按行块处理 dask 数据帧

Question

我有一个使用某个 blocksize 的块创建的 dask 数据框：

df = dd.read_csv(filepath, blocksize = blocksize * 1024 * 1024)

我可以像这样分块处理它：

partial_results = []
for partition in df.partitions:
    partial = trivial_func(partition[var])
    partial_results.append(partial)
result = delayed(sum)(partial_results)

（这里我尝试使用map_partitions，但最终只使用了for 循环）。直到这部分一切正常。

现在，我需要对相同的数据运行一个函数，但是这个函数需要接收一定数量的数据帧（例如rows_per_chunk=60），这可以实现吗？有了熊猫，我会这样做：

partial_results = []
for i in range(int(len_df/rows_per_chunk)): # I think ceil would be better if decimal
    arg_data = df.iloc[i*rows_per_chunk:(i+1)*rows_per_chunk]
    partial = not_so_trivial_func(arg_data)
    partial_results.append(partial)
result = sum(partial_results)

是否可以用 dask 做这样的事情？我知道由于延迟评估，无法使用iloc，但是否可以以不同的方式对数据帧进行分区？如果不是，那么使用 dask 实现这一目标的最有效方法是什么？数据框有数百万行。

Answer 1

您可以沿着定义如何跨分区分配索引值的分区重新分区数据帧（假设唯一索引）。

import dask.dataframe as dd
import pandas as pd

df = pd.DataFrame(range(15), columns=['x'])
ddf = dd.from_pandas(df, npartitions=3)

# there will 5 rows per partition
print(ddf.map_partitions(len).compute())

# you can see that ddf is split along these index values
print(ddf.divisions)

# change the divisions to have the desired spacing
new_divisions = (0, 3, 6, 9, 12, 14)
new_ddf = ddf.repartition(divisions=new_divisions)

# now there will be 3 rows per partition
print(new_ddf.map_partitions(len).compute())

如果索引未知，则可以创建一个新索引（假设行不需要排序）并沿计算的分区重新分区：

import dask.dataframe as dd
import pandas as pd

# save some data into unindexed csv
num_rows = 15
df = pd.DataFrame(range(num_rows), columns=['x'])
df.to_csv('dask_test.csv', index=False)


# read from csv
ddf = dd.read_csv('dask_test.csv', blocksize=10)

# assume that rows are already ordered (so no sorting is needed)
# then can modify the index using the lengths of partitions
cumlens = ddf.map_partitions(len).compute().cumsum()

# since processing will be done on a partition-by-partition basis, save them
# individually
new_partitions = [ddf.partitions[0]]
for npart, partition in enumerate(ddf.partitions[1:].partitions):
    partition.index = partition.index + cumlens[npart]
    new_partitions.append(partition)

# this is our new ddf
ddf = dd.concat(new_partitions)

#  set divisions based on cumulative lengths
ddf.divisions = tuple([0] + cumlens.tolist())

# change the divisions to have the desired spacing
new_partition_size = 12
max_rows = cumlens.tolist()[-1]
new_divisions = list(range(0, max_rows, new_partition_size))
if new_divisions[-1]<max_rows:
    new_divisions.append(max_rows)
new_ddf = ddf.repartition(divisions=new_divisions)

# now there will be desired rows per partition
print(new_ddf.map_partitions(len).compute())