xarray
E459722
xarray is an open-source Python library that provides labeled, N-dimensional arrays and datasets for more intuitive and efficient analysis of multi-dimensional scientific data.
All labels observed (1)
| Label | Occurrences |
|---|---|
| xarray canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4599951 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: xarray Context triple: [Python scientific stack, hasComponent, xarray]
-
A.
Dask
Dask is an open-source parallel computing library for Python that enables scalable, distributed data processing and analytics using familiar interfaces like NumPy, pandas, and scikit-learn.
-
B.
NumPy
NumPy is a fundamental Python library that provides efficient multi-dimensional arrays and numerical computing tools widely used in scientific computing and data analysis.
-
C.
pandas
pandas is a popular open-source Python library that provides powerful, easy-to-use data structures and tools for data analysis and manipulation.
-
D.
NCL
NCL is the commonly used abbreviation for the Nice Classification, an international system for categorizing goods and services for trademark registration.
-
E.
NCL
NCL is the IATA airport code for Newcastle International Airport, a major airport serving Newcastle upon Tyne and the surrounding region in northeast England.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: xarray Target entity description: xarray is an open-source Python library that provides labeled, N-dimensional arrays and datasets for more intuitive and efficient analysis of multi-dimensional scientific data.
-
A.
Dask
Dask is an open-source parallel computing library for Python that enables scalable, distributed data processing and analytics using familiar interfaces like NumPy, pandas, and scikit-learn.
-
B.
NumPy
NumPy is a fundamental Python library that provides efficient multi-dimensional arrays and numerical computing tools widely used in scientific computing and data analysis.
-
C.
pandas
pandas is a popular open-source Python library that provides powerful, easy-to-use data structures and tools for data analysis and manipulation.
-
D.
NCL
NCL is the commonly used abbreviation for the Nice Classification, an international system for categorizing goods and services for trademark registration.
-
E.
NCL
NCL is the IATA airport code for Newcastle International Airport, a major airport serving Newcastle upon Tyne and the surrounding region in northeast England.
- F. None of above. chosen
Statements (81)
| Predicate | Object |
|---|---|
| instanceOf |
Python library
ⓘ
data analysis library ⓘ open-source software ⓘ |
| category |
data science
ⓘ
geospatial data processing ⓘ scientific computing ⓘ |
| compatibleWith |
Cartopy
NERFINISHED
ⓘ
Dask NERFINISHED ⓘ Matplotlib NERFINISHED ⓘ NumPy NERFINISHED ⓘ SciPy NERFINISHED ⓘ pandas ⓘ |
| documentationURL | https://docs.xarray.dev ⓘ |
| enables |
efficient analysis of multi-dimensional data
ⓘ
intuitive analysis of multi-dimensional data ⓘ |
| hostedOn | GitHub NERFINISHED ⓘ |
| inspiredBy |
netCDF data model
NERFINISHED
ⓘ
pandas NERFINISHED ⓘ |
| license | Apache License 2.0 ⓘ |
| maintainedBy |
PyData community
NERFINISHED
ⓘ
xarray developers ⓘ |
| partOf | PyData ecosystem ⓘ |
| programmingLanguage | Python ⓘ |
| provides |
datasets abstraction
ⓘ
labeled N-dimensional arrays ⓘ |
| repositoryURL | https://github.com/pydata/xarray ⓘ |
| supports |
N-dimensional arrays
ⓘ
labeled arrays ⓘ labeled datasets ⓘ multi-dimensional scientific data ⓘ |
| supportsBackend |
HDF5 via netCDF4
ⓘ
Pydap NERFINISHED ⓘ SciDB NERFINISHED ⓘ Zarr NERFINISHED ⓘ cfgrib ⓘ h5netcdf NERFINISHED ⓘ netCDF4 NERFINISHED ⓘ rasterio NERFINISHED ⓘ |
| supportsComputation |
Fourier transforms via integration with SciPy
ⓘ
aggregation operations ⓘ interpolation ⓘ regridding via external libraries ⓘ |
| supportsDataStructure |
Coordinates
ⓘ
DataArray NERFINISHED ⓘ Dataset ⓘ Variable ⓘ |
| supportsDimensionType |
latitude
ⓘ
level ⓘ longitude ⓘ time ⓘ |
| supportsFeature |
CF-compliant metadata handling
ⓘ
broadcasting ⓘ coordinate-based indexing ⓘ groupby operations ⓘ label-based indexing ⓘ lazy evaluation with Dask ⓘ metadata preservation ⓘ missing data handling ⓘ out-of-core computation ⓘ parallel computation ⓘ resampling ⓘ rolling window operations ⓘ vectorized operations ⓘ |
| supportsIndexing |
isel
ⓘ
loc-like indexing ⓘ sel ⓘ |
| supportsIOFormat |
GRIB via cfgrib
ⓘ
GeoTIFF via rasterio ⓘ HDF5 via netCDF4 ⓘ NetCDF NERFINISHED ⓘ OPeNDAP NERFINISHED ⓘ Zarr NERFINISHED ⓘ |
| supportsLanguage | Python ⓘ |
| supportsPlotting |
facet plots
ⓘ
quicklook plots via Matplotlib ⓘ |
| usedIn |
climate science
ⓘ
environmental science ⓘ geosciences ⓘ meteorology ⓘ oceanography ⓘ remote sensing ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: xarray Description of subject: xarray is an open-source Python library that provides labeled, N-dimensional arrays and datasets for more intuitive and efficient analysis of multi-dimensional scientific data.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.