From 1943f2b4f11a1d53fc5b7523986cd7f55baffd8d Mon Sep 17 00:00:00 2001
From: Michael Tiemann <72577720+MichaelTiemannOSC@users.noreply.github.com>
Date: Mon, 29 Jul 2024 17:56:08 -0400
Subject: [PATCH] Pr/137 compat check (#245)

* _get_common_dtype must check PintTypes compat

PR number 137 adds _get_common_dtype to PintType so that `PintType` operations can be performed on a mix of `PintType` and numeric values (with the later being promoted to the `PintType` for the purposes of the operation).  However, when there are multiple `PintType` elements present, it is important that all elements are in fact compatible, lest the operation attempt to combine two `PintType` elements that are not unit-compatible.

* Fix pre-commit and add CHANGES

Clean up PR for submission.

* Update CHANGES

* Update pint_array.py

* fix issue

---------

Co-authored-by: andrewgsavage <andrewgsavage@gmail.com>
---
 CHANGES                              |  4 ++--
 pint_pandas/pint_array.py            | 13 +++++++++--
 pint_pandas/testsuite/test_issues.py | 34 ++++++++++++++++++++++++++++
 3 files changed, 47 insertions(+), 4 deletions(-)

diff --git a/CHANGES b/CHANGES
index e945626..b733508 100644
--- a/CHANGES
+++ b/CHANGES
@@ -2,10 +2,10 @@ pint-pandas Changelog
 =====================
 
 
-0.7 (unreleased)
+0.6.2 (2024-07-29)
 --------------------
 
-- Nothing added yet.
+- Fix PintType._get_common_dtype (added via #137) to check compatibility of all `PintType`s in `dtypes`
 
 
 0.6.1 (2024-07-13)
diff --git a/pint_pandas/pint_array.py b/pint_pandas/pint_array.py
index 9c5efab..93c787e 100644
--- a/pint_pandas/pint_array.py
+++ b/pint_pandas/pint_array.py
@@ -201,8 +201,9 @@ def _get_common_dtype(self, dtypes):
         If this function is called this means at least on of the ``dtypes``
         list is a ``PintType``
 
-        In order to be able to be able to perform operation on ``PintType``
+        In order to be able to perform operation on ``PintType``
         with scalars, mix of ``PintType`` and numeric values are allowed.
+        But all ``PintType`` elements must be compatible.
 
 
         Parameters
@@ -213,10 +214,18 @@ def _get_common_dtype(self, dtypes):
         -------
         returns self for acceptable cases or None otherwise
         """
+        # Return self (PintType with same units) if possible
         if all(
-            isinstance(x, PintType) or pd.api.types.is_numeric_dtype(x) for x in dtypes
+            isinstance(dtype, PintType) and dtype.units == self.units
+            for dtype in dtypes
         ):
             return self
+        # Otherwise return PintType with undefined units
+        elif all(
+            isinstance(dtype, PintType) or pd.api.types.is_numeric_dtype(dtype)
+            for dtype in dtypes
+        ):
+            return PintType
         else:
             return None
 
diff --git a/pint_pandas/testsuite/test_issues.py b/pint_pandas/testsuite/test_issues.py
index 25709c5..74f26eb 100644
--- a/pint_pandas/testsuite/test_issues.py
+++ b/pint_pandas/testsuite/test_issues.py
@@ -277,3 +277,37 @@ def test_eval(self):
         )
         tm.assert_series_equal(df.eval("a / b"), df["a"] / df["b"])
         tm.assert_series_equal(df.eval("a / c"), df["a"] / df["c"])
+        tm.assert_series_equal(df.eval("a / a"), df["a"] / df["a"])
+
+    def test_mixed_df(self):
+        df = pd.DataFrame(
+            {
+                "a": pd.Series([1.0, 2.0, 3.0], dtype="pint[meter]"),
+                "b": pd.Series([4.0, 5.0, 6.0], dtype="pint[second]"),
+                "c": [1.0, 2.0, 3.0],
+            }
+        )
+
+        assert df["a"][0] == df.iloc[0, 0]
+
+
+class TestIssue246(BaseExtensionTests):
+    def test_issue246(self):
+        df = pd.DataFrame(
+            {
+                "a": [1, 2, 3],
+                "b": [4, 5, 6],
+                "c": [7, 8, 9],
+            }
+        )
+
+        df = df.astype(
+            {
+                "a": "pint[m]",
+                "b": "pint[m/s]",
+                "c": "pint[kN]",
+            }
+        )
+
+        # now an operation where each cell is independent from each other
+        df.apply(lambda x: x * 2, axis=1)