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()

Answer 1

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() 

Answer 2

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)