Hello, I have question regarding best practices with Apache Arrow. 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. My 2D numpy array would just be the collection of rows from the pyarrow.Table transformed in such a way. 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.