Xarray set_index example
xarray.Dataset.set_index¶. Set Dataset (multi-)indexes using one or more existing coordinates or variables. Mapping from names matching dimensions and values given by (lists of) the names of existing coordinates or variables to set as new (multi-)index. If True, append the supplied index(es) to the existing index(es). Unlike pandas, xarray does not guess whether you provide index levels or dimensions when using loc in some ambiguous cases. For example, for mda.loc[{'one': 'a', 'two': 0}] and mda.loc['a', 0] xarray always interprets (‘one Using apply_ufunc¶. Applying unvectorized functions with apply_ufunc. Load data; No errors are raised so our interpolation is working. I use xarray 0.9.6 for both examples below. With pandas 0.20.3, Dataset.set_index gives me what I expect (i.e., the grid__x data variable becomes a coordinate x): xarray supports four kind of indexing. Since we have assigned coordinate labels to the x dimension we can use label-based indexing along that dimension just like pandas. The four examples below all yield the same result (the value at x=10) but at varying levels of convenience and intuitiveness.
xarray.DataArray.set_index¶. Set DataArray (multi-)indexes using one or more existing coordinates. Mapping from names matching dimensions and values given by (lists of) the names of existing coordinates or variables to set as new (multi-)index. If True, append the supplied index(es) to the existing index(es).
xarray.Dataset.set_index¶. Set Dataset (multi-)indexes using one or more existing coordinates or variables. Mapping from names matching dimensions and values given by (lists of) the names of existing coordinates or variables to set as new (multi-)index. If True, append the supplied index(es) to the existing index(es). Unlike pandas, xarray does not guess whether you provide index levels or dimensions when using loc in some ambiguous cases. For example, for mda.loc[{'one': 'a', 'two': 0}] and mda.loc['a', 0] xarray always interprets (‘one Using apply_ufunc¶. Applying unvectorized functions with apply_ufunc. Load data; No errors are raised so our interpolation is working. I use xarray 0.9.6 for both examples below. With pandas 0.20.3, Dataset.set_index gives me what I expect (i.e., the grid__x data variable becomes a coordinate x):
xarray supports four kind of indexing. Since we have assigned coordinate labels to the x dimension we can use label-based indexing along that dimension just like pandas. The four examples below all yield the same result (the value at x=10) but at varying levels of convenience and intuitiveness.
16 Nov 2017 I use xarray 0.9.6 for both examples below. With pandas 0.20.3, Dataset. set_index gives me what I expect (i.e., the grid__x data variable 28 Jan 2017 Let's say I want to use xarray to work on some daily weather data, organized in a way that is similar to this example from xarray's documentation 3 Oct 2017 For example, we might imagine that "Indexes" are no longer coordinates, but instead their own entry in the repr:
I use xarray 0.9.6 for both examples below. With pandas 0.20.3, Dataset.set_index gives me what I expect (i.e., the grid__x data variable becomes a coordinate x):
One of indexes or indexes_kwargs must be provided. Returns. obj – Another DataArray, with this data but replaced coordinates. Return type. DataArray. Examples. obj – Another dataset, with this dataset's data but replaced coordinates. Return type. Dataset. Examples. >>> arr = xr 16 Nov 2017 I use xarray 0.9.6 for both examples below. With pandas 0.20.3, Dataset. set_index gives me what I expect (i.e., the grid__x data variable 28 Jan 2017 Let's say I want to use xarray to work on some daily weather data, organized in a way that is similar to this example from xarray's documentation 3 Oct 2017 For example, we might imagine that "Indexes" are no longer coordinates, but instead their own entry in the repr:
28 Jan 2017 Let's say I want to use xarray to work on some daily weather data, organized in a way that is similar to this example from xarray's documentation
Note that set_index() method does not modify the original DataFrame, but returns the DataFrame with the column set as index. Example 1: Set Column as Index in xarray.DataArray.set_index¶ DataArray.set_index (self, indexes: Mapping[Hashable, Union[Hashable, Sequence[Hashable]]] = None, append: bool = False, inplace: bool xarray.Dataset.set_index¶. Set Dataset (multi-)indexes using one or more existing coordinates or variables. Mapping from names matching dimensions and values given by (lists of) the names of existing coordinates or variables to set as new (multi-)index. If True, append the supplied index(es) to the existing index(es).
* add Dataset.set_index method * add set_index and reset_index methods for dataarray and dataset * add reorder_levels method for dataset and dataarray * add tests * update doc * fix tests py27 * review changes * fix unresolved rebase conflict * fix reset_index example in docs * fix docstring * change signature of reset_index * add type annotations * update missing coordinate dims * fix and set_index seems like the correct choice, however, each of our coordinates indexes itself (unlike the example in set_index docs where x indexes both x and a coordinates) which means that set_index cannot be used in this particular case. To add on to Joe's comment, definitely take a look at the working with pandas section of the xarray docs to see if that helps. If you can set the appropriate pandas.MultiIndex for your data, converting to xarray is usually quite easy. – shoyer Oct 17 '17 at 23:59