func[, keep_attrs, args])	Apply a function to each data variable in this dataset
Dataset.reduce(func[, dim, keep_attrs, ...])	Reduce this dataset by applying func along some dimension(s).
Dataset.groupby(group[, squeeze, ...])	Returns a DatasetGroupBy object for performing grouped operations.
Dataset.groupby_bins(group, bins[, right, ...])	Returns a DatasetGroupBy object for performing grouped operations.
Dataset.rolling([dim, min_periods, center])	Rolling window object for Datasets.
Dataset.rolling_exp([window, window_type])	Exponentially-weighted moving window.
Dataset.weighted(weights)	Weighted Dataset operations.
Dataset.coarsen([dim, boundary, side, ...])	Coarsen object for Datasets.
Dataset.resample([indexer, skipna, closed, ...])	Returns a Resample object for performing resampling operations.
Dataset.diff(dim[, n, label])	Calculate the n-th order discrete difference along given axis.
Dataset.quantile(q[, dim, method, ...])	Compute the qth quantile of the data along the specified dimension.
Dataset.differentiate(coord[, edge_order, ...])	Differentiate with the second order accurate central differences.
Dataset.integrate(coord[, datetime_unit])	Integrate along the given coordinate using the trapezoidal rule.
Dataset.map_blocks(func[, args, kwargs, ...])	Apply a function to each block of this Dataset.
Dataset.polyfit(dim, deg[, skipna, rcond, ...])	Least squares polynomial fit.
Dataset.curvefit(coords, func[, ...])	Curve fitting optimization for arbitrary functions.

Aggregation

Dataset.all([dim, keep_attrs])	Reduce this Dataset's data by applying all along some dimension(s).
Dataset.any([dim, keep_attrs])	Reduce this Dataset's data by applying any along some dimension(s).
Dataset.argmax([dim])	Indices of the maxima of the member variables.
Dataset.argmin([dim])	Indices of the minima of the member variables.
Dataset.count([dim, keep_attrs])	Reduce this Dataset's data by applying count along some dimension(s).
Dataset.idxmax([dim, skipna, fill_value, ...])	Return the coordinate label of the maximum value along a dimension.
Dataset.idxmin([dim, skipna, fill_value, ...])	Return the coordinate label of the minimum value along a dimension.
Dataset.max([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying max along some dimension(s).
Dataset.min([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying min along some dimension(s).
Dataset.mean([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying mean along some dimension(s).
Dataset.median([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying median along some dimension(s).
Dataset.prod([dim, skipna, min_count, ...])	Reduce this Dataset's data by applying prod along some dimension(s).
Dataset.sum([dim, skipna, min_count, keep_attrs])	Reduce this Dataset's data by applying sum along some dimension(s).
Dataset.std([dim, skipna, ddof, keep_attrs])	Reduce this Dataset's data by applying std along some dimension(s).
Dataset.var([dim, skipna, ddof, keep_attrs])	Reduce this Dataset's data by applying var along some dimension(s).
Dataset.cumsum([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying cumsum along some dimension(s).
Dataset.cumprod([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying cumprod along some dimension(s).

ndarray methods

Dataset.argsort([axis, kind, order])	Returns the indices that would sort this array.
Dataset.astype(dtype, *[, order, casting, ...])	Copy of the xarray object, with data cast to a specified type.
Dataset.clip([min, max, keep_attrs])	Return an array whose values are limited to [min, max].
Dataset.conj()	Complex-conjugate all elements.
Dataset.conjugate()	Return the complex conjugate, element-wise.
Dataset.imag	The imaginary part of each data variable.
Dataset.round(*args, **kwargs)	Evenly round to the given number of decimals.
Dataset.real	The real part of each data variable.
Dataset.rank(dim[, pct, keep_attrs])	Ranks the data.

Reshaping and reorganizing

Dataset.transpose(*dims[, missing_dims])	Return a new Dataset object with all array dimensions transposed.
Dataset.stack([dimensions, create_index, ...])	Stack any number of existing dimensions into a single new dimension.
Dataset.unstack([dim, fill_value, sparse])	Unstack existing dimensions corresponding to MultiIndexes into multiple new dimensions.
Dataset.to_stacked_array(new_dim, sample_dims)	Combine variables of differing dimensionality into a DataArray without broadcasting.
Dataset.shift([shifts, fill_value])	Shift this dataset by an offset along one or more dimensions.
Dataset.roll([shifts, roll_coords])	Roll this dataset by an offset along one or more dimensions.
Dataset.pad([pad_width, mode, stat_length, ...])	Pad this dataset along one or more dimensions.
Dataset.sortby(variables[, ascending])	Sort object by labels or values (along an axis).
Dataset.broadcast_like(other[, exclude])	Broadcast this DataArray against another Dataset or DataArray.

DataArray

DataArray([data, coords, dims, name, attrs, ...])

N-dimensional array with labeled coordinates and dimensions.

Attributes

DataArray.values	The array's data as a numpy.ndarray.
DataArray.data	The DataArray's data as an array.
DataArray.coords	Dictionary-like container of coordinate arrays.
DataArray.dims	Tuple of dimension names associated with this array.
DataArray.sizes	Ordered mapping from dimension names to lengths.
DataArray.name	The name of this array.
DataArray.attrs	Dictionary storing arbitrary metadata with this array.
DataArray.encoding	Dictionary of format-specific settings for how this array should be serialized.
DataArray.indexes	Mapping of pandas.Index objects used for label based indexing.
DataArray.chunksizes	Mapping from dimension names to block lengths for this dataarray's data, or None if the underlying data is not a dask array.

ndarray attributes

DataArray.ndim	Number of array dimensions.
DataArray.nbytes	Total bytes consumed by the elements of this DataArray's data.
DataArray.shape	Tuple of array dimensions.
DataArray.size	Number of elements in the array.
DataArray.dtype	Data-type of the array’s elements.
DataArray.chunks	Tuple of block lengths for this dataarray's data, in order of dimensions, or None if the underlying data is not a dask array.

DataArray contents

DataArray.assign_coords([coords])	Assign new coordinates to this object.
DataArray.assign_attrs(*args, **kwargs)	Assign new attrs to this object.
DataArray.pipe(func, *args, **kwargs)	Apply func(self, *args, **kwargs)
DataArray.rename([new_name_or_name_dict])	Returns a new DataArray with renamed coordinates, dimensions or a new name.
DataArray.swap_dims([dims_dict])	Returns a new DataArray with swapped dimensions.
DataArray.expand_dims([dim, axis])	Return a new object with an additional axis (or axes) inserted at the corresponding position in the array shape.
DataArray.drop_vars(names, *[, errors])	Returns an array with dropped variables.
DataArray.drop_indexes(coord_names, *[, errors])	Drop the indexes assigned to the given coordinates.
DataArray.drop_duplicates(dim[, keep])	Returns a new DataArray with duplicate dimension values removed.
DataArray.reset_coords([names, drop])	Given names of coordinates, reset them to become variables.
DataArray.reset_encoding()	Return a new DataArray without encoding on the array or any attached coords.
DataArray.copy([deep, data])	Returns a copy of this array.
DataArray.convert_calendar(calendar[, dim, ...])	Convert the DataArray to another calendar.
DataArray.interp_calendar(target[, dim])	Interpolates the DataArray to another calendar based on decimal year measure.
DataArray.get_index(key)	Get an index for a dimension, with fall-back to a default RangeIndex
DataArray.astype(dtype, *[, order, casting, ...])	Copy of the xarray object, with data cast to a specified type.
DataArray.item(*args)	Copy an element of an array to a standard Python scalar and return it.

Indexing

DataArray.__getitem__(key)
DataArray.__setitem__(key, value)
DataArray.loc	Attribute for location based indexing like pandas.
DataArray.isel([indexers, drop, missing_dims])	Return a new DataArray whose data is given by selecting indexes along the specified dimension(s).
DataArray.sel([indexers, method, tolerance, ...])	Return a new DataArray whose data is given by selecting index labels along the specified dimension(s).
DataArray.drop_sel([labels, errors])	Drop index labels from this DataArray.
DataArray.drop_isel([indexers])	Drop index positions from this DataArray.
DataArray.head([indexers])	Return a new DataArray whose data is given by the the first n values along the specified dimension(s).
DataArray.tail([indexers])	Return a new DataArray whose data is given by the the last n values along the specified dimension(s).
DataArray.thin([indexers])	Return a new DataArray whose data is given by each n value along the specified dimension(s).
DataArray.squeeze([dim, drop, axis])	Return a new object with squeezed data.
DataArray.interp([coords, method, ...])	Interpolate a DataArray onto new coordinates
DataArray.interp_like(other[, method, ...])	Interpolate this object onto the coordinates of another object, filling out of range values with NaN.
DataArray.reindex([indexers, method, ...])	Conform this object onto the indexes of another object, filling in missing values with fill_value.
DataArray.reindex_like(other[, method, ...])	Conform this object onto the indexes of another object, filling in missing values with fill_value.
DataArray.set_index([indexes, append])	Set DataArray (multi-)indexes using one or more existing coordinates.
DataArray.reset_index(dims_or_levels[, drop])	Reset the specified index(es) or multi-index level(s).
DataArray.set_xindex(coord_names[, index_cls])	Set a new, Xarray-compatible index from one or more existing coordinate(s).
DataArray.reorder_levels([dim_order])	Rearrange index levels using input order.
DataArray.query([queries, parser, engine, ...])	Return a new data array indexed along the specified dimension(s), where the indexers are given as strings containing Python expressions to be evaluated against the values in the array.

Missing value handling

DataArray.isnull([keep_attrs])	Test each value in the array for whether it is a missing value.
DataArray.notnull([keep_attrs])	Test each value in the array for whether it is not a missing value.
DataArray.combine_first(other)	Combine two DataArray objects, with union of coordinates.
DataArray.count([dim, keep_attrs])	Reduce this DataArray's data by applying count along some dimension(s).
DataArray.dropna(dim[, how, thresh])	Returns a new array with dropped labels for missing values along the provided dimension.
DataArray.fillna(value)	Fill missing values in this object.
DataArray.ffill(dim[, limit])	Fill NaN values by propagating values forward
DataArray.bfill(dim[, limit])	Fill NaN values by propagating values backward
DataArray.interpolate_na([dim, method, ...])	Fill in NaNs by interpolating according to different methods.
DataArray.where(cond[, other, drop])	Filter elements from this object according to a condition.
DataArray.isin(test_elements)	Tests each value in the array for whether it is in test elements.

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 the array name and attributes, and attributes on all coordinates.
DataArray.broadcast_equals(other)	Two DataArrays are broadcast equal if they are equal after broadcasting them against each other such that they have the same dimensions.

Computation

DataArray.reduce(func[, dim, axis, ...])	Reduce this array by applying func along some dimension(s).
DataArray.groupby(group[, squeeze, ...])	Returns a DataArrayGroupBy object for performing grouped operations.
DataArray.groupby_bins(group, bins[, right, ...])	Returns a DataArrayGroupBy object for performing grouped operations.
DataArray.rolling([dim, min_periods, center])	Rolling window object for DataArrays.
DataArray.rolling_exp([window, window_type])	Exponentially-weighted moving window.
DataArray.weighted(weights)	Weighted DataArray operations.
DataArray.coarsen([dim, boundary, side, ...])	Coarsen object for DataArrays.
DataArray.resample([indexer, skipna, ...])	Returns a Resample object for performing resampling operations.
DataArray.get_axis_num(dim)	Return axis number(s) corresponding to dimension(s) in this array.
DataArray.diff(dim[, n, label])	Calculate the n-th order discrete difference along given axis.
DataArray.dot(other[, dims])	Perform dot product of two DataArrays along their shared dims.
DataArray.quantile(q[, dim, method, ...])	Compute the qth quantile of the data along the specified dimension.
DataArray.differentiate(coord[, edge_order, ...])	Differentiate the array with the second order accurate central differences.
DataArray.integrate([coord, datetime_unit])	Integrate along the given coordinate using the trapezoidal rule.
DataArray.polyfit(dim, deg[, skipna, rcond, ...])	Least squares polynomial fit.
DataArray.map_blocks(func[, args, kwargs, ...])	Apply a function to each block of this DataArray.
DataArray.curvefit(coords, func[, ...])	Curve fitting optimization for arbitrary functions.

Aggregation

DataArray.all([dim, keep_attrs])	Reduce this DataArray's data by applying all along some dimension(s).
DataArray.any([dim, keep_attrs])	Reduce this DataArray's data by applying any along some dimension(s).
DataArray.argmax([dim, axis, keep_attrs, skipna])	Index or indices of the maximum of the DataArray over one or more dimensions.
DataArray.argmin([dim, axis, keep_attrs, skipna])	Index or indices of the minimum of the DataArray over one or more dimensions.
DataArray.count([dim, keep_attrs])	Reduce this DataArray's data by applying count along some dimension(s).
DataArray.idxmax([dim, skipna, fill_value, ...])	Return the coordinate label of the maximum value along a dimension.
DataArray.idxmin([dim, skipna, fill_value, ...])	Return the coordinate label of the minimum value along a dimension.
DataArray.max([dim, skipna, keep_attrs])	Reduce this DataArray's data by applying max along some dimension(s).
DataArray.min([dim, skipna, keep_attrs])	Reduce this DataArray's data by applying min along some dimension(s).
DataArray.mean([dim, skipna, keep_attrs])	Reduce this DataArray's data by applying mean along some dimension(s).
DataArray.median([dim, skipna, keep_attrs])	Reduce this DataArray's data by applying median along some dimension(s).
DataArray.prod([dim, skipna, min_count, ...])	Reduce this DataArray's data by applying prod along some dimension(s).
DataArray.sum([dim, skipna, min_count, ...])	Reduce this DataArray's data by applying sum along some dimension(s).
DataArray.std([dim, skipna, ddof, keep_attrs])	Reduce this DataArray's data by applying std along some dimension(s).
DataArray.var([dim, skipna, ddof, keep_attrs])	Reduce this DataArray's data by applying var along some dimension(s).
DataArray.cumsum([dim, skipna, keep_attrs])	Reduce this DataArray's data by applying cumsum along some dimension(s).
DataArray.cumprod([dim, skipna, keep_attrs])	Reduce this DataArray's data by applying cumprod along some dimension(s).

ndarray methods

DataArray.argsort([axis, kind, order])	Returns the indices that would sort this array.
DataArray.clip([min, max, keep_attrs])	Return an array whose values are limited to [min, max].
DataArray.conj()	Complex-conjugate all elements.
DataArray.conjugate()	Return the complex conjugate, element-wise.
DataArray.imag	The imaginary part of the array.
DataArray.searchsorted(v[, side, sorter])	Find indices where elements of v should be inserted in a to maintain order.
DataArray.round(*args, **kwargs)	Evenly round to the given number of decimals.
DataArray.real	The real part of the array.
DataArray.T
DataArray.rank(dim[, pct, keep_attrs])	Ranks the data.

String manipulation

DataArray.str

DataArray.str.capitalize()	Convert strings in the array to be capitalized.
DataArray.str.casefold()	Convert strings in the array to be casefolded.
DataArray.str.cat(*others[, sep])	Concatenate strings elementwise in the DataArray with other strings.
DataArray.str.center(width[, fillchar])	Pad left and right side of each string in the array.
DataArray.str.contains(pat[, case, flags, regex])	Test if pattern or regex is contained within each string of the array.
DataArray.str.count(pat[, flags, case])	Count occurrences of pattern in each string of the array.
DataArray.str.decode(encoding[, errors])	Decode character string in the array using indicated encoding.
DataArray.str.encode(encoding[, errors])	Encode character string in the array using indicated encoding.
DataArray.str.endswith(pat)	Test if the end of each string in the array matches a pattern.
DataArray.str.extract(pat, dim[, case, flags])	Extract the first match of capture groups in the regex pat as a new dimension in a DataArray.
DataArray.str.extractall(pat, group_dim, ...)	Extract all matches of capture groups in the regex pat as new dimensions in a DataArray.
DataArray.str.find(sub[, start, end, side])	Return lowest or highest indexes in each strings in the array where the substring is fully contained between [start:end].
DataArray.str.findall(pat[, case, flags])	Find all occurrences of pattern or regular expression in the DataArray.
DataArray.str.format(*args, **kwargs)	Perform python string formatting on each element of the DataArray.
DataArray.str.get(i[, default])	Extract character number i from each string in the array.
DataArray.str.get_dummies(dim[, sep])	Return DataArray of dummy/indicator variables.
DataArray.str.index(sub[, start, end, side])	Return lowest or highest indexes in each strings where the substring is fully contained between [start:end].
DataArray.str.isalnum()	Check whether all characters in each string are alphanumeric.
DataArray.str.isalpha()	Check whether all characters in each string are alphabetic.
DataArray.str.isdecimal()	Check whether all characters in each string are decimal.
DataArray.str.isdigit()	Check whether all characters in each string are digits.
DataArray.str.islower()	Check whether all characters in each string are lowercase.
DataArray.str.isnumeric()	Check whether all characters in each string are numeric.
DataArray.str.isspace()	Check whether all characters in each string are spaces.
DataArray.str.istitle()	Check whether all characters in each string are titlecase.
DataArray.str.isupper()	Check whether all characters in each string are uppercase.
DataArray.str.join([dim, sep])	Concatenate strings in a DataArray along a particular dimension.
DataArray.str.len()	Compute the length of each string in the array.
DataArray.str.ljust(width[, fillchar])	Pad right side of each string in the array.
DataArray.str.lower()	Convert strings in the array to lowercase.
DataArray.str.lstrip([to_strip])	Remove leading characters.
DataArray.str.match(pat[, case, flags])	Determine if each string in the array matches a regular expression.
DataArray.str.normalize(form)	Return the Unicode normal form for the strings in the datarray.
DataArray.str.pad(width[, side, fillchar])	Pad strings in the array up to width.
DataArray.str.partition(dim[, sep])	Split the strings in the DataArray at the first occurrence of separator sep.
DataArray.str.repeat(repeats)	Repeat each string in the array.
DataArray.str.replace(pat, repl[, n, case, ...])	Replace occurrences of pattern/regex in the array with some string.
DataArray.str.rfind(sub[, start, end])	Return highest indexes in each strings in the array where the substring is fully contained between [start:end].
DataArray.str.rindex(sub[, start, end])	Return highest indexes in each strings where the substring is fully contained between [start:end].
DataArray.str.rjust(width[, fillchar])	Pad left side of each string in the array.
DataArray.str.rpartition(dim[, sep])	Split the strings in the DataArray at the last occurrence of separator sep.
DataArray.str.rsplit(dim[, sep, maxsplit])	Split strings in a DataArray around the given separator/delimiter sep.
DataArray.str.rstrip([to_strip])	Remove trailing characters.
DataArray.str.slice([start, stop, step])	Slice substrings from each string in the array.
DataArray.str.slice_replace([start, stop, repl])	Replace a positional slice of a string with another value.
DataArray.str.split(dim[, sep, maxsplit])	Split strings in a DataArray around the given separator/delimiter sep.
DataArray.str.startswith(pat)	Test if the start of each string in the array matches a pattern.
DataArray.str.strip([to_strip, side])	Remove leading and trailing characters.
DataArray.str.swapcase()	Convert strings in the array to be swapcased.
DataArray.str.title()	Convert strings in the array to titlecase.
DataArray.str.translate(table)	Map characters of each string through the given mapping table.
DataArray.str.upper()	Convert strings in the array to uppercase.
DataArray.str.wrap(width, **kwargs)	Wrap long strings in the array in paragraphs with length less than width.
DataArray.str.zfill(width)	Pad each string in the array by prepending '0' characters.

Datetimelike properties

Datetime properties:

DataArray.dt.year	The year of the datetime
DataArray.dt.month	The month as January=1, December=12
DataArray.dt.day	The days of the datetime
DataArray.dt.hour	The hours of the datetime
DataArray.dt.minute	The minutes of the datetime
DataArray.dt.second	The seconds of the datetime
DataArray.dt.microsecond	The microseconds of the datetime
DataArray.dt.nanosecond	The nanoseconds of the datetime
DataArray.dt.dayofweek	The day of the week with Monday=0, Sunday=6
DataArray.dt.weekday	The day of the week with Monday=0, Sunday=6
DataArray.dt.weekday_name	The name of day in a week
DataArray.dt.dayofyear	The ordinal day of the year
DataArray.dt.quarter	The quarter of the date
DataArray.dt.days_in_month	The number of days in the month
DataArray.dt.daysinmonth	The number of days in the month
DataArray.dt.season	Season of the year
DataArray.dt.time	Timestamps corresponding to datetimes
DataArray.dt.date	Date corresponding to datetimes
DataArray.dt.calendar	The name of the calendar of the dates.
DataArray.dt.is_month_start	Indicate whether the date is the first day of the month
DataArray.dt.is_month_end	Indicate whether the date is the last day of the month
DataArray.dt.is_quarter_end	Indicate whether the date is the last day of a quarter
DataArray.dt.is_year_start	Indicate whether the date is the first day of a year
DataArray.dt.is_leap_year	Indicate if the date belongs to a leap year

Datetime methods:

DataArray.dt.floor(freq)	Round timestamps downward to specified frequency resolution.
DataArray.dt.ceil(freq)	Round timestamps upward to specified frequency resolution.
DataArray.dt.isocalendar()	Dataset containing ISO year, week number, and weekday.
DataArray.dt.round(freq)	Round timestamps to specified frequency resolution.
DataArray.dt.strftime(date_format)	Return an array of formatted strings specified by date_format, which supports the same string format as the python standard library.

Timedelta properties:

DataArray.dt.days	Number of days for each element
DataArray.dt.seconds	Number of seconds (>= 0 and less than 1 day) for each element
DataArray.dt.microseconds	Number of microseconds (>= 0 and less than 1 second) for each element
DataArray.dt.nanoseconds	Number of nanoseconds (>= 0 and less than 1 microsecond) for each element

Timedelta methods:

DataArray.dt.floor(freq)	Round timestamps downward to specified frequency resolution.
DataArray.dt.ceil(freq)	Round timestamps upward to specified frequency resolution.
DataArray.dt.round(freq)	Round timestamps to specified frequency resolution.

Reshaping and reorganizing

DataArray.transpose(*dims[, ...])	Return a new DataArray object with transposed dimensions.
DataArray.stack([dimensions, create_index, ...])	Stack any number of existing dimensions into a single new dimension.
DataArray.unstack([dim, fill_value, sparse])	Unstack existing dimensions corresponding to MultiIndexes into multiple new dimensions.
DataArray.to_unstacked_dataset(dim[, level])	Unstack DataArray expanding to Dataset along a given level of a stacked coordinate.
DataArray.shift([shifts, fill_value])	Shift this DataArray by an offset along one or more dimensions.
DataArray.roll([shifts, roll_coords])	Roll this array by an offset along one or more dimensions.
DataArray.pad([pad_width, mode, ...])	Pad this array along one or more dimensions.
DataArray.sortby(variables[, ascending])	Sort object by labels or values (along an axis).
DataArray.broadcast_like(other[, exclude])	Broadcast this DataArray against another Dataset or DataArray.

IO / Conversion

Dataset methods

load_dataset(filename_or_obj, **kwargs)	Open, load into memory, and close a Dataset from a file or file-like object.
open_dataset(filename_or_obj, *[, engine, ...])	Open and decode a dataset from a file or file-like object.
open_mfdataset(paths[, chunks, concat_dim, ...])	Open multiple files as a single dataset.
open_zarr(store[, group, synchronizer, ...])	Load and decode a dataset from a Zarr store.
save_mfdataset(datasets, paths[, mode, ...])	Write multiple datasets to disk as netCDF files simultaneously.
Dataset.as_numpy()	Coerces wrapped data and coordinates into numpy arrays, returning a Dataset.
Dataset.from_dataframe(dataframe[, sparse])	Convert a pandas.DataFrame into an xarray.Dataset
Dataset.from_dict(d)	Convert a dictionary into an xarray.Dataset.
Dataset.to_array([dim, name])	Convert this dataset into an xarray.DataArray
Dataset.to_dataframe([dim_order])	Convert this dataset into a pandas.DataFrame.
Dataset.to_dask_dataframe([dim_order, set_index])	Convert this dataset into a dask.dataframe.DataFrame.
Dataset.to_dict([data, encoding])	Convert this dataset to a dictionary following xarray naming conventions.
Dataset.to_netcdf([path, mode, format, ...])	Write dataset contents to a netCDF file.
Dataset.to_pandas()	Convert this dataset into a pandas object without changing the number of dimensions.
Dataset.to_zarr([store, chunk_store, mode, ...])	Write dataset contents to a zarr group.
Dataset.chunk([chunks, name_prefix, token, ...])	Coerce all arrays in this dataset into dask arrays with the given chunks.
Dataset.close()	Release any resources linked to this object.
Dataset.compute(**kwargs)	Manually trigger loading and/or computation of this dataset's data from disk or a remote source into memory and return a new dataset.
Dataset.filter_by_attrs(**kwargs)	Returns a Dataset with variables that match specific conditions.
Dataset.info([buf])	Concise summary of a Dataset variables and attributes.
Dataset.load(**kwargs)	Manually trigger loading and/or computation of this dataset's data from disk or a remote source into memory and return this dataset.
Dataset.persist(**kwargs)	Trigger computation, keeping data as dask arrays
Dataset.unify_chunks()	Unify chunk size along all chunked dimensions of this Dataset.

DataArray methods

load_dataarray(filename_or_obj, **kwargs)	Open, load into memory, and close a DataArray from a file or file-like object containing a single data variable.
open_dataarray(filename_or_obj, *[, engine, ...])	Open an DataArray from a file or file-like object containing a single data variable.
DataArray.as_numpy()	Coerces wrapped data and coordinates into numpy arrays, returning a DataArray.
DataArray.from_cdms2(variable)	Convert a cdms2.Variable into an xarray.DataArray
DataArray.from_dict(d)	Convert a dictionary into an xarray.DataArray
DataArray.from_iris(cube)	Convert a iris.cube.Cube into an xarray.DataArray
DataArray.from_series(series[, sparse])	Convert a pandas.Series into an xarray.DataArray.
DataArray.to_cdms2()	Convert this array into a cdms2.Variable
DataArray.to_dataframe([name, dim_order])	Convert this array and its coordinates into a tidy pandas.DataFrame.
DataArray.to_dataset([dim, name, promote_attrs])	Convert a DataArray to a Dataset.
DataArray.to_dict([data, encoding])	Convert this xarray.DataArray into a dictionary following xarray naming conventions.
DataArray.to_index()	Convert this variable to a pandas.Index.
DataArray.to_iris()	Convert this array into a iris.cube.Cube
DataArray.to_masked_array([copy])	Convert this array into a numpy.ma.MaskedArray
DataArray.to_netcdf([path, mode, format, ...])	Write DataArray contents to a netCDF file.
DataArray.to_numpy()	Coerces wrapped data to numpy and returns a numpy.ndarray.
DataArray.to_pandas()	Convert this array into a pandas object with the same shape.
DataArray.to_series()	Convert this array into a pandas.Series.
DataArray.to_zarr([store, chunk_store, ...])	Write DataArray contents to a Zarr store
DataArray.chunk([chunks, name_prefix, ...])	Coerce this array's data into a dask arrays with the given chunks.
DataArray.close()	Release any resources linked to this object.
DataArray.compute(**kwargs)	Manually trigger loading of this array's data from disk or a remote source into memory and return a new array.
DataArray.persist(**kwargs)	Trigger computation in constituent dask arrays
DataArray.load(**kwargs)	Manually trigger loading of this array's data from disk or a remote source into memory and return this array.
DataArray.unify_chunks()	Unify chunk size along all chunked dimensions of this DataArray.

Coordinates objects

Dataset

core.coordinates.DatasetCoordinates(dataset)	Dictionary like container for Dataset coordinates.
core.coordinates.DatasetCoordinates.dtypes	Mapping from coordinate names to dtypes.

DataArray

core.coordinates.DataArrayCoordinates(dataarray)	Dictionary like container for DataArray coordinates.
core.coordinates.DataArrayCoordinates.dtypes	Mapping from coordinate names to dtypes.

Plotting

Dataset

Dataset.plot.scatter(*args[, x, y, z, hue, ...])	Scatter variables against each other.
Dataset.plot.quiver(*args[, x, y, u, v, ...])	Quiver plot of Dataset variables.
Dataset.plot.streamplot(*args[, x, y, u, v, ...])	Plot streamlines of Dataset variables.

DataArray

DataArray.plot(*[, row, col, col_wrap, ax, ...])

Default plot of DataArray using matplotlib.pyplot.

DataArray.plot.contourf(*args[, x, y, ...])	Filled contour plot of 2D DataArray.
DataArray.plot.contour(*args[, x, y, ...])	Contour plot of 2D DataArray.
DataArray.plot.hist(*args[, figsize, size, ...])	Histogram of DataArray.
DataArray.plot.imshow(*args[, x, y, ...])	Image plot of 2D DataArray.
DataArray.plot.line(*args[, row, col, ...])	Line plot of DataArray values.
DataArray.plot.pcolormesh(*args[, x, y, ...])	Pseudocolor plot of 2D DataArray.
DataArray.plot.step(*args[, where, ...])	Step plot of DataArray values.
DataArray.plot.scatter(*args[, x, y, z, ...])	Scatter variables against each other.
DataArray.plot.surface(*args[, x, y, ...])	Surface plot of 2D DataArray.

Faceting

plot.FacetGrid(data[, col, row, col_wrap, ...])	Initialize the Matplotlib figure and FacetGrid object.
plot.FacetGrid.add_colorbar(**kwargs)	Draw a colorbar.
plot.FacetGrid.add_legend(*[, label, ...])
plot.FacetGrid.add_quiverkey(u, v, **kwargs)
plot.FacetGrid.map(func, *args, **kwargs)	Apply a plotting function to each facet's subset of the data.
plot.FacetGrid.map_dataarray(func, x, y, ...)	Apply a plotting function to a 2d facet's subset of the data.
plot.FacetGrid.map_dataarray_line(func, x, ...)
plot.FacetGrid.map_dataset(func[, x, y, ...])
plot.FacetGrid.map_plot1d(func, x, y, *[, ...])	Apply a plotting function to a 1d facet's subset of the data.
plot.FacetGrid.set_axis_labels(*axlabels)	Set axis labels on the left column and bottom row of the grid.
plot.FacetGrid.set_ticks([max_xticks, ...])	Set and control tick behavior.
plot.FacetGrid.set_titles([template, ...])	Draw titles either above each facet or on the grid margins.
plot.FacetGrid.set_xlabels([label])	Label the x axis on the bottom row of the grid.
plot.FacetGrid.set_ylabels([label])	Label the y axis on the left column of the grid.

GroupBy objects

Dataset

DatasetGroupBy(obj, group[, squeeze, ...])
DatasetGroupBy.map(func[, args, shortcut])	Apply a function to each Dataset in the group and concatenate them together into a new Dataset.
DatasetGroupBy.reduce(func[, dim, axis, ...])	Reduce the items in this group by applying func along some dimension(s).
DatasetGroupBy.assign(**kwargs)	Assign data variables by group.
DatasetGroupBy.assign_coords([coords])	Assign coordinates by group.
DatasetGroupBy.first([skipna, keep_attrs])	Return the first element of each group along the group dimension
DatasetGroupBy.last([skipna, keep_attrs])	Return the last element of each group along the group dimension
DatasetGroupBy.fillna(value)	Fill missing values in this object by group.
DatasetGroupBy.quantile(q[, dim, method, ...])	Compute the qth quantile over each array in the groups and concatenate them together into a new array.
DatasetGroupBy.where(cond[, other])	Return elements from self or other depending on cond.
DatasetGroupBy.all([dim, keep_attrs])	Reduce this Dataset's data by applying all along some dimension(s).
DatasetGroupBy.any([dim, keep_attrs])	Reduce this Dataset's data by applying any along some dimension(s).
DatasetGroupBy.count([dim, keep_attrs])	Reduce this Dataset's data by applying count along some dimension(s).
DatasetGroupBy.cumsum([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying cumsum along some dimension(s).
DatasetGroupBy.cumprod([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying cumprod along some dimension(s).
DatasetGroupBy.max([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying max along some dimension(s).
DatasetGroupBy.mean([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying mean along some dimension(s).
DatasetGroupBy.median([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying median along some dimension(s).
DatasetGroupBy.min([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying min along some dimension(s).
DatasetGroupBy.prod([dim, skipna, ...])	Reduce this Dataset's data by applying prod along some dimension(s).
DatasetGroupBy.std([dim, skipna, ddof, ...])	Reduce this Dataset's data by applying std along some dimension(s).
DatasetGroupBy.sum([dim, skipna, min_count, ...])	Reduce this Dataset's data by applying sum along some dimension(s).
DatasetGroupBy.var([dim, skipna, ddof, ...])	Reduce this Dataset's data by applying var along some dimension(s).
DatasetGroupBy.dims
DatasetGroupBy.groups	Mapping from group labels to indices.

DataArray

DataArrayGroupBy(obj, group[, squeeze, ...])
DataArrayGroupBy.map(func[, args, shortcut])	Apply a function to each array in the group and concatenate them together into a new array.
DataArrayGroupBy.reduce(func[, dim, axis, ...])	Reduce the items in this group by applying func along some dimension(s).
DataArrayGroupBy.assign_coords([coords])	Assign coordinates by group.
DataArrayGroupBy.first([skipna, keep_attrs])	Return the first element of each group along the group dimension
DataArrayGroupBy.last([skipna, keep_attrs])	Return the last element of each group along the group dimension
DataArrayGroupBy.fillna(value)	Fill missing values in this object by group.
DataArrayGroupBy.quantile(q[, dim, method, ...])	Compute the qth quantile over each array in the groups and concatenate them together into a new array.
DataArrayGroupBy.where(cond[, other])	Return elements from self or other depending on cond.
DataArrayGroupBy.all([dim, keep_attrs])	Reduce this DataArray's data by applying all along some dimension(s).
DataArrayGroupBy.any([dim, keep_attrs])	Reduce this DataArray's data by applying any along some dimension(s).
DataArrayGroupBy.count([dim, keep_attrs])	Reduce this DataArray's data by applying count along some dimension(s).
DataArrayGroupBy.cumsum([dim, skipna, ...])	Reduce this DataArray's data by applying cumsum along some dimension(s).
DataArrayGroupBy.cumprod([dim, skipna, ...])	Reduce this DataArray's data by applying cumprod along some dimension(s).
DataArrayGroupBy.max([dim, skipna, keep_attrs])	Reduce this DataArray's data by applying max along some dimension(s).
DataArrayGroupBy.mean([dim, skipna, keep_attrs])	Reduce this DataArray's data by applying mean along some dimension(s).
DataArrayGroupBy.median([dim, skipna, ...])	Reduce this DataArray's data by applying median along some dimension(s).
DataArrayGroupBy.min([dim, skipna, keep_attrs])	Reduce this DataArray's data by applying min along some dimension(s).
DataArrayGroupBy.prod([dim, skipna, ...])	Reduce this DataArray's data by applying prod along some dimension(s).
DataArrayGroupBy.std([dim, skipna, ddof, ...])	Reduce this DataArray's data by applying std along some dimension(s).
DataArrayGroupBy.sum([dim, skipna, ...])	Reduce this DataArray's data by applying sum along some dimension(s).
DataArrayGroupBy.var([dim, skipna, ddof, ...])	Reduce this DataArray's data by applying var along some dimension(s).
DataArrayGroupBy.dims
DataArrayGroupBy.groups	Mapping from group labels to indices.

Rolling objects

Dataset

DatasetRolling(obj, windows[, min_periods, ...])
DatasetRolling.construct([window_dim, ...])	Convert this rolling object to xr.Dataset, where the window dimension is stacked as a new dimension
DatasetRolling.reduce(func[, keep_attrs])	Reduce the items in this group by applying func along some dimension(s).
DatasetRolling.argmax([keep_attrs])	Reduce this object's data windows by applying argmax along its dimension.
DatasetRolling.argmin([keep_attrs])	Reduce this object's data windows by applying argmin along its dimension.
DatasetRolling.count([keep_attrs])	Reduce this object's data windows by applying count along its dimension.
DatasetRolling.max([keep_attrs])	Reduce this object's data windows by applying max along its dimension.
DatasetRolling.mean([keep_attrs])	Reduce this object's data windows by applying mean along its dimension.
DatasetRolling.median([keep_attrs])	Reduce this object's data windows by applying median along its dimension.
DatasetRolling.min([keep_attrs])	Reduce this object's data windows by applying min along its dimension.
DatasetRolling.prod([keep_attrs])	Reduce this object's data windows by applying prod along its dimension.
DatasetRolling.std([keep_attrs])	Reduce this object's data windows by applying std along its dimension.
DatasetRolling.sum([keep_attrs])	Reduce this object's data windows by applying sum along its dimension.
DatasetRolling.var([keep_attrs])	Reduce this object's data windows by applying var along its dimension.

DataArray

DataArrayRolling(obj, windows[, ...])
DataArrayRolling.__iter__()
DataArrayRolling.construct([window_dim, ...])	Convert this rolling object to xr.DataArray, where the window dimension is stacked as a new dimension
DataArrayRolling.reduce(func[, keep_attrs])	Reduce the items in this group by applying func along some dimension(s).
DataArrayRolling.argmax([keep_attrs])	Reduce this object's data windows by applying argmax along its dimension.
DataArrayRolling.argmin([keep_attrs])	Reduce this object's data windows by applying argmin along its dimension.
DataArrayRolling.count([keep_attrs])	Reduce this object's data windows by applying count along its dimension.
DataArrayRolling.max([keep_attrs])	Reduce this object's data windows by applying max along its dimension.
DataArrayRolling.mean([keep_attrs])	Reduce this object's data windows by applying mean along its dimension.
DataArrayRolling.median([keep_attrs])	Reduce this object's data windows by applying median along its dimension.
DataArrayRolling.min([keep_attrs])	Reduce this object's data windows by applying min along its dimension.
DataArrayRolling.prod([keep_attrs])	Reduce this object's data windows by applying prod along its dimension.
DataArrayRolling.std([keep_attrs])	Reduce this object's data windows by applying std along its dimension.
DataArrayRolling.sum([keep_attrs])	Reduce this object's data windows by applying sum along its dimension.
DataArrayRolling.var([keep_attrs])	Reduce this object's data windows by applying var along its dimension.

Coarsen objects

Dataset

DatasetCoarsen(obj, windows, boundary, side, ...)
DatasetCoarsen.all([keep_attrs])	Reduce this DatasetCoarsen's data by applying all along some dimension(s).
DatasetCoarsen.any([keep_attrs])	Reduce this DatasetCoarsen's data by applying any along some dimension(s).
DatasetCoarsen.construct([window_dim, ...])	Convert this Coarsen object to a DataArray or Dataset, where the coarsening dimension is split or reshaped to two new dimensions.
DatasetCoarsen.count([keep_attrs])	Reduce this DatasetCoarsen's data by applying count along some dimension(s).
DatasetCoarsen.max([keep_attrs])	Reduce this DatasetCoarsen's data by applying max along some dimension(s).
DatasetCoarsen.mean([keep_attrs])	Reduce this DatasetCoarsen's data by applying mean along some dimension(s).
DatasetCoarsen.median([keep_attrs])	Reduce this DatasetCoarsen's data by applying median along some dimension(s).
DatasetCoarsen.min([keep_attrs])	Reduce this DatasetCoarsen's data by applying min along some dimension(s).
DatasetCoarsen.prod([keep_attrs])	Reduce this DatasetCoarsen's data by applying prod along some dimension(s).
DatasetCoarsen.reduce(func[, keep_attrs])	Reduce the items in this group by applying func along some dimension(s).
DatasetCoarsen.std([keep_attrs])	Reduce this DatasetCoarsen's data by applying std along some dimension(s).
DatasetCoarsen.sum([keep_attrs])	Reduce this DatasetCoarsen's data by applying sum along some dimension(s).
DatasetCoarsen.var([keep_attrs])	Reduce this DatasetCoarsen's data by applying var along some dimension(s).

DataArray

DataArrayCoarsen(obj, windows, boundary, ...)
DataArrayCoarsen.all([keep_attrs])	Reduce this DataArrayCoarsen's data by applying all along some dimension(s).
DataArrayCoarsen.any([keep_attrs])	Reduce this DataArrayCoarsen's data by applying any along some dimension(s).
DataArrayCoarsen.construct([window_dim, ...])	Convert this Coarsen object to a DataArray or Dataset, where the coarsening dimension is split or reshaped to two new dimensions.
DataArrayCoarsen.count([keep_attrs])	Reduce this DataArrayCoarsen's data by applying count along some dimension(s).
DataArrayCoarsen.max([keep_attrs])	Reduce this DataArrayCoarsen's data by applying max along some dimension(s).
DataArrayCoarsen.mean([keep_attrs])	Reduce this DataArrayCoarsen's data by applying mean along some dimension(s).
DataArrayCoarsen.median([keep_attrs])	Reduce this DataArrayCoarsen's data by applying median along some dimension(s).
DataArrayCoarsen.min([keep_attrs])	Reduce this DataArrayCoarsen's data by applying min along some dimension(s).
DataArrayCoarsen.prod([keep_attrs])	Reduce this DataArrayCoarsen's data by applying prod along some dimension(s).
DataArrayCoarsen.reduce(func[, keep_attrs])	Reduce the items in this group by applying func along some dimension(s).
DataArrayCoarsen.std([keep_attrs])	Reduce this DataArrayCoarsen's data by applying std along some dimension(s).
DataArrayCoarsen.sum([keep_attrs])	Reduce this DataArrayCoarsen's data by applying sum along some dimension(s).
DataArrayCoarsen.var([keep_attrs])	Reduce this DataArrayCoarsen's data by applying var along some dimension(s).

Exponential rolling objects

RollingExp(obj, windows[, window_type])	Exponentially-weighted moving window object.
RollingExp.mean([keep_attrs])	Exponentially weighted moving average.
RollingExp.sum([keep_attrs])	Exponentially weighted moving sum.

Weighted objects

Dataset

DatasetWeighted(obj, weights)
DatasetWeighted.mean([dim, skipna, keep_attrs])	Reduce this Dataset's data by a weighted mean along some dimension(s).
DatasetWeighted.quantile(q, *[, dim, ...])	Apply a weighted quantile to this Dataset's data along some dimension(s).
DatasetWeighted.sum([dim, skipna, keep_attrs])	Reduce this Dataset's data by a weighted sum along some dimension(s).
DatasetWeighted.std([dim, skipna, keep_attrs])	Reduce this Dataset's data by a weighted std along some dimension(s).
DatasetWeighted.var([dim, skipna, keep_attrs])	Reduce this Dataset's data by a weighted var along some dimension(s).
DatasetWeighted.sum_of_weights([dim, keep_attrs])	Calculate the sum of weights, accounting for missing values in the data.
DatasetWeighted.sum_of_squares([dim, ...])	Reduce this Dataset's data by a weighted sum_of_squares along some dimension(s).

DataArray

DataArrayWeighted(obj, weights)
DataArrayWeighted.mean([dim, skipna, keep_attrs])	Reduce this Dataset's data by a weighted mean along some dimension(s).
DataArrayWeighted.quantile(q, *[, dim, ...])	Apply a weighted quantile to this Dataset's data along some dimension(s).
DataArrayWeighted.sum([dim, skipna, keep_attrs])	Reduce this Dataset's data by a weighted sum along some dimension(s).
DataArrayWeighted.std([dim, skipna, keep_attrs])	Reduce this Dataset's data by a weighted std along some dimension(s).
DataArrayWeighted.var([dim, skipna, keep_attrs])	Reduce this Dataset's data by a weighted var along some dimension(s).
DataArrayWeighted.sum_of_weights([dim, ...])	Calculate the sum of weights, accounting for missing values in the data.
DataArrayWeighted.sum_of_squares([dim, ...])	Reduce this Dataset's data by a weighted sum_of_squares along some dimension(s).

Resample objects

Dataset

DatasetResample(*args[, dim, resample_dim])	DatasetGroupBy object specialized to resampling a specified dimension
DatasetResample.asfreq()	Return values of original object at the new up-sampling frequency; essentially a re-index with new times set to NaN.
DatasetResample.backfill([tolerance])	Backward fill new values at up-sampled frequency.
DatasetResample.interpolate([kind])	Interpolate up-sampled data using the original data as knots.
DatasetResample.nearest([tolerance])	Take new values from nearest original coordinate to up-sampled frequency coordinates.
DatasetResample.pad([tolerance])	Forward fill new values at up-sampled frequency.
DatasetResample.all([dim, keep_attrs])	Reduce this Dataset's data by applying all along some dimension(s).
DatasetResample.any([dim, keep_attrs])	Reduce this Dataset's data by applying any along some dimension(s).
DatasetResample.apply(func[, args, shortcut])	Backward compatible implementation of map
DatasetResample.assign(**kwargs)	Assign data variables by group.
DatasetResample.assign_coords([coords])	Assign coordinates by group.
DatasetResample.bfill([tolerance])	Backward fill new values at up-sampled frequency.
DatasetResample.count([dim, keep_attrs])	Reduce this Dataset's data by applying count along some dimension(s).
DatasetResample.ffill([tolerance])	Forward fill new values at up-sampled frequency.
DatasetResample.fillna(value)	Fill missing values in this object by group.
DatasetResample.first([skipna, keep_attrs])	Return the first element of each group along the group dimension
DatasetResample.last([skipna, keep_attrs])	Return the last element of each group along the group dimension
DatasetResample.map(func[, args, shortcut])	Apply a function over each Dataset in the groups generated for resampling and concatenate them together into a new Dataset.
DatasetResample.max([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying max along some dimension(s).
DatasetResample.mean([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying mean along some dimension(s).
DatasetResample.median([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying median along some dimension(s).
DatasetResample.min([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying min along some dimension(s).
DatasetResample.prod([dim, skipna, ...])	Reduce this Dataset's data by applying prod along some dimension(s).
DatasetResample.quantile(q[, dim, method, ...])	Compute the qth quantile over each array in the groups and concatenate them together into a new array.
DatasetResample.reduce(func[, dim, axis, ...])	Reduce the items in this group by applying func along the pre-defined resampling dimension.
DatasetResample.std([dim, skipna, ddof, ...])	Reduce this Dataset's data by applying std along some dimension(s).
DatasetResample.sum([dim, skipna, ...])	Reduce this Dataset's data by applying sum along some dimension(s).
DatasetResample.var([dim, skipna, ddof, ...])	Reduce this Dataset's data by applying var along some dimension(s).
DatasetResample.where(cond[, other])	Return elements from self or other depending on cond.
DatasetResample.dims
DatasetResample.groups	Mapping from group labels to indices.

DataArray

DataArrayResample(*args[, dim, resample_dim])	DataArrayGroupBy object specialized to time resampling operations over a specified dimension
DataArrayResample.asfreq()	Return values of original object at the new up-sampling frequency; essentially a re-index with new times set to NaN.
DataArrayResample.backfill([tolerance])	Backward fill new values at up-sampled frequency.
DataArrayResample.interpolate([kind])	Interpolate up-sampled data using the original data as knots.
DataArrayResample.nearest([tolerance])	Take new values from nearest original coordinate to up-sampled frequency coordinates.
DataArrayResample.pad([tolerance])	Forward fill new values at up-sampled frequency.
DataArrayResample.all([dim, keep_attrs])	Reduce this DataArray's data by applying all along some dimension(s).
DataArrayResample.any([dim, keep_attrs])	Reduce this DataArray's data by applying any along some dimension(s).
DataArrayResample.apply(func[, args, shortcut])	Backward compatible implementation of map
DataArrayResample.assign_coords([coords])	Assign coordinates by group.
DataArrayResample.bfill([tolerance])	Backward fill new values at up-sampled frequency.
DataArrayResample.count([dim, keep_attrs])	Reduce this DataArray's data by applying count along some dimension(s).
DataArrayResample.ffill([tolerance])	Forward fill new values at up-sampled frequency.
DataArrayResample.fillna(value)	Fill missing values in this object by group.
DataArrayResample.first([skipna, keep_attrs])	Return the first element of each group along the group dimension
DataArrayResample.last([skipna, keep_attrs])	Return the last element of each group along the group dimension
DataArrayResample.map(func[, args, shortcut])	Apply a function to each array in the group and concatenate them together into a new array.
DataArrayResample.max([dim, skipna, keep_attrs])	Reduce this DataArray's data by applying max along some dimension(s).
DataArrayResample.mean([dim, skipna, keep_attrs])	Reduce this DataArray's data by applying mean along some dimension(s).
DataArrayResample.median([dim, skipna, ...])	Reduce this DataArray's data by applying median along some dimension(s).
DataArrayResample.min([dim, skipna, keep_attrs])	Reduce this DataArray's data by applying min along some dimension(s).
DataArrayResample.prod([dim, skipna, ...])	Reduce this DataArray's data by applying prod along some dimension(s).
DataArrayResample.quantile(q[, dim, method, ...])	Compute the qth quantile over each array in the groups and concatenate them together into a new array.
DataArrayResample.reduce(func[, dim, axis, ...])	Reduce the items in this group by applying func along some dimension(s).
DataArrayResample.std([dim, skipna, ddof, ...])	Reduce this DataArray's data by applying std along some dimension(s).
DataArrayResample.sum([dim, skipna, ...])	Reduce this DataArray's data by applying sum along some dimension(s).
DataArrayResample.var([dim, skipna, ddof, ...])	Reduce this DataArray's data by applying var along some dimension(s).
DataArrayResample.where(cond[, other])	Return elements from self or other depending on cond.
DataArrayResample.dims
DataArrayResample.groups	Mapping from group labels to indices.

Accessors

core.accessor_dt.DatetimeAccessor(obj)	Access datetime fields for DataArrays with datetime-like dtypes.
core.accessor_dt.TimedeltaAccessor(obj)	Access Timedelta fields for DataArrays with Timedelta-like dtypes.
core.accessor_str.StringAccessor(obj)	Vectorized string functions for string-like arrays.

Custom Indexes

CFTimeIndex(data[, name])

Custom Index for working with CF calendars and dates

Creating custom indexes

cftime_range([start, end, periods, freq, ...])	Return a fixed frequency CFTimeIndex.
date_range([start, end, periods, freq, tz, ...])	Return a fixed frequency datetime index.
date_range_like(source, calendar[, use_cftime])	Generate a datetime array with the same frequency, start and end as another one, but in a different calendar.

Tutorial

tutorial.open_dataset(name[, cache, ...])	Open a dataset from the online repository (requires internet).
tutorial.load_dataset(*args, **kwargs)	Open, load into memory, and close a dataset from the online repository (requires internet).

Testing

testing.assert_equal(a, b)	Like numpy.testing.assert_array_equal(), but for xarray objects.
testing.assert_identical(a, b)	Like xarray.testing.assert_equal(), but also matches the objects' names and attributes.
testing.assert_allclose(a, b[, rtol, atol, ...])	Like numpy.testing.assert_allclose(), but for xarray objects.
testing.assert_chunks_equal(a, b)	Assert that chunksizes along chunked dimensions are equal.

Exceptions

MergeError	Error class for merge failures due to incompatible arguments.
SerializationWarning	Warnings about encoding/decoding issues in serialization.

Advanced API

Dataset.variables	Low level interface to Dataset contents as dict of Variable objects.
DataArray.variable	Low level interface to the Variable object for this DataArray.
Variable(dims, data[, attrs, encoding, fastpath])	A netcdf-like variable consisting of dimensions, data and attributes which describe a single Array.
IndexVariable(dims, data[, attrs, encoding, ...])	Wrapper for accommodating a pandas.Index in an xarray.Variable.
as_variable(obj[, name])	Convert an object into a Variable.
indexes.Index()	Base class inherited by all xarray-compatible indexes.
Context(func)	object carrying the information of a call
register_dataset_accessor(name)	Register a custom property on xarray.Dataset objects.
register_dataarray_accessor(name)	Register a custom accessor on xarray.DataArray objects.
Dataset.set_close(close)	Register the function that releases any resources linked to this object.
backends.BackendArray()
backends.BackendEntrypoint()	BackendEntrypoint is a class container and it is the main interface for the backend plugins, see BackendEntrypoint subclassing.
backends.list_engines()	Return a dictionary of available engines and their BackendEntrypoint objects.
backends.refresh_engines()	Refreshes the backend engines based on installed packages.

Default, pandas-backed indexes built-in Xarray:

indexes.PandasIndex indexes.PandasMultiIndex

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 load_store and dump_to_store Dataset methods:

backends.NetCDF4DataStore(manager[, group, ...])	Store for reading and writing data via the Python-NetCDF4 library.
backends.H5NetCDFStore(manager[, group, ...])	Store for reading and writing data via h5netcdf
backends.PseudoNetCDFDataStore(manager[, lock])	Store for accessing datasets via PseudoNetCDF
backends.PydapDataStore(ds)	Store for accessing OpenDAP datasets with pydap.
backends.ScipyDataStore(filename_or_obj[, ...])	Store for reading and writing data via scipy.io.netcdf.
backends.ZarrStore(zarr_group[, mode, ...])	Store for reading and writing data via zarr
backends.FileManager()	Manager for acquiring and closing a file object.
backends.CachingFileManager(opener, *args[, ...])	Wrapper for automatically opening and closing file objects.
backends.DummyFileManager(value)	FileManager that simply wraps an open file in the FileManager interface.

These BackendEntrypoints provide a basic interface to the most commonly used filetypes in the xarray universe.

backends.NetCDF4BackendEntrypoint()	Backend for netCDF files based on the netCDF4 package.
backends.H5netcdfBackendEntrypoint()	Backend for netCDF files based on the h5netcdf package.
backends.PseudoNetCDFBackendEntrypoint()	Backend for netCDF-like data formats in the air quality field based on the PseudoNetCDF package.
backends.PydapBackendEntrypoint()	Backend for steaming datasets over the internet using the Data Access Protocol, also known as DODS or OPeNDAP based on the pydap package.
backends.ScipyBackendEntrypoint()	Backend for netCDF files based on the scipy package.
backends.StoreBackendEntrypoint()
backends.ZarrBackendEntrypoint()	Backend for ".zarr" files based on the zarr package.

Deprecated / Pending Deprecation

Dataset.drop([labels, dim, errors])	Backward compatible method based on drop_vars and drop_sel
DataArray.drop([labels, dim, errors])	Backward compatible method based on drop_vars and drop_sel
Dataset.apply(func[, keep_attrs, args])	Backward compatible implementation of map
core.groupby.DataArrayGroupBy.apply(func[, ...])	Backward compatible implementation of map
core.groupby.DatasetGroupBy.apply(func[, ...])	Backward compatible implementation of map

DataArray.dt.weekofyear	The week ordinal of the year
DataArray.dt.week	The week ordinal of the year