TST/REF: collect indexing tests by method (#37729)

Author: jbrockmendel

pandas/tests/frame/indexing/test_getitem.py

@@ -7,11 +7,13 @@
     CategoricalIndex,
     DataFrame,
     MultiIndex,
+    Series,
     Timestamp,
     get_dummies,
     period_range,
 )
 import pandas._testing as tm
+from pandas.core.arrays import SparseArray
 
 
 class TestGetitem:
@@ -50,6 +52,21 @@ def test_getitem_list_of_labels_categoricalindex_cols(self):
         result = dummies[list(dummies.columns)]
         tm.assert_frame_equal(result, expected)
 
+    def test_getitem_sparse_column_return_type_and_dtype(self):
+        # https://github.com/pandas-dev/pandas/issues/23559
+        data = SparseArray([0, 1])
+        df = DataFrame({"A": data})
+        expected = Series(data, name="A")
+        result = df["A"]
+        tm.assert_series_equal(result, expected)
+
+        # Also check iloc and loc while we're here
+        result = df.iloc[:, 0]
+        tm.assert_series_equal(result, expected)
+
+        result = df.loc[:, "A"]
+        tm.assert_series_equal(result, expected)
+
 
 class TestGetitemCallable:
     def test_getitem_callable(self, float_frame):

pandas/tests/frame/indexing/test_sparse.py

@@ -17,6 +17,16 @@ def test_at_timezone():
     tm.assert_frame_equal(result, expected)
 
 
+class TestAtSetItem:
+    def test_at_setitem_mixed_index_assignment(self):
+        # GH#19860
+        ser = Series([1, 2, 3, 4, 5], index=["a", "b", "c", 1, 2])
+        ser.at["a"] = 11
+        assert ser.iat[0] == 11
+        ser.at[1] = 22
+        assert ser.iat[3] == 22
+
+
 class TestAtWithDuplicates:
     def test_at_with_duplicate_axes_requires_scalar_lookup(self):
         # GH#33041 check that falling back to loc doesn't allow non-scalar
@@ -108,3 +118,11 @@ def test_at_frame_raises_key_error2(self):
             df.at["a", 0]
         with pytest.raises(KeyError, match="^0$"):
             df.loc["a", 0]
+
+    def test_at_getitem_mixed_index_no_fallback(self):
+        # GH#19860
+        ser = Series([1, 2, 3, 4, 5], index=["a", "b", "c", 1, 2])
+        with pytest.raises(KeyError, match="^0$"):
+            ser.at[0]
+        with pytest.raises(KeyError, match="^4$"):
+            ser.at[4]

pandas/tests/indexing/test_at.py

@@ -1,17 +1,9 @@
-import re
-
 import numpy as np
 import pytest
 
 from pandas import DataFrame, Float64Index, Index, Int64Index, RangeIndex, Series
 import pandas._testing as tm
 
-# We pass through the error message from numpy
-_slice_iloc_msg = re.escape(
-    "only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) "
-    "and integer or boolean arrays are valid indices"
-)
-
 
 def gen_obj(klass, index):
     if klass is Series:
@@ -40,24 +32,6 @@ def check(self, result, original, indexer, getitem):
 
         tm.assert_almost_equal(result, expected)
 
-    def test_scalar_error(self, series_with_simple_index):
-
-        # GH 4892
-        # float_indexers should raise exceptions
-        # on appropriate Index types & accessors
-        # this duplicates the code below
-        # but is specifically testing for the error
-        # message
-
-        s = series_with_simple_index
-
-        msg = "Cannot index by location index with a non-integer key"
-        with pytest.raises(TypeError, match=msg):
-            s.iloc[3.0]
-
-        with pytest.raises(IndexError, match=_slice_iloc_msg):
-            s.iloc[3.0] = 0
-
     @pytest.mark.parametrize(
         "index_func",
         [
@@ -69,40 +43,32 @@ def test_scalar_error(self, series_with_simple_index):
             tm.makePeriodIndex,
         ],
     )
-    @pytest.mark.parametrize("klass", [Series, DataFrame])
-    def test_scalar_non_numeric(self, index_func, klass):
+    def test_scalar_non_numeric(self, index_func, frame_or_series):
 
         # GH 4892
         # float_indexers should raise exceptions
         # on appropriate Index types & accessors
 
         i = index_func(5)
-        s = gen_obj(klass, i)
+        s = gen_obj(frame_or_series, i)
 
         # getting
         with pytest.raises(KeyError, match="^3.0$"):
             s[3.0]
 
-        msg = "Cannot index by location index with a non-integer key"
-        with pytest.raises(TypeError, match=msg):
-            s.iloc[3.0]
-
         with pytest.raises(KeyError, match="^3.0$"):
             s.loc[3.0]
 
         # contains
         assert 3.0 not in s
 
-        # setting with a float fails with iloc
-        with pytest.raises(IndexError, match=_slice_iloc_msg):
-            s.iloc[3.0] = 0
-
         # setting with an indexer
         if s.index.inferred_type in ["categorical"]:
             # Value or Type Error
             pass
         elif s.index.inferred_type in ["datetime64", "timedelta64", "period"]:
 
+            # FIXME: dont leave commented-out
             # these should prob work
             # and are inconsistent between series/dataframe ATM
             # for idxr in [lambda x: x]:
@@ -151,10 +117,6 @@ def test_scalar_with_mixed(self):
         with pytest.raises(KeyError, match="^1.0$"):
             s2[1.0]
 
-        msg = "Cannot index by location index with a non-integer key"
-        with pytest.raises(TypeError, match=msg):
-            s2.iloc[1.0]
-
         with pytest.raises(KeyError, match=r"^1\.0$"):
             s2.loc[1.0]
 
@@ -171,9 +133,6 @@ def test_scalar_with_mixed(self):
         expected = 2
         assert result == expected
 
-        msg = "Cannot index by location index with a non-integer key"
-        with pytest.raises(TypeError, match=msg):
-            s3.iloc[1.0]
         with pytest.raises(KeyError, match=r"^1\.0$"):
             s3.loc[1.0]
 
@@ -182,14 +141,13 @@ def test_scalar_with_mixed(self):
         assert result == expected
 
     @pytest.mark.parametrize("index_func", [tm.makeIntIndex, tm.makeRangeIndex])
-    @pytest.mark.parametrize("klass", [Series, DataFrame])
-    def test_scalar_integer(self, index_func, klass):
+    def test_scalar_integer(self, index_func, frame_or_series):
 
         # test how scalar float indexers work on int indexes
 
         # integer index
         i = index_func(5)
-        obj = gen_obj(klass, i)
+        obj = gen_obj(frame_or_series, i)
 
         # coerce to equal int
         for idxr, getitem in [(lambda x: x.loc, False), (lambda x: x, True)]:
@@ -226,12 +184,11 @@ def compare(x, y):
         # coerce to equal int
         assert 3.0 in obj
 
-    @pytest.mark.parametrize("klass", [Series, DataFrame])
-    def test_scalar_float(self, klass):
+    def test_scalar_float(self, frame_or_series):
 
         # scalar float indexers work on a float index
         index = Index(np.arange(5.0))
-        s = gen_obj(klass, index)
+        s = gen_obj(frame_or_series, index)
 
         # assert all operations except for iloc are ok
         indexer = index[3]
@@ -262,14 +219,6 @@ def test_scalar_float(self, klass):
         result = s2.iloc[3]
         self.check(result, s, 3, False)
 
-        # iloc raises with a float
-        msg = "Cannot index by location index with a non-integer key"
-        with pytest.raises(TypeError, match=msg):
-            s.iloc[3.0]
-
-        with pytest.raises(IndexError, match=_slice_iloc_msg):
-            s2.iloc[3.0] = 0
-
     @pytest.mark.parametrize(
         "index_func",
         [
@@ -281,15 +230,14 @@ def test_scalar_float(self, klass):
         ],
     )
     @pytest.mark.parametrize("l", [slice(3.0, 4), slice(3, 4.0), slice(3.0, 4.0)])
-    @pytest.mark.parametrize("klass", [Series, DataFrame])
-    def test_slice_non_numeric(self, index_func, l, klass):
+    def test_slice_non_numeric(self, index_func, l, frame_or_series):
 
         # GH 4892
         # float_indexers should raise exceptions
         # on appropriate Index types & accessors
 
         index = index_func(5)
-        s = gen_obj(klass, index)
+        s = gen_obj(frame_or_series, index)
 
         # getitem
         msg = (
@@ -509,12 +457,11 @@ def test_float_slice_getitem_with_integer_index_raises(self, l, index_func):
             s[l]
 
     @pytest.mark.parametrize("l", [slice(3.0, 4), slice(3, 4.0), slice(3.0, 4.0)])
-    @pytest.mark.parametrize("klass", [Series, DataFrame])
-    def test_slice_float(self, l, klass):
+    def test_slice_float(self, l, frame_or_series):
 
         # same as above, but for floats
         index = Index(np.arange(5.0)) + 0.1
-        s = gen_obj(klass, index)
+        s = gen_obj(frame_or_series, index)
 
         expected = s.iloc[3:4]
         for idxr in [lambda x: x.loc, lambda x: x]: