删除Dask中的空分区

我发现过滤Dask数据帧(例如按日期)通常会导致空分区。如果您在使用带有空分区的数据帧时遇到问题,根据MRocklin的指导,这里有一个函数可以剔除它们:

def cull_empty_partitions(df):
    ll = list(df.map_partitions(len).compute())
    df_delayed = df.to_delayed()
    df_delayed_new = list()
    pempty = None
    for ix, n in enumerate(ll):
        if 0 == n:
            pempty = df.get_partition(ix)
        else:
            df_delayed_new.append(df_delayed[ix])
    if pempty is not None:
        df = dd.from_delayed(df_delayed_new, meta=pempty)
    return df

对于处理行李(非数据帧)的任何人,此功能将实现以下功能:

def cull_empty_partitions(bag):
    """
    When bags are created by filtering or grouping from a different bag,
    it retains the original bag's partition count, even if a lot of the
    partitions become empty.
    Those extra partitions add overhead, so it's nice to discard them.
    This function drops the empty partitions.
    """
    bag = bag.persist()
    def get_len(partition):
        # If the bag is the result of bag.filter(),
        # then each partition is actually a 'filter' object,
        # which has no __len__.
        # In that case, we must convert it to a list first.
        if hasattr(partition, '__len__'):
            return len(partition)
        return len(list(partition))
    partition_lengths = bag.map_partitions(get_len).compute()

    # Convert bag partitions into a list of 'delayed' objects
    lengths_and_partitions = zip(partition_lengths, bag.to_delayed())

    # Drop the ones with empty partitions
    partitions = (p for l,p in lengths_and_partitions if l > 0)

    # Convert from list of delayed objects back into a Bag.
    return dask.bag.from_delayed(partitions)

没有简单的API可以做到这一点。你可以打电话 df.map_partitions(len) 要确定哪些分区是空的,然后显式删除它们,可以使用 df.to_delayed() 和 dask.dataframe.from_delayed(...) .

如果您将来发现一个函数不能很好地与空分区配合使用,如果您愿意提出问题,我们将不胜感激。 https://github.com/dask/dask/issues/new

下面是我删除空分区的尝试:

import numpy as np

def remove_empty_partitions(ddf):
    """ remove empty partitions """
    partition_lens = ddf.map_partitions(len).compute()
    ids_of_empty_partitions = np.where(partition_lens==0)
    if len(ids_of_empty_partitions) == len(partition_lens):
        # all partitions are empty
        ddf_nonzero = ddf.partitions[0]
    elif len(ids_of_empty_partitions)>0:
        ddf_nonzero = dd.concat([
            ddf.get_partition(num_partition) for num_partition, partition in enumerate(ddf.partitions)
            if num_partition not in ids_of_empty_partitions
        ])
    return ddf_nonzero

FWIW,@tpegbert的答案似乎在获取过滤数据帧所需的任务数量方面更有效。