Dataset

Dataset([data_vars, coords, attrs])

A multi-dimensional, in memory, array database.

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	Mapping of DataArray objects for 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.xindexes	Mapping of Index objects.
Dataset.chunks	Mapping from dimension names to block lengths for this dataset's data.
Dataset.chunksizes	Mapping from dimension names to block lengths for this dataset's data.
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.
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])	Return a copy of this dataset.
Dataset.assign([variables])	Assign new data variables to a Dataset.
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])	Return an object with renamed variables, coordinates and dimensions.
Dataset.rename_vars([name_dict])	Return an object with renamed variables including coordinates.
Dataset.rename_dims([dims_dict])	Return an object with renamed dimensions only.
Dataset.swap_dims([dims_dict])	Return a new object with swapped dimensions.
Dataset.expand_dims([dim, axis, ...])	Return a new object with an additional axis (or axes).
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])	Return a new Dataset with duplicate dimension values removed.
Dataset.drop_dims(drop_dims, *[, errors])	Drop dimensions and associated variables from this dataset.
Dataset.drop_encoding()	Return a new Dataset without encoding.
Dataset.drop_attrs(*[, deep])	Remove all attributes from the Dataset and its variables.
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.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)	Check whether two Datasets are equal.
Dataset.identical(other)	Check whether two Datasets are identical.
Dataset.broadcast_equals(other)	Check whether two Datasets are broadcast equal.

Indexing

Dataset.loc	Attribute for location based indexing.
Dataset.isel([indexers, drop, missing_dims])	Return a new dataset with each array indexed along the given dimension(s).
Dataset.sel([indexers, method, tolerance, drop])	Return a new dataset with each array indexed by tick labels.
Dataset.drop_sel([labels, errors])	Drop index labels from this dataset.
Dataset.drop_isel([indexers])	Drop index positions from this Dataset.
Dataset.head([indexers])	Return a new dataset with the first n values of each array.
Dataset.tail([indexers])	Return a new dataset with the last n values of each array.
Dataset.thin([indexers])	Return a new dataset with each array indexed along every n-th value.
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.
Dataset.reindex([indexers, method, ...])	Conform this object onto a new set of indexes.
Dataset.reindex_like(other[, method, ...])	Conform this object onto the indexes of another object.
Dataset.set_index([indexes, append])	Set Dataset (multi-)indexes using one or more existing coords 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 by query strings.

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])	Return a new dataset with dropped labels for missing values.
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)	Test 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, ...])	Return a DatasetGroupBy object for performing grouped operations.
Dataset.groupby_bins(group, bins, *[, ...])	Return a DatasetGroupBy object for performing grouped operations.
Dataset.rolling([dim, min_periods, center])	Return a rolling window object for Datasets.
Dataset.rolling_exp([window, window_type])	Exponentially-weighted moving window.
Dataset.cumulative(dim[, min_periods])	Return an accumulating object for Datasets.
Dataset.weighted(weights)	Weighted Dataset operations.
Dataset.coarsen([dim, boundary, side, ...])	Coarsen object for Datasets.
Dataset.resample([indexer, skipna, closed, ...])	Return 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.
Dataset.eval(statement, *[, parser])	Calculate an expression supplied as a string in the context of the dataset.

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])	Return the indices of the maxima of the member variables.
Dataset.argmin([dim])	Return the 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 (i.e., maximum) along some dimension(s).
Dataset.min([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying min (i.e., minimum) 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 (i.e., product) 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 (i.e., standard deviation) along some dimension(s).
Dataset.var([dim, skipna, ddof, keep_attrs])	Reduce this Dataset's data by applying var (i.e., variance) along some dimension(s).
Dataset.cumsum([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying cumsum (i.e., cumulative sum) along some dimension(s).
Dataset.cumprod([dim, skipna, keep_attrs])	Reduce this Dataset's data by applying cumprod (i.e., cumulative product) along some dimension(s).

ndarray methods

Dataset.argsort([axis, kind, order, stable, ...])	Returns the indices that would sort this array.
Dataset.astype(dtype, *[, order, casting, ...])	Return a 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(*args, **kwargs)	a.conj()
Dataset.imag	The imaginary part of each data variable.
Dataset.round(*args, **kwargs)
Dataset.real	The real part of each data variable.
Dataset.rank(dim, *[, pct, keep_attrs])	Ranks the data.

Reshaping and reorganizing

Dataset.transpose(*dim[, missing_dims])	Return a new Dataset object with all array dimensions transposed.
Dataset.stack([dim, create_index, index_cls])	Stack any number of existing dimensions into a single new dimension.
Dataset.unstack([dim, fill_value, sparse])	Unstack existing dimensions corresponding to MultiIndexes into new dimensions.
Dataset.to_stacked_array(new_dim, sample_dims)	Combine variables of differing dimensionality into a DataArray.
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.