python - Spark Dataframe 中 `float` 与 `np.nan` 的比较

Question

这是预期的行为吗？我想提出一个 Spark 问题，但这似乎是一个基本功能，很难想象这里有一个错误。我错过了什么？

python

import numpy as np

>>> np.nan < 0.0
False

>>> np.nan > 0.0
False

PySpark

from pyspark.sql.functions import col

df = spark.createDataFrame([(np.nan, 0.0),(0.0, np.nan)])
df.show()
#+---+---+
#| _1| _2|
#+---+---+
#|NaN|0.0|
#|0.0|NaN|
#+---+---+

df.printSchema()
#root
# |-- _1: double (nullable = true)
# |-- _2: double (nullable = true)

df.select(col("_1")> col("_2")).show()
#+---------+
#|(_1 > _2)|
#+---------+
#|     true|
#|    false|
#+---------+

Answer 1

这既是预期的行为，也是记录在案的行为。引用NaN Semantics段官Spark SQL Guide (强调我的):

There is specially handling for not-a-number (NaN) when dealing with float or double types that does not exactly match standard floating point semantics. Specifically:

• NaN = NaN returns true.
• In aggregations, all NaN values are grouped together.
• NaN is treated as a normal value in join keys.
• NaN values go last when in ascending order, larger than any other numeric value.

Ad 如您所见，与 Python NaN 相比，排序行为并不是唯一的区别。特别是 Spark 认为 NaN 等于:

spark.sql("""
    WITH table AS (SELECT CAST('NaN' AS float) AS x, cast('NaN' AS float) AS y) 
    SELECT x = y, x != y FROM table
""").show()

+-------+-------------+
|(x = y)|(NOT (x = y))|
+-------+-------------+
|   true|        false|
+-------+-------------+

而普通的 Python

float("NaN") == float("NaN"), float("NaN") != float("NaN")

(False, True)

和 NumPy

np.nan == np.nan, np.nan != np.nan

(False, True)

不要。

可以查看eqNullSafe docstring更多示例。

因此，为了获得所需的结果，您必须明确检查 NaN

from pyspark.sql.functions import col, isnan, when

when(isnan("_1") | isnan("_2"), False).otherwise(col("_1") > col("_2"))