1
我想知道爲什麼dd.from_bcolz（）在調用時立即開始做一些處理（當N列上升並且存在字符串類型列時，它會很快增長）。dask.dataframe.from_bcolz（）開始立即處理
而且dd.read_hdf（）調用時沒有做太多的處理，但只有當dask.dataframe使用 - 那麼read_hdf（）讀取和處理HDF5由大塊大塊......
我怎麼樣read_hdf現在工作，唯一的問題是hdf5表不能有多於1200列，而dataframe不支持列數組。並且hdf5格式畢竟不是列的...
In [1]: import dask.dataframe as dd
In [2]: import pandas as pd
In [3]: import bcolz, random
In [4]: import numpy as np
In [5]: N = int(1e7)
In [6]: int_col = np.linspace(0, 1, N)
In [7]: ct_disk = bcolz.fromiter(((i,i) for i in range(N)), dtype="i8,i8",\
...: count=N, rootdir=r'/mnt/nfs/ct_.bcolz')
In [8]: for i in range(10): ct_disk.addcol(int_col)
In [9]: import dask.dataframe as dd
In [10]: %time dd.from_bcolz(r'/mnt/nfs/ct_.bcolz', chunksize=1000000, lock=False)
CPU times: user 8 ms, sys: 16 ms, total: 24 ms
Wall time: 32.6 ms
Out[10]: dd.DataFrame<from_bc..., npartitions=10, divisions=(0, 1000000, 2000000, ..., 9000000, 9999999)>
In [11]: str_col= [''.join(random.choice('ABCD1234') for _ in range(5)) for i in range(int(N/10))]*10
In [12]: ct_disk.addcol(str_col, dtype='S5')
In [13]: %time dd.from_bcolz(r'/mnt/nfs/ct_.bcolz', chunksize=1000000, lock=False)
CPU times: user 2.36 s, sys: 56 ms, total: 2.42 s
Wall time: 2.44 s
Out[13]: dd.DataFrame<from_bc..., npartitions=10, divisions=(0, 1000000, 2000000, ..., 9000000, 9999999)>
In [14]: for i in range(10): ct_disk.addcol(str_col, dtype='S5')
In [15]: %time dd.from_bcolz(r'/mnt/nfs/ct_.bcolz', chunksize=1000000, lock=False)
CPU times: user 25.3 s, sys: 511 ms, total: 25.8 s
Wall time: 25.9 s
Out[15]: dd.DataFrame<from_bc..., npartitions=10, divisions=(0, 1000000, 2000000, ..., 9000000, 9999999)>
而且，當N（nrows）長大時它會變得更糟。