amine000 opened a new issue #8767:
URL: https://github.com/apache/arrow/issues/8767
Hello, I have a very large dataset (10's of millions of rows) stored on a
partitioned parquet dataset on disk. I load this dataset into memory into a
pyarrow.Table, and drop all columns except one, which is of type MapType
mapping integers to floats. This column represents sparse feature vector data
to be used in an ML context. Call the number of rows "num_rows". My job is to
transform this column to a 2D numpy array of shape ("num_rows" x "num_cols")
where both rows and cols are known before hand. If one of my pyarrow.Table rows
looks like `[(1, 3.4), (2, 4.4), (4, 5.4), (6, 6.4)]` and "num_cols" = 10, then
that row in the numpy array would look like [0, 3.4, 4.4, 0, 5.4, 0, 6.4, 0,
0, 0, 0], where unmapped values are just 0. What is the best, most efficient
way to accomplish this, considering I have 10's of millions of rows? Assume I
have enough memory to fit the entire dataset.
Note that I can use `table.to_pandas()` to get a pandas DF, and then map
functions on the pandas series, if that would help in the solution. So far I
have been stumped, however. `df.to_numpy()` has not been helpful here.
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