array_schema = ArrayType(StructType([ StructField("col1", StringType(), True), StructField("col2", StringType(), True), StructField("col3", StringType(), True) ])) 将数组的数组转换为结构的数组: 代码语言:txt 复制 array_of_structs = spark.createDataFrame([(row,) for row in ...
当您访问时,array of structs我们需要给出我们需要访问数组0,1,2..中的哪个元素,即等。 如果我们需要选择数组的所有元素,那么我们需要使用explode(). Example: df.printSchema()#root# |-- result_set: struct (nullable = true)# | |-- currency: string (nullable = true)# | |-- dates: array (nu...
return isinstance(dtype, (MapType, StructType, ArrayType)) def complex_dtypes_to_json(df): """Converts all columns with complex dtypes to JSON Args: df: Spark dataframe Returns: tuple: Spark dataframe and dictionary of converted columns and their data types """ conv_cols = dict() selects...
是的，这很慢。所以一个更好的方法是不要在一开始就创建副本。也许你可以通过在爆炸前先调用array_...
schema) # flattened_schema = ["root-element", # "root-element-array-primitive", # "root-element-array-of-structs.d1.d2", # "nested-structure.n1", # "nested-structure.d1.d2"] Hash Replace a nested field by its SHA-2 hash value. By default the number of bits in the output ...
It looks like you are using a scalar pandas_udf type, which doesn't support returning structs currently. I believe the return type you want is an array of strings, which is supported, so this should work. Try this: @pandas_udf("array<string>") def stringClassifier(x,y,z): # return...
是的，这很慢。所以一个更好的方法是不要在一开始就创建副本。也许你可以通过在爆炸前先调用array_...
。如果列类型有任何差异，请在使用UDF之前将列转换为通用类型。你可以简单地使用f.array，但是你必须在...
assert len(set(dtypes))==1, 'All columns have to be of the same type' # Create and explode an array of (column_name, column_value) structs kvs = explode(array([struct(lit(c).alias('key1'), col(c).alias('val')) for c in cols])).alias('kvs') ...
Using Pyspark to Flatten Dataframe with ArrayType of Nested Structs Question: I have a dataframe with this schema root |-- AUTHOR_ID: integer (nullable = false) |-- NAME: string (nullable = true) |-- Books: array (nullable = false) | |-- element: struct (containsNull = false) | ...