pyspark在大 pandas 中的flatMap [英] pyspark's flatMap in pandas

问题描述

熊猫中是否存在与 flatMap示例:

flatMap example:

>>> rdd = sc.parallelize([2, 3, 4])
>>> sorted(rdd.flatMap(lambda x: range(1, x)).collect())
[1, 1, 1, 2, 2, 3]

到目前为止，我想到的是apply，然后是itertools.chain，但我想知道是否有一个单步解决方案.

So far I can think of apply followed by itertools.chain, but I am wondering if there is a one-step solution.

推荐答案

有一个hack.我经常做类似的事情

There's a hack. I often do something like

In [1]: import pandas as pd

In [2]: df = pd.DataFrame({'x': [[1, 2], [3, 4, 5]]})

In [3]: df['x'].apply(pd.Series).unstack().reset_index(drop=True)
Out[3]:
0     1
1     3
2     2
3     4
4   NaN
5     5
dtype: float64

NaN的引入是因为中间对象创建了MultiIndex，但是对于很多事情，您可以将其删除:

The introduction of NaN is because the intermediate object creates a MultiIndex, but for a lot of things you can just drop that:

In [4]: df['x'].apply(pd.Series).unstack().reset_index(drop=True).dropna()
Out[4]:
0    1
1    3
2    2
3    4
5    5
dtype: float64

此技巧使用了所有的熊猫代码，因此我希望它是相当有效的，尽管它可能不喜欢大小不同的列表之类的东西.

This trick uses all pandas code, so I would expect it to be reasonably efficient, though it might not like things like very different sized lists.

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