Statements (65)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:Company
|
| gptkbp:can_be |
Pickle
lazy evaluation |
| gptkbp:compatibleWith |
gptkb:Dask_Bag
NumPy |
| gptkbp:createdBy |
2015
|
| gptkbp:deployedTo |
pip
|
| https://www.w3.org/2000/01/rdf-schema#label |
Dask-Array
|
| gptkbp:is_available_in |
gptkb:PyPI
Dask_dashboard |
| gptkbp:is_designed_to |
cloud computing environments
multi-core processors high-dimensional data out-of-core computation |
| gptkbp:is_integrated_with |
gptkb:Pandas
other data processing libraries. |
| gptkbp:is_known_for |
scalability
flexibility in data handling |
| gptkbp:is_part_of |
open-source software
data analysis workflows data engineering pipelines |
| gptkbp:is_recognized_for |
Python
|
| gptkbp:is_supported_by |
gptkb:Anaconda
|
| gptkbp:is_used_in |
gptkb:Apache_Spark
large datasets machine learning data preprocessing Jupyter notebooks academic research financial modeling scientific computing big data applications streamline data workflows matrix computations optimize resource usage data visualization projects data transformation tasks support data-driven decision making analyze time series data build machine learning models create visualizations enhance performance of data applications facilitate collaboration in data science teams implement algorithms manage large datasets efficiently perform exploratory data analysis perform simulations perform statistical analysis process large images reduce computation time scale computations GPU_acceleration |
| gptkbp:isFacilitatedBy |
multi-dimensional arrays
|
| gptkbp:isPartOf |
Dask_ecosystem
|
| gptkbp:isUsedFor |
Dask DataFrame
existing NumPy arrays |
| gptkbp:maintainedBy |
Dask_community
|
| gptkbp:performance |
distributed computing
|
| gptkbp:provides |
parallel computing capabilities
|
| gptkbp:suitableFor |
real-time data processing
data science applications image processing tasks |
| gptkbp:supports |
Numpy-like operations
block-wise operations |
| gptkbp:transferFee |
NumPy arrays
|