Dask-Array

GPTKB entity

Statements (60)
Predicate Object
gptkbp:instance_of gptkb:Dask
gptkb:Matrix
gptkbp:allows lazy evaluation
gptkbp:analyzes Dask dashboard
gptkbp:can_be_aggregated_with reduce operations
gptkbp:can_be_combined_with other Dask collections
gptkbp:can_be_parallelized_across multiple cores
multiple machines
gptkbp:can_be_reshaped_into different dimensions
gptkbp:can_be_sliced_like Num Py arrays
gptkbp:can_be_used_for image processing
matrix operations
data visualization
financial modeling
signal processing
gptkbp:can_be_used_in gptkb:machine_learning
scientific computing
gptkbp:can_be_used_with gptkb:Pandas
gptkb:Sci_Py
gptkb:Num_Py
gptkbp:can_handle missing data
gptkbp:can_perform element-wise operations
gptkbp:can_transform_into map operations
gptkbp:created_by lists
Pandas Data Frames
Num Py arrays
gptkbp:developed_by gptkb:Dask
gptkbp:first_released gptkb:2016
gptkbp:functionality array operations
https://www.w3.org/2000/01/rdf-schema#label Dask-Array
gptkbp:is_accessible_by pip
conda
gptkbp:is_compatible_with gptkb:Jupyter_notebooks
distributed systems
GPU computing
gptkbp:is_designed_for large datasets
gptkbp:is_documented_in Dask documentation
gptkbp:is_effective_against large-scale computations
gptkbp:is_integrated_with gptkb:Dask-Data_Frame
gptkbp:is_maintained_by community contributors
gptkbp:is_open_source gptkb:true
gptkbp:is_optimized_for memory usage
gptkbp:is_part_of data analysis workflows
Dask ecosystem
gptkbp:is_scalable cloud environments
gptkbp:is_used_by gptkb:developers
gptkb:researchers
data analysts
data engineers
gptkbp:is_used_in data science
gptkbp:language gptkb:Python
gptkbp:provides N-dimensional arrays
gptkbp:serialization gptkb:pickles
gptkbp:suitable_for real-time data processing
big data applications
gptkbp:supports multi-dimensional data
out-of-core computation
gptkbp:used_for parallel computing
gptkbp:bfsParent gptkb:Dask
gptkbp:bfsLayer 5