xarray.core.resample.DatasetResample¶

class xarray.core.resample.DatasetResample(*args, dim=None, resample_dim=None, **kwargs)[source]¶

DatasetGroupBy object specialized to resampling a specified dimension

__init__(*args, dim=None, resample_dim=None, **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

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

Attributes

`dims`
`groups`	Mapping from group labels to indices.