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Description
from dask_expr.datasets import timeseries
from dask.utils import M
df = timeseries()
df.x.map_partitions(M.min).compute()---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[4], line 1
----> 1 df.x.map_partitions(M.min).compute()
File ~/workspace/dask-expr/dask_expr/_collection.py:265, in FrameBase.compute(self, fuse, **kwargs)
263 out = out.repartition(npartitions=1)
264 out = out.optimize(fuse=fuse)
--> 265 return DaskMethodsMixin.compute(out, **kwargs)
File ~/workspace/dask/dask/base.py:342, in DaskMethodsMixin.compute(self, **kwargs)
318 def compute(self, **kwargs):
319 """Compute this dask collection
320
321 This turns a lazy Dask collection into its in-memory equivalent.
(...)
340 dask.compute
341 """
--> 342 (result,) = compute(self, traverse=False, **kwargs)
343 return result
File ~/workspace/dask/dask/base.py:628, in compute(traverse, optimize_graph, scheduler, get, *args, **kwargs)
625 postcomputes.append(x.__dask_postcompute__())
627 with shorten_traceback():
--> 628 results = schedule(dsk, keys, **kwargs)
630 return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])
File ~/workspace/dask/dask/dataframe/dispatch.py:67, in concat(dfs, axis, join, uniform, filter_warning, ignore_index, **kwargs)
65 return dfs[0]
66 else:
---> 67 func = concat_dispatch.dispatch(type(dfs[0]))
68 return func(
69 dfs,
70 axis=axis,
(...)
75 **kwargs,
76 )
File ~/workspace/dask/dask/utils.py:635, in Dispatch.dispatch(self, cls)
633 self._lazy.pop(toplevel, None)
634 return self.dispatch(cls) # recurse
--> 635 raise TypeError(f"No dispatch for {cls}")
TypeError: No dispatch for <class 'numpy.float64'>So far, I believe the reason for this is that the min is returning a scalar float (the type that is tried to be dispatched) instead of a Series with one element. This then fails in the concat operation when computing the series.