How to rename duplicated columns after join? [duplicate] - apache-spark

Question

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How to avoid duplicate columns after join?
(10 answers)
Closed 4 years ago.
I want to use join with 3 dataframe, but there are some columns we don't need or have some duplicate name with other dataframes, so I want to drop some columns like below:
result_df = (aa_df.join(bb_df, 'id', 'left')
.join(cc_df, 'id', 'left')
.withColumnRenamed(bb_df.status, 'user_status'))
Please note that status column is in two dataframes, i.e. aa_df and bb_df.
The above doesn't work. I also tried to use withColumn, but the new column is created, and the old column is still existed.

Answer 1

If you are trying to rename the status column of bb_df dataframe then you can do so while joining as
result_df = aa_df.join(bb_df.withColumnRenamed('status', 'user_status'),'id', 'left').join(cc_df, 'id', 'left')

Answer 2

I want to use join with 3 dataframe, but there are some columns we don't need or have some duplicate name with other dataframes
That's a fine use case for aliasing a Dataset using alias or as operators.
alias(alias: String): Dataset[T] or alias(alias: Symbol): Dataset[T]
Returns a new Dataset with an alias set. Same as as.
as(alias: String): Dataset[T] or as(alias: Symbol): Dataset[T]
Returns a new Dataset with an alias set.
(And honestly I did only now see the Symbol-based variants.)
NOTE There are two as operators, as for aliasing and as for type mapping. Consult the Dataset API.
After you've aliases a Dataset, you can reference columns using [alias].[columnName] format. This is particularly handy with joins and star column dereferencing using *.
val ds1 = spark.range(5)
scala> ds1.as('one).select($"one.*").show
+---+
| id|
+---+
| 0|
| 1|
| 2|
| 3|
| 4|
+---+
val ds2 = spark.range(10)
// Using joins with aliased datasets
// where clause is in a longer form to demo how ot reference columns by alias
scala> ds1.as('one).join(ds2.as('two)).where($"one.id" === $"two.id").show
+---+---+
| id| id|
+---+---+
| 0| 0|
| 1| 1|
| 2| 2|
| 3| 3|
| 4| 4|
+---+---+
so I want to drop some columns like below
My general recommendation is not to drop columns, but select what you want to include in the result. That makes life more predictable as you know what you get (not what you don't). I was told that our brains work by positives which could also make a point for select.
So, as you asked and I showed in the above example, the result has two columns of the same name id. The question is how to have only one.
There are at least two answers with using the variant of join operator with the join columns or condition included (as you did show in your question), but that would not answer your real question about "dropping unwanted columns", would it?
Given I prefer select (over drop), I'd do the following to have a single id column:
val q = ds1.as('one)
.join(ds2.as('two))
.where($"one.id" === $"two.id")
.select("one.*") // <-- select columns from "one" dataset
scala> q.show
+---+
| id|
+---+
| 0|
| 1|
| 2|
| 3|
| 4|
+---+
Regardless of the reasons why you asked the question (which could also be answered with the points I raised above), let me answer the (burning) question how to use withColumnRenamed when there are two matching columns (after join).
Let's assume you ended up with the following query and so you've got two id columns (per join side).
val q = ds1.as('one)
.join(ds2.as('two))
.where($"one.id" === $"two.id")
scala> q.show
+---+---+
| id| id|
+---+---+
| 0| 0|
| 1| 1|
| 2| 2|
| 3| 3|
| 4| 4|
+---+---+
withColumnRenamed won't work for this use case since it does not accept aliased column names.
scala> q.withColumnRenamed("one.id", "one_id").show
+---+---+
| id| id|
+---+---+
| 0| 0|
| 1| 1|
| 2| 2|
| 3| 3|
| 4| 4|
+---+---+
You could select the columns you're interested in as follows:
scala> q.select("one.id").show
+---+
| id|
+---+
| 0|
| 1|
| 2|
| 3|
| 4|
+---+
scala> q.select("two.*").show
+---+
| id|
+---+
| 0|
| 1|
| 2|
| 3|
| 4|
+---+

Answer 3

Please see the docs : withColumnRenamed()
You need to pass the name of the existing column and the new name to the function. Both of these should be strings.
result_df = aa_df.join(bb_df,'id', 'left').join(cc_df, 'id', 'left').withColumnRenamed('status', 'user_status')
If you have 'status' columns in 2 dataframes, you can use them in the join as aa_df.join(bb_df, ['id','status'], 'left') assuming aa_df and bb_df have the common column. This way you will not end up having 2 'status' columns.