我有一个由字典列表组成的数据框，想要拆分每个字典并根据其中一个键值创建一行。
样本数据：
[{"col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD"},{"col.1":"13ABC","col.2":"141","col.3":"","col.4":"ABCD"}]
输入数据框：
+---------------------------------------------------------------------------------------------------------------------------+
|ID|DATASET |
+---------------------------------------------------------------------------------------------------------------------------+
|4A|[{"col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD"},{"col.1":"13ABC","col.2":"141","col.3":"","col.4":"ABCD"}] |
|4B|[] |
+---------------------------------------------------------------------------------------------------------------------------+
预期结果：
+-----------------------------------------------------------------+
|ID|col_1 | col_2 | col |
+-----------------------------------------------------------------+
|4A|"12ABC"|"{"col.2":"141","col.3":"","col.4":"ABCD"}" |
|4A|"13ABC"|"{"col.2":"141","col.3":"","col.4":"ABCD"}" |
|4B|""|"" |
+-----------------------------------------------------------------+
试图为数据集col创建模式并分离数据，但不确定是否将它们分组并基于col.1值进行合并
schema = spark.read.json（df.rdd.map（lambda row：row.dataset））。schema
，也称为Convert Pyspark dataframe to dictionary
预先感谢
已编辑
df2.withColumn("col", f.to_json(f.struct("`col.1`","`col.2`"))).show(truncate=False)
结果：
+---+-----+-----+-----+-----+-------------------------------+
|ID |col.1|col.2|col.3|col.4|col |
+---+-----+-----+-----+-----+-------------------------------+
|4A |12ABC|141 | |ABCD |{"col.1":"12ABC","col.2":"141"}|
|4A |13ABC|141 | |ABCD |{"col.1":"13ABC","col.2":"141"}|
+---+-----+-----+-----+-----+-------------------------------+
答案 0 :(得分：0)
我尝试了以下方法，并且有效。不确定是否是优化方法，是否愿意提供投入和改进。
>>> df.show(truncate=False)
+--------------------------------------------------------------------------------------------------------------------+---+-----+-----+
|DATASET |ID |count|level|
+--------------------------------------------------------------------------------------------------------------------+---+-----+-----+
|[{"col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD"},{"col.1":"13ABC","col.2":"141","col.3":"","col.4":"FCD"}]|4A |156 |36 |
|[] |4B |179 |258 |
|[{"col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD"}] |4C |222 |158 |
|[{"col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD"},{"col.1":"12ABC","col.2":"1","col.3":"","col.4":"ECD"}] |4D |222 |158 |
+--------------------------------------------------------------------------------------------------------------------+---+-----+-----+
# Splitting list of dictionaries into rows
>>> df2 = df.withColumn("data",explode(split(regexp_replace(col("DATASET"), "(^\[)|(\]$)", ""), ", "))).withColumn("data",explode(split('data','},'))).withColumn("data",explode(split(regexp_replace(col("data"), "(^\{)|(\}$)", ""), ", ")))
>>> df2 = df2.drop(df2.DATASET)
>>> df2.show(truncate=False)
+---+-----+-----+-------------------------------------------------------+
|ID |count|level|data |
+---+-----+-----+-------------------------------------------------------+
|4A |156 |36 |"col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD"|
|4A |156 |36 |"col.1":"13ABC","col.2":"141","col.3":"","col.4":"FCD" |
|4B |179 |258 | |
|4C |222 |158 |"col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD"|
|4D |222 |158 |"col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD"|
|4D |222 |158 |"col.1":"12ABC","col.2":"1","col.3":"","col.4":"ECD" |
+---+-----+-----+-------------------------------------------------------+
#Getting col.1 value from the seperated dict
>>> col_1 = F.split(df2['data'],',')
>>> df3 = df2.withColumn('col_1', col_1.getItem(0))
>>> df3.show(truncate=False)
+---+-----+-----+-------------------------------------------------------+---------------+
|ID |count|level|data |col_1 |
+---+-----+-----+-------------------------------------------------------+---------------+
|4A |156 |36 |"col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD"|"col.1":"12ABC"|
|4A |156 |36 |"col.1":"13ABC","col.2":"141","col.3":"","col.4":"FCD" |"col.1":"13ABC"|
|4B |179 |258 | | |
|4C |222 |158 |"col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD"|"col.1":"12ABC"|
|4D |222 |158 |"col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD"|"col.1":"12ABC"|
|4D |222 |158 |"col.1":"12ABC","col.2":"1","col.3":"","col.4":"ECD" |"col.1":"12ABC"|
+---+-----+-----+-------------------------------------------------------+---------------+
#Getting value of col.1
>>> col_1 = F.split(df3['col_1'],':')
>>> df3 =df3.withColumn('col_1',col_1.getItem(1)).drop(df3.col_1)
>>> df3.show(truncate=False)
+---+-----+-----+-------------------------------------------------------+-------+
|ID |count|level|data |col_1 |
+---+-----+-----+-------------------------------------------------------+-------+
|4A |156 |36 |"col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD"|"12ABC"|
|4A |156 |36 |"col.1":"13ABC","col.2":"141","col.3":"","col.4":"FCD" |"13ABC"|
|4B |179 |258 | |null |
|4C |222 |158 |"col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD"|"12ABC"|
|4D |222 |158 |"col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD"|"12ABC"|
|4D |222 |158 |"col.1":"12ABC","col.2":"1","col.3":"","col.4":"ECD" |"12ABC"|
+---+-----+-----+-------------------------------------------------------+-------+
#Grouping data by ID and col_1 columns
>>> grp_df = df3.groupBy(grouping_cols).agg(collect_list("data").name("dataset"))
>>> grp_df.show(truncate=False)
+-------+---+---------------------------------------------------------------------------------------------------------------+
|col_1 |ID |dataset |
+-------+---+---------------------------------------------------------------------------------------------------------------+
|"13ABC"|4A |["col.1":"13ABC","col.2":"141","col.3":"","col.4":"FCD"] |
|null |4B |[] |
|"12ABC"|4D |["col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD", "col.1":"12ABC","col.2":"1","col.3":"","col.4":"ECD"]|
|"12ABC"|4A |["col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD"] |
|"12ABC"|4C |["col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD"] |
+-------+---+---------------------------------------------------------------------------------------------------------------+
>>> grp_df = grp_df.selectExpr("col_1 as grp_col", "ID as ID","dataset as dataset")
>>> grp_df.show(truncate=False)
+-------+---+---------------------------------------------------------------------------------------------------------------+
|grp_col|ID |dataset |
+-------+---+---------------------------------------------------------------------------------------------------------------+
|"13ABC"|4A |["col.1":"13ABC","col.2":"141","col.3":"","col.4":"FCD"] |
|null |4B |[] |
|"12ABC"|4D |["col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD", "col.1":"12ABC","col.2":"1","col.3":"","col.4":"ECD"]|
|"12ABC"|4A |["col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD"] |
|"12ABC"|4C |["col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD"] |
+-------+---+---------------------------------------------------------------------------------------------------------------+
# joining grouped df with main dataframe and removing dupes
>>> final_df = df3.join(grp_df,'ID').dropDuplicates().drop('data').drop('col_1')
>>> final_df.show(truncate=False)
+---+-----+-----+-------+---------------------------------------------------------------------------------------------------------------+
|ID |count|level|grp_col|dataset |
+---+-----+-----+-------+---------------------------------------------------------------------------------------------------------------+
|4B |179 |258 |null |[] |
|4C |222 |158 |"12ABC"|["col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD"] |
|4A |156 |36 |"13ABC"|["col.1":"13ABC","col.2":"141","col.3":"","col.4":"FCD"] |
|4A |156 |36 |"12ABC"|["col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD"] |
|4A |156 |36 |"13ABC"|["col.1":"13ABC","col.2":"141","col.3":"","col.4":"FCD"] |
|4A |156 |36 |"12ABC"|["col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD"] |
|4D |222 |158 |"12ABC"|["col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD", "col.1":"12ABC","col.2":"1","col.3":"","col.4":"ECD"]|
|4D |222 |158 |"12ABC"|["col.1":"12ABC","col.2":"141","col.3":"","col.4":"ABCD", "col.1":"12ABC","col.2":"1","col.3":"","col.4":"ECD"]|
+---+-----+-----+-------+---------------------------------------------------------------------------------------------------------------+
答案 1 :(得分：0)
我们可以使用posexplode和concat_ws选项代替正则表达式。
df2.select(
"*",
F.posexplode(F.split("DATASET", ",")).alias("pos", "token")
)\
.where("pos > 0")\
.groupBy("ID", "DATASET")\
.agg(F.concat_ws("_" ,F.collect_list("token")).alias("data_cols"))\
.select(
"ID",
F.split("DATASET", ",").getItem(0).alias("col_id"),
"data"
)\
.show(truncate=False)