问题
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 |
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.
Taken the list of values which starts with '&'
df_new = df_inp.select(inp_val).where(df.inp_val.substr(0, 1) == '&')
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)
)
But, I'm getting error as below:
Java.lang.RuntimeException: Unsupported literal tpe class java.util.ArrayList [[5], [6]]
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??
Updated lines of code:
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')))
But, the above code doesn't work on the multiple iterations
Updated Expected Output:
|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'] |
回答1:
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]|
#+-------+---------+---------+-------+
回答2:
You can simply use regex_replace like this:
df.withColumn("new_col", regex_replace(col("inp_val"), "&", ""))
回答3:
Can you tryout this solution. Your approach may run into whole lot of problems.
import pyspark.sql.functions as F
from pyspark.sql.functions import col
from pyspark.sql.window import Window
#Test data
tst = sqlContext.createDataFrame([(1,'a','3'),(1,'a','4'),(1,'b','5'),(1,'b','7'),(2,'c','&b'),(2,'c','&a'),(2,'d','&b')],schema=['col1','col2','col3'])
# extract the special character out
tst_1 = tst.withColumn("col3_extract",F.substring(F.col('col3'),2,1))
# Selecct 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')))
Results :
+----+----+----+------------+------------+------+
|col1|col2|col3|col3_extract|col3_collect|result|
+----+----+----+------------+------------+------+
| 2| c| &a| a| [3, 4]|[3, 4]|
| 2| c| &b| b| [7, 5]|[7, 5]|
| 2| d| &b| b| [7, 5]|[7, 5]|
| 1| a| 3| | null| [3]|
| 1| a| 4| | null| [4]|
| 1| b| 5| | null| [5]|
| 1| b| 7| | null| [7]|
+----+----+----+------------+------------+------+
来源：https://stackoverflow.com/questions/62741589/pyspark-dataframe-withcolumn-command-not-working