Minor Description
In PySpark, the fillna function of DataFrame inadvertently casts bools to ints, so fillna cannot be used to fill True/False.
e.g. `spark.createDataFrame([Row(a=True),Row(a=None)]).fillna(True).collect()`
yields
`[Row(a=True), Row(a=None)]`
It should be a=True for the second Row
The cause is this bit of code:
if isinstance(value, (int, long)): value = float(value)
There needs to be a separate check for isinstance(bool), since in python, bools are ints too
Additionally there's another anomaly:
Spark (and pyspark) supports filling of bools if you specify the args as a map:
fillna({"a": False})
, but not if you specify it as
fillna(False)
This is because (scala-)Spark has no
def fill(value: Boolean): DataFrame = fill(value, df.columns)
method. I find that strange/buggy
