Decimal

Author: MomIsBestFriend

pandas/core/arrays/categorical.py

@@ -299,7 +299,7 @@ class Categorical(ExtensionArray, PandasObject):
 
     # For comparisons, so that numpy uses our implementation if the compare
     # ops, which raise
-    __array_priority__ = 1_000
+    __array_priority__ = 1000
     _dtype = CategoricalDtype(ordered=False)
     # tolist is not actually deprecated, just suppressed in the __dir__
     _deprecations = PandasObject._deprecations | frozenset(["tolist", "itemsize"])

pandas/core/arrays/timedeltas.py

@@ -157,7 +157,7 @@ class TimedeltaArray(dtl.DatetimeLikeArrayMixin, dtl.TimelikeOps):
 
     _typ = "timedeltaarray"
     _scalar_type = Timedelta
-    __array_priority__ = 1_000
+    __array_priority__ = 1000
     # define my properties & methods for delegation
     _other_ops: List[str] = []
     _bool_ops: List[str] = []

pandas/core/base.py

@@ -618,7 +618,7 @@ class IndexOpsMixin:
     """
 
     # ndarray compatibility
-    __array_priority__ = 1_000
+    __array_priority__ = 1000
     _deprecations: FrozenSet[str] = frozenset(
         ["tolist", "item"]  # tolist is not deprecated, just suppressed in the __dir__
     )

pandas/tests/io/formats/test_format.py

@@ -286,11 +286,11 @@ def test_repr_chop_threshold_column_below(self):
             )
 
     def test_repr_obeys_max_seq_limit(self):
-        with option_context("display.max_seq_items", 2_000):
-            assert len(printing.pprint_thing(list(range(1_000)))) > 1_000
+        with option_context("display.max_seq_items", 2000):
+            assert len(printing.pprint_thing(list(range(1000)))) > 1000
 
         with option_context("display.max_seq_items", 5):
-            assert len(printing.pprint_thing(list(range(1_000)))) < 100
+            assert len(printing.pprint_thing(list(range(1000)))) < 100
 
     def test_repr_set(self):
         assert printing.pprint_thing({1}) == "{1}"
@@ -354,10 +354,10 @@ def test_expand_frame_repr(self):
     def test_repr_non_interactive(self):
         # in non interactive mode, there can be no dependency on the
         # result of terminal auto size detection
-        df = DataFrame("hello", index=range(1_000), columns=range(5))
+        df = DataFrame("hello", index=range(1000), columns=range(5))
 
         with option_context(
-            "mode.sim_interactive", False, "display.width", 0, "display.max_rows", 5_000
+            "mode.sim_interactive", False, "display.width", 0, "display.max_rows", 5000
         ):
             assert not has_truncated_repr(df)
             assert not has_expanded_repr(df)
@@ -1339,7 +1339,7 @@ def test_wide_repr_wide_long_columns(self):
             assert "ddddd" in result
 
     def test_long_series(self):
-        n = 1_000
+        n = 1000
         s = Series(
             np.random.randint(-50, 50, n),
             index=["s{x:04d}".format(x=x) for x in range(n)],
@@ -1545,7 +1545,7 @@ def test_to_string_float_formatting(self):
         )
 
         df = DataFrame(
-            {"x": [0, 0.25, 3_456.000, 12e45, 1.64e6, 1.7e8, 1.253_456, np.pi, -1e6]}
+            {"x": [0, 0.25, 3456.000, 12e45, 1.64e6, 1.7e8, 1.253_456, np.pi, -1e6]}
         )
 
         df_s = df.to_string()
@@ -1566,7 +1566,7 @@ def test_to_string_float_formatting(self):
             )
         assert df_s == expected
 
-        df = DataFrame({"x": [3_234, 0.253]})
+        df = DataFrame({"x": [3234, 0.253]})
         df_s = df.to_string()
 
         expected = "          x\n0  3234.000\n1     0.253"
@@ -1636,9 +1636,9 @@ def test_to_string_complex_float_formatting(self):
             df = DataFrame(
                 {
                     "x": [
-                        (0.4_467_846_931_321_966 + 0.0_715_185_102_060_818j),
-                        (0.2_739_442_392_974_528 + 0.23_515_228_785_438_969j),
-                        (0.26_974_928_742_135_185 + 0.3_250_604_054_898_979j),
+                        (0.4467846931321966 + 0.0715185102060818j),
+                        (0.2739442392974528 + 0.23515228785438969j),
+                        (0.26974928742135185 + 0.3250604054898979j),
                         (-1j),
                     ]
                 }
@@ -1690,7 +1690,7 @@ def test_to_string_index_formatter(self):
 
     def test_to_string_left_justify_cols(self):
         tm.reset_display_options()
-        df = DataFrame({"x": [3_234, 0.253]})
+        df = DataFrame({"x": [3234, 0.253]})
         df_s = df.to_string(justify="left")
         expected = "   x       \n0  3234.000\n1     0.253"
         assert df_s == expected
@@ -1699,7 +1699,7 @@ def test_to_string_format_na(self):
         tm.reset_display_options()
         df = DataFrame(
             {
-                "A": [np.nan, -1, -2.1_234, 3, 4],
+                "A": [np.nan, -1, -2.1234, 3, 4],
                 "B": [np.nan, "foo", "foooo", "fooooo", "bar"],
             }
         )
@@ -1738,7 +1738,7 @@ def test_to_string_format_inf(self):
         tm.reset_display_options()
         df = DataFrame(
             {
-                "A": [-np.inf, np.inf, -1, -2.1_234, 3, 4],
+                "A": [-np.inf, np.inf, -1, -2.1234, 3, 4],
                 "B": [-np.inf, np.inf, "foo", "foooo", "fooooo", "bar"],
             }
         )
@@ -2076,11 +2076,11 @@ def __getitem__(self, key):
 
     def test_float_trim_zeros(self):
         vals = [
-            2.08_430_917_305e10,
-            3.52_205_017_305e10,
-            2.30_674_817_305e10,
-            2.03_954_217_305e10,
-            5.59_897_817_305e10,
+            2.08430917305e10,
+            3.52205017305e10,
+            2.30674817305e10,
+            2.03954217305e10,
+            5.59897817305e10,
         ]
         skip = True
         for line in repr(DataFrame({"A": vals})).split("\n")[:-2]:
@@ -2140,7 +2140,7 @@ def test_repr_unicode(self):
         s = Series(["\u03c3"] * 10)
         repr(s)
 
-        a = Series(["\u05d0"] * 1_000)
+        a = Series(["\u05d0"] * 1000)
         a.name = "title1"
         repr(a)
 
@@ -2202,7 +2202,7 @@ def test_to_string_mixed(self):
         assert result == expected
 
     def test_to_string_float_na_spacing(self):
-        s = Series([0.0, 1.5_678, 2.0, -3.0, 4.0])
+        s = Series([0.0, 1.5678, 2.0, -3.0, 4.0])
         s[::2] = np.nan
 
         result = s.to_string()
@@ -2261,7 +2261,7 @@ def test_east_asian_unicode_series(self):
         idx = pd.MultiIndex.from_tuples(
             [("あ", "いい"), ("う", "え"), ("おおお", "かかかか"), ("き", "くく")]
         )
-        s = Series([1, 22, 3_333, 44_444], index=idx)
+        s = Series([1, 22, 3333, 44_444], index=idx)
         expected = (
             "あ    いい          1\n"
             "う    え          22\n"
@@ -2271,15 +2271,15 @@ def test_east_asian_unicode_series(self):
         assert repr(s) == expected
 
         # object dtype, shorter than unicode repr
-        s = Series([1, 22, 3_333, 44_444], index=[1, "AB", np.nan, "あああ"])
+        s = Series([1, 22, 3333, 44_444], index=[1, "AB", np.nan, "あああ"])
         expected = (
             "1          1\nAB        22\nNaN     3333\nあああ    44444\ndtype: int64"
         )
         assert repr(s) == expected
 
         # object dtype, longer than unicode repr
         s = Series(
-            [1, 22, 3_333, 44_444], index=[1, "AB", pd.Timestamp("2011-01-01"), "あああ"]
+            [1, 22, 3333, 44_444], index=[1, "AB", pd.Timestamp("2011-01-01"), "あああ"]
         )
         expected = (
             "1                          1\n"
@@ -2356,7 +2356,7 @@ def test_east_asian_unicode_series(self):
             idx = pd.MultiIndex.from_tuples(
                 [("あ", "いい"), ("う", "え"), ("おおお", "かかかか"), ("き", "くく")]
             )
-            s = Series([1, 22, 3_333, 44_444], index=idx)
+            s = Series([1, 22, 3333, 44_444], index=idx)
             expected = (
                 "あ      いい            1\n"
                 "う      え             22\n"
@@ -2367,7 +2367,7 @@ def test_east_asian_unicode_series(self):
             assert repr(s) == expected
 
             # object dtype, shorter than unicode repr
-            s = Series([1, 22, 3_333, 44_444], index=[1, "AB", np.nan, "あああ"])
+            s = Series([1, 22, 3333, 44_444], index=[1, "AB", np.nan, "あああ"])
             expected = (
                 "1             1\nAB           22\nNaN        3333\n"
                 "あああ    44444\ndtype: int64"
@@ -2376,7 +2376,7 @@ def test_east_asian_unicode_series(self):
 
             # object dtype, longer than unicode repr
             s = Series(
-                [1, 22, 3_333, 44_444],
+                [1, 22, 3333, 44_444],
                 index=[1, "AB", pd.Timestamp("2011-01-01"), "あああ"],
             )
             expected = (
@@ -2420,11 +2420,11 @@ def test_east_asian_unicode_series(self):
 
     def test_float_trim_zeros(self):
         vals = [
-            2.08_430_917_305e10,
-            3.52_205_017_305e10,
-            2.30_674_817_305e10,
-            2.03_954_217_305e10,
-            5.59_897_817_305e10,
+            2.08430917305e10,
+            3.52205017305e10,
+            2.30674817305e10,
+            2.03954217305e10,
+            5.59897817305e10,
         ]
         for line in repr(Series(vals)).split("\n"):
             if line.startswith("dtype:"):
@@ -2624,7 +2624,7 @@ def test_max_multi_index_display(self):
 
     # Make sure #8532 is fixed
     def test_consistent_format(self):
-        s = pd.Series([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9_999, 1, 1] * 10)
+        s = pd.Series([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9999, 1, 1] * 10)
         with option_context("display.max_rows", 10, "display.show_dimensions", False):
             res = repr(s)
         exp = (
@@ -2824,19 +2824,19 @@ def test_output_significant_digits(self):
                     "col1": [
                         9.999e-8,
                         1e-7,
-                        1.0_001e-7,
+                        1.0001e-7,
                         2e-7,
                         4.999e-7,
                         5e-7,
-                        5.0_001e-7,
+                        5.0001e-7,
                         6e-7,
                         9.999e-7,
                         1e-6,
-                        1.0_001e-6,
+                        1.0001e-6,
                         2e-6,
                         4.999e-6,
                         5e-6,
-                        5.0_001e-6,
+                        5.0001e-6,
                         6e-6,
                     ]
                 }
@@ -2891,7 +2891,7 @@ def test_too_long(self):
         with pd.option_context("display.precision", 4):
             # need both a number > 1e6 and something that normally formats to
             # having length > display.precision + 6
-            df = pd.DataFrame(dict(x=[12_345.6_789]))
+            df = pd.DataFrame(dict(x=[12_345.6789]))
             assert str(df) == "            x\n0  12345.6789"
             df = pd.DataFrame(dict(x=[2e6]))
             assert str(df) == "           x\n0  2000000.0"

pandas/tests/io/parser/test_usecols.py

@@ -197,12 +197,9 @@ def test_usecols_with_whitespace(all_parsers):
     "usecols,expected",
     [
         # Column selection by index.
-        ([0, 1], DataFrame(data=[[1_000, 2_000], [4_000, 5_000]], columns=["2", "0"])),
+        ([0, 1], DataFrame(data=[[1000, 2000], [4000, 5000]], columns=["2", "0"])),
         # Column selection by name.
-        (
-            ["0", "1"],
-            DataFrame(data=[[2_000, 3_000], [5_000, 6_000]], columns=["0", "1"]),
-        ),
+        (["0", "1"], DataFrame(data=[[2000, 3000], [5000, 6000]], columns=["0", "1"]),),
     ],
 )
 def test_usecols_with_integer_like_header(all_parsers, usecols, expected):
@@ -252,7 +249,7 @@ def test_usecols_with_parse_dates2(all_parsers):
         ],
         name="date",
     )
-    cols = {"values": [1_032.43, 1_042.54, 1_051.65]}
+    cols = {"values": [1032.43, 1042.54, 1051.65]}
     expected = DataFrame(cols, index=index)
 
     result = parser.read_csv(
@@ -352,11 +349,7 @@ def test_usecols_with_unicode_strings(all_parsers):
     parser = all_parsers
 
     exp_data = {
-        "AAA": {
-            0: 0.056_674_972_999_999_997,
-            1: 2.6_132_309_819_999_997,
-            2: 3.5_689_350_380_000_002,
-        },
+        "AAA": {0: 0.056674972999999997, 1: 2.6132309819999997, 2: 3.5689350380000002,},
         "BBB": {0: 8, 1: 2, 2: 7},
     }
     expected = DataFrame(exp_data)
@@ -374,11 +367,7 @@ def test_usecols_with_single_byte_unicode_strings(all_parsers):
     parser = all_parsers
 
     exp_data = {
-        "A": {
-            0: 0.056_674_972_999_999_997,
-            1: 2.6_132_309_819_999_997,
-            2: 3.5_689_350_380_000_002,
-        },
+        "A": {0: 0.056674972999999997, 1: 2.6132309819999997, 2: 3.5689350380000002,},
         "B": {0: 8, 1: 2, 2: 7},
     }
     expected = DataFrame(exp_data)
@@ -408,11 +397,7 @@ def test_usecols_with_multi_byte_characters(all_parsers, usecols):
     parser = all_parsers
 
     exp_data = {
-        "あああ": {
-            0: 0.056_674_972_999_999_997,
-            1: 2.6_132_309_819_999_997,
-            2: 3.5_689_350_380_000_002,
-        },
+        "あああ": {0: 0.056674972999999997, 1: 2.6132309819999997, 2: 3.5689350380000002,},
         "いい": {0: 8, 1: 2, 2: 7},
     }
     expected = DataFrame(exp_data)
@@ -449,9 +434,9 @@ def test_np_array_usecols(all_parsers):
             DataFrame(
                 {
                     "AaA": {
-                        0: 0.056_674_972_999_999_997,
-                        1: 2.6_132_309_819_999_997,
-                        2: 3.5_689_350_380_000_002,
+                        0: 0.056674972999999997,
+                        1: 2.6132309819999997,
+                        2: 3.5689350380000002,
                     },
                     "bBb": {0: 8, 1: 2, 2: 7},
                     "ddd": {0: "a", 1: "b", 2: "a"},