Pyspark在用0替换null时出错

Question

我正在研究 PySpark Python，我已经提到了代码并遇到了一些问题，我想知道是否有人知道以下问题？

我的样本数据集

data = [('12er',None), ('4xcf',None), ('6hjk',None), \
      ('45fh',2000),('56gh',2000),('hj45',None)]


columns= ["Id","Amount"]
pivotDF= spark.createDataFrame(data = data, schema = columns)
pivotDF.show(truncate=False)

原始数据集的片段 我正在使用 fill 将 null 替换为零

pivotDF.na.fill(0).show(n=2)

虽然我可以在示例数据集中但在我的 pspark 数据框中执行此操作 我收到此错误

Fail to execute line 1: pivotDF.na.fill(0).show(n=2)
Traceback (most recent call last):
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/utils.py", line 63, in deco
    return f(*a, **kw)
  File "/usr/lib/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value
    format(target_id, ".", name), value)
py4j.protocol.Py4JJavaError: An error occurred while calling o244.fill.
: org.apache.spark.sql.AnalysisException: Reference 'Cancel' is ambiguous, could be: Cancel, Cancel.;
    at org.apache.spark.sql.catalyst.expressions.package$AttributeSeq.resolve(package.scala:259)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveQuoted(LogicalPlan.scala:126)
    at org.apache.spark.sql.Dataset.resolve(Dataset.scala:222)
    at org.apache.spark.sql.Dataset.col(Dataset.scala:1269)
    at org.apache.spark.sql.DataFrameNaFunctions.org$apache$spark$sql$DataFrameNaFunctions$$fillCol(DataFrameNaFunctions.scala:443)
    at org.apache.spark.sql.DataFrameNaFunctions$$anonfun$7.apply(DataFrameNaFunctions.scala:502)
    at org.apache.spark.sql.DataFrameNaFunctions$$anonfun$7.apply(DataFrameNaFunctions.scala:492)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
    at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
    at org.apache.spark.sql.DataFrameNaFunctions.fillValue(DataFrameNaFunctions.scala:492)
    at org.apache.spark.sql.DataFrameNaFunctions.fill(DataFrameNaFunctions.scala:179)
    at org.apache.spark.sql.DataFrameNaFunctions.fill(DataFrameNaFunctions.scala:163)
    at org.apache.spark.sql.DataFrameNaFunctions.fill(DataFrameNaFunctions.scala:140)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:238)
    at java.lang.Thread.run(Thread.java:748)


During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/tmp/zeppelin_pyspark-3253703896.py", line 473, in <module>
    exec(code, _zcUserQueryNameSpace)
  File "<stdin>", line 1, in <module>
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/dataframe.py", line 2241, in fill
    return self.df.fillna(value=value, subset=subset)
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/dataframe.py", line 1665, in fillna
    return DataFrame(self._jdf.na().fill(value), self.sql_ctx)
  File "/usr/lib/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1257, in __call__
    answer, self.gateway_client, self.target_id, self.name)
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/utils.py", line 69, in deco
    raise AnalysisException(s.split(': ', 1)[1], stackTrace)
pyspark.sql.utils.AnalysisException: "Reference 'Cancel' is ambiguous, could be: Cancel, Cancel.;"

我的架构

Answer 1

使用df.fillna

data = [('12er',None), ('4xcf',None), ('6hjk',None), \
      ('45fh',2000),('56gh',2000),('hj45',None)]

columns= ["Id","Amount"]
pivotDF= spark.createDataFrame(data = data, schema = columns)

#fillna on whole dataframe
pivotDF.fillna(0).show(truncate=False)

#fillna on specific columns. { 'col1':0,'col2':10}
pivotDF.fillna( { 'Amount':0} ).show()

输出：

+----+------+
|  Id|Amount|
+----+------+
|12er|     0|
|4xcf|     0|
|6hjk|     0|
|45fh|  2000|
|56gh|  2000|
|hj45|     0|
+----+------+

Answer 2

我有大约 1000 列，有些列名是相似的，但区分大小写。

我做到了

spark.sql('set spark.sql.caseSensitive = True')