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
|