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我目前正在尝试保存和读取从 dask 到镶木地板文件的信息。但是，当尝试使用 dask "to_parquet" 保存数据帧并随后使用 "read_parquet" 再次加载时，似乎分区信息丢失了。

>>df.divisions
(Timestamp('2014-10-01 17:25:17.928000'), Timestamp('2014-10-01 17:27:18.000860'), Timestamp('2014-10-01 17:29:19.000860'), Timestamp('2014-10-01 17:31:19.000860'), Timestamp('2014-10-01 17:33:20.000860'), Timestamp('2014-10-01 17:35:20.763000'), Timestamp('2014-10-01 17:36:12.992860'))
>>df.to_parquet(folder)
>>del df
>>df = dask.dataframe.read_parquet(folder)
>>df.divisions
(None, None, None, None, None, None, None)

这是故意的吗？我目前的解决方法是在加载后再次设置索引，但这需要很长时间。

>> df = dask.dataframe.read_parquet(folder,index=False).set_index('timestamp', sorted=True)
>> df.divisions
(Timestamp('2014-10-01 17:25:17.928000'), Timestamp('2014-10-01 17:27:18.000860'), Timestamp('2014-10-01 17:29:19.000860'), Timestamp('2014-10-01 17:31:19.000860'), Timestamp('2014-10-01 17:33:20.000860'), Timestamp('2014-10-01 17:35:20.763000'), Timestamp('2014-10-01 17:36:12.992860'))

或者我在保存和加载时错过了一些选项？

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1 回答 1

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用 fastparquet 后端测试，似乎可以工作：

> import pandas.util.testing as tm
> df = tm.makeTimeDataFrame()
> df
                   A         B         C         D
2000-01-03 -0.414197  0.459438  1.105962 -0.791487
2000-01-04 -0.875873  0.987601  0.881839 -1.339756
2000-01-05  0.552543  3.415769  1.008780  0.127757
...
> d = dd.from_pandas(df, 2)
> d.to_parquet('temp.parq')
> dd.read_parquet('temp.parq').divisions
(Timestamp('2000-01-03 00:00:00'),
 Timestamp('2000-01-24 00:00:00'),
 Timestamp('2000-02-11 00:00:00'))
于 2017-11-23T23:25:44.513 回答