从列表PySpark的列表创建单行数据框 [英] Create single row dataframe from list of list PySpark

问题描述

我有一个像这样的数据data = [[1.1, 1.2], [1.3, 1.4], [1.5, 1.6]]我想创建一个PySpark数据框

I have a data like this data = [[1.1, 1.2], [1.3, 1.4], [1.5, 1.6]] I want to create a PySpark dataframe

我已经使用

dataframe = SQLContext.createDataFrame(data, ['features'])

但我总是得到

+--------+---+
|features| _2|
+--------+---+
|     1.1|1.2|
|     1.3|1.4|
|     1.5|1.6|
+--------+---+

如何获得如下所示的结果?

how can I get result like below?

+----------+
|features  |
+----------+
|[1.1, 1.2]|
|[1.3, 1.4]|
|[1.5, 1.6]|
+----------+

推荐答案

我发现将createDataFrame()的参数视为元组列表很有用，其中列表中的每个条目对应于DataFrame中的一行，每个元组的元素对应于一列.

I find it's useful to think of the argument to createDataFrame() as a list of tuples where each entry in the list corresponds to a row in the DataFrame and each element of the tuple corresponds to a column.

您可以通过将列表中的每个元素设置为元组来获得所需的输出:

You can get your desired output by making each element in the list a tuple:

data = [([1.1, 1.2],), ([1.3, 1.4],), ([1.5, 1.6],)]
dataframe = sqlCtx.createDataFrame(data, ['features'])
dataframe.show()
#+----------+
#|  features|
#+----------+
#|[1.1, 1.2]|
#|[1.3, 1.4]|
#|[1.5, 1.6]|
#+----------+

或者，如果更改来源很麻烦，您也可以这样做:

Or if changing the source is cumbersome, you can equivalently do:

data = [[1.1, 1.2], [1.3, 1.4], [1.5, 1.6]]
dataframe = sqlCtx.createDataFrame(map(lambda x: (x, ), data), ['features'])
dataframe.show()
#+----------+
#|  features|
#+----------+
#|[1.1, 1.2]|
#|[1.3, 1.4]|
#|[1.5, 1.6]|
#+----------+

这篇关于从列表PySpark的列表创建单行数据框的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！