How do I remove words numerics pyspark
0
I'm trying to remove only words that are numerical from my words array, but the function I created is not working correctly. When I try to view the information from my dataframe the following error message appears
First I converted my string and word tokens
from pyspark.ml.feature import RegexTokenizer
regexTokenizer = RegexTokenizer(
inputCol="description",
outputCol="words_withnumber",
pattern="\W"
)
data = regexTokenizer.transform(data)
I created the function to remove only the numbers
from pyspark.sql.functions import when,udf
from pyspark.sql.types import BooleanType
def is_digit(value):
if value:
return value.isdigit()
else:
return False
is_digit_udf = udf(is_digit, BooleanType())
Call function
data = data.withColumn(
'words_withoutnumber',
when(~is_digit_udf(data['words_withnumber']), data['words_withnumber'])
)
Error:
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 5.0 failed 4 times, most recent failure: Lost task 0.3 in stage 5.0 (TID 14, 10.139.64.4, executor 0): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
Sample Dataframe
+-----------+-----------------------------------------------------------+
|categoryid |description |
+-----------+-----------------------------------------------------------+
| 33004|["short","sarja", "40567","detalhe","couro"] |
| 22033|["multipane","6768686868686867868888","220v","branco"] |
+-----------+-----------------------------------------------------------+
expected result
+-----------+-----------------------------------------------------------+
|categoryid |description |
+-----------+-----------------------------------------------------------+
| 33004|["short","sarja","detalhe","couro"] |
| 22033|["multipane","220v","branco"] |
+-----------+-----------------------------------------------------------+
pyspark apache-spark-sql apache-spark-ml
edited Dec 28 '18 at 17:49
pault
14.2k31947
asked Dec 27 '18 at 21:49
user3661384
11818
|
show 1 more comment
0
I'm trying to remove only words that are numerical from my words array, but the function I created is not working correctly. When I try to view the information from my dataframe the following error message appears
First I converted my string and word tokens
from pyspark.ml.feature import RegexTokenizer
regexTokenizer = RegexTokenizer(
inputCol="description",
outputCol="words_withnumber",
pattern="\W"
)
data = regexTokenizer.transform(data)
I created the function to remove only the numbers
from pyspark.sql.functions import when,udf
from pyspark.sql.types import BooleanType
def is_digit(value):
if value:
return value.isdigit()
else:
return False
is_digit_udf = udf(is_digit, BooleanType())
Call function
data = data.withColumn(
'words_withoutnumber',
when(~is_digit_udf(data['words_withnumber']), data['words_withnumber'])
)
Error:
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 5.0 failed 4 times, most recent failure: Lost task 0.3 in stage 5.0 (TID 14, 10.139.64.4, executor 0): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
Sample Dataframe
+-----------+-----------------------------------------------------------+
|categoryid |description |
+-----------+-----------------------------------------------------------+
| 33004|["short","sarja", "40567","detalhe","couro"] |
| 22033|["multipane","6768686868686867868888","220v","branco"] |
+-----------+-----------------------------------------------------------+
expected result
+-----------+-----------------------------------------------------------+
|categoryid |description |
+-----------+-----------------------------------------------------------+
| 33004|["short","sarja","detalhe","couro"] |
| 22033|["multipane","220v","branco"] |
+-----------+-----------------------------------------------------------+
pyspark apache-spark-sql apache-spark-ml
edited Dec 28 '18 at 17:49
pault
14.2k31947
asked Dec 27 '18 at 21:49
user3661384
11818
1
You udf expects a string, but you are passing an array to it. Also in your sample data frame shoulddescriptionbewords_withnumber?
– Psidom
Dec 27 '18 at 22:01
You need to iterate over the array and filter out the desired words. What version of spark are you using?
– pault
Dec 27 '18 at 22:05
@Psidom, I tried to loop through array but I got the following error message "name 'ArrayType' is not defined
– user3661384
Dec 28 '18 at 12:22
@pault version spark 2.3.1. I see link, i try this filter_udf = udf(lambda row: [x for x in row if is_digit(x)], ArrayType(StringType())) , i receive error "name 'ArrayType' is not defined" – user3661384 25 mins ago
– user3661384
Dec 28 '18 at 12:44
1
from pyspark.sql.types import ArrayType
– pault
Dec 28 '18 at 13:00
|
show 1 more comment
0
0
0
I'm trying to remove only words that are numerical from my words array, but the function I created is not working correctly. When I try to view the information from my dataframe the following error message appears
First I converted my string and word tokens
from pyspark.ml.feature import RegexTokenizer
regexTokenizer = RegexTokenizer(
inputCol="description",
outputCol="words_withnumber",
pattern="\W"
)
data = regexTokenizer.transform(data)
I created the function to remove only the numbers
from pyspark.sql.functions import when,udf
from pyspark.sql.types import BooleanType
def is_digit(value):
if value:
return value.isdigit()
else:
return False
is_digit_udf = udf(is_digit, BooleanType())
Call function
data = data.withColumn(
'words_withoutnumber',
when(~is_digit_udf(data['words_withnumber']), data['words_withnumber'])
)
Error:
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 5.0 failed 4 times, most recent failure: Lost task 0.3 in stage 5.0 (TID 14, 10.139.64.4, executor 0): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
Sample Dataframe
+-----------+-----------------------------------------------------------+
|categoryid |description |
+-----------+-----------------------------------------------------------+
| 33004|["short","sarja", "40567","detalhe","couro"] |
| 22033|["multipane","6768686868686867868888","220v","branco"] |
+-----------+-----------------------------------------------------------+
expected result
+-----------+-----------------------------------------------------------+
|categoryid |description |
+-----------+-----------------------------------------------------------+
| 33004|["short","sarja","detalhe","couro"] |
| 22033|["multipane","220v","branco"] |
+-----------+-----------------------------------------------------------+
pyspark apache-spark-sql apache-spark-ml
edited Dec 28 '18 at 17:49
pault
14.2k31947
asked Dec 27 '18 at 21:49
user3661384
11818
I'm trying to remove only words that are numerical from my words array, but the function I created is not working correctly. When I try to view the information from my dataframe the following error message appears
First I converted my string and word tokens
from pyspark.ml.feature import RegexTokenizer
regexTokenizer = RegexTokenizer(
inputCol="description",
outputCol="words_withnumber",
pattern="\W"
)
data = regexTokenizer.transform(data)
I created the function to remove only the numbers
from pyspark.sql.functions import when,udf
from pyspark.sql.types import BooleanType
def is_digit(value):
if value:
return value.isdigit()
else:
return False
is_digit_udf = udf(is_digit, BooleanType())
Call function
data = data.withColumn(
'words_withoutnumber',
when(~is_digit_udf(data['words_withnumber']), data['words_withnumber'])
)
Error:
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 5.0 failed 4 times, most recent failure: Lost task 0.3 in stage 5.0 (TID 14, 10.139.64.4, executor 0): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
Sample Dataframe
+-----------+-----------------------------------------------------------+
|categoryid |description |
+-----------+-----------------------------------------------------------+
| 33004|["short","sarja", "40567","detalhe","couro"] |
| 22033|["multipane","6768686868686867868888","220v","branco"] |
+-----------+-----------------------------------------------------------+
expected result
+-----------+-----------------------------------------------------------+
|categoryid |description |
+-----------+-----------------------------------------------------------+
| 33004|["short","sarja","detalhe","couro"] |
| 22033|["multipane","220v","branco"] |
+-----------+-----------------------------------------------------------+
pyspark apache-spark-sql apache-spark-ml
pyspark apache-spark-sql apache-spark-ml
edited Dec 28 '18 at 17:49
pault
14.2k31947
asked Dec 27 '18 at 21:49
user3661384
11818
edited Dec 28 '18 at 17:49
pault
14.2k31947
asked Dec 27 '18 at 21:49
user3661384
11818
edited Dec 28 '18 at 17:49
pault
14.2k31947
edited Dec 28 '18 at 17:49
pault
14.2k31947
edited Dec 28 '18 at 17:49
pault
14.2k31947
14.2k31947
asked Dec 27 '18 at 21:49
user3661384
11818
asked Dec 27 '18 at 21:49
user3661384
11818
asked Dec 27 '18 at 21:49
user3661384
11818
11818
1
You udf expects a string, but you are passing an array to it. Also in your sample data frame shoulddescriptionbewords_withnumber?
– Psidom
Dec 27 '18 at 22:01
You need to iterate over the array and filter out the desired words. What version of spark are you using?
– pault
Dec 27 '18 at 22:05
@Psidom, I tried to loop through array but I got the following error message "name 'ArrayType' is not defined
– user3661384
Dec 28 '18 at 12:22
@pault version spark 2.3.1. I see link, i try this filter_udf = udf(lambda row: [x for x in row if is_digit(x)], ArrayType(StringType())) , i receive error "name 'ArrayType' is not defined" – user3661384 25 mins ago
– user3661384
Dec 28 '18 at 12:44
1
from pyspark.sql.types import ArrayType
– pault
Dec 28 '18 at 13:00
|
show 1 more comment
1
You udf expects a string, but you are passing an array to it. Also in your sample data frame shoulddescriptionbewords_withnumber?
– Psidom
Dec 27 '18 at 22:01
You need to iterate over the array and filter out the desired words. What version of spark are you using?
– pault
Dec 27 '18 at 22:05
@Psidom, I tried to loop through array but I got the following error message "name 'ArrayType' is not defined
– user3661384
Dec 28 '18 at 12:22
@pault version spark 2.3.1. I see link, i try this filter_udf = udf(lambda row: [x for x in row if is_digit(x)], ArrayType(StringType())) , i receive error "name 'ArrayType' is not defined" – user3661384 25 mins ago
– user3661384
Dec 28 '18 at 12:44
1
from pyspark.sql.types import ArrayType
– pault
Dec 28 '18 at 13:00
1
1
You udf expects a string, but you are passing an array to it. Also in your sample data frame should
description be words_withnumber?– Psidom
Dec 27 '18 at 22:01
You udf expects a string, but you are passing an array to it. Also in your sample data frame should
description be words_withnumber?– Psidom
Dec 27 '18 at 22:01
You need to iterate over the array and filter out the desired words. What version of spark are you using?
– pault
Dec 27 '18 at 22:05
You need to iterate over the array and filter out the desired words. What version of spark are you using?
– pault
Dec 27 '18 at 22:05
@Psidom, I tried to loop through array but I got the following error message "name 'ArrayType' is not defined
– user3661384
Dec 28 '18 at 12:22
@Psidom, I tried to loop through array but I got the following error message "name 'ArrayType' is not defined
– user3661384
Dec 28 '18 at 12:22
@pault version spark 2.3.1. I see link, i try this filter_udf = udf(lambda row: [x for x in row if is_digit(x)], ArrayType(StringType())) , i receive error "name 'ArrayType' is not defined" – user3661384 25 mins ago
– user3661384
Dec 28 '18 at 12:44
@pault version spark 2.3.1. I see link, i try this filter_udf = udf(lambda row: [x for x in row if is_digit(x)], ArrayType(StringType())) , i receive error "name 'ArrayType' is not defined" – user3661384 25 mins ago
– user3661384
Dec 28 '18 at 12:44
1
1
from pyspark.sql.types import ArrayType– pault
Dec 28 '18 at 13:00
from pyspark.sql.types import ArrayType– pault
Dec 28 '18 at 13:00
|
show 1 more comment
2 Answers
2
active
oldest
votes
0
As a help @pault the solution was this.
from pyspark.sql.functions import when,udf
from pyspark.sql.types import BooleanType
def is_digit(value):
if value:
return value.isdigit()
else:
return False
is_digit_udf = udf(is_digit, BooleanType()
Call function
from pyspark.sql.types import ArrayType, StringType
from pyspark.sql.types import StructType
filter_length_udf = udf(lambda row: [x for x in row if not is_digit(x)], ArrayType(StringType()))
data = data.withColumn('words_clean', filter_length_udf(col('words_withnumber')))
answered Dec 28 '18 at 16:31
user3661384
11818
add a comment |
0
If you want to avoid udf() for performance reasons and if comma won't appear in your "description" column, then below scala solution would work. The df.withColumn() should be similar in pyspark.
Note: I also added a third record to show that the solution works when the numbers appear at the start/end of the array. Try it out.
scala> val df = Seq((33004,Array("short","sarja", "40567","detalhe","couro")), (22033,Array("multipane","6768686868686867868888","220v","branco")), (33033,Array("0123","x220","220v","889"))).toDF("categoryid","description")
df: org.apache.spark.sql.DataFrame = [categoryid: int, description: array<string>]
scala> df.show(false)
+----------+-------------------------------------------------+
|categoryid|description |
+----------+-------------------------------------------------+
|33004 |[short, sarja, 40567, detalhe, couro] |
|22033 |[multipane, 6768686868686867868888, 220v, branco]|
|33033 |[0123, x220, 220v, 889] |
+----------+-------------------------------------------------+
scala> df.withColumn("newc",split(regexp_replace(regexp_replace(regexp_replace(concat_ws(",",'description),"""bd+b""",""),"""^,|,$""",""),",,",","),",")).show(false)
+----------+-------------------------------------------------+------------------------------+
|categoryid|description |newc |
+----------+-------------------------------------------------+------------------------------+
|33004 |[short, sarja, 40567, detalhe, couro] |[short, sarja, detalhe, couro]|
|22033 |[multipane, 6768686868686867868888, 220v, branco]|[multipane, 220v, branco] |
|33033 |[0123, x220, 220v, 889] |[x220, 220v] |
+----------+-------------------------------------------------+------------------------------+
scala>
Spark 2.4 answer
Using spark-sql in version 2.4 onwards, you can use the filter() higher order function and get the results
scala> val df = Seq((33004,Array("short","sarja", "40567","detalhe","couro")), (22033,Array("multipane","6768686868686867868888","220v","branco")), (33033,Array("0123","x220","220v","889"))).toDF("categoryid","description")
df: org.apache.spark.sql.DataFrame = [categoryid: int, description: array<string>]
scala> df.createOrReplaceTempView("tab")
scala> spark.sql(""" select categoryid, filter(description, x -> lower(x)!=upper(x)) fw from tab """).show(false)
+----------+------------------------------+
|categoryid|fw |
+----------+------------------------------+
|33004 |[short, sarja, detalhe, couro]|
|22033 |[multipane, 220v, branco] |
|33033 |[x220, 220v] |
+----------+------------------------------+
scala>
answered Dec 31 '18 at 20:14
stack0114106
2,2211417
add a comment |
Your Answer
StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
draft saved
draft discarded
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53951215%2fhow-do-i-remove-words-numerics-pyspark%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
0
As a help @pault the solution was this.
from pyspark.sql.functions import when,udf
from pyspark.sql.types import BooleanType
def is_digit(value):
if value:
return value.isdigit()
else:
return False
is_digit_udf = udf(is_digit, BooleanType()
Call function
from pyspark.sql.types import ArrayType, StringType
from pyspark.sql.types import StructType
filter_length_udf = udf(lambda row: [x for x in row if not is_digit(x)], ArrayType(StringType()))
data = data.withColumn('words_clean', filter_length_udf(col('words_withnumber')))
answered Dec 28 '18 at 16:31
user3661384
11818
add a comment |
0
As a help @pault the solution was this.
from pyspark.sql.functions import when,udf
from pyspark.sql.types import BooleanType
def is_digit(value):
if value:
return value.isdigit()
else:
return False
is_digit_udf = udf(is_digit, BooleanType()
Call function
from pyspark.sql.types import ArrayType, StringType
from pyspark.sql.types import StructType
filter_length_udf = udf(lambda row: [x for x in row if not is_digit(x)], ArrayType(StringType()))
data = data.withColumn('words_clean', filter_length_udf(col('words_withnumber')))
answered Dec 28 '18 at 16:31
user3661384
11818
add a comment |
0
0
0
As a help @pault the solution was this.
from pyspark.sql.functions import when,udf
from pyspark.sql.types import BooleanType
def is_digit(value):
if value:
return value.isdigit()
else:
return False
is_digit_udf = udf(is_digit, BooleanType()
Call function
from pyspark.sql.types import ArrayType, StringType
from pyspark.sql.types import StructType
filter_length_udf = udf(lambda row: [x for x in row if not is_digit(x)], ArrayType(StringType()))
data = data.withColumn('words_clean', filter_length_udf(col('words_withnumber')))
answered Dec 28 '18 at 16:31
user3661384
11818
As a help @pault the solution was this.
from pyspark.sql.functions import when,udf
from pyspark.sql.types import BooleanType
def is_digit(value):
if value:
return value.isdigit()
else:
return False
is_digit_udf = udf(is_digit, BooleanType()
Call function
from pyspark.sql.types import ArrayType, StringType
from pyspark.sql.types import StructType
filter_length_udf = udf(lambda row: [x for x in row if not is_digit(x)], ArrayType(StringType()))
data = data.withColumn('words_clean', filter_length_udf(col('words_withnumber')))
answered Dec 28 '18 at 16:31
user3661384
11818
answered Dec 28 '18 at 16:31
user3661384
11818
answered Dec 28 '18 at 16:31
user3661384
11818
answered Dec 28 '18 at 16:31
user3661384
11818
11818
add a comment |
add a comment |
0
If you want to avoid udf() for performance reasons and if comma won't appear in your "description" column, then below scala solution would work. The df.withColumn() should be similar in pyspark.
Note: I also added a third record to show that the solution works when the numbers appear at the start/end of the array. Try it out.
scala> val df = Seq((33004,Array("short","sarja", "40567","detalhe","couro")), (22033,Array("multipane","6768686868686867868888","220v","branco")), (33033,Array("0123","x220","220v","889"))).toDF("categoryid","description")
df: org.apache.spark.sql.DataFrame = [categoryid: int, description: array<string>]
scala> df.show(false)
+----------+-------------------------------------------------+
|categoryid|description |
+----------+-------------------------------------------------+
|33004 |[short, sarja, 40567, detalhe, couro] |
|22033 |[multipane, 6768686868686867868888, 220v, branco]|
|33033 |[0123, x220, 220v, 889] |
+----------+-------------------------------------------------+
scala> df.withColumn("newc",split(regexp_replace(regexp_replace(regexp_replace(concat_ws(",",'description),"""bd+b""",""),"""^,|,$""",""),",,",","),",")).show(false)
+----------+-------------------------------------------------+------------------------------+
|categoryid|description |newc |
+----------+-------------------------------------------------+------------------------------+
|33004 |[short, sarja, 40567, detalhe, couro] |[short, sarja, detalhe, couro]|
|22033 |[multipane, 6768686868686867868888, 220v, branco]|[multipane, 220v, branco] |
|33033 |[0123, x220, 220v, 889] |[x220, 220v] |
+----------+-------------------------------------------------+------------------------------+
scala>
Spark 2.4 answer
Using spark-sql in version 2.4 onwards, you can use the filter() higher order function and get the results
scala> val df = Seq((33004,Array("short","sarja", "40567","detalhe","couro")), (22033,Array("multipane","6768686868686867868888","220v","branco")), (33033,Array("0123","x220","220v","889"))).toDF("categoryid","description")
df: org.apache.spark.sql.DataFrame = [categoryid: int, description: array<string>]
scala> df.createOrReplaceTempView("tab")
scala> spark.sql(""" select categoryid, filter(description, x -> lower(x)!=upper(x)) fw from tab """).show(false)
+----------+------------------------------+
|categoryid|fw |
+----------+------------------------------+
|33004 |[short, sarja, detalhe, couro]|
|22033 |[multipane, 220v, branco] |
|33033 |[x220, 220v] |
+----------+------------------------------+
scala>
answered Dec 31 '18 at 20:14
stack0114106
2,2211417
add a comment |
0
If you want to avoid udf() for performance reasons and if comma won't appear in your "description" column, then below scala solution would work. The df.withColumn() should be similar in pyspark.
Note: I also added a third record to show that the solution works when the numbers appear at the start/end of the array. Try it out.
scala> val df = Seq((33004,Array("short","sarja", "40567","detalhe","couro")), (22033,Array("multipane","6768686868686867868888","220v","branco")), (33033,Array("0123","x220","220v","889"))).toDF("categoryid","description")
df: org.apache.spark.sql.DataFrame = [categoryid: int, description: array<string>]
scala> df.show(false)
+----------+-------------------------------------------------+
|categoryid|description |
+----------+-------------------------------------------------+
|33004 |[short, sarja, 40567, detalhe, couro] |
|22033 |[multipane, 6768686868686867868888, 220v, branco]|
|33033 |[0123, x220, 220v, 889] |
+----------+-------------------------------------------------+
scala> df.withColumn("newc",split(regexp_replace(regexp_replace(regexp_replace(concat_ws(",",'description),"""bd+b""",""),"""^,|,$""",""),",,",","),",")).show(false)
+----------+-------------------------------------------------+------------------------------+
|categoryid|description |newc |
+----------+-------------------------------------------------+------------------------------+
|33004 |[short, sarja, 40567, detalhe, couro] |[short, sarja, detalhe, couro]|
|22033 |[multipane, 6768686868686867868888, 220v, branco]|[multipane, 220v, branco] |
|33033 |[0123, x220, 220v, 889] |[x220, 220v] |
+----------+-------------------------------------------------+------------------------------+
scala>
Spark 2.4 answer
Using spark-sql in version 2.4 onwards, you can use the filter() higher order function and get the results
scala> val df = Seq((33004,Array("short","sarja", "40567","detalhe","couro")), (22033,Array("multipane","6768686868686867868888","220v","branco")), (33033,Array("0123","x220","220v","889"))).toDF("categoryid","description")
df: org.apache.spark.sql.DataFrame = [categoryid: int, description: array<string>]
scala> df.createOrReplaceTempView("tab")
scala> spark.sql(""" select categoryid, filter(description, x -> lower(x)!=upper(x)) fw from tab """).show(false)
+----------+------------------------------+
|categoryid|fw |
+----------+------------------------------+
|33004 |[short, sarja, detalhe, couro]|
|22033 |[multipane, 220v, branco] |
|33033 |[x220, 220v] |
+----------+------------------------------+
scala>
answered Dec 31 '18 at 20:14
stack0114106
2,2211417
add a comment |
0
0
0
If you want to avoid udf() for performance reasons and if comma won't appear in your "description" column, then below scala solution would work. The df.withColumn() should be similar in pyspark.
Note: I also added a third record to show that the solution works when the numbers appear at the start/end of the array. Try it out.
scala> val df = Seq((33004,Array("short","sarja", "40567","detalhe","couro")), (22033,Array("multipane","6768686868686867868888","220v","branco")), (33033,Array("0123","x220","220v","889"))).toDF("categoryid","description")
df: org.apache.spark.sql.DataFrame = [categoryid: int, description: array<string>]
scala> df.show(false)
+----------+-------------------------------------------------+
|categoryid|description |
+----------+-------------------------------------------------+
|33004 |[short, sarja, 40567, detalhe, couro] |
|22033 |[multipane, 6768686868686867868888, 220v, branco]|
|33033 |[0123, x220, 220v, 889] |
+----------+-------------------------------------------------+
scala> df.withColumn("newc",split(regexp_replace(regexp_replace(regexp_replace(concat_ws(",",'description),"""bd+b""",""),"""^,|,$""",""),",,",","),",")).show(false)
+----------+-------------------------------------------------+------------------------------+
|categoryid|description |newc |
+----------+-------------------------------------------------+------------------------------+
|33004 |[short, sarja, 40567, detalhe, couro] |[short, sarja, detalhe, couro]|
|22033 |[multipane, 6768686868686867868888, 220v, branco]|[multipane, 220v, branco] |
|33033 |[0123, x220, 220v, 889] |[x220, 220v] |
+----------+-------------------------------------------------+------------------------------+
scala>
Spark 2.4 answer
Using spark-sql in version 2.4 onwards, you can use the filter() higher order function and get the results
scala> val df = Seq((33004,Array("short","sarja", "40567","detalhe","couro")), (22033,Array("multipane","6768686868686867868888","220v","branco")), (33033,Array("0123","x220","220v","889"))).toDF("categoryid","description")
df: org.apache.spark.sql.DataFrame = [categoryid: int, description: array<string>]
scala> df.createOrReplaceTempView("tab")
scala> spark.sql(""" select categoryid, filter(description, x -> lower(x)!=upper(x)) fw from tab """).show(false)
+----------+------------------------------+
|categoryid|fw |
+----------+------------------------------+
|33004 |[short, sarja, detalhe, couro]|
|22033 |[multipane, 220v, branco] |
|33033 |[x220, 220v] |
+----------+------------------------------+
scala>
answered Dec 31 '18 at 20:14
stack0114106
2,2211417
If you want to avoid udf() for performance reasons and if comma won't appear in your "description" column, then below scala solution would work. The df.withColumn() should be similar in pyspark.
Note: I also added a third record to show that the solution works when the numbers appear at the start/end of the array. Try it out.
scala> val df = Seq((33004,Array("short","sarja", "40567","detalhe","couro")), (22033,Array("multipane","6768686868686867868888","220v","branco")), (33033,Array("0123","x220","220v","889"))).toDF("categoryid","description")
df: org.apache.spark.sql.DataFrame = [categoryid: int, description: array<string>]
scala> df.show(false)
+----------+-------------------------------------------------+
|categoryid|description |
+----------+-------------------------------------------------+
|33004 |[short, sarja, 40567, detalhe, couro] |
|22033 |[multipane, 6768686868686867868888, 220v, branco]|
|33033 |[0123, x220, 220v, 889] |
+----------+-------------------------------------------------+
scala> df.withColumn("newc",split(regexp_replace(regexp_replace(regexp_replace(concat_ws(",",'description),"""bd+b""",""),"""^,|,$""",""),",,",","),",")).show(false)
+----------+-------------------------------------------------+------------------------------+
|categoryid|description |newc |
+----------+-------------------------------------------------+------------------------------+
|33004 |[short, sarja, 40567, detalhe, couro] |[short, sarja, detalhe, couro]|
|22033 |[multipane, 6768686868686867868888, 220v, branco]|[multipane, 220v, branco] |
|33033 |[0123, x220, 220v, 889] |[x220, 220v] |
+----------+-------------------------------------------------+------------------------------+
scala>
Spark 2.4 answer
Using spark-sql in version 2.4 onwards, you can use the filter() higher order function and get the results
scala> val df = Seq((33004,Array("short","sarja", "40567","detalhe","couro")), (22033,Array("multipane","6768686868686867868888","220v","branco")), (33033,Array("0123","x220","220v","889"))).toDF("categoryid","description")
df: org.apache.spark.sql.DataFrame = [categoryid: int, description: array<string>]
scala> df.createOrReplaceTempView("tab")
scala> spark.sql(""" select categoryid, filter(description, x -> lower(x)!=upper(x)) fw from tab """).show(false)
+----------+------------------------------+
|categoryid|fw |
+----------+------------------------------+
|33004 |[short, sarja, detalhe, couro]|
|22033 |[multipane, 220v, branco] |
|33033 |[x220, 220v] |
+----------+------------------------------+
scala>
answered Dec 31 '18 at 20:14
stack0114106
2,2211417
edited Jan 1 at 0:08
answered Dec 31 '18 at 20:14
stack0114106
2,2211417
answered Dec 31 '18 at 20:14
stack0114106
2,2211417
answered Dec 31 '18 at 20:14
stack0114106
2,2211417
2,2211417
add a comment |
add a comment |
draft saved
draft discarded
Thanks for contributing an answer to Stack Overflow!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
Some of your past answers have not been well-received, and you're in danger of being blocked from answering.
Please pay close attention to the following guidance:
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
draft saved
draft discarded
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53951215%2fhow-do-i-remove-words-numerics-pyspark%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
1
You udf expects a string, but you are passing an array to it. Also in your sample data frame should
descriptionbewords_withnumber?– Psidom
Dec 27 '18 at 22:01
You need to iterate over the array and filter out the desired words. What version of spark are you using?
– pault
Dec 27 '18 at 22:05
@Psidom, I tried to loop through array but I got the following error message "name 'ArrayType' is not defined
– user3661384
Dec 28 '18 at 12:22
@pault version spark 2.3.1. I see link, i try this filter_udf = udf(lambda row: [x for x in row if is_digit(x)], ArrayType(StringType())) , i receive error "name 'ArrayType' is not defined" – user3661384 25 mins ago
– user3661384
Dec 28 '18 at 12:44
1
from pyspark.sql.types import ArrayType– pault
Dec 28 '18 at 13:00