Wgy does df[“index”].compute() compute the entire dataframe, I just need this one column, the issue exists even when I make another df with just this column it fully computes the base df as well. are there any workarounds to this or any optimizations that can be done?
short answer - it depends.
long answer:
When you execute df["index"].compute(), dask may end up computing more of the dataframe than just the “index” column. This behavior depends on several factors:
- Index Knowledge: If the dataframe’s index is unknown (i.e., dask doesn’t know the divisions), certain operations may require a full scan or shuffle of the data to compute the result. This can lead to the entire dataset being read or computed.
- Column Dependencies: If the column you’re computing depends on other columns or requires computations that involve the entire dataset (like aggregations, sorts, etc.), dask will need to process more data.
ddf["index"]: This accesses a column named “index” in your dataframe. If you have a column explicitly named “index,” this will select that column.ddf.index: This accesses the index of the dataframe itself. The index could be any column you’ve set as the index or the default integer index if none is set.
Accessing ddf.index might be more efficient because dask often knows about the index divisions, especially if you’ve set the index explicitly. Accessing ddf["index"] treats “index” as a regular column, which might not have known divisions, leading to more computation.
easiest way to know is to look at the graph using
ddf.dask
or
ddf.visualize()
please keep in mind there could be even more reasons to this - i just used the index version as i saw you ask specifically about it.
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