从列表 PySpark 创建单行数据框 [英] Create single row dataframe from list of list PySpark
问题描述
我有这样的数据 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
我已经用了
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|
+--------+---+
我怎样才能得到如下结果?
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,元组的每个元素对应一列.
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]|
#+----------+
或者如果更改源很麻烦,您可以等效地执行:
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]|
#+----------+
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