AFAICT, maybe Spark SQL built-in functions[1] can help as below:
scala> df.show()
+----+-------+
| age| name|
+----+-------+
|null|Michael|
| 30| Andy|
| 19| Justin|
+----+-------+
scala> df.filter("length(name) == 4 or substring(name, 1, 1) == 'J'").show()
+---+------+
|age| name|
+---+------+
| 30| Andy|
| 19|Justin|
+---+------+
--
Cheers,
-z
[1] https://spark.apache.org/docs/latest/api/sql/index.html
On Wed, 29 Apr 2020 08:45:26 +0100
Mich Talebzadeh <mich.talebza...@gmail.com> wrote:
> Hi,
>
>
>
> Trying to filter a dataframe with multiple conditions using OR "||" as below
>
>
>
> val rejectedDF = newDF.withColumn("target_mobile_no",
> col("target_mobile_no").cast(StringType)).
>
> filter(length(col("target_mobile_no")) !== 10 ||
> substring(col("target_mobile_no"),1,1) !== "7")
>
>
>
> This throws this error
>
>
>
> res12: org.apache.spark.sql.DataFrame = []
>
> <console>:49: error: value || is not a member of Int
>
> filter(length(col("target_mobile_no")) !== 10 ||
> substring(col("target_mobile_no"),1,1) !== "7")
>
>
>
> Try another way
>
>
>
> val rejectedDF = newDF.withColumn("target_mobile_no",
> col("target_mobile_no").cast(StringType)).
>
> filter(length(col("target_mobile_no")) !=== 10 ||
> substring(col("target_mobile_no"),1,1) !=== "7")
>
> rejectedDF.createOrReplaceTempView("tmp")
>
>
>
> Tried few options but I am still getting this error
>
>
>
> <console>:49: error: value !=== is not a member of
> org.apache.spark.sql.Column
>
> filter(length(col("target_mobile_no")) !=== 10 ||
> substring(col("target_mobile_no"),1,1) !=== "7")
>
> ^
>
> <console>:49: error: value || is not a member of Int
>
> filter(length(col("target_mobile_no")) !=== 10 ||
> substring(col("target_mobile_no"),1,1) !=== "7")
>
>
>
> I can create a dataframe for each filter but that does not look efficient
> to me?
>
>
>
> Thanks
>
>
>
> Dr Mich Talebzadeh
>
>
>
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>
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