Aggregating in chunks refers to the process of computing aggregate statistics on a large dataset by dividing it into smaller chunks or blocks, processing the blocks in parallel, and then combining the results. This approach can be more efficient than computing the aggregate statistics on the entire dataset at once, especially when the dataset is too large to fit in memory.
Dask provides a convenient way to perform chunk-wise aggregation on arrays using the da.map_blocks function. Here is an example:
import dask.array as daimport numpy as np# Create a Dask arrayx = da.random.random((10000, 10000), chunks=(1000, 1000))# Compute the mean of each blockmean_blocks = da.map_blocks(np.mean, x, dtype=float)# Compute the mean of the entire arraymean = mean_blocks.mean().compute()print(mean) |
In this example, we first create a Dask array x of shape (10000, 10000) with chunk size (1000, 1000). We then use the map_blocks function to compute the mean of each block of the array using the NumPy mean function. The result is a Dask array mean_blocks of shape (10, 10) with chunk size (1, 1).
Finally, we compute the mean of the entire array mean_blocks using the mean method and the compute method to perform the computation and obtain the result.
Note that the da.map_blocks function can be used with any function that takes an array as input and returns an array as output. The input array is divided into chunks, and the function is applied to each chunk independently. The output is a Dask array with the same shape as the input array, where each chunk has been replaced by the result of applying the function to that chunk.