At the moment you can freely compare with mixed object dtype column:
>>> ser_string = pd.Series(["a", "b"])
>>> ser_mixed = pd.Series([1, "b"])
>>> ser_string == ser_mixed
0 False
1 True
dtype: bool
But with the string dtype enabled (using pyarrow), this now raises an error:
>>> pd.options.future.infer_string = True
>>> ser_string = pd.Series(["a", "b"])
>>> ser_mixed = pd.Series([1, "b"])
>>> ser_string == ser_mixed
...
File ~/scipy/repos/pandas/pandas/core/arrays/arrow/array.py:510, in ArrowExtensionArray._box_pa_array(cls, value, pa_type, copy)
...
--> 510 pa_array = pa.array(value, from_pandas=True)
...
ArrowInvalid: Could not convert 'b' with type str: tried to convert to int64
This happens because the ArrowEA tries to convert the other operand to Arrow as well, which fails for mixed types.
In general, I think our rule is that == comparison never fails, but then just gives False for when values are not comparable.
At the moment you can freely compare with mixed object dtype column:
But with the string dtype enabled (using pyarrow), this now raises an error:
This happens because the ArrowEA tries to convert the
otheroperand to Arrow as well, which fails for mixed types.In general, I think our rule is that
==comparison never fails, but then just gives False for when values are not comparable.