1

我的数据是一个 10GB 的文件，格式如下：

[ 1234567890 ][ 2020052701020201 ][ value1 ][ value2 ][ key3 = value3 ]...[ keyn = valuen ]

笔记：

  1. 可以有任意数量的 [ key = value ] 块。
  2. 字符[和]值本身，例如：[ hello = wo[rld] ]
  3. 我无法控制 abinput 文件，除非我可以在我的脚本中更改/处理它。
  4. 我只需要几列，但是它们有字符[和]值。

在我的简单for line in f:功能中，我可以按' ][ '模式拆分。但是考虑到文件的大小，dask 非常有利可图。

我知道engine='c'我不能使用多字符分隔符，但切换到engine='python'会导致不可预测的结果。这是一个例子：

def init_ddf(filename):
    return ddf.read_csv(
        filename,
        blocksize="1GB",
        sep="]",
        usecols=[1, 8],
        na_filter=False,
        names=["hello", World" ],
        engine="c",
    )

上面的代码如预期的那样导致ParserError: Too many columns specified: expected 25 and found 24. 这个错误很难重现，因为它只是由于一些我很难识别的特定行而发生。每次有更多列时都不会发生这种情况。所以在上面的函数中我改变了：engine="python"和sep=" \]\[ "。这适用于我测试的小样本数据。但在 10G 文件中，我得到以下不可预测的行为：

def init_pyddf(filename, usecols, names):
    return ddf.read_csv(
        filename,
        blocksize="1GB",
        sep=" \]\[ ",
        usecols=usecols,
        na_filter=False,
        names=names,
        engine="python",
    )

In [50]: !head   /tmp/foo /tmp/bar
==> /tmp/foo <==
[ 1234567890 ][ 2020052701020201 ][ value1 ][ value2 ][ key3 = value3 ][ keyn = valuen ]
[ 1590471107 ][ 20200526T0731460 ][ THEOQQ ][ e = CL ][ Even = 175134 ][ rded = a12344 ][ blah = INVALID ][ N = T ][ ED = 13606 ]                       

==> /tmp/bar <==
[ 1234567890 ][ 2020052701020201 ][ value1 ][ value2 ][ key3 = value3 ][ keyn = valuen ]
[ 1590471107 ][ 20200526T0731460 ][ THEOQQ ][ e = CL ][ Even = 175134 ][ rded = a12344 ]

In [51]: init_pyddf("/tmp/foo", [1,2], ["time", "name"]).compute()
Out[51]: 
                                               time             name
[ 1234567890 2020052701020201 value1  key3 = value3  keyn = valuen ]
[ 1590471107 20200526T0731460 THEOQQ  Even = 175134    rded = a12344

In [52]: init_pyddf("/tmp/bar", [1,2], ["time", "name"]).compute()
Out[52]: 
               time    name
0  2020052701020201  value1
1  20200526T0731460  THEOQQ

更多示例：

In [110]: !cat /tmp/dummy
[ 0 ][ 000000000000000000000000000 ][ 0 ][ 0 ][ 0 ][ 0 ][ 0 ][ 0 ][ 0 ][ 0 ]
[ 1 ][ 20200526T073146.901861+0200 ][ T ][ E ][ E ][ F ][ W ][ N ][ E ][ E ][ 5 ]

In [111]: init_pyddf("/tmp/dummy", [1,7], ["time", "name"]).compute().head()
Out[111]: 
    time name
[ 0    0    0
[ 1    T    E

In [112]: !cat /tmp/dummy
[ 0 ][ 000000000000000000000000000 ][ 0 ][ 0 ][ 0 ][ 0 ][ 0 ][ 0 ][ 0 ][ 0 ]
[ 1 ][ 20200526T073146.901861+0200 ][ T ][ E ][ E ][ F ][ W ][ N ][ E ][ E ]

In [113]: init_pyddf("/tmp/dummy", [1,7], ["time", "name"]).compute().head()
Out[113]: 
                          time name
0  000000000000000000000000000    0
1  20200526T073146.901861+0200    N

In [119]: !cat /tmp/dummy
[ 0 ][ 000000000000000 ][ 0 ][ 0 ][ 0 ][ 0 ][ 0 ][ 0 ][ 0 ][ 0 ]
[ 1 ][ 20200526T073146 ][ T ][ D ][ F ][ W ][ e ][ E ][ E ][ I ][ T ][ T ][ S ][ S ][ B ][ A ][ E ][ F ][ S ][ P][ T = Y ][ 0 ]

In [120]: init_pyddf("/tmp/dummy", [1,7], ["time", "name"]).compute()
Out[120]: 
                                           time  name
[ 0 000000000000000 0 0 0 0 0 0 0 0 ] NaN  None  None
[ 1 20200526T073146 T D F W e E E I   T       S     S
4

1 回答 1

0

鉴于您有更复杂的基于文本的文件格式，您可能首先从 Dask Bag 开始，使用普通 Python 函数生成 Python 字典，然后使用该to_dataframe方法将该 Bag 转换为 Dask Dataframe。

import dask.bag

b = dask.bag.read_text("my-files.*.txt")

def parse(line: str) -> dict:
    ...

records = b.map(parse)
df = b.to_dataframe()
于 2020-06-13T15:13:13.177 回答