从 PySpark 中的数据框中删除重复项

Question

我在本地处理 pyspark 1.4 中的数据框，并且在使dropDuplicates方法正常工作时遇到问题。 它不断返回错误：

“AttributeError：‘list’对象没有属性‘dropDuplicates’”

不太清楚为什么，因为我似乎遵循最新文档中的语法。

#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

这不是进口问题。 您只需在错误的对象上调用.dropDuplicates()即可。 虽然sqlContext.createDataFrame(rdd1, ...)的类是pyspark.sql.dataframe.DataFrame ，但应用.collect()后它是一个普通的 Python list ，并且列表不提供dropDuplicates方法。 你想要的是这样的：

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

 df1.collect()

Answer 2

如果您有一个数据框并且想要删除所有重复项 - 参考特定列中的重复项（称为“colName”）：

重复数据删除前的计数：

df.count()

执行重复数据删除（将要重复数据删除的列转换为字符串类型）：

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

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

可以使用排序的 groupby 来检查是否已删除重复项：

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

Answer 3

总而言之， distinct()和dropDuplicates()方法删除重复项有一个区别，这是必不可少的。

dropDuplicates()更适合只考虑列的一个子集

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|
+----------+-----------+---------+-----+------+------+