Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/16792#discussion_r99471688
--- Diff: python/pyspark/sql/dataframe.py ---
@@ -1272,16 +1272,18 @@ def replace(self, to_replace, value, subset=None):
"""Returns a new :class:`DataFrame` replacing a value with another
value.
:func:`DataFrame.replace` and :func:`DataFrameNaFunctions.replace`
are
aliases of each other.
+ Values `to_replace` and `value` should be homogeneous. Mixed
string and numeric
--- End diff --
I don't think we need to cast the types - if you look inside of `replace0`
all of the numerics are turned into doubles in the map (but we should probably
- in your other PR - add a test around that so that if the internals change we
know we need to update the Python side).
Doing `sc.parallelize([Row(name='Alice', age=0,
height=80)]).toDF().replace(0, 12.5).collect()` is what I was talking about
cutting of the deciminal component (so while it runs it arguable doesn't do
what the user expects - but that ).
What about something along the lines of: `to_replace` and `value` should
contain either all numerics, all booleans, or all strings. When replacing, the
new value will be cast to the type of the existing column."
I think this more clearly communicates the requirements, but is still a bit
awkward -- can you think of something better?)
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