High Level Expressions for Dask
Reason this release was yanked:
Wrong Dask Version Pin
Project description
Dask Expressions
Dask DataFrames with query optimization.
This is a proof-of-concept rewrite of Dask DataFrame that includes query optimization and generally improved organization.
More in our blog posts:
Example
import dask_expr as dx
df = dx.datasets.timeseries()
df.head()
df.groupby("name").x.mean().compute()
Query Representation
Dask-expr encodes user code in an expression tree:
>>> df.x.mean().pprint()
Mean:
Projection: columns='x'
Timeseries: seed=1896674884
This expression tree will be optimized and modified before execution:
>>> df.x.mean().optimize().pprint()
Div:
Sum:
Fused(375f9):
| Projection: columns='x'
| Timeseries: dtypes={'x': <class 'float'>} seed=1896674884
Count:
Fused(375f9):
| Projection: columns='x'
| Timeseries: dtypes={'x': <class 'float'>} seed=1896674884
Stability
This project is a work in progress and will be changed without notice or deprecation warning. Please provide feedback, but it's best to avoid use in production settings.
API Coverage
Dask-Expr covers almost everything of the Dask DataFrame API. The only missing features are:
melt- named GroupBy Aggregations
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