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How to remove rows from pyspark dataframe using pattern matching?


By : bek
Date : October 18 2020, 06:10 PM
fixed the issue. Will look into that further I don't really understand your regular expression, but when you want to match all strings containing 0x0 (+any number of zeros), then you can use ^0x0+$. Filtering with regular expression can be achieved with rlike and the tilde negates the match.
code :
l = [('20190503', 'par1', 'feat2', '0x0'),
('20190503', 'par1', 'feat3', '0x01'),
('20190501', 'par2', 'feat4', '0x0f32'),
('20190501', 'par5', 'feat9', '0x00'),
('20190506', 'par8', 'feat2', '0x00f45'),
('20190507', 'par1', 'feat6', '0x0e62300000'),
('20190501', 'par11', 'feat3', '0x000000000'),
('20190501', 'par21', 'feat5', '0x03efff'),
('20190501', 'par3', 'feat9', '0x000'),
('20190501', 'par6', 'feat5', '0x000000'),
('20190506', 'par5', 'feat8', '0x034edc45'),
('20190506', 'par8', 'feat1', '0x00000'),
('20190508', 'par3', 'feat6', '0x00000000'),
('20190503', 'par4', 'feat3', '0x0c0deffe21'),
('20190503', 'par6', 'feat4', '0x0000000000'),
('20190501', 'par3', 'feat6', '0x0123fe'),
('20190501', 'par7', 'feat4', '0x00000d0')]

columns = ['date', 'part', 'feature', 'value']

df=spark.createDataFrame(l, columns)

expr = "^0x0+$"
df.filter(~ df["value"].rlike(expr)).show()
+--------+-----+-------+------------+ 
|    date| part|feature|       value| 
+--------+-----+-------+------------+ 
|20190503| par1|  feat3|        0x01| 
|20190501| par2|  feat4|      0x0f32| 
|20190506| par8|  feat2|     0x00f45| 
|20190507| par1|  feat6|0x0e62300000| 
|20190501|par21|  feat5|    0x03efff| 
|20190506| par5|  feat8|  0x034edc45| 
|20190503| par4|  feat3|0x0c0deffe21| 
|20190501| par3|  feat6|    0x0123fe| 
|20190501| par7|  feat4|   0x00000d0| 
+--------+-----+-------+------------+


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pyspark dataframe filtering doesn't really remove rows?


By : shtaki
Date : March 29 2020, 07:55 AM
hope this fix your issue My dataframe undergoes two consecutive filtering passes each using a boolean-valued UDF. The first filtering removes all rows whose columns are not present as keys in some broadcast dictionary. The second filtering imposes thresholds on values that this dictionary associates with the present keys. , This
code :
df1 = df.where( udf_indict.asNondeterministic()('name'))
df1.where( udf_bigenough.asNondeterministic()('name') ).show()
@udf(BooleanType())
   def udf_bigenough(x):
      try:
          return mydict_bc.get(x) > 5
      except TypeError:
          pass

How to remove 'duplicate' rows from joining the same pyspark dataframe?


By : Mark Copenhaver
Date : March 29 2020, 07:55 AM
I wish this help you Here's a way to do it using DataFrame functions. Compare the two columns alphabetically and assign values such that artist1 will always sort lexicographically before artist2. Then select the distinct rows.
code :
import pyspark.sql.functions as f

df.select(
    'knownForTitle',
    f.when(f.col('artist1') < f.col('artist2'), f.col('artist1')).otherwise(f.col('artist2')).alias('artist1'),
    f.when(f.col('artist1') < f.col('artist2'), f.col('artist2')).otherwise(f.col('artist1')).alias('artist2'),
).distinct().show()
#+-------------+----------------+----------------+
#|knownForTitle|         artist1|         artist2|
#+-------------+----------------+----------------+
#|    tt0070735| George Roy Hill|  Robert Redford|
#|    tt0022958|   Joan Crawford|Lionel Barrymore|
#|    tt0022958|   Joan Crawford|   Wallace Beery|
#|    tt0022958|Lionel Barrymore|   Wallace Beery|
#+-------------+----------------+----------------+

Remove rows from dataframe based on condition in pyspark


By : user1804399
Date : March 29 2020, 07:55 AM
will help you Another possible way could be using a where function of DF.
For example this:
code :
val output = df.where("col1>col2")
+----+----+
|col1|col2|
+----+----+
|  22|12.2|
|  77|33.3|
+----+----+

Remove duplicate rows from pyspark dataframe which have same value but in different column


By : user3229518
Date : March 29 2020, 07:55 AM
it helps some times You can do it using spark sql:
I assume your original dataframe name as mobiles and code to remove duplicates:
code :
mobiles.createTempView('tablename')

newDF= spark.sql("select * from tablename where name<=alt_name")

newDF.show()

Remove rows where value is string in pyspark dataframe


By : user3393225
Date : March 29 2020, 07:55 AM
this one helps. This issue can be solved by providing data types when loading the data as follows,
code :
inputdf = my_spark.read.format("mongo").load(schema=StructType(
    [StructField("decimalLatitude", DoubleType(), True),
     StructField("decimalLongitude", DoubleType(), True)]))
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