如何在 PySpark 中将字典转换为数据帧？

Question

我正在尝试将字典：转换 data_dict = {'t1': '1', 't2': '2', 't3': '3'}为数据框：

key   |   value|
----------------
t1          1
t2          2
t3          3

为此，我尝试了：

schema = StructType([StructField("key", StringType(), True), StructField("value", StringType(), True)])
ddf = spark.createDataFrame(data_dict, schema)

但我收到以下错误：

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/Cellar/apache-spark/2.4.5/libexec/python/pyspark/sql/session.py", line 748, in createDataFrame
    rdd, schema = self._createFromLocal(map(prepare, data), schema)
  File "/usr/local/Cellar/apache-spark/2.4.5/libexec/python/pyspark/sql/session.py", line 413, in _createFromLocal
    data = list(data)
  File "/usr/local/Cellar/apache-spark/2.4.5/libexec/python/pyspark/sql/session.py", line 730, in prepare
    verify_func(obj)
  File "/usr/local/Cellar/apache-spark/2.4.5/libexec/python/pyspark/sql/types.py", line 1389, in verify
    verify_value(obj)
  File "/usr/local/Cellar/apache-spark/2.4.5/libexec/python/pyspark/sql/types.py", line 1377, in verify_struct
    % (obj, type(obj))))
TypeError: StructType can not accept object 't1' in type <class 'str'>

所以我尝试了这个，没有指定任何模式，而只是列数据类型： ddf = spark.createDataFrame(data_dict, StringType()&ddf = spark.createDataFrame(data_dict, StringType(), StringType())

但两者都会产生一个具有一列的数据框，该列是字典的关键，如下所示：

+-----+
|value|
+-----+
|t1   |
|t2   |
|t3   |
+-----+

谁能告诉我如何将字典转换为 PySpark 中的 Spark 数据框？

Answer 1

您可以使用data_dict.items()列出键/值对：

spark.createDataFrame(data_dict.items()).show()

哪个打印

+---+---+
| _1| _2|
+---+---+
| t1|  1|
| t2|  2|
| t3|  3|
+---+---+

当然，您可以指定您的架构：

spark.createDataFrame(data_dict.items(), 
                      schema=StructType(fields=[
                          StructField("key", StringType()), 
                          StructField("value", StringType())])).show()

导致

+---+-----+
|key|value|
+---+-----+
| t1|    1|
| t2|    2|
| t3|    3|
+---+-----+