I'm not able to process this block using the distributed cluster.
import pandas as pd
from dask import dataframe as dd
import dask
df = pd.DataFrame({'reid_encod': [[1,2,3,4,5,6,7,8,9,10],[1,2,3,4,5,6,7,8,9,10],[1,2,3,4,5,6,7,8,9,10],[1,2,3,4,5,6,7,8,9,10],[1,2,3,4,5,6,7,8,9,10],[1,2,3,4,5,6,7,8,9,10]]})
dask_df = dd.from_pandas(df, npartitions=3)
save_val = []
def add(dask_df):
for _, outer_row in dask_df.iterrows():
for _, inner_row in dask_df.iterrows():
for base_encod in outer_row['reid_encod']:
for compare_encod in inner_row['reid_encod']:
val = base_encod + compare_encod
save_val.append(val)
return save_val
from dask.distributed import Client
client = Client(...)
dask_compute = dask.delayed(add)(dask_df)
dask_compute.compute()
Also I have few queries
Does dask.delayed use the available clusters to do the computation.
Can I paralleize the for loop iteratition of this pandas DF using delayed, and use multiple computers present in the cluster to do computations.
does dask.distributed work on pandas dataframe.
can we use dask.delayed in dask.distributed.
If the above programming approach is wrong, can you guide me whether to choose delayed or dask DF for the above scenario.