auto_combine(datasets[, concat_dim, compat, …]) |
Attempt to auto-magically combine the given datasets into one. |
Dataset.nbytes |
|
Dataset.chunks |
Block dimensions for this dataset’s data or None if it’s not a dask array. |
Dataset.all([dim]) |
Reduce this Dataset’s data by applying all along some dimension(s). |
Dataset.any([dim]) |
Reduce this Dataset’s data by applying any along some dimension(s). |
Dataset.argmax([dim, skipna]) |
Reduce this Dataset’s data by applying argmax along some dimension(s). |
Dataset.argmin([dim, skipna]) |
Reduce this Dataset’s data by applying argmin along some dimension(s). |
Dataset.max([dim, skipna]) |
Reduce this Dataset’s data by applying max along some dimension(s). |
Dataset.min([dim, skipna]) |
Reduce this Dataset’s data by applying min along some dimension(s). |
Dataset.mean([dim, skipna]) |
Reduce this Dataset’s data by applying mean along some dimension(s). |
Dataset.median([dim, skipna]) |
Reduce this Dataset’s data by applying median along some dimension(s). |
Dataset.prod([dim, skipna]) |
Reduce this Dataset’s data by applying prod along some dimension(s). |
Dataset.sum([dim, skipna]) |
Reduce this Dataset’s data by applying sum along some dimension(s). |
Dataset.std([dim, skipna]) |
Reduce this Dataset’s data by applying std along some dimension(s). |
Dataset.var([dim, skipna]) |
Reduce this Dataset’s data by applying var along some dimension(s). |
core.coordinates.DatasetCoordinates.get(k[,d]) |
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core.coordinates.DatasetCoordinates.items() |
|
core.coordinates.DatasetCoordinates.keys() |
|
core.coordinates.DatasetCoordinates.merge(other) |
Merge two sets of coordinates to create a new Dataset |
core.coordinates.DatasetCoordinates.to_dataset() |
Convert these coordinates into a new Dataset |
core.coordinates.DatasetCoordinates.to_index(…) |
Convert all index coordinates into a pandas.Index. |
core.coordinates.DatasetCoordinates.update(…) |
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core.coordinates.DatasetCoordinates.values() |
|
core.coordinates.DatasetCoordinates.dims |
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core.coordinates.DatasetCoordinates.indexes |
|
core.coordinates.DatasetCoordinates.variables |
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core.rolling.DatasetCoarsen.all(**kwargs) |
Reduce this DatasetCoarsen’s data by applying all along some dimension(s). |
core.rolling.DatasetCoarsen.any(**kwargs) |
Reduce this DatasetCoarsen’s data by applying any along some dimension(s). |
core.rolling.DatasetCoarsen.argmax(**kwargs) |
Reduce this DatasetCoarsen’s data by applying argmax along some dimension(s). |
core.rolling.DatasetCoarsen.argmin(**kwargs) |
Reduce this DatasetCoarsen’s data by applying argmin along some dimension(s). |
core.rolling.DatasetCoarsen.count(**kwargs) |
Reduce this DatasetCoarsen’s data by applying count along some dimension(s). |
core.rolling.DatasetCoarsen.max(**kwargs) |
Reduce this DatasetCoarsen’s data by applying max along some dimension(s). |
core.rolling.DatasetCoarsen.mean(**kwargs) |
Reduce this DatasetCoarsen’s data by applying mean along some dimension(s). |
core.rolling.DatasetCoarsen.median(**kwargs) |
Reduce this DatasetCoarsen’s data by applying median along some dimension(s). |
core.rolling.DatasetCoarsen.min(**kwargs) |
Reduce this DatasetCoarsen’s data by applying min along some dimension(s). |
core.rolling.DatasetCoarsen.prod(**kwargs) |
Reduce this DatasetCoarsen’s data by applying prod along some dimension(s). |
core.rolling.DatasetCoarsen.std(**kwargs) |
Reduce this DatasetCoarsen’s data by applying std along some dimension(s). |
core.rolling.DatasetCoarsen.sum(**kwargs) |
Reduce this DatasetCoarsen’s data by applying sum along some dimension(s). |
core.rolling.DatasetCoarsen.var(**kwargs) |
Reduce this DatasetCoarsen’s data by applying var along some dimension(s). |
core.rolling.DatasetCoarsen.boundary |
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core.rolling.DatasetCoarsen.coord_func |
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core.rolling.DatasetCoarsen.obj |
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core.rolling.DatasetCoarsen.side |
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core.rolling.DatasetCoarsen.trim_excess |
|
core.rolling.DatasetCoarsen.windows |
|
core.groupby.DatasetGroupBy.assign(**kwargs) |
Assign data variables by group. |
core.groupby.DatasetGroupBy.assign_coords([…]) |
Assign coordinates by group. |
core.groupby.DatasetGroupBy.first([skipna, …]) |
Return the first element of each group along the group dimension |
core.groupby.DatasetGroupBy.last([skipna, …]) |
Return the last element of each group along the group dimension |
core.groupby.DatasetGroupBy.fillna(value) |
Fill missing values in this object by group. |
core.groupby.DatasetGroupBy.quantile(q[, …]) |
Compute the qth quantile over each array in the groups and concatenate them together into a new array. |
core.groupby.DatasetGroupBy.where(cond[, other]) |
Return elements from self or other depending on cond. |
core.groupby.DatasetGroupBy.all([dim]) |
Reduce this DatasetGroupBy’s data by applying all along some dimension(s). |
core.groupby.DatasetGroupBy.any([dim]) |
Reduce this DatasetGroupBy’s data by applying any along some dimension(s). |
core.groupby.DatasetGroupBy.argmax([dim, skipna]) |
Reduce this DatasetGroupBy’s data by applying argmax along some dimension(s). |
core.groupby.DatasetGroupBy.argmin([dim, skipna]) |
Reduce this DatasetGroupBy’s data by applying argmin along some dimension(s). |
core.groupby.DatasetGroupBy.count([dim]) |
Reduce this DatasetGroupBy’s data by applying count along some dimension(s). |
core.groupby.DatasetGroupBy.max([dim, skipna]) |
Reduce this DatasetGroupBy’s data by applying max along some dimension(s). |
core.groupby.DatasetGroupBy.mean([dim, skipna]) |
Reduce this DatasetGroupBy’s data by applying mean along some dimension(s). |
core.groupby.DatasetGroupBy.median([dim, skipna]) |
Reduce this DatasetGroupBy’s data by applying median along some dimension(s). |
core.groupby.DatasetGroupBy.min([dim, skipna]) |
Reduce this DatasetGroupBy’s data by applying min along some dimension(s). |
core.groupby.DatasetGroupBy.prod([dim, skipna]) |
Reduce this DatasetGroupBy’s data by applying prod along some dimension(s). |
core.groupby.DatasetGroupBy.std([dim, skipna]) |
Reduce this DatasetGroupBy’s data by applying std along some dimension(s). |
core.groupby.DatasetGroupBy.sum([dim, skipna]) |
Reduce this DatasetGroupBy’s data by applying sum along some dimension(s). |
core.groupby.DatasetGroupBy.var([dim, skipna]) |
Reduce this DatasetGroupBy’s data by applying var along some dimension(s). |
core.groupby.DatasetGroupBy.dims |
|
core.groupby.DatasetGroupBy.groups |
|
core.resample.DatasetResample.all([dim]) |
Reduce this DatasetResample’s data by applying all along some dimension(s). |
core.resample.DatasetResample.any([dim]) |
Reduce this DatasetResample’s data by applying any along some dimension(s). |
core.resample.DatasetResample.apply(func[, …]) |
Backward compatible implementation of map |
core.resample.DatasetResample.argmax([dim, …]) |
Reduce this DatasetResample’s data by applying argmax along some dimension(s). |
core.resample.DatasetResample.argmin([dim, …]) |
Reduce this DatasetResample’s data by applying argmin along some dimension(s). |
core.resample.DatasetResample.assign(**kwargs) |
Assign data variables by group. |
core.resample.DatasetResample.assign_coords([…]) |
Assign coordinates by group. |
core.resample.DatasetResample.bfill([tolerance]) |
Backward fill new values at up-sampled frequency. |
core.resample.DatasetResample.count([dim]) |
Reduce this DatasetResample’s data by applying count along some dimension(s). |
core.resample.DatasetResample.ffill([tolerance]) |
Forward fill new values at up-sampled frequency. |
core.resample.DatasetResample.fillna(value) |
Fill missing values in this object by group. |
core.resample.DatasetResample.first([…]) |
Return the first element of each group along the group dimension |
core.resample.DatasetResample.last([skipna, …]) |
Return the last element of each group along the group dimension |
core.resample.DatasetResample.map(func[, …]) |
Apply a function over each Dataset in the groups generated for resampling and concatenate them together into a new Dataset. |
core.resample.DatasetResample.max([dim, skipna]) |
Reduce this DatasetResample’s data by applying max along some dimension(s). |
core.resample.DatasetResample.mean([dim, skipna]) |
Reduce this DatasetResample’s data by applying mean along some dimension(s). |
core.resample.DatasetResample.median([dim, …]) |
Reduce this DatasetResample’s data by applying median along some dimension(s). |
core.resample.DatasetResample.min([dim, skipna]) |
Reduce this DatasetResample’s data by applying min along some dimension(s). |
core.resample.DatasetResample.prod([dim, skipna]) |
Reduce this DatasetResample’s data by applying prod along some dimension(s). |
core.resample.DatasetResample.quantile(q[, …]) |
Compute the qth quantile over each array in the groups and concatenate them together into a new array. |
core.resample.DatasetResample.reduce(func[, …]) |
Reduce the items in this group by applying func along the pre-defined resampling dimension. |
core.resample.DatasetResample.std([dim, skipna]) |
Reduce this DatasetResample’s data by applying std along some dimension(s). |
core.resample.DatasetResample.sum([dim, skipna]) |
Reduce this DatasetResample’s data by applying sum along some dimension(s). |
core.resample.DatasetResample.var([dim, skipna]) |
Reduce this DatasetResample’s data by applying var along some dimension(s). |
core.resample.DatasetResample.where(cond[, …]) |
Return elements from self or other depending on cond. |
core.resample.DatasetResample.dims |
|
core.resample.DatasetResample.groups |
|
core.rolling.DatasetRolling.argmax(**kwargs) |
Reduce this object’s data windows by applying argmax along its dimension. |
core.rolling.DatasetRolling.argmin(**kwargs) |
Reduce this object’s data windows by applying argmin along its dimension. |
core.rolling.DatasetRolling.count() |
Reduce this object’s data windows by applying count along its dimension. |
core.rolling.DatasetRolling.max(**kwargs) |
Reduce this object’s data windows by applying max along its dimension. |
core.rolling.DatasetRolling.mean(**kwargs) |
Reduce this object’s data windows by applying mean along its dimension. |
core.rolling.DatasetRolling.median(**kwargs) |
Reduce this object’s data windows by applying median along its dimension. |
core.rolling.DatasetRolling.min(**kwargs) |
Reduce this object’s data windows by applying min along its dimension. |
core.rolling.DatasetRolling.prod(**kwargs) |
Reduce this object’s data windows by applying prod along its dimension. |
core.rolling.DatasetRolling.std(**kwargs) |
Reduce this object’s data windows by applying std along its dimension. |
core.rolling.DatasetRolling.sum(**kwargs) |
Reduce this object’s data windows by applying sum along its dimension. |
core.rolling.DatasetRolling.var(**kwargs) |
Reduce this object’s data windows by applying var along its dimension. |
core.rolling.DatasetRolling.center |
|
core.rolling.DatasetRolling.dim |
|
core.rolling.DatasetRolling.min_periods |
|
core.rolling.DatasetRolling.obj |
|
core.rolling.DatasetRolling.rollings |
|
core.rolling.DatasetRolling.window |
|
core.rolling_exp.RollingExp.mean() |
Exponentially weighted moving average |
Dataset.argsort([axis, kind, order]) |
Returns the indices that would sort this array. |
Dataset.astype(dtype[, order, casting, …]) |
Copy of the array, cast to a specified type. |
Dataset.clip([min, max, out]) |
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 |
|
Dataset.round(*args, **kwargs) |
|
Dataset.real |
|
Dataset.cumsum([dim, skipna]) |
Apply cumsum along some dimension of Dataset. |
Dataset.cumprod([dim, skipna]) |
Apply cumprod along some dimension of Dataset. |
Dataset.rank(dim[, pct, keep_attrs]) |
Ranks the data. |
Dataset.load_store(store[, decoder]) |
Create a new dataset from the contents of a backends.*DataStore object |
Dataset.dump_to_store(store, **kwargs) |
Store dataset contents to a backends.*DataStore object. |
DataArray.ndim |
|
DataArray.nbytes |
|
DataArray.shape |
|
DataArray.size |
|
DataArray.dtype |
|
DataArray.nbytes |
|
DataArray.chunks |
Block dimensions for this array’s data or None if it’s not a dask array. |
DataArray.astype(dtype[, order, casting, …]) |
Copy of the array, cast to a specified type. |
DataArray.item(*args) |
Copy an element of an array to a standard Python scalar and return it. |
DataArray.all([dim, axis]) |
Reduce this DataArray’s data by applying all along some dimension(s). |
DataArray.any([dim, axis]) |
Reduce this DataArray’s data by applying any along some dimension(s). |
DataArray.argmax([dim, axis, skipna]) |
Reduce this DataArray’s data by applying argmax along some dimension(s). |
DataArray.argmin([dim, axis, skipna]) |
Reduce this DataArray’s data by applying argmin along some dimension(s). |
DataArray.max([dim, axis, skipna]) |
Reduce this DataArray’s data by applying max along some dimension(s). |
DataArray.min([dim, axis, skipna]) |
Reduce this DataArray’s data by applying min along some dimension(s). |
DataArray.mean([dim, axis, skipna]) |
Reduce this DataArray’s data by applying mean along some dimension(s). |
DataArray.median([dim, axis, skipna]) |
Reduce this DataArray’s data by applying median along some dimension(s). |
DataArray.prod([dim, axis, skipna]) |
Reduce this DataArray’s data by applying prod along some dimension(s). |
DataArray.sum([dim, axis, skipna]) |
Reduce this DataArray’s data by applying sum along some dimension(s). |
DataArray.std([dim, axis, skipna]) |
Reduce this DataArray’s data by applying std along some dimension(s). |
DataArray.var([dim, axis, skipna]) |
Reduce this DataArray’s data by applying var along some dimension(s). |
core.coordinates.DataArrayCoordinates.get(k[,d]) |
|
core.coordinates.DataArrayCoordinates.items() |
|
core.coordinates.DataArrayCoordinates.keys() |
|
core.coordinates.DataArrayCoordinates.merge(other) |
Merge two sets of coordinates to create a new Dataset |
core.coordinates.DataArrayCoordinates.to_dataset() |
|
core.coordinates.DataArrayCoordinates.to_index(…) |
Convert all index coordinates into a pandas.Index. |
core.coordinates.DataArrayCoordinates.update(…) |
|
core.coordinates.DataArrayCoordinates.values() |
|
core.coordinates.DataArrayCoordinates.dims |
|
core.coordinates.DataArrayCoordinates.indexes |
|
core.coordinates.DataArrayCoordinates.variables |
|
core.rolling.DataArrayCoarsen.all(**kwargs) |
Reduce this DataArrayCoarsen’s data by applying all along some dimension(s). |
core.rolling.DataArrayCoarsen.any(**kwargs) |
Reduce this DataArrayCoarsen’s data by applying any along some dimension(s). |
core.rolling.DataArrayCoarsen.argmax(**kwargs) |
Reduce this DataArrayCoarsen’s data by applying argmax along some dimension(s). |
core.rolling.DataArrayCoarsen.argmin(**kwargs) |
Reduce this DataArrayCoarsen’s data by applying argmin along some dimension(s). |
core.rolling.DataArrayCoarsen.count(**kwargs) |
Reduce this DataArrayCoarsen’s data by applying count along some dimension(s). |
core.rolling.DataArrayCoarsen.max(**kwargs) |
Reduce this DataArrayCoarsen’s data by applying max along some dimension(s). |
core.rolling.DataArrayCoarsen.mean(**kwargs) |
Reduce this DataArrayCoarsen’s data by applying mean along some dimension(s). |
core.rolling.DataArrayCoarsen.median(**kwargs) |
Reduce this DataArrayCoarsen’s data by applying median along some dimension(s). |
core.rolling.DataArrayCoarsen.min(**kwargs) |
Reduce this DataArrayCoarsen’s data by applying min along some dimension(s). |
core.rolling.DataArrayCoarsen.prod(**kwargs) |
Reduce this DataArrayCoarsen’s data by applying prod along some dimension(s). |
core.rolling.DataArrayCoarsen.std(**kwargs) |
Reduce this DataArrayCoarsen’s data by applying std along some dimension(s). |
core.rolling.DataArrayCoarsen.sum(**kwargs) |
Reduce this DataArrayCoarsen’s data by applying sum along some dimension(s). |
core.rolling.DataArrayCoarsen.var(**kwargs) |
Reduce this DataArrayCoarsen’s data by applying var along some dimension(s). |
core.rolling.DataArrayCoarsen.boundary |
|
core.rolling.DataArrayCoarsen.coord_func |
|
core.rolling.DataArrayCoarsen.obj |
|
core.rolling.DataArrayCoarsen.side |
|
core.rolling.DataArrayCoarsen.trim_excess |
|
core.rolling.DataArrayCoarsen.windows |
|
core.groupby.DataArrayGroupBy.assign_coords([…]) |
Assign coordinates by group. |
core.groupby.DataArrayGroupBy.first([…]) |
Return the first element of each group along the group dimension |
core.groupby.DataArrayGroupBy.last([skipna, …]) |
Return the last element of each group along the group dimension |
core.groupby.DataArrayGroupBy.fillna(value) |
Fill missing values in this object by group. |
core.groupby.DataArrayGroupBy.quantile(q[, …]) |
Compute the qth quantile over each array in the groups and concatenate them together into a new array. |
core.groupby.DataArrayGroupBy.where(cond[, …]) |
Return elements from self or other depending on cond. |
core.groupby.DataArrayGroupBy.all([dim, axis]) |
Reduce this DataArrayGroupBy’s data by applying all along some dimension(s). |
core.groupby.DataArrayGroupBy.any([dim, axis]) |
Reduce this DataArrayGroupBy’s data by applying any along some dimension(s). |
core.groupby.DataArrayGroupBy.argmax([dim, …]) |
Reduce this DataArrayGroupBy’s data by applying argmax along some dimension(s). |
core.groupby.DataArrayGroupBy.argmin([dim, …]) |
Reduce this DataArrayGroupBy’s data by applying argmin along some dimension(s). |
core.groupby.DataArrayGroupBy.count([dim, axis]) |
Reduce this DataArrayGroupBy’s data by applying count along some dimension(s). |
core.groupby.DataArrayGroupBy.max([dim, …]) |
Reduce this DataArrayGroupBy’s data by applying max along some dimension(s). |
core.groupby.DataArrayGroupBy.mean([dim, …]) |
Reduce this DataArrayGroupBy’s data by applying mean along some dimension(s). |
core.groupby.DataArrayGroupBy.median([dim, …]) |
Reduce this DataArrayGroupBy’s data by applying median along some dimension(s). |
core.groupby.DataArrayGroupBy.min([dim, …]) |
Reduce this DataArrayGroupBy’s data by applying min along some dimension(s). |
core.groupby.DataArrayGroupBy.prod([dim, …]) |
Reduce this DataArrayGroupBy’s data by applying prod along some dimension(s). |
core.groupby.DataArrayGroupBy.std([dim, …]) |
Reduce this DataArrayGroupBy’s data by applying std along some dimension(s). |
core.groupby.DataArrayGroupBy.sum([dim, …]) |
Reduce this DataArrayGroupBy’s data by applying sum along some dimension(s). |
core.groupby.DataArrayGroupBy.var([dim, …]) |
Reduce this DataArrayGroupBy’s data by applying var along some dimension(s). |
core.groupby.DataArrayGroupBy.dims |
|
core.groupby.DataArrayGroupBy.groups |
|
core.resample.DataArrayResample.all([dim, axis]) |
Reduce this DataArrayResample’s data by applying all along some dimension(s). |
core.resample.DataArrayResample.any([dim, axis]) |
Reduce this DataArrayResample’s data by applying any along some dimension(s). |
core.resample.DataArrayResample.apply(func) |
Backward compatible implementation of map |
core.resample.DataArrayResample.argmax([…]) |
Reduce this DataArrayResample’s data by applying argmax along some dimension(s). |
core.resample.DataArrayResample.argmin([…]) |
Reduce this DataArrayResample’s data by applying argmin along some dimension(s). |
core.resample.DataArrayResample.assign_coords([…]) |
Assign coordinates by group. |
core.resample.DataArrayResample.bfill([…]) |
Backward fill new values at up-sampled frequency. |
core.resample.DataArrayResample.count([dim, …]) |
Reduce this DataArrayResample’s data by applying count along some dimension(s). |
core.resample.DataArrayResample.ffill([…]) |
Forward fill new values at up-sampled frequency. |
core.resample.DataArrayResample.fillna(value) |
Fill missing values in this object by group. |
core.resample.DataArrayResample.first([…]) |
Return the first element of each group along the group dimension |
core.resample.DataArrayResample.last([…]) |
Return the last element of each group along the group dimension |
core.resample.DataArrayResample.map(func[, …]) |
Apply a function to each array in the group and concatenate them together into a new array. |
core.resample.DataArrayResample.max([dim, …]) |
Reduce this DataArrayResample’s data by applying max along some dimension(s). |
core.resample.DataArrayResample.mean([dim, …]) |
Reduce this DataArrayResample’s data by applying mean along some dimension(s). |
core.resample.DataArrayResample.median([…]) |
Reduce this DataArrayResample’s data by applying median along some dimension(s). |
core.resample.DataArrayResample.min([dim, …]) |
Reduce this DataArrayResample’s data by applying min along some dimension(s). |
core.resample.DataArrayResample.prod([dim, …]) |
Reduce this DataArrayResample’s data by applying prod along some dimension(s). |
core.resample.DataArrayResample.quantile(q) |
Compute the qth quantile over each array in the groups and concatenate them together into a new array. |
core.resample.DataArrayResample.reduce(func) |
Reduce the items in this group by applying func along some dimension(s). |
core.resample.DataArrayResample.std([dim, …]) |
Reduce this DataArrayResample’s data by applying std along some dimension(s). |
core.resample.DataArrayResample.sum([dim, …]) |
Reduce this DataArrayResample’s data by applying sum along some dimension(s). |
core.resample.DataArrayResample.var([dim, …]) |
Reduce this DataArrayResample’s data by applying var along some dimension(s). |
core.resample.DataArrayResample.where(cond) |
Return elements from self or other depending on cond. |
core.resample.DataArrayResample.dims |
|
core.resample.DataArrayResample.groups |
|
core.rolling.DataArrayRolling.argmax(**kwargs) |
Reduce this object’s data windows by applying argmax along its dimension. |
core.rolling.DataArrayRolling.argmin(**kwargs) |
Reduce this object’s data windows by applying argmin along its dimension. |
core.rolling.DataArrayRolling.count() |
Reduce this object’s data windows by applying count along its dimension. |
core.rolling.DataArrayRolling.max(**kwargs) |
Reduce this object’s data windows by applying max along its dimension. |
core.rolling.DataArrayRolling.mean(**kwargs) |
Reduce this object’s data windows by applying mean along its dimension. |
core.rolling.DataArrayRolling.median(**kwargs) |
Reduce this object’s data windows by applying median along its dimension. |
core.rolling.DataArrayRolling.min(**kwargs) |
Reduce this object’s data windows by applying min along its dimension. |
core.rolling.DataArrayRolling.prod(**kwargs) |
Reduce this object’s data windows by applying prod along its dimension. |
core.rolling.DataArrayRolling.std(**kwargs) |
Reduce this object’s data windows by applying std along its dimension. |
core.rolling.DataArrayRolling.sum(**kwargs) |
Reduce this object’s data windows by applying sum along its dimension. |
core.rolling.DataArrayRolling.var(**kwargs) |
Reduce this object’s data windows by applying var along its dimension. |
core.rolling.DataArrayRolling.center |
|
core.rolling.DataArrayRolling.dim |
|
core.rolling.DataArrayRolling.min_periods |
|
core.rolling.DataArrayRolling.obj |
|
core.rolling.DataArrayRolling.window |
|
core.rolling.DataArrayRolling.window_labels |
|
DataArray.argsort([axis, kind, order]) |
Returns the indices that would sort this array. |
DataArray.clip([min, max, out]) |
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 |
|
DataArray.searchsorted(v[, side, sorter]) |
Find indices where elements of v should be inserted in a to maintain order. |
DataArray.round(*args, **kwargs) |
|
DataArray.real |
|
DataArray.T |
|
DataArray.cumsum([dim, axis, skipna]) |
Apply cumsum along some dimension of DataArray. |
DataArray.cumprod([dim, axis, skipna]) |
Apply cumprod along some dimension of DataArray. |
DataArray.rank(dim, pct, keep_attrs) |
Ranks the data. |
core.accessor_dt.DatetimeAccessor.ceil(freq) |
Round timestamps upward to specified frequency resolution. |
core.accessor_dt.DatetimeAccessor.floor(freq) |
Round timestamps downward to specified frequency resolution. |
core.accessor_dt.DatetimeAccessor.round(freq) |
Round timestamps to specified frequency resolution. |
core.accessor_dt.DatetimeAccessor.strftime(…) |
Return an array of formatted strings specified by date_format, which supports the same string format as the python standard library. |
core.accessor_dt.DatetimeAccessor.day |
The days of the datetime |
core.accessor_dt.DatetimeAccessor.dayofweek |
The day of the week with Monday=0, Sunday=6 |
core.accessor_dt.DatetimeAccessor.dayofyear |
The ordinal day of the year |
core.accessor_dt.DatetimeAccessor.days_in_month |
The number of days in the month |
core.accessor_dt.DatetimeAccessor.daysinmonth |
The number of days in the month |
core.accessor_dt.DatetimeAccessor.hour |
The hours of the datetime |
core.accessor_dt.DatetimeAccessor.microsecond |
The microseconds of the datetime |
core.accessor_dt.DatetimeAccessor.minute |
The minutes of the datetime |
core.accessor_dt.DatetimeAccessor.month |
The month as January=1, December=12 |
core.accessor_dt.DatetimeAccessor.nanosecond |
The nanoseconds of the datetime |
core.accessor_dt.DatetimeAccessor.quarter |
The quarter of the date |
core.accessor_dt.DatetimeAccessor.season |
Season of the year |
core.accessor_dt.DatetimeAccessor.second |
The seconds of the datetime |
core.accessor_dt.DatetimeAccessor.time |
Timestamps corresponding to datetimes |
core.accessor_dt.DatetimeAccessor.week |
The week ordinal of the year |
core.accessor_dt.DatetimeAccessor.weekday |
The day of the week with Monday=0, Sunday=6 |
core.accessor_dt.DatetimeAccessor.weekday_name |
The name of day in a week |
core.accessor_dt.DatetimeAccessor.weekofyear |
The week ordinal of the year |
core.accessor_dt.DatetimeAccessor.year |
The year of the datetime |
core.accessor_str.StringAccessor.capitalize() |
Convert strings in the array to be capitalized. |
core.accessor_str.StringAccessor.center(width) |
Filling left and right side of strings in the array with an additional character. |
core.accessor_str.StringAccessor.contains(pat) |
Test if pattern or regex is contained within a string of the array. |
core.accessor_str.StringAccessor.count(pat) |
Count occurrences of pattern in each string of the array. |
core.accessor_str.StringAccessor.decode(encoding) |
Decode character string in the array using indicated encoding. |
core.accessor_str.StringAccessor.encode(encoding) |
Encode character string in the array using indicated encoding. |
core.accessor_str.StringAccessor.endswith(pat) |
Test if the end of each string element matches a pattern. |
core.accessor_str.StringAccessor.find(sub[, …]) |
Return lowest or highest indexes in each strings in the array where the substring is fully contained between [start:end]. |
core.accessor_str.StringAccessor.get(i) |
Extract element from indexable in each element in the array. |
core.accessor_str.StringAccessor.index(sub) |
Return lowest or highest indexes in each strings where the substring is fully contained between [start:end]. |
core.accessor_str.StringAccessor.isalnum() |
Check whether all characters in each string are alphanumeric. |
core.accessor_str.StringAccessor.isalpha() |
Check whether all characters in each string are alphabetic. |
core.accessor_str.StringAccessor.isdecimal() |
Check whether all characters in each string are decimal. |
core.accessor_str.StringAccessor.isdigit() |
Check whether all characters in each string are digits. |
core.accessor_str.StringAccessor.islower() |
Check whether all characters in each string are lowercase. |
core.accessor_str.StringAccessor.isnumeric() |
Check whether all characters in each string are numeric. |
core.accessor_str.StringAccessor.isspace() |
Check whether all characters in each string are spaces. |
core.accessor_str.StringAccessor.istitle() |
Check whether all characters in each string are titlecase. |
core.accessor_str.StringAccessor.isupper() |
Check whether all characters in each string are uppercase. |
core.accessor_str.StringAccessor.len() |
Compute the length of each element in the array. |
core.accessor_str.StringAccessor.ljust(width) |
Filling right side of strings in the array with an additional character. |
core.accessor_str.StringAccessor.lower() |
Convert strings in the array to lowercase. |
core.accessor_str.StringAccessor.lstrip([…]) |
Remove leading and trailing characters. |
core.accessor_str.StringAccessor.match(pat) |
Determine if each string matches a regular expression. |
core.accessor_str.StringAccessor.pad(width) |
Pad strings in the array up to width. |
core.accessor_str.StringAccessor.repeat(repeats) |
Duplicate each string in the array. |
core.accessor_str.StringAccessor.replace(…) |
Replace occurrences of pattern/regex in the array with some string. |
core.accessor_str.StringAccessor.rfind(sub) |
Return highest indexes in each strings in the array where the substring is fully contained between [start:end]. |
core.accessor_str.StringAccessor.rindex(sub) |
Return highest indexes in each strings where the substring is fully contained between [start:end]. |
core.accessor_str.StringAccessor.rjust(width) |
Filling left side of strings in the array with an additional character. |
core.accessor_str.StringAccessor.rstrip([…]) |
Remove leading and trailing characters. |
core.accessor_str.StringAccessor.slice([…]) |
Slice substrings from each element in the array. |
core.accessor_str.StringAccessor.slice_replace([…]) |
Replace a positional slice of a string with another value. |
core.accessor_str.StringAccessor.startswith(pat) |
Test if the start of each string element matches a pattern. |
core.accessor_str.StringAccessor.strip([…]) |
Remove leading and trailing characters. |
core.accessor_str.StringAccessor.swapcase() |
Convert strings in the array to be swapcased. |
core.accessor_str.StringAccessor.title() |
Convert strings in the array to titlecase. |
core.accessor_str.StringAccessor.translate(table) |
Map all characters in the string through the given mapping table. |
core.accessor_str.StringAccessor.upper() |
Convert strings in the array to uppercase. |
core.accessor_str.StringAccessor.wrap(width, …) |
Wrap long strings in the array to be formatted in paragraphs with length less than a given width. |
core.accessor_str.StringAccessor.zfill(width) |
Pad strings in the array by prepending ‘0’ characters. |
Variable.all([dim, axis]) |
Reduce this Variable’s data by applying all along some dimension(s). |
Variable.any([dim, axis]) |
Reduce this Variable’s data by applying any along some dimension(s). |
Variable.argmax([dim, axis, skipna]) |
Reduce this Variable’s data by applying argmax along some dimension(s). |
Variable.argmin([dim, axis, skipna]) |
Reduce this Variable’s data by applying argmin along some dimension(s). |
Variable.argsort([axis, kind, order]) |
Returns the indices that would sort this array. |
Variable.astype(dtype[, order, casting, …]) |
Copy of the array, cast to a specified type. |
Variable.broadcast_equals(other[, equiv]) |
True if two Variables have the values after being broadcast against each other; otherwise False. |
Variable.chunk([chunks, name, lock]) |
Coerce this array’s data into a dask arrays with the given chunks. |
Variable.clip([min, max, out]) |
Return an array whose values are limited to [min, max]. |
Variable.coarsen(windows, func[, boundary, side]) |
Apply reduction function. |
Variable.compute(**kwargs) |
Manually trigger loading of this variable’s data from disk or a remote source into memory and return a new variable. |
Variable.concat(variables[, dim, positions, …]) |
Concatenate variables along a new or existing dimension. |
Variable.conj() |
Complex-conjugate all elements. |
Variable.conjugate() |
Return the complex conjugate, element-wise. |
Variable.copy([deep, data]) |
Returns a copy of this object. |
Variable.count([dim, axis]) |
Reduce this Variable’s data by applying count along some dimension(s). |
Variable.cumprod([dim, axis, skipna]) |
Apply cumprod along some dimension of Variable. |
Variable.cumsum([dim, axis, skipna]) |
Apply cumsum along some dimension of Variable. |
Variable.equals(other[, equiv]) |
True if two Variables have the same dimensions and values; otherwise False. |
Variable.fillna(value) |
|
Variable.get_axis_num(dim, Iterable[Hashable]]) |
Return axis number(s) corresponding to dimension(s) in this array. |
Variable.identical(other[, equiv]) |
Like equals, but also checks attributes. |
Variable.isel(indexers, Any] = None, …) |
Return a new array indexed along the specified dimension(s). |
Variable.isnull(*args, **kwargs) |
|
Variable.item(*args) |
Copy an element of an array to a standard Python scalar and return it. |
Variable.load(**kwargs) |
Manually trigger loading of this variable’s data from disk or a remote source into memory and return this variable. |
Variable.max([dim, axis, skipna]) |
Reduce this Variable’s data by applying max along some dimension(s). |
Variable.mean([dim, axis, skipna]) |
Reduce this Variable’s data by applying mean along some dimension(s). |
Variable.median([dim, axis, skipna]) |
Reduce this Variable’s data by applying median along some dimension(s). |
Variable.min([dim, axis, skipna]) |
Reduce this Variable’s data by applying min along some dimension(s). |
Variable.no_conflicts(other[, equiv]) |
True if the intersection of two Variable’s non-null data is equal; otherwise false. |
Variable.notnull(*args, **kwargs) |
|
Variable.pad_with_fill_value([pad_widths, …]) |
Return a new Variable with paddings. |
Variable.prod([dim, axis, skipna]) |
Reduce this Variable’s data by applying prod along some dimension(s). |
Variable.quantile(q[, dim, interpolation, …]) |
Compute the qth quantile of the data along the specified dimension. |
Variable.rank(dim[, pct]) |
Ranks the data. |
Variable.reduce(func[, dim, axis, …]) |
Reduce this array by applying func along some dimension(s). |
Variable.roll([shifts]) |
Return a new Variable with rolld data. |
Variable.rolling_window(dim, window, window_dim) |
Make a rolling_window along dim and add a new_dim to the last place. |
Variable.round(*args, **kwargs) |
|
Variable.searchsorted(v[, side, sorter]) |
Find indices where elements of v should be inserted in a to maintain order. |
Variable.set_dims(dims[, shape]) |
Return a new variable with given set of dimensions. |
Variable.shift([shifts, fill_value]) |
Return a new Variable with shifted data. |
Variable.squeeze([dim]) |
Return a new object with squeezed data. |
Variable.stack([dimensions]) |
Stack any number of existing dimensions into a single new dimension. |
Variable.std([dim, axis, skipna]) |
Reduce this Variable’s data by applying std along some dimension(s). |
Variable.sum([dim, axis, skipna]) |
Reduce this Variable’s data by applying sum along some dimension(s). |
Variable.to_base_variable() |
Return this variable as a base xarray.Variable |
Variable.to_coord() |
to_coord has been deprecated. |
Variable.to_dict([data]) |
Dictionary representation of variable. |
Variable.to_index() |
Convert this variable to a pandas.Index |
Variable.to_index_variable() |
Return this variable as an xarray.IndexVariable |
Variable.to_variable() |
to_variable has been deprecated. |
Variable.transpose(*dims) |
Return a new Variable object with transposed dimensions. |
Variable.unstack([dimensions]) |
Unstack an existing dimension into multiple new dimensions. |
Variable.var([dim, axis, skipna]) |
Reduce this Variable’s data by applying var along some dimension(s). |
Variable.where(cond[, other]) |
|
Variable.T |
|
Variable.attrs |
Dictionary of local attributes on this variable. |
Variable.chunks |
Block dimensions for this array’s data or None if it’s not a dask array. |
Variable.data |
|
Variable.dims |
Tuple of dimension names with which this variable is associated. |
Variable.dtype |
|
Variable.encoding |
Dictionary of encodings on this variable. |
Variable.imag |
|
Variable.nbytes |
|
Variable.ndim |
|
Variable.real |
|
Variable.shape |
|
Variable.size |
|
Variable.sizes |
Ordered mapping from dimension names to lengths. |
Variable.values |
The variable’s data as a numpy.ndarray |
IndexVariable.all([dim, axis]) |
Reduce this Variable’s data by applying all along some dimension(s). |
IndexVariable.any([dim, axis]) |
Reduce this Variable’s data by applying any along some dimension(s). |
IndexVariable.argmax([dim, axis, skipna]) |
Reduce this Variable’s data by applying argmax along some dimension(s). |
IndexVariable.argmin([dim, axis, skipna]) |
Reduce this Variable’s data by applying argmin along some dimension(s). |
IndexVariable.argsort([axis, kind, order]) |
Returns the indices that would sort this array. |
IndexVariable.astype(dtype[, order, …]) |
Copy of the array, cast to a specified type. |
IndexVariable.broadcast_equals(other[, equiv]) |
True if two Variables have the values after being broadcast against each other; otherwise False. |
IndexVariable.chunk([chunks, name, lock]) |
Coerce this array’s data into a dask arrays with the given chunks. |
IndexVariable.clip([min, max, out]) |
Return an array whose values are limited to [min, max]. |
IndexVariable.coarsen(windows, func[, …]) |
Apply reduction function. |
IndexVariable.compute(**kwargs) |
Manually trigger loading of this variable’s data from disk or a remote source into memory and return a new variable. |
IndexVariable.concat(variables[, dim, …]) |
Specialized version of Variable.concat for IndexVariable objects. |
IndexVariable.conj() |
Complex-conjugate all elements. |
IndexVariable.conjugate() |
Return the complex conjugate, element-wise. |
IndexVariable.copy([deep, data]) |
Returns a copy of this object. |
IndexVariable.count([dim, axis]) |
Reduce this Variable’s data by applying count along some dimension(s). |
IndexVariable.cumprod([dim, axis, skipna]) |
Apply cumprod along some dimension of Variable. |
IndexVariable.cumsum([dim, axis, skipna]) |
Apply cumsum along some dimension of Variable. |
IndexVariable.equals(other[, equiv]) |
True if two Variables have the same dimensions and values; otherwise False. |
IndexVariable.fillna(value) |
|
IndexVariable.get_axis_num(dim, …) |
Return axis number(s) corresponding to dimension(s) in this array. |
IndexVariable.get_level_variable(level) |
Return a new IndexVariable from a given MultiIndex level. |
IndexVariable.identical(other[, equiv]) |
Like equals, but also checks attributes. |
IndexVariable.isel(indexers, Any] = None, …) |
Return a new array indexed along the specified dimension(s). |
IndexVariable.isnull(*args, **kwargs) |
|
IndexVariable.item(*args) |
Copy an element of an array to a standard Python scalar and return it. |
IndexVariable.load() |
Manually trigger loading of this variable’s data from disk or a remote source into memory and return this variable. |
IndexVariable.max([dim, axis, skipna]) |
Reduce this Variable’s data by applying max along some dimension(s). |
IndexVariable.mean([dim, axis, skipna]) |
Reduce this Variable’s data by applying mean along some dimension(s). |
IndexVariable.median([dim, axis, skipna]) |
Reduce this Variable’s data by applying median along some dimension(s). |
IndexVariable.min([dim, axis, skipna]) |
Reduce this Variable’s data by applying min along some dimension(s). |
IndexVariable.no_conflicts(other[, equiv]) |
True if the intersection of two Variable’s non-null data is equal; otherwise false. |
IndexVariable.notnull(*args, **kwargs) |
|
IndexVariable.pad_with_fill_value([…]) |
Return a new Variable with paddings. |
IndexVariable.prod([dim, axis, skipna]) |
Reduce this Variable’s data by applying prod along some dimension(s). |
IndexVariable.quantile(q[, dim, …]) |
Compute the qth quantile of the data along the specified dimension. |
IndexVariable.rank(dim[, pct]) |
Ranks the data. |
IndexVariable.reduce(func[, dim, axis, …]) |
Reduce this array by applying func along some dimension(s). |
IndexVariable.roll([shifts]) |
Return a new Variable with rolld data. |
IndexVariable.rolling_window(dim, window, …) |
Make a rolling_window along dim and add a new_dim to the last place. |
IndexVariable.round(*args, **kwargs) |
|
IndexVariable.searchsorted(v[, side, sorter]) |
Find indices where elements of v should be inserted in a to maintain order. |
IndexVariable.set_dims(dims[, shape]) |
Return a new variable with given set of dimensions. |
IndexVariable.shift([shifts, fill_value]) |
Return a new Variable with shifted data. |
IndexVariable.squeeze([dim]) |
Return a new object with squeezed data. |
IndexVariable.stack([dimensions]) |
Stack any number of existing dimensions into a single new dimension. |
IndexVariable.std([dim, axis, skipna]) |
Reduce this Variable’s data by applying std along some dimension(s). |
IndexVariable.sum([dim, axis, skipna]) |
Reduce this Variable’s data by applying sum along some dimension(s). |
IndexVariable.to_base_variable() |
Return this variable as a base xarray.Variable |
IndexVariable.to_coord() |
to_coord has been deprecated. |
IndexVariable.to_dict([data]) |
Dictionary representation of variable. |
IndexVariable.to_index() |
Convert this variable to a pandas.Index |
IndexVariable.to_index_variable() |
Return this variable as an xarray.IndexVariable |
IndexVariable.to_variable() |
to_variable has been deprecated. |
IndexVariable.transpose(*dims) |
Return a new Variable object with transposed dimensions. |
IndexVariable.unstack([dimensions]) |
Unstack an existing dimension into multiple new dimensions. |
IndexVariable.var([dim, axis, skipna]) |
Reduce this Variable’s data by applying var along some dimension(s). |
IndexVariable.where(cond[, other]) |
|
IndexVariable.T |
|
IndexVariable.attrs |
Dictionary of local attributes on this variable. |
IndexVariable.chunks |
Block dimensions for this array’s data or None if it’s not a dask array. |
IndexVariable.data |
|
IndexVariable.dims |
Tuple of dimension names with which this variable is associated. |
IndexVariable.dtype |
|
IndexVariable.encoding |
Dictionary of encodings on this variable. |
IndexVariable.imag |
|
IndexVariable.level_names |
Return MultiIndex level names or None if this IndexVariable has no MultiIndex. |
IndexVariable.name |
|
IndexVariable.nbytes |
|
IndexVariable.ndim |
|
IndexVariable.real |
|
IndexVariable.shape |
|
IndexVariable.size |
|
IndexVariable.sizes |
Ordered mapping from dimension names to lengths. |
IndexVariable.values |
The variable’s data as a numpy.ndarray |
ufuncs.angle |
xarray specific variant of numpy.angle. |
ufuncs.arccos |
xarray specific variant of numpy.arccos. |
ufuncs.arccosh |
xarray specific variant of numpy.arccosh. |
ufuncs.arcsin |
xarray specific variant of numpy.arcsin. |
ufuncs.arcsinh |
xarray specific variant of numpy.arcsinh. |
ufuncs.arctan |
xarray specific variant of numpy.arctan. |
ufuncs.arctan2 |
xarray specific variant of numpy.arctan2. |
ufuncs.arctanh |
xarray specific variant of numpy.arctanh. |
ufuncs.ceil |
xarray specific variant of numpy.ceil. |
ufuncs.conj |
xarray specific variant of numpy.conj. |
ufuncs.copysign |
xarray specific variant of numpy.copysign. |
ufuncs.cos |
xarray specific variant of numpy.cos. |
ufuncs.cosh |
xarray specific variant of numpy.cosh. |
ufuncs.deg2rad |
xarray specific variant of numpy.deg2rad. |
ufuncs.degrees |
xarray specific variant of numpy.degrees. |
ufuncs.exp |
xarray specific variant of numpy.exp. |
ufuncs.expm1 |
xarray specific variant of numpy.expm1. |
ufuncs.fabs |
xarray specific variant of numpy.fabs. |
ufuncs.fix |
xarray specific variant of numpy.fix. |
ufuncs.floor |
xarray specific variant of numpy.floor. |
ufuncs.fmax |
xarray specific variant of numpy.fmax. |
ufuncs.fmin |
xarray specific variant of numpy.fmin. |
ufuncs.fmod |
xarray specific variant of numpy.fmod. |
ufuncs.fmod |
xarray specific variant of numpy.fmod. |
ufuncs.frexp |
xarray specific variant of numpy.frexp. |
ufuncs.hypot |
xarray specific variant of numpy.hypot. |
ufuncs.imag |
xarray specific variant of numpy.imag. |
ufuncs.iscomplex |
xarray specific variant of numpy.iscomplex. |
ufuncs.isfinite |
xarray specific variant of numpy.isfinite. |
ufuncs.isinf |
xarray specific variant of numpy.isinf. |
ufuncs.isnan |
xarray specific variant of numpy.isnan. |
ufuncs.isreal |
xarray specific variant of numpy.isreal. |
ufuncs.ldexp |
xarray specific variant of numpy.ldexp. |
ufuncs.log |
xarray specific variant of numpy.log. |
ufuncs.log10 |
xarray specific variant of numpy.log10. |
ufuncs.log1p |
xarray specific variant of numpy.log1p. |
ufuncs.log2 |
xarray specific variant of numpy.log2. |
ufuncs.logaddexp |
xarray specific variant of numpy.logaddexp. |
ufuncs.logaddexp2 |
xarray specific variant of numpy.logaddexp2. |
ufuncs.logical_and |
xarray specific variant of numpy.logical_and. |
ufuncs.logical_not |
xarray specific variant of numpy.logical_not. |
ufuncs.logical_or |
xarray specific variant of numpy.logical_or. |
ufuncs.logical_xor |
xarray specific variant of numpy.logical_xor. |
ufuncs.maximum |
xarray specific variant of numpy.maximum. |
ufuncs.minimum |
xarray specific variant of numpy.minimum. |
ufuncs.nextafter |
xarray specific variant of numpy.nextafter. |
ufuncs.rad2deg |
xarray specific variant of numpy.rad2deg. |
ufuncs.radians |
xarray specific variant of numpy.radians. |
ufuncs.real |
xarray specific variant of numpy.real. |
ufuncs.rint |
xarray specific variant of numpy.rint. |
ufuncs.sign |
xarray specific variant of numpy.sign. |
ufuncs.signbit |
xarray specific variant of numpy.signbit. |
ufuncs.sin |
xarray specific variant of numpy.sin. |
ufuncs.sinh |
xarray specific variant of numpy.sinh. |
ufuncs.sqrt |
xarray specific variant of numpy.sqrt. |
ufuncs.square |
xarray specific variant of numpy.square. |
ufuncs.tan |
xarray specific variant of numpy.tan. |
ufuncs.tanh |
xarray specific variant of numpy.tanh. |
ufuncs.trunc |
xarray specific variant of numpy.trunc. |
plot.FacetGrid.map_dataarray(func, x, y, …) |
Apply a plotting function to a 2d facet’s subset of the data. |
plot.FacetGrid.set_titles([template, …]) |
Draw titles either above each facet or on the grid margins. |
plot.FacetGrid.set_ticks([max_xticks, …]) |
Set and control tick behavior |
plot.FacetGrid.map(func, *args, **kwargs) |
Apply a plotting function to each facet’s subset of the data. |
CFTimeIndex.all(*args, **kwargs) |
Return whether all elements are True. |
CFTimeIndex.any(*args, **kwargs) |
Return whether any element is True. |
CFTimeIndex.append(other) |
Append a collection of Index options together. |
CFTimeIndex.argmax([axis, skipna]) |
Return an ndarray of the maximum argument indexer. |
CFTimeIndex.argmin([axis, skipna]) |
Return a ndarray of the minimum argument indexer. |
CFTimeIndex.argsort(*args, **kwargs) |
Return the integer indices that would sort the index. |
CFTimeIndex.asof(label) |
Return the label from the index, or, if not present, the previous one. |
CFTimeIndex.asof_locs(where, mask) |
Find the locations (indices) of the labels from the index for every entry in the where argument. |
CFTimeIndex.astype(dtype[, copy]) |
Create an Index with values cast to dtypes. |
CFTimeIndex.contains(key) |
Needed for .loc based partial-string indexing |
CFTimeIndex.copy([name, deep, dtype]) |
Make a copy of this object. |
CFTimeIndex.delete(loc) |
Make new Index with passed location(-s) deleted. |
CFTimeIndex.difference(other[, sort]) |
Return a new Index with elements from the index that are not in other. |
CFTimeIndex.drop(labels[, errors]) |
Make new Index with passed list of labels deleted. |
CFTimeIndex.drop_duplicates([keep]) |
Return Index with duplicate values removed. |
CFTimeIndex.droplevel([level]) |
Return index with requested level(s) removed. |
CFTimeIndex.dropna([how]) |
Return Index without NA/NaN values |
CFTimeIndex.duplicated([keep]) |
Indicate duplicate index values. |
CFTimeIndex.equals(other) |
Determine if two Index objects contain the same elements. |
CFTimeIndex.factorize([sort, na_sentinel]) |
Encode the object as an enumerated type or categorical variable. |
CFTimeIndex.fillna([value, downcast]) |
Fill NA/NaN values with the specified value |
CFTimeIndex.format([name, formatter]) |
Render a string representation of the Index. |
CFTimeIndex.get_indexer(target[, method, …]) |
Compute indexer and mask for new index given the current index. |
CFTimeIndex.get_indexer_for(target, **kwargs) |
Guaranteed return of an indexer even when non-unique. |
CFTimeIndex.get_indexer_non_unique(target) |
Compute indexer and mask for new index given the current index. |
CFTimeIndex.get_level_values(level) |
Return an Index of values for requested level. |
CFTimeIndex.get_loc(key[, method, tolerance]) |
Adapted from pandas.tseries.index.DatetimeIndex.get_loc |
CFTimeIndex.get_slice_bound(label, side, kind) |
Calculate slice bound that corresponds to given label. |
CFTimeIndex.get_value(series, key) |
Adapted from pandas.tseries.index.DatetimeIndex.get_value |
CFTimeIndex.groupby(values) |
Group the index labels by a given array of values. |
CFTimeIndex.holds_integer() |
Whether the type is an integer type. |
CFTimeIndex.identical(other) |
Similar to equals, but check that other comparable attributes are also equal. |
CFTimeIndex.insert(loc, item) |
Make new Index inserting new item at location. |
CFTimeIndex.intersection(other[, sort]) |
Form the intersection of two Index objects. |
CFTimeIndex.is_(other) |
More flexible, faster check like is but that works through views. |
CFTimeIndex.is_boolean() |
|
CFTimeIndex.is_categorical() |
Check if the Index holds categorical data. |
CFTimeIndex.is_floating() |
|
CFTimeIndex.is_integer() |
|
CFTimeIndex.is_interval() |
|
CFTimeIndex.is_mixed() |
|
CFTimeIndex.is_numeric() |
|
CFTimeIndex.is_object() |
|
CFTimeIndex.is_type_compatible(kind) |
Whether the index type is compatible with the provided type. |
CFTimeIndex.isin(values[, level]) |
Return a boolean array where the index values are in values. |
CFTimeIndex.isna() |
Detect missing values. |
CFTimeIndex.isnull() |
Detect missing values. |
CFTimeIndex.item() |
Return the first element of the underlying data as a python scalar. |
CFTimeIndex.join(other[, how, level, …]) |
Compute join_index and indexers to conform data structures to the new index. |
CFTimeIndex.map(mapper[, na_action]) |
Map values using input correspondence (a dict, Series, or function). |
CFTimeIndex.max([axis, skipna]) |
Return the maximum value of the Index. |
CFTimeIndex.memory_usage([deep]) |
Memory usage of the values |
CFTimeIndex.min([axis, skipna]) |
Return the minimum value of the Index. |
CFTimeIndex.notna() |
Detect existing (non-missing) values. |
CFTimeIndex.notnull() |
Detect existing (non-missing) values. |
CFTimeIndex.nunique([dropna]) |
Return number of unique elements in the object. |
CFTimeIndex.putmask(mask, value) |
Return a new Index of the values set with the mask. |
CFTimeIndex.ravel([order]) |
Return an ndarray of the flattened values of the underlying data. |
CFTimeIndex.reindex(target[, method, level, …]) |
Create index with target’s values (move/add/delete values as necessary). |
CFTimeIndex.rename(name[, inplace]) |
Alter Index or MultiIndex name. |
CFTimeIndex.repeat(repeats[, axis]) |
Repeat elements of a Index. |
CFTimeIndex.searchsorted(value[, side, sorter]) |
Find indices where elements should be inserted to maintain order. |
CFTimeIndex.set_names(names[, level, inplace]) |
Set Index or MultiIndex name. |
CFTimeIndex.set_value(arr, key, value) |
Fast lookup of value from 1-dimensional ndarray. |
CFTimeIndex.shift(n, freq) |
Shift the CFTimeIndex a multiple of the given frequency. |
CFTimeIndex.slice_indexer([start, end, …]) |
For an ordered or unique index, compute the slice indexer for input labels and step. |
CFTimeIndex.slice_locs([start, end, step, kind]) |
Compute slice locations for input labels. |
CFTimeIndex.sort(*args, **kwargs) |
Use sort_values instead. |
CFTimeIndex.sort_values([return_indexer, …]) |
Return a sorted copy of the index. |
CFTimeIndex.sortlevel([level, ascending, …]) |
For internal compatibility with with the Index API. |
CFTimeIndex.strftime(date_format) |
Return an Index of formatted strings specified by date_format, which supports the same string format as the python standard library. |
CFTimeIndex.symmetric_difference(other[, …]) |
Compute the symmetric difference of two Index objects. |
CFTimeIndex.take(indices[, axis, …]) |
Return a new Index of the values selected by the indices. |
CFTimeIndex.to_datetimeindex([unsafe]) |
If possible, convert this index to a pandas.DatetimeIndex. |
CFTimeIndex.to_flat_index() |
Identity method. |
CFTimeIndex.to_frame([index, name]) |
Create a DataFrame with a column containing the Index. |
CFTimeIndex.to_list() |
Return a list of the values. |
CFTimeIndex.to_native_types([slicer]) |
Format specified values of self and return them. |
CFTimeIndex.to_numpy([dtype, copy]) |
A NumPy ndarray representing the values in this Series or Index. |
CFTimeIndex.to_series([index, name]) |
Create a Series with both index and values equal to the index keys useful with map for returning an indexer based on an index. |
CFTimeIndex.tolist() |
Return a list of the values. |
CFTimeIndex.transpose(*args, **kwargs) |
Return the transpose, which is by definition self. |
CFTimeIndex.union(other[, sort]) |
Form the union of two Index objects. |
CFTimeIndex.unique([level]) |
Return unique values in the index. |
CFTimeIndex.value_counts([normalize, sort, …]) |
Return a Series containing counts of unique values. |
CFTimeIndex.view([cls]) |
|
CFTimeIndex.where(cond[, other]) |
Return an Index of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from other. |
CFTimeIndex.T |
Return the transpose, which is by definition self. |
CFTimeIndex.array |
The ExtensionArray of the data backing this Series or Index. |
CFTimeIndex.asi8 |
Integer representation of the values. |
CFTimeIndex.date_type |
|
CFTimeIndex.day |
The days of the datetime |
CFTimeIndex.dayofweek |
The day of week of the datetime |
CFTimeIndex.dayofyear |
The ordinal day of year of the datetime |
CFTimeIndex.dtype |
Return the dtype object of the underlying data. |
CFTimeIndex.empty |
|
CFTimeIndex.has_duplicates |
|
CFTimeIndex.hasnans |
Return if I have any nans; enables various perf speedups. |
CFTimeIndex.hour |
The hours of the datetime |
CFTimeIndex.inferred_type |
Return a string of the type inferred from the values. |
CFTimeIndex.is_all_dates |
|
CFTimeIndex.is_monotonic |
Alias for is_monotonic_increasing. |
CFTimeIndex.is_monotonic_increasing |
Return if the index is monotonic increasing (only equal or increasing) values. |
CFTimeIndex.is_monotonic_decreasing |
Return if the index is monotonic decreasing (only equal or decreasing) values. |
CFTimeIndex.is_unique |
Return if the index has unique values. |
CFTimeIndex.microsecond |
The microseconds of the datetime |
CFTimeIndex.minute |
The minutes of the datetime |
CFTimeIndex.month |
The month of the datetime |
CFTimeIndex.name |
|
CFTimeIndex.names |
|
CFTimeIndex.nbytes |
Return the number of bytes in the underlying data. |
CFTimeIndex.ndim |
Number of dimensions of the underlying data, by definition 1. |
CFTimeIndex.nlevels |
Number of levels. |
CFTimeIndex.second |
The seconds of the datetime |
CFTimeIndex.shape |
Return a tuple of the shape of the underlying data. |
CFTimeIndex.size |
Return the number of elements in the underlying data. |
CFTimeIndex.values |
Return an array representing the data in the Index. |
CFTimeIndex.year |
The year of the datetime |
backends.NetCDF4DataStore.close(**kwargs) |
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backends.NetCDF4DataStore.encode(variables, …) |
Encode the variables and attributes in this store |
backends.NetCDF4DataStore.encode_attribute(a) |
encode one attribute |
backends.NetCDF4DataStore.encode_variable(…) |
encode one variable |
backends.NetCDF4DataStore.get(k[,d]) |
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backends.NetCDF4DataStore.get_attrs() |
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backends.NetCDF4DataStore.get_dimensions() |
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backends.NetCDF4DataStore.get_encoding() |
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backends.NetCDF4DataStore.get_variables() |
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backends.NetCDF4DataStore.items() |
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backends.NetCDF4DataStore.keys() |
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backends.NetCDF4DataStore.load() |
This loads the variables and attributes simultaneously. |
backends.NetCDF4DataStore.open(filename[, …]) |
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backends.NetCDF4DataStore.open_store_variable(…) |
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backends.NetCDF4DataStore.prepare_variable(…) |
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backends.NetCDF4DataStore.set_attribute(key, …) |
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backends.NetCDF4DataStore.set_attributes(…) |
This provides a centralized method to set the dataset attributes on the data store. |
backends.NetCDF4DataStore.set_dimension(…) |
|
backends.NetCDF4DataStore.set_dimensions(…) |
This provides a centralized method to set the dimensions on the data store. |
backends.NetCDF4DataStore.set_variable(k, v) |
|
backends.NetCDF4DataStore.set_variables(…) |
This provides a centralized method to set the variables on the data store. |
backends.NetCDF4DataStore.store(variables, …) |
Top level method for putting data on this store, this method: |
backends.NetCDF4DataStore.store_dataset(dataset) |
in stores, variables are all variables AND coordinates in xarray.Dataset variables are variables NOT coordinates, so here we pass the whole dataset in instead of doing dataset.variables |
backends.NetCDF4DataStore.sync() |
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backends.NetCDF4DataStore.values() |
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backends.NetCDF4DataStore.attrs |
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backends.NetCDF4DataStore.autoclose |
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backends.NetCDF4DataStore.dimensions |
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backends.NetCDF4DataStore.ds |
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backends.NetCDF4DataStore.format |
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backends.NetCDF4DataStore.is_remote |
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backends.NetCDF4DataStore.lock |
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backends.NetCDF4DataStore.variables |
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backends.H5NetCDFStore.close(**kwargs) |
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backends.H5NetCDFStore.encode(variables, …) |
Encode the variables and attributes in this store |
backends.H5NetCDFStore.encode_attribute(a) |
encode one attribute |
backends.H5NetCDFStore.encode_variable(variable) |
encode one variable |
backends.H5NetCDFStore.get(k[,d]) |
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backends.H5NetCDFStore.get_attrs() |
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backends.H5NetCDFStore.get_dimensions() |
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backends.H5NetCDFStore.get_encoding() |
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backends.H5NetCDFStore.get_variables() |
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backends.H5NetCDFStore.items() |
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backends.H5NetCDFStore.keys() |
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backends.H5NetCDFStore.load() |
This loads the variables and attributes simultaneously. |
backends.H5NetCDFStore.open_store_variable(…) |
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backends.H5NetCDFStore.prepare_variable(…) |
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backends.H5NetCDFStore.set_attribute(key, value) |
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backends.H5NetCDFStore.set_attributes(attributes) |
This provides a centralized method to set the dataset attributes on the data store. |
backends.H5NetCDFStore.set_dimension(name, …) |
|
backends.H5NetCDFStore.set_dimensions(variables) |
This provides a centralized method to set the dimensions on the data store. |
backends.H5NetCDFStore.set_variable(k, v) |
|
backends.H5NetCDFStore.set_variables(…[, …]) |
This provides a centralized method to set the variables on the data store. |
backends.H5NetCDFStore.store(variables, …) |
Top level method for putting data on this store, this method: |
backends.H5NetCDFStore.store_dataset(dataset) |
in stores, variables are all variables AND coordinates in xarray.Dataset variables are variables NOT coordinates, so here we pass the whole dataset in instead of doing dataset.variables |
backends.H5NetCDFStore.sync() |
|
backends.H5NetCDFStore.values() |
|
backends.H5NetCDFStore.attrs |
|
backends.H5NetCDFStore.dimensions |
|
backends.H5NetCDFStore.ds |
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backends.H5NetCDFStore.variables |
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backends.PydapDataStore.close() |
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backends.PydapDataStore.get(k[,d]) |
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backends.PydapDataStore.get_attrs() |
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backends.PydapDataStore.get_dimensions() |
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backends.PydapDataStore.get_encoding() |
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backends.PydapDataStore.get_variables() |
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backends.PydapDataStore.items() |
|
backends.PydapDataStore.keys() |
|
backends.PydapDataStore.load() |
This loads the variables and attributes simultaneously. |
backends.PydapDataStore.open(url[, session]) |
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backends.PydapDataStore.open_store_variable(var) |
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backends.PydapDataStore.values() |
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backends.PydapDataStore.attrs |
|
backends.PydapDataStore.dimensions |
|
backends.PydapDataStore.variables |
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backends.ScipyDataStore.close() |
|
backends.ScipyDataStore.encode(variables, …) |
Encode the variables and attributes in this store |
backends.ScipyDataStore.encode_attribute(a) |
encode one attribute |
backends.ScipyDataStore.encode_variable(variable) |
encode one variable |
backends.ScipyDataStore.get(k[,d]) |
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backends.ScipyDataStore.get_attrs() |
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backends.ScipyDataStore.get_dimensions() |
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backends.ScipyDataStore.get_encoding() |
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backends.ScipyDataStore.get_variables() |
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backends.ScipyDataStore.items() |
|
backends.ScipyDataStore.keys() |
|
backends.ScipyDataStore.load() |
This loads the variables and attributes simultaneously. |
backends.ScipyDataStore.open_store_variable(…) |
|
backends.ScipyDataStore.prepare_variable(…) |
|
backends.ScipyDataStore.set_attribute(key, value) |
|
backends.ScipyDataStore.set_attributes(…) |
This provides a centralized method to set the dataset attributes on the data store. |
backends.ScipyDataStore.set_dimension(name, …) |
|
backends.ScipyDataStore.set_dimensions(variables) |
This provides a centralized method to set the dimensions on the data store. |
backends.ScipyDataStore.set_variable(k, v) |
|
backends.ScipyDataStore.set_variables(…[, …]) |
This provides a centralized method to set the variables on the data store. |
backends.ScipyDataStore.store(variables, …) |
Top level method for putting data on this store, this method: |
backends.ScipyDataStore.store_dataset(dataset) |
in stores, variables are all variables AND coordinates in xarray.Dataset variables are variables NOT coordinates, so here we pass the whole dataset in instead of doing dataset.variables |
backends.ScipyDataStore.sync() |
|
backends.ScipyDataStore.values() |
|
backends.ScipyDataStore.attrs |
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backends.ScipyDataStore.dimensions |
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backends.ScipyDataStore.ds |
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backends.ScipyDataStore.variables |
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backends.FileManager.acquire([needs_lock]) |
Acquire the file object from this manager. |
backends.FileManager.acquire_context([…]) |
Context manager for acquiring a file. |
backends.FileManager.close([needs_lock]) |
Close the file object associated with this manager, if needed. |
backends.CachingFileManager.acquire([needs_lock]) |
Acquire a file object from the manager. |
backends.CachingFileManager.acquire_context([…]) |
Context manager for acquiring a file. |
backends.CachingFileManager.close([needs_lock]) |
Explicitly close any associated file object (if necessary). |
backends.DummyFileManager.acquire([needs_lock]) |
Acquire the file object from this manager. |
backends.DummyFileManager.acquire_context([…]) |
Context manager for acquiring a file. |
backends.DummyFileManager.close([needs_lock]) |
Close the file object associated with this manager, if needed. |