Round 3

Author: TomAugspurger

pandas/tests/dtypes/test_common.py

@@ -21,7 +21,6 @@
     UNSIGNED_EA_INT_DTYPES,
     UNSIGNED_INT_DTYPES,
 )
-from pandas.core.sparse.api import SparseDtype
 import pandas.util.testing as tm
 
 ignore_sparse_warning = pytest.mark.filterwarnings("ignore:Sparse:FutureWarning")
@@ -182,7 +181,6 @@ def test_is_object():
 @ignore_sparse_warning
 def test_is_sparse(check_scipy):
     assert com.is_sparse(pd.SparseArray([1, 2, 3]))
-    assert com.is_sparse(pd.SparseSeries([1, 2, 3]))
 
     assert not com.is_sparse(np.array([1, 2, 3]))
 
@@ -200,7 +198,6 @@ def test_is_scipy_sparse():
     assert com.is_scipy_sparse(bsr_matrix([1, 2, 3]))
 
     assert not com.is_scipy_sparse(pd.SparseArray([1, 2, 3]))
-    assert not com.is_scipy_sparse(pd.SparseSeries([1, 2, 3]))
 
 
 def test_is_categorical():
@@ -596,7 +593,6 @@ def test_is_extension_type(check_scipy):
     assert com.is_extension_type(cat)
     assert com.is_extension_type(pd.Series(cat))
     assert com.is_extension_type(pd.SparseArray([1, 2, 3]))
-    assert com.is_extension_type(pd.SparseSeries([1, 2, 3]))
     assert com.is_extension_type(pd.DatetimeIndex(["2000"], tz="US/Eastern"))
 
     dtype = DatetimeTZDtype("ns", tz="US/Eastern")
@@ -664,14 +660,6 @@ def test__get_dtype(input_param, result):
     assert com._get_dtype(input_param) == result
 
 
-@ignore_sparse_warning
-def test__get_dtype_sparse():
-    ser = pd.SparseSeries([1, 2], dtype="int32")
-    expected = SparseDtype("int32")
-    assert com._get_dtype(ser) == expected
-    assert com._get_dtype(ser.dtype) == expected
-
-
 @pytest.mark.parametrize(
     "input_param,expected_error_message",
     [
@@ -723,11 +711,3 @@ def test__get_dtype_fails(input_param, expected_error_message):
 )
 def test__is_dtype_type(input_param, result):
     assert com._is_dtype_type(input_param, lambda tipo: tipo == result)
-
-
-@ignore_sparse_warning
-def test__is_dtype_type_sparse():
-    ser = pd.SparseSeries([1, 2], dtype="int32")
-    result = np.dtype("int32")
-    assert com._is_dtype_type(ser, lambda tipo: tipo == result)
-    assert com._is_dtype_type(ser.dtype, lambda tipo: tipo == result)

pandas/tests/dtypes/test_generic.py

@@ -1,4 +1,4 @@
-from warnings import catch_warnings, simplefilter
+from warnings import catch_warnings
 
 import numpy as np
 
@@ -17,11 +17,6 @@ class TestABCClasses:
     categorical = pd.Categorical([1, 2, 3], categories=[2, 3, 1])
     categorical_df = pd.DataFrame({"values": [1, 2, 3]}, index=categorical)
     df = pd.DataFrame({"names": ["a", "b", "c"]}, index=multi_index)
-    with catch_warnings():
-        simplefilter("ignore", FutureWarning)
-        sparse_series = pd.Series([1, 2, 3]).to_sparse()
-        sparse_frame = pd.SparseDataFrame({"a": [1, -1, None]})
-
     sparse_array = pd.SparseArray(np.random.randn(10))
     datetime_array = pd.core.arrays.DatetimeArray(datetime_index)
     timedelta_array = pd.core.arrays.TimedeltaArray(timedelta_index)

pandas/tests/io/json/test_pandas.py

@@ -1296,25 +1296,6 @@ def test_datetime_tz(self):
         s_naive = Series(tz_naive)
         assert stz.to_json() == s_naive.to_json()
 
-    @pytest.mark.filterwarnings("ignore:Sparse:FutureWarning")
-    @pytest.mark.filterwarnings("ignore:DataFrame.to_sparse:FutureWarning")
-    @pytest.mark.filterwarnings("ignore:Series.to_sparse:FutureWarning")
-    def test_sparse(self):
-        # GH4377 df.to_json segfaults with non-ndarray blocks
-        df = pd.DataFrame(np.random.randn(10, 4))
-        df.loc[:8] = np.nan
-
-        sdf = df.to_sparse()
-        expected = df.to_json()
-        assert expected == sdf.to_json()
-
-        s = pd.Series(np.random.randn(10))
-        s.loc[:8] = np.nan
-        ss = s.to_sparse()
-
-        expected = s.to_json()
-        assert expected == ss.to_json()
-
     def test_tz_is_utc(self):
         from pandas.io.json import dumps