Updated script that can be controled by Nodejs web app

lib/python3.13/site-packages/pandas/tests/io/parser/common/test_ints.py

@@ -0,0 +1,231 @@
+"""
+Tests that work on both the Python and C engines but do not have a
+specific classification into the other test modules.
+"""
+from io import StringIO
+
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Series,
+)
+import pandas._testing as tm
+
+pytestmark = pytest.mark.filterwarnings(
+    "ignore:Passing a BlockManager to DataFrame:DeprecationWarning"
+)
+
+xfail_pyarrow = pytest.mark.usefixtures("pyarrow_xfail")
+skip_pyarrow = pytest.mark.usefixtures("pyarrow_skip")
+
+
+def test_int_conversion(all_parsers):
+    data = """A,B
+1.0,1
+2.0,2
+3.0,3
+"""
+    parser = all_parsers
+    result = parser.read_csv(StringIO(data))
+
+    expected = DataFrame([[1.0, 1], [2.0, 2], [3.0, 3]], columns=["A", "B"])
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data,kwargs,expected",
+    [
+        (
+            "A,B\nTrue,1\nFalse,2\nTrue,3",
+            {},
+            DataFrame([[True, 1], [False, 2], [True, 3]], columns=["A", "B"]),
+        ),
+        (
+            "A,B\nYES,1\nno,2\nyes,3\nNo,3\nYes,3",
+            {"true_values": ["yes", "Yes", "YES"], "false_values": ["no", "NO", "No"]},
+            DataFrame(
+                [[True, 1], [False, 2], [True, 3], [False, 3], [True, 3]],
+                columns=["A", "B"],
+            ),
+        ),
+        (
+            "A,B\nTRUE,1\nFALSE,2\nTRUE,3",
+            {},
+            DataFrame([[True, 1], [False, 2], [True, 3]], columns=["A", "B"]),
+        ),
+        (
+            "A,B\nfoo,bar\nbar,foo",
+            {"true_values": ["foo"], "false_values": ["bar"]},
+            DataFrame([[True, False], [False, True]], columns=["A", "B"]),
+        ),
+    ],
+)
+def test_parse_bool(all_parsers, data, kwargs, expected):
+    parser = all_parsers
+    result = parser.read_csv(StringIO(data), **kwargs)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_parse_integers_above_fp_precision(all_parsers):
+    data = """Numbers
+17007000002000191
+17007000002000191
+17007000002000191
+17007000002000191
+17007000002000192
+17007000002000192
+17007000002000192
+17007000002000192
+17007000002000192
+17007000002000194"""
+    parser = all_parsers
+    result = parser.read_csv(StringIO(data))
+    expected = DataFrame(
+        {
+            "Numbers": [
+                17007000002000191,
+                17007000002000191,
+                17007000002000191,
+                17007000002000191,
+                17007000002000192,
+                17007000002000192,
+                17007000002000192,
+                17007000002000192,
+                17007000002000192,
+                17007000002000194,
+            ]
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("sep", [" ", r"\s+"])
+def test_integer_overflow_bug(all_parsers, sep):
+    # see gh-2601
+    data = "65248E10 11\n55555E55 22\n"
+    parser = all_parsers
+    if parser.engine == "pyarrow" and sep != " ":
+        msg = "the 'pyarrow' engine does not support regex separators"
+        with pytest.raises(ValueError, match=msg):
+            parser.read_csv(StringIO(data), header=None, sep=sep)
+        return
+
+    result = parser.read_csv(StringIO(data), header=None, sep=sep)
+    expected = DataFrame([[6.5248e14, 11], [5.5555e59, 22]])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_int64_min_issues(all_parsers):
+    # see gh-2599
+    parser = all_parsers
+    data = "A,B\n0,0\n0,"
+    result = parser.read_csv(StringIO(data))
+
+    expected = DataFrame({"A": [0, 0], "B": [0, np.nan]})
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("conv", [None, np.int64, np.uint64])
+def test_int64_overflow(all_parsers, conv, request):
+    data = """ID
+00013007854817840016671868
+00013007854817840016749251
+00013007854817840016754630
+00013007854817840016781876
+00013007854817840017028824
+00013007854817840017963235
+00013007854817840018860166"""
+    parser = all_parsers
+
+    if conv is None:
+        # 13007854817840016671868 > UINT64_MAX, so this
+        # will overflow and return object as the dtype.
+        if parser.engine == "pyarrow":
+            mark = pytest.mark.xfail(reason="parses to float64")
+            request.applymarker(mark)
+
+        result = parser.read_csv(StringIO(data))
+        expected = DataFrame(
+            [
+                "00013007854817840016671868",
+                "00013007854817840016749251",
+                "00013007854817840016754630",
+                "00013007854817840016781876",
+                "00013007854817840017028824",
+                "00013007854817840017963235",
+                "00013007854817840018860166",
+            ],
+            columns=["ID"],
+        )
+        tm.assert_frame_equal(result, expected)
+    else:
+        # 13007854817840016671868 > UINT64_MAX, so attempts
+        # to cast to either int64 or uint64 will result in
+        # an OverflowError being raised.
+        msg = "|".join(
+            [
+                "Python int too large to convert to C long",
+                "long too big to convert",
+                "int too big to convert",
+            ]
+        )
+        err = OverflowError
+        if parser.engine == "pyarrow":
+            err = ValueError
+            msg = "The 'converters' option is not supported with the 'pyarrow' engine"
+
+        with pytest.raises(err, match=msg):
+            parser.read_csv(StringIO(data), converters={"ID": conv})
+
+
+@skip_pyarrow  # CSV parse error: Empty CSV file or block
+@pytest.mark.parametrize(
+    "val", [np.iinfo(np.uint64).max, np.iinfo(np.int64).max, np.iinfo(np.int64).min]
+)
+def test_int64_uint64_range(all_parsers, val):
+    # These numbers fall right inside the int64-uint64
+    # range, so they should be parsed as string.
+    parser = all_parsers
+    result = parser.read_csv(StringIO(str(val)), header=None)
+
+    expected = DataFrame([val])
+    tm.assert_frame_equal(result, expected)
+
+
+@skip_pyarrow  # CSV parse error: Empty CSV file or block
+@pytest.mark.parametrize(
+    "val", [np.iinfo(np.uint64).max + 1, np.iinfo(np.int64).min - 1]
+)
+def test_outside_int64_uint64_range(all_parsers, val):
+    # These numbers fall just outside the int64-uint64
+    # range, so they should be parsed as string.
+    parser = all_parsers
+    result = parser.read_csv(StringIO(str(val)), header=None)
+
+    expected = DataFrame([str(val)])
+    tm.assert_frame_equal(result, expected)
+
+
+@xfail_pyarrow  # gets float64 dtype instead of object
+@pytest.mark.parametrize("exp_data", [[str(-1), str(2**63)], [str(2**63), str(-1)]])
+def test_numeric_range_too_wide(all_parsers, exp_data):
+    # No numerical dtype can hold both negative and uint64
+    # values, so they should be cast as string.
+    parser = all_parsers
+    data = "\n".join(exp_data)
+    expected = DataFrame(exp_data)
+
+    result = parser.read_csv(StringIO(data), header=None)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_integer_precision(all_parsers):
+    # Gh 7072
+    s = """1,1;0;0;0;1;1;3844;3844;3844;1;1;1;1;1;1;0;0;1;1;0;0,,,4321583677327450765
+5,1;0;0;0;1;1;843;843;843;1;1;1;1;1;1;0;0;1;1;0;0,64.0,;,4321113141090630389"""
+    parser = all_parsers
+    result = parser.read_csv(StringIO(s), header=None)[4]
+    expected = Series([4321583677327450765, 4321113141090630389], name=4)
+    tm.assert_series_equal(result, expected)