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Mysterious 'pyarrow.lib.ArrowInvalid: Floating point value truncated' ERROR when use toPandas() on a DataFrame in pyspark

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I use toPandas() on a DataFrame which is not very large, but I get the following exception:
18/10/31 19:13:19 ERROR Executor: Exception in task 127.2 in stage 13.0 (TID 2264)
org.apache.spark.api.python.PythonException: Traceback (most recent call last):
    File "/home/hadoop/spark2.3.1/python/lib/pyspark.zip/pyspark/worker.py", line 230, in main
      process()
    File "/home/hadoop/spark2.3.1/python/lib/pyspark.zip/pyspark/worker.py", line 225, in process
      serializer.dump_stream(func(split_index, iterator), outfile)
    File "/home/hadoop/spark2.3.1/python/lib/pyspark.zip/pyspark/serializers.py", line 261, in dump_stream
      batch = _create_batch(series, self._timezone)
    File "/home/hadoop/spark2.3.1/python/lib/pyspark.zip/pyspark/serializers.py", line 239, in _create_batch
      arrs = [create_array(s, t) for s, t in series]
    File "/home/hadoop/spark2.3.1/python/lib/pyspark.zip/pyspark/serializers.py", line 239, in <listcomp>
      arrs = [create_array(s, t) for s, t in series]
    File "/home/hadoop/spark2.3.1/python/lib/pyspark.zip/pyspark/serializers.py", line 237, in create_array
      return pa.Array.from_pandas(s, mask=mask, type=t)
    File "pyarrow/array.pxi", line 474, in pyarrow.lib.Array.from_pandas
    File "pyarrow/array.pxi", line 169, in pyarrow.lib.array
    File "pyarrow/array.pxi", line 69, in pyarrow.lib._ndarray_to_array
    File "pyarrow/error.pxi", line 81, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Floating point value truncated
        at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:298)
        at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:171)
        at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:121)
        at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:252)
        at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
        at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:439)
        at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
        at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage19.agg_doAggregateWithKeys_0$(Unknown Source)
        at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage19.processNext(Unknown Source)
        at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
        at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.hasNext(WholeStageCodegenExec.scala:614)
        at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
        at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
        at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
        at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
        at org.apache.spark.scheduler.Task.run(Task.scala:109)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
        at java.lang.Thread.run(Thread.java:745)
Sometimes, this exception can be ignored and I can get the right result, but more often, the program exited. Does anyone know about this mysterious error?
                That's likely because your data contains NaN when int is expected - issues.apache.org/jira/browse/…
– 10465355
                Oct 31, 2018 at 12:40
                I checked the data, there's no NaN in the field expecting int. The code runs well in local mode, but fails in the cluster.
– Hao
                Nov 1, 2018 at 13:02
I came across the same error. I think @bryanc is right that you need to safely cast the type. In my case the data is in bigint while the function needs float/double. So I did
from pyspark.sql.types import DoubleType
df = df.withColumn("x_dbl", df["x"].cast(DoubleType()))
following the approach in how to change a Dataframe column from String type to Double type in pyspark
Then instead of applying the function on "x", I did on "x_dbl" and it worked. Hope this helps! 
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