Remove duplicates from a dataframe in PySpark

Question

I'm messing around with dataframes in pyspark 1.4 locally and am having issues getting the dropDuplicates method to work. It keeps returning the error:

"AttributeError: 'list' object has no attribute 'dropDuplicates'"

Not quite sure why as I seem to be following the syntax in the latest documentation.

#loading the CSV file into an RDD in order to start working with the data
rdd1 = sc.textFile("C:\myfilename.csv").map(lambda line: (line.split(",")[0], line.split(",")[1], line.split(",")[2], line.split(",")[3])).collect()

#loading the RDD object into a dataframe and assigning column names
df1 = sqlContext.createDataFrame(rdd1, ['column1', 'column2', 'column3', 'column4']).collect()

#dropping duplicates from the dataframe
df1.dropDuplicates().show()

zero323 · Accepted Answer · 2015-06-26 03:27:55Z

50

It is not an import problem. You simply call .dropDuplicates() on a wrong object. While class of sqlContext.createDataFrame(rdd1, ...) is pyspark.sql.dataframe.DataFrame, after you apply .collect() it is a plain Python list, and lists don't provide dropDuplicates method. What you want is something like this:

 (df1 = sqlContext
     .createDataFrame(rdd1, ['column1', 'column2', 'column3', 'column4'])
     .dropDuplicates())

 df1.collect()

edited Jun 26, 2015 at 3:27

answered Jun 26, 2015 at 3:22

zero323

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Comments

Grant Shannon · Accepted Answer · 2018-01-11 15:57:19Z

25

if you have a data frame and want to remove all duplicates -- with reference to duplicates in a specific column (called 'colName'):

count before dedupe:

df.count()

do the de-dupe (convert the column you are de-duping to string type):

from pyspark.sql.functions import col
df = df.withColumn('colName',col('colName').cast('string'))

df.drop_duplicates(subset=['colName']).count()

can use a sorted groupby to check to see that duplicates have been removed:

df.groupBy('colName').count().toPandas().set_index("count").sort_index(ascending=False)

edited Jan 11, 2018 at 15:57

answered Jan 2, 2018 at 14:40

Grant Shannon

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Comments

Jorge Tovar · Accepted Answer · 2022-06-07 17:32:01Z

In summary, distinct() and dropDuplicates() methods remove duplicates with one difference, which is essential.

dropDuplicates() is more suitable by considering only a subset of the columns

data = [("James","","Smith","36636","M",60000),
        ("James","Rose","","40288","M",70000),
        ("Robert","","Williams","42114","",400000),
        ("Maria","Anne","Jones","39192","F",500000),
        ("Maria","Mary","Brown","","F",0)]

columns = ["first_name","middle_name","last_name","dob","gender","salary"]
df = spark.createDataFrame(data = data, schema = columns)
df.printSchema()
df.show(truncate=False)

df.groupBy('first_name').agg(count(
  'first_name').alias("count_duplicates")).filter(
  col('count_duplicates') >= 2).show()

df.dropDuplicates(['first_name']).show()

# output

+----------+-----------+---------+-----+------+------+
|first_name|middle_name|last_name|dob  |gender|salary|
+----------+-----------+---------+-----+------+------+
|James     |           |Smith    |36636|M     |60000 |
|James     |Rose       |         |40288|M     |70000 |
|Robert    |           |Williams |42114|      |400000|
|Maria     |Anne       |Jones    |39192|F     |500000|
|Maria     |Mary       |Brown    |     |F     |0     |
+----------+-----------+---------+-----+------+------+

+----------+----------------+
|first_name|count_duplicates|
+----------+----------------+
|     James|               2|
|     Maria|               2|
+----------+----------------+

+----------+-----------+---------+-----+------+------+
|first_name|middle_name|last_name|  dob|gender|salary|
+----------+-----------+---------+-----+------+------+
|     James|           |    Smith|36636|     M| 60000|
|     Maria|       Anne|    Jones|39192|     F|500000|
|    Robert|           | Williams|42114|      |400000|
+----------+-----------+---------+-----+------+------+

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