在Apache Spark中使用pyspark进行数据帧转置

本文介绍了在Apache Spark中使用pyspark进行数据帧转置的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我有一个数据帧df，其结构如下:

I have a dataframe df that have following structure:

+-----+-----+-----+-------+ | s |col_1|col_2|col_...| +-----+-----+-----+-------+ | f1 | 0.0| 0.6| ... | | f2 | 0.6| 0.7| ... | | f3 | 0.5| 0.9| ... | | ...| ...| ...| ... |

我想计算此数据帧的转置，所以它看起来像

And I want to calculate the transpose of this dataframe so it will be look like

+-------+-----+-----+-------+------+ | s | f1 | f2 | f3 | ...| +-------+-----+-----+-------+------+ |col_1 | 0.0| 0.6| 0.5 | ...| |col_2 | 0.6| 0.7| 0.9 | ...| |col_...| ...| ...| ... | ...|

我捆绑了这两个解决方案，但返回的数据框没有指定的使用方法:

I tied this two solutions but it returns that dataframe has not the specified used method:

方法1:

for x in df.columns: df = df.pivot(x)

方法2:

df = sc.parallelize([ (k,) + tuple(v[0:]) for k,v in df.items()]).toDF()

我该如何解决.

推荐答案

如果数据足够小以至于可以转置(不随聚合而变化)，则可以将其转换为Pandas DataFrame:

If data is small enough to be transposed (not pivoted with aggregation) you can just convert it to Pandas DataFrame:

df = sc.parallelize([ ("f1", 0.0, 0.6, 0.5), ("f2", 0.6, 0.7, 0.9)]).toDF(["s", "col_1", "col_2", "col_3"]) df.toPandas().set_index("s").transpose() s f1 f2 col_1 0.0 0.6 col_2 0.6 0.7 col_3 0.5 0.9

如果它太大，Spark将无济于事. Spark DataFrame按行分配数据(尽管在本地使用列式存储)，因此单个行的大小仅限于本地内存.

If it is to large for this, Spark won't help. Spark DataFrame distributes data by row (although locally uses columnar storage), therefore size of a individual rows is limited to local memory.