pyspark 数据框 withColumn 命令不起作用 [英] pyspark dataframe withColumn command not working
问题描述
我有一个输入数据框:df_input(更新的 df_input)
I have a input dataframe: df_input (updated df_input)
|comment|inp_col|inp_val|
|11 |a |a1 |
|12 |a |a2 |
|15 |b |b3 |
|16 |b |b4 |
|17 |c |&b |
|17 |c |c5 |
|17 |d |&c |
|17 |d |d6 |
|17 |e |&d |
|17 |e |e7 |
我想将 inp_val 列中的变量替换为其值.我已尝试使用以下代码创建一个新列.
I want to replace the variable in inp_val column to its value. I have tried with the below code to create a new column.
取以'&'开头的值列表
Taken the list of values which starts with '&'
df_new = df_inp.select(inp_val).where(df.inp_val.substr(0, 1) == '&')
现在我正在遍历列表以替换&"列值数据到它的原始列表.
Now I'm iterating over the list to replace the '&' column value data to it original list.
for a in [row[inp_val] for row in df_new.collect()]
df_inp = df_inp.withColumn
(
'new_col',
when(df.inp_val.substr(0, 1) == '&',
[row[inp_val] for row in df.select(df.inp_val).where(df.inp_col == a[1:]).collect()])
.otherwise(df.inp_val)
)
但是,我收到如下错误:
But, I'm getting error as below:
Java.lang.RuntimeException: Unsupported literal tpe class java.util.ArrayList [[5], [6]]
基本上我想要如下输出.请检查并告诉我错误在哪里???.我在想,我想按照上面的代码插入两种类型的数据类型值??
Basically I want the output as below. Please check and let me know where is the error???. I was thinking that two type of datatype values I'm trying to insert as per the above code??
更新的代码行:
tst_1 = tst.withColumn("col3_extract", when(tst.col3.substr(0, 1) == '&', regexp_replace(tst.col3, "&", "")).otherwise(""))
# Select which values need to be replaced; withColumnRenamed will also solve spark self join issues
# The substring search can also be done using regex function
tst_filter=tst.where(~F.col('col3').contains('&')).withColumnRenamed('col2','col2_collect')
# For the selected data, perform a collect list
tst_clct = tst_filter.groupby('col2_collect').agg(F.collect_list('col3').alias('col3_collect'))
#%% Join the main table with the collected list
tst_join = tst_1.join(tst_clct,on=tst_1.col3_extract==tst_clct.col2_collect,how='left').drop('col2_collect')
#%% In the column3 replace the values such as a, b
tst_result = tst_join.withColumn("result",F.when(~F.col('col3').contains('&'),F.array(F.col('col3'))).otherwise(F.col('col3_collect')))
但是,上面的代码不适用于多次迭代
更新的预期输出:
|comment|inp_col|inp_val|new_col |
|11 |a |a1 |['a1'] |
|12 |a |a2 |['a2'] |
|15 |b |b3 |['b3'] |
|16 |b |b4 |['b4'] |
|17 |c |&b |['b3', 'b4'] |
|18 |c |c5 |['c5'] |
|19 |d |&c |['b3', 'b4', 'c5'] |
|20 |d |d6 |['d6'] |
|21 |e |&d |['b3', 'b4', 'c5', 'd6'] |
|22 |e |e7 |['e7'] |
推荐答案
试试这个,self-join
with collected list
在 rlike 加入条件
是要走的路.
Try this, self-join
with collected list
on rlike join condition
is the way to go.
df.show() #sampledataframe
#+-------+---------+---------+
#|comment|input_col|input_val|
#+-------+---------+---------+
#| 11| a| 1|
#| 12| a| 2|
#| 15| b| 5|
#| 16| b| 6|
#| 17| c| &b|
#| 17| c| 7|
#+-------+---------+---------+
df.join(df.groupBy("input_col").agg(F.collect_list("input_val").alias("y1"))
.withColumnRenamed("input_col","x1"),F.expr("""input_val rlike x1"""),'left')
.withColumn("new_col", F.when(F.col("input_val").cast("int").isNotNull(), F.array("input_val"))
.otherwise(F.col("y1"))).drop("x1","y1").show()
#+-------+---------+---------+-------+
#|comment|input_col|input_val|new_col|
#+-------+---------+---------+-------+
#| 11| a| 1| [1]|
#| 12| a| 2| [2]|
#| 15| b| 5| [5]|
#| 16| b| 6| [6]|
#| 17| c| &b| [5, 6]|
#| 17| c| 7| [7]|
#+-------+---------+---------+-------+
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