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values | stars int64 0 1k | created_date stringdate 2023-07-10 19:21:08 2025-07-09 19:11:45 | license stringclasses 4
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import numpy as np\nimport pytest\n\nimport pandas as pd\nfrom pandas import Series\nimport pandas._testing as tm\n\n\nclass TestSeriesRound:\n def test_round(self, datetime_series):\n datetime_series.index.name = "index_name"\n result = datetime_series.round(2)\n expected = Series(\n np.round(datetime_series.values, 2), index=datetime_series.index, name="ts"\n )\n tm.assert_series_equal(result, expected)\n assert result.name == datetime_series.name\n\n def test_round_numpy(self, any_float_dtype):\n # See GH#12600\n ser = Series([1.53, 1.36, 0.06], dtype=any_float_dtype)\n out = np.round(ser, decimals=0)\n expected = Series([2.0, 1.0, 0.0], dtype=any_float_dtype)\n tm.assert_series_equal(out, expected)\n\n msg = "the 'out' parameter is not supported"\n with pytest.raises(ValueError, match=msg):\n np.round(ser, decimals=0, out=ser)\n\n def test_round_numpy_with_nan(self, any_float_dtype):\n # See GH#14197\n ser = Series([1.53, np.nan, 0.06], dtype=any_float_dtype)\n with tm.assert_produces_warning(None):\n result = ser.round()\n expected = Series([2.0, np.nan, 0.0], dtype=any_float_dtype)\n tm.assert_series_equal(result, expected)\n\n def test_round_builtin(self, any_float_dtype):\n ser = Series(\n [1.123, 2.123, 3.123],\n index=range(3),\n dtype=any_float_dtype,\n )\n result = round(ser)\n expected_rounded0 = Series(\n [1.0, 2.0, 3.0], index=range(3), dtype=any_float_dtype\n )\n tm.assert_series_equal(result, expected_rounded0)\n\n decimals = 2\n expected_rounded = Series(\n [1.12, 2.12, 3.12], index=range(3), dtype=any_float_dtype\n )\n result = round(ser, decimals)\n tm.assert_series_equal(result, expected_rounded)\n\n @pytest.mark.parametrize("method", ["round", "floor", "ceil"])\n @pytest.mark.parametrize("freq", ["s", "5s", "min", "5min", "h", "5h"])\n def test_round_nat(self, method, freq, unit):\n # GH14940, GH#56158\n ser = Series([pd.NaT], dtype=f"M8[{unit}]")\n expected = Series(pd.NaT, dtype=f"M8[{unit}]")\n round_method = getattr(ser.dt, method)\n result = round_method(freq)\n tm.assert_series_equal(result, expected)\n\n def test_round_ea_boolean(self):\n # GH#55936\n ser = Series([True, False], dtype="boolean")\n expected = ser.copy()\n result = ser.round(2)\n tm.assert_series_equal(result, expected)\n result.iloc[0] = False\n tm.assert_series_equal(ser, expected)\n\n def test_round_dtype_object(self):\n # GH#61206\n ser = Series([0.2], dtype="object")\n msg = "Expected numeric dtype, got object instead."\n with pytest.raises(TypeError, match=msg):\n ser.round()\n | .venv\Lib\site-packages\pandas\tests\series\methods\test_round.py | test_round.py | Python | 2,888 | 0.95 | 0.098765 | 0.071429 | react-lib | 807 | 2024-07-19T08:15:24.960255 | Apache-2.0 | true | 92f05ead0c08ffb987464fd44bc0fd7c |
import numpy as np\nimport pytest\n\nimport pandas as pd\nfrom pandas import (\n Series,\n Timestamp,\n date_range,\n)\nimport pandas._testing as tm\nfrom pandas.api.types import is_scalar\n\n\nclass TestSeriesSearchSorted:\n def test_searchsorted(self):\n ser = Series([1, 2, 3])\n\n result = ser.searchsorted(1, side="left")\n assert is_scalar(result)\n assert result == 0\n\n result = ser.searchsorted(1, side="right")\n assert is_scalar(result)\n assert result == 1\n\n def test_searchsorted_numeric_dtypes_scalar(self):\n ser = Series([1, 2, 90, 1000, 3e9])\n res = ser.searchsorted(30)\n assert is_scalar(res)\n assert res == 2\n\n res = ser.searchsorted([30])\n exp = np.array([2], dtype=np.intp)\n tm.assert_numpy_array_equal(res, exp)\n\n def test_searchsorted_numeric_dtypes_vector(self):\n ser = Series([1, 2, 90, 1000, 3e9])\n res = ser.searchsorted([91, 2e6])\n exp = np.array([3, 4], dtype=np.intp)\n tm.assert_numpy_array_equal(res, exp)\n\n def test_searchsorted_datetime64_scalar(self):\n ser = Series(date_range("20120101", periods=10, freq="2D"))\n val = Timestamp("20120102")\n res = ser.searchsorted(val)\n assert is_scalar(res)\n assert res == 1\n\n def test_searchsorted_datetime64_scalar_mixed_timezones(self):\n # GH 30086\n ser = Series(date_range("20120101", periods=10, freq="2D", tz="UTC"))\n val = Timestamp("20120102", tz="America/New_York")\n res = ser.searchsorted(val)\n assert is_scalar(res)\n assert res == 1\n\n def test_searchsorted_datetime64_list(self):\n ser = Series(date_range("20120101", periods=10, freq="2D"))\n vals = [Timestamp("20120102"), Timestamp("20120104")]\n res = ser.searchsorted(vals)\n exp = np.array([1, 2], dtype=np.intp)\n tm.assert_numpy_array_equal(res, exp)\n\n def test_searchsorted_sorter(self):\n # GH8490\n ser = Series([3, 1, 2])\n res = ser.searchsorted([0, 3], sorter=np.argsort(ser))\n exp = np.array([0, 2], dtype=np.intp)\n tm.assert_numpy_array_equal(res, exp)\n\n def test_searchsorted_dataframe_fail(self):\n # GH#49620\n ser = Series([1, 2, 3, 4, 5])\n vals = pd.DataFrame([[1, 2], [3, 4]])\n msg = "Value must be 1-D array-like or scalar, DataFrame is not supported"\n with pytest.raises(ValueError, match=msg):\n ser.searchsorted(vals)\n | .venv\Lib\site-packages\pandas\tests\series\methods\test_searchsorted.py | test_searchsorted.py | Python | 2,493 | 0.95 | 0.116883 | 0.046875 | node-utils | 861 | 2023-10-31T17:25:45.930195 | GPL-3.0 | true | e5a04b81e2dfc9c5297804058bbdd77b |
from datetime import datetime\n\nfrom pandas import Series\n\n\nclass TestSetName:\n def test_set_name(self):\n ser = Series([1, 2, 3])\n ser2 = ser._set_name("foo")\n assert ser2.name == "foo"\n assert ser.name is None\n assert ser is not ser2\n\n def test_set_name_attribute(self):\n ser = Series([1, 2, 3])\n ser2 = Series([1, 2, 3], name="bar")\n for name in [7, 7.0, "name", datetime(2001, 1, 1), (1,), "\u05D0"]:\n ser.name = name\n assert ser.name == name\n ser2.name = name\n assert ser2.name == name\n | .venv\Lib\site-packages\pandas\tests\series\methods\test_set_name.py | test_set_name.py | Python | 595 | 0.85 | 0.190476 | 0 | awesome-app | 725 | 2023-10-04T04:54:47.859033 | MIT | true | 6ef2f82d71309525900a5511f86765f6 |
import pytest\n\nfrom pandas import Series\n\n\n@pytest.mark.parametrize(\n "data, index, expected",\n [\n ([1, 2, 3], None, 3),\n ({"a": 1, "b": 2, "c": 3}, None, 3),\n ([1, 2, 3], ["x", "y", "z"], 3),\n ([1, 2, 3, 4, 5], ["x", "y", "z", "w", "n"], 5),\n ([1, 2, 3], None, 3),\n ([1, 2, 3], ["x", "y", "z"], 3),\n ([1, 2, 3, 4], ["x", "y", "z", "w"], 4),\n ],\n)\ndef test_series(data, index, expected):\n # GH#52897\n ser = Series(data, index=index)\n assert ser.size == expected\n assert isinstance(ser.size, int)\n | .venv\Lib\site-packages\pandas\tests\series\methods\test_size.py | test_size.py | Python | 566 | 0.95 | 0.045455 | 0.052632 | vue-tools | 845 | 2024-04-21T11:35:35.799693 | MIT | true | 032e2ddb1278b583e24ed638e7550c52 |
import numpy as np\nimport pytest\n\nfrom pandas import (\n DatetimeIndex,\n IntervalIndex,\n MultiIndex,\n Series,\n)\nimport pandas._testing as tm\n\n\n@pytest.fixture(params=["quicksort", "mergesort", "heapsort", "stable"])\ndef sort_kind(request):\n return request.param\n\n\nclass TestSeriesSortIndex:\n def test_sort_index_name(self, datetime_series):\n result = datetime_series.sort_index(ascending=False)\n assert result.name == datetime_series.name\n\n def test_sort_index(self, datetime_series):\n datetime_series.index = datetime_series.index._with_freq(None)\n\n rindex = list(datetime_series.index)\n np.random.default_rng(2).shuffle(rindex)\n\n random_order = datetime_series.reindex(rindex)\n sorted_series = random_order.sort_index()\n tm.assert_series_equal(sorted_series, datetime_series)\n\n # descending\n sorted_series = random_order.sort_index(ascending=False)\n tm.assert_series_equal(\n sorted_series, datetime_series.reindex(datetime_series.index[::-1])\n )\n\n # compat on level\n sorted_series = random_order.sort_index(level=0)\n tm.assert_series_equal(sorted_series, datetime_series)\n\n # compat on axis\n sorted_series = random_order.sort_index(axis=0)\n tm.assert_series_equal(sorted_series, datetime_series)\n\n msg = "No axis named 1 for object type Series"\n with pytest.raises(ValueError, match=msg):\n random_order.sort_values(axis=1)\n\n sorted_series = random_order.sort_index(level=0, axis=0)\n tm.assert_series_equal(sorted_series, datetime_series)\n\n with pytest.raises(ValueError, match=msg):\n random_order.sort_index(level=0, axis=1)\n\n def test_sort_index_inplace(self, datetime_series):\n datetime_series.index = datetime_series.index._with_freq(None)\n\n # For GH#11402\n rindex = list(datetime_series.index)\n np.random.default_rng(2).shuffle(rindex)\n\n # descending\n random_order = datetime_series.reindex(rindex)\n result = random_order.sort_index(ascending=False, inplace=True)\n\n assert result is None\n expected = datetime_series.reindex(datetime_series.index[::-1])\n expected.index = expected.index._with_freq(None)\n tm.assert_series_equal(random_order, expected)\n\n # ascending\n random_order = datetime_series.reindex(rindex)\n result = random_order.sort_index(ascending=True, inplace=True)\n\n assert result is None\n expected = datetime_series.copy()\n expected.index = expected.index._with_freq(None)\n tm.assert_series_equal(random_order, expected)\n\n def test_sort_index_level(self):\n mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC"))\n s = Series([1, 2], mi)\n backwards = s.iloc[[1, 0]]\n\n res = s.sort_index(level="A")\n tm.assert_series_equal(backwards, res)\n\n res = s.sort_index(level=["A", "B"])\n tm.assert_series_equal(backwards, res)\n\n res = s.sort_index(level="A", sort_remaining=False)\n tm.assert_series_equal(s, res)\n\n res = s.sort_index(level=["A", "B"], sort_remaining=False)\n tm.assert_series_equal(s, res)\n\n @pytest.mark.parametrize("level", ["A", 0]) # GH#21052\n def test_sort_index_multiindex(self, level):\n mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC"))\n s = Series([1, 2], mi)\n backwards = s.iloc[[1, 0]]\n\n # implicit sort_remaining=True\n res = s.sort_index(level=level)\n tm.assert_series_equal(backwards, res)\n\n # GH#13496\n # sort has no effect without remaining lvls\n res = s.sort_index(level=level, sort_remaining=False)\n tm.assert_series_equal(s, res)\n\n def test_sort_index_kind(self, sort_kind):\n # GH#14444 & GH#13589: Add support for sort algo choosing\n series = Series(index=[3, 2, 1, 4, 3], dtype=object)\n expected_series = Series(index=[1, 2, 3, 3, 4], dtype=object)\n\n index_sorted_series = series.sort_index(kind=sort_kind)\n tm.assert_series_equal(expected_series, index_sorted_series)\n\n def test_sort_index_na_position(self):\n series = Series(index=[3, 2, 1, 4, 3, np.nan], dtype=object)\n expected_series_first = Series(index=[np.nan, 1, 2, 3, 3, 4], dtype=object)\n\n index_sorted_series = series.sort_index(na_position="first")\n tm.assert_series_equal(expected_series_first, index_sorted_series)\n\n expected_series_last = Series(index=[1, 2, 3, 3, 4, np.nan], dtype=object)\n\n index_sorted_series = series.sort_index(na_position="last")\n tm.assert_series_equal(expected_series_last, index_sorted_series)\n\n def test_sort_index_intervals(self):\n s = Series(\n [np.nan, 1, 2, 3], IntervalIndex.from_arrays([0, 1, 2, 3], [1, 2, 3, 4])\n )\n\n result = s.sort_index()\n expected = s\n tm.assert_series_equal(result, expected)\n\n result = s.sort_index(ascending=False)\n expected = Series(\n [3, 2, 1, np.nan], IntervalIndex.from_arrays([3, 2, 1, 0], [4, 3, 2, 1])\n )\n tm.assert_series_equal(result, expected)\n\n @pytest.mark.parametrize("inplace", [True, False])\n @pytest.mark.parametrize(\n "original_list, sorted_list, ascending, ignore_index, output_index",\n [\n ([2, 3, 6, 1], [2, 3, 6, 1], True, True, [0, 1, 2, 3]),\n ([2, 3, 6, 1], [2, 3, 6, 1], True, False, [0, 1, 2, 3]),\n ([2, 3, 6, 1], [1, 6, 3, 2], False, True, [0, 1, 2, 3]),\n ([2, 3, 6, 1], [1, 6, 3, 2], False, False, [3, 2, 1, 0]),\n ],\n )\n def test_sort_index_ignore_index(\n self, inplace, original_list, sorted_list, ascending, ignore_index, output_index\n ):\n # GH 30114\n ser = Series(original_list)\n expected = Series(sorted_list, index=output_index)\n kwargs = {\n "ascending": ascending,\n "ignore_index": ignore_index,\n "inplace": inplace,\n }\n\n if inplace:\n result_ser = ser.copy()\n result_ser.sort_index(**kwargs)\n else:\n result_ser = ser.sort_index(**kwargs)\n\n tm.assert_series_equal(result_ser, expected)\n tm.assert_series_equal(ser, Series(original_list))\n\n def test_sort_index_ascending_list(self):\n # GH#16934\n\n # Set up a Series with a three level MultiIndex\n arrays = [\n ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],\n ["one", "two", "one", "two", "one", "two", "one", "two"],\n [4, 3, 2, 1, 4, 3, 2, 1],\n ]\n tuples = zip(*arrays)\n mi = MultiIndex.from_tuples(tuples, names=["first", "second", "third"])\n ser = Series(range(8), index=mi)\n\n # Sort with boolean ascending\n result = ser.sort_index(level=["third", "first"], ascending=False)\n expected = ser.iloc[[4, 0, 5, 1, 6, 2, 7, 3]]\n tm.assert_series_equal(result, expected)\n\n # Sort with list of boolean ascending\n result = ser.sort_index(level=["third", "first"], ascending=[False, True])\n expected = ser.iloc[[0, 4, 1, 5, 2, 6, 3, 7]]\n tm.assert_series_equal(result, expected)\n\n @pytest.mark.parametrize(\n "ascending",\n [\n None,\n (True, None),\n (False, "True"),\n ],\n )\n def test_sort_index_ascending_bad_value_raises(self, ascending):\n ser = Series(range(10), index=[0, 3, 2, 1, 4, 5, 7, 6, 8, 9])\n match = 'For argument "ascending" expected type bool'\n with pytest.raises(ValueError, match=match):\n ser.sort_index(ascending=ascending)\n\n\nclass TestSeriesSortIndexKey:\n def test_sort_index_multiindex_key(self):\n mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC"))\n s = Series([1, 2], mi)\n backwards = s.iloc[[1, 0]]\n\n result = s.sort_index(level="C", key=lambda x: -x)\n tm.assert_series_equal(s, result)\n\n result = s.sort_index(level="C", key=lambda x: x) # nothing happens\n tm.assert_series_equal(backwards, result)\n\n def test_sort_index_multiindex_key_multi_level(self):\n mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC"))\n s = Series([1, 2], mi)\n backwards = s.iloc[[1, 0]]\n\n result = s.sort_index(level=["A", "C"], key=lambda x: -x)\n tm.assert_series_equal(s, result)\n\n result = s.sort_index(level=["A", "C"], key=lambda x: x) # nothing happens\n tm.assert_series_equal(backwards, result)\n\n def test_sort_index_key(self):\n series = Series(np.arange(6, dtype="int64"), index=list("aaBBca"))\n\n result = series.sort_index()\n expected = series.iloc[[2, 3, 0, 1, 5, 4]]\n tm.assert_series_equal(result, expected)\n\n result = series.sort_index(key=lambda x: x.str.lower())\n expected = series.iloc[[0, 1, 5, 2, 3, 4]]\n tm.assert_series_equal(result, expected)\n\n result = series.sort_index(key=lambda x: x.str.lower(), ascending=False)\n expected = series.iloc[[4, 2, 3, 0, 1, 5]]\n tm.assert_series_equal(result, expected)\n\n def test_sort_index_key_int(self):\n series = Series(np.arange(6, dtype="int64"), index=np.arange(6, dtype="int64"))\n\n result = series.sort_index()\n tm.assert_series_equal(result, series)\n\n result = series.sort_index(key=lambda x: -x)\n expected = series.sort_index(ascending=False)\n tm.assert_series_equal(result, expected)\n\n result = series.sort_index(key=lambda x: 2 * x)\n tm.assert_series_equal(result, series)\n\n def test_sort_index_kind_key(self, sort_kind, sort_by_key):\n # GH #14444 & #13589: Add support for sort algo choosing\n series = Series(index=[3, 2, 1, 4, 3], dtype=object)\n expected_series = Series(index=[1, 2, 3, 3, 4], dtype=object)\n\n index_sorted_series = series.sort_index(kind=sort_kind, key=sort_by_key)\n tm.assert_series_equal(expected_series, index_sorted_series)\n\n def test_sort_index_kind_neg_key(self, sort_kind):\n # GH #14444 & #13589: Add support for sort algo choosing\n series = Series(index=[3, 2, 1, 4, 3], dtype=object)\n expected_series = Series(index=[4, 3, 3, 2, 1], dtype=object)\n\n index_sorted_series = series.sort_index(kind=sort_kind, key=lambda x: -x)\n tm.assert_series_equal(expected_series, index_sorted_series)\n\n def test_sort_index_na_position_key(self, sort_by_key):\n series = Series(index=[3, 2, 1, 4, 3, np.nan], dtype=object)\n expected_series_first = Series(index=[np.nan, 1, 2, 3, 3, 4], dtype=object)\n\n index_sorted_series = series.sort_index(na_position="first", key=sort_by_key)\n tm.assert_series_equal(expected_series_first, index_sorted_series)\n\n expected_series_last = Series(index=[1, 2, 3, 3, 4, np.nan], dtype=object)\n\n index_sorted_series = series.sort_index(na_position="last", key=sort_by_key)\n tm.assert_series_equal(expected_series_last, index_sorted_series)\n\n def test_changes_length_raises(self):\n s = Series([1, 2, 3])\n with pytest.raises(ValueError, match="change the shape"):\n s.sort_index(key=lambda x: x[:1])\n\n def test_sort_values_key_type(self):\n s = Series([1, 2, 3], DatetimeIndex(["2008-10-24", "2008-11-23", "2007-12-22"]))\n\n result = s.sort_index(key=lambda x: x.month)\n expected = s.iloc[[0, 1, 2]]\n tm.assert_series_equal(result, expected)\n\n result = s.sort_index(key=lambda x: x.day)\n expected = s.iloc[[2, 1, 0]]\n tm.assert_series_equal(result, expected)\n\n result = s.sort_index(key=lambda x: x.year)\n expected = s.iloc[[2, 0, 1]]\n tm.assert_series_equal(result, expected)\n\n result = s.sort_index(key=lambda x: x.month_name())\n expected = s.iloc[[2, 1, 0]]\n tm.assert_series_equal(result, expected)\n\n @pytest.mark.parametrize(\n "ascending",\n [\n [True, False],\n [False, True],\n ],\n )\n def test_sort_index_multi_already_monotonic(self, ascending):\n # GH 56049\n mi = MultiIndex.from_product([[1, 2], [3, 4]])\n ser = Series(range(len(mi)), index=mi)\n result = ser.sort_index(ascending=ascending)\n if ascending == [True, False]:\n expected = ser.take([1, 0, 3, 2])\n elif ascending == [False, True]:\n expected = ser.take([2, 3, 0, 1])\n tm.assert_series_equal(result, expected)\n | .venv\Lib\site-packages\pandas\tests\series\methods\test_sort_index.py | test_sort_index.py | Python | 12,634 | 0.95 | 0.089021 | 0.068702 | awesome-app | 513 | 2024-04-26T22:05:47.080630 | GPL-3.0 | true | 5bd9ce69ab7166aeae0230e8fa9942fb |
import numpy as np\nimport pytest\n\nfrom pandas import (\n Categorical,\n DataFrame,\n Series,\n)\nimport pandas._testing as tm\n\n\nclass TestSeriesSortValues:\n def test_sort_values(self, datetime_series, using_copy_on_write):\n # check indexes are reordered corresponding with the values\n ser = Series([3, 2, 4, 1], ["A", "B", "C", "D"])\n expected = Series([1, 2, 3, 4], ["D", "B", "A", "C"])\n result = ser.sort_values()\n tm.assert_series_equal(expected, result)\n\n ts = datetime_series.copy()\n ts[:5] = np.nan\n vals = ts.values\n\n result = ts.sort_values()\n assert np.isnan(result[-5:]).all()\n tm.assert_numpy_array_equal(result[:-5].values, np.sort(vals[5:]))\n\n # na_position\n result = ts.sort_values(na_position="first")\n assert np.isnan(result[:5]).all()\n tm.assert_numpy_array_equal(result[5:].values, np.sort(vals[5:]))\n\n # something object-type\n ser = Series(["A", "B"], [1, 2])\n # no failure\n ser.sort_values()\n\n # ascending=False\n ordered = ts.sort_values(ascending=False)\n expected = np.sort(ts.dropna().values)[::-1]\n tm.assert_almost_equal(expected, ordered.dropna().values)\n ordered = ts.sort_values(ascending=False, na_position="first")\n tm.assert_almost_equal(expected, ordered.dropna().values)\n\n # ascending=[False] should behave the same as ascending=False\n ordered = ts.sort_values(ascending=[False])\n expected = ts.sort_values(ascending=False)\n tm.assert_series_equal(expected, ordered)\n ordered = ts.sort_values(ascending=[False], na_position="first")\n expected = ts.sort_values(ascending=False, na_position="first")\n tm.assert_series_equal(expected, ordered)\n\n msg = 'For argument "ascending" expected type bool, received type NoneType.'\n with pytest.raises(ValueError, match=msg):\n ts.sort_values(ascending=None)\n msg = r"Length of ascending \(0\) must be 1 for Series"\n with pytest.raises(ValueError, match=msg):\n ts.sort_values(ascending=[])\n msg = r"Length of ascending \(3\) must be 1 for Series"\n with pytest.raises(ValueError, match=msg):\n ts.sort_values(ascending=[1, 2, 3])\n msg = r"Length of ascending \(2\) must be 1 for Series"\n with pytest.raises(ValueError, match=msg):\n ts.sort_values(ascending=[False, False])\n msg = 'For argument "ascending" expected type bool, received type str.'\n with pytest.raises(ValueError, match=msg):\n ts.sort_values(ascending="foobar")\n\n # inplace=True\n ts = datetime_series.copy()\n return_value = ts.sort_values(ascending=False, inplace=True)\n assert return_value is None\n tm.assert_series_equal(ts, datetime_series.sort_values(ascending=False))\n tm.assert_index_equal(\n ts.index, datetime_series.sort_values(ascending=False).index\n )\n\n # GH#5856/5853\n # Series.sort_values operating on a view\n df = DataFrame(np.random.default_rng(2).standard_normal((10, 4)))\n s = df.iloc[:, 0]\n\n msg = (\n "This Series is a view of some other array, to sort in-place "\n "you must create a copy"\n )\n if using_copy_on_write:\n s.sort_values(inplace=True)\n tm.assert_series_equal(s, df.iloc[:, 0].sort_values())\n else:\n with pytest.raises(ValueError, match=msg):\n s.sort_values(inplace=True)\n\n def test_sort_values_categorical(self):\n c = Categorical(["a", "b", "b", "a"], ordered=False)\n cat = Series(c.copy())\n\n # sort in the categories order\n expected = Series(\n Categorical(["a", "a", "b", "b"], ordered=False), index=[0, 3, 1, 2]\n )\n result = cat.sort_values()\n tm.assert_series_equal(result, expected)\n\n cat = Series(Categorical(["a", "c", "b", "d"], ordered=True))\n res = cat.sort_values()\n exp = np.array(["a", "b", "c", "d"], dtype=np.object_)\n tm.assert_numpy_array_equal(res.__array__(), exp)\n\n cat = Series(\n Categorical(\n ["a", "c", "b", "d"], categories=["a", "b", "c", "d"], ordered=True\n )\n )\n res = cat.sort_values()\n exp = np.array(["a", "b", "c", "d"], dtype=np.object_)\n tm.assert_numpy_array_equal(res.__array__(), exp)\n\n res = cat.sort_values(ascending=False)\n exp = np.array(["d", "c", "b", "a"], dtype=np.object_)\n tm.assert_numpy_array_equal(res.__array__(), exp)\n\n raw_cat1 = Categorical(\n ["a", "b", "c", "d"], categories=["a", "b", "c", "d"], ordered=False\n )\n raw_cat2 = Categorical(\n ["a", "b", "c", "d"], categories=["d", "c", "b", "a"], ordered=True\n )\n s = ["a", "b", "c", "d"]\n df = DataFrame(\n {"unsort": raw_cat1, "sort": raw_cat2, "string": s, "values": [1, 2, 3, 4]}\n )\n\n # Cats must be sorted in a dataframe\n res = df.sort_values(by=["string"], ascending=False)\n exp = np.array(["d", "c", "b", "a"], dtype=np.object_)\n tm.assert_numpy_array_equal(res["sort"].values.__array__(), exp)\n assert res["sort"].dtype == "category"\n\n res = df.sort_values(by=["sort"], ascending=False)\n exp = df.sort_values(by=["string"], ascending=True)\n tm.assert_series_equal(res["values"], exp["values"])\n assert res["sort"].dtype == "category"\n assert res["unsort"].dtype == "category"\n\n # unordered cat, but we allow this\n df.sort_values(by=["unsort"], ascending=False)\n\n # multi-columns sort\n # GH#7848\n df = DataFrame(\n {"id": [6, 5, 4, 3, 2, 1], "raw_grade": ["a", "b", "b", "a", "a", "e"]}\n )\n df["grade"] = Categorical(df["raw_grade"], ordered=True)\n df["grade"] = df["grade"].cat.set_categories(["b", "e", "a"])\n\n # sorts 'grade' according to the order of the categories\n result = df.sort_values(by=["grade"])\n expected = df.iloc[[1, 2, 5, 0, 3, 4]]\n tm.assert_frame_equal(result, expected)\n\n # multi\n result = df.sort_values(by=["grade", "id"])\n expected = df.iloc[[2, 1, 5, 4, 3, 0]]\n tm.assert_frame_equal(result, expected)\n\n @pytest.mark.parametrize("inplace", [True, False])\n @pytest.mark.parametrize(\n "original_list, sorted_list, ignore_index, output_index",\n [\n ([2, 3, 6, 1], [6, 3, 2, 1], True, [0, 1, 2, 3]),\n ([2, 3, 6, 1], [6, 3, 2, 1], False, [2, 1, 0, 3]),\n ],\n )\n def test_sort_values_ignore_index(\n self, inplace, original_list, sorted_list, ignore_index, output_index\n ):\n # GH 30114\n ser = Series(original_list)\n expected = Series(sorted_list, index=output_index)\n kwargs = {"ignore_index": ignore_index, "inplace": inplace}\n\n if inplace:\n result_ser = ser.copy()\n result_ser.sort_values(ascending=False, **kwargs)\n else:\n result_ser = ser.sort_values(ascending=False, **kwargs)\n\n tm.assert_series_equal(result_ser, expected)\n tm.assert_series_equal(ser, Series(original_list))\n\n def test_mergesort_descending_stability(self):\n # GH 28697\n s = Series([1, 2, 1, 3], ["first", "b", "second", "c"])\n result = s.sort_values(ascending=False, kind="mergesort")\n expected = Series([3, 2, 1, 1], ["c", "b", "first", "second"])\n tm.assert_series_equal(result, expected)\n\n def test_sort_values_validate_ascending_for_value_error(self):\n # GH41634\n ser = Series([23, 7, 21])\n\n msg = 'For argument "ascending" expected type bool, received type str.'\n with pytest.raises(ValueError, match=msg):\n ser.sort_values(ascending="False")\n\n @pytest.mark.parametrize("ascending", [False, 0, 1, True])\n def test_sort_values_validate_ascending_functional(self, ascending):\n # GH41634\n ser = Series([23, 7, 21])\n expected = np.sort(ser.values)\n\n sorted_ser = ser.sort_values(ascending=ascending)\n if not ascending:\n expected = expected[::-1]\n\n result = sorted_ser.values\n tm.assert_numpy_array_equal(result, expected)\n\n\nclass TestSeriesSortingKey:\n def test_sort_values_key(self):\n series = Series(np.array(["Hello", "goodbye"]))\n\n result = series.sort_values(axis=0)\n expected = series\n tm.assert_series_equal(result, expected)\n\n result = series.sort_values(axis=0, key=lambda x: x.str.lower())\n expected = series[::-1]\n tm.assert_series_equal(result, expected)\n\n def test_sort_values_key_nan(self):\n series = Series(np.array([0, 5, np.nan, 3, 2, np.nan]))\n\n result = series.sort_values(axis=0)\n expected = series.iloc[[0, 4, 3, 1, 2, 5]]\n tm.assert_series_equal(result, expected)\n\n result = series.sort_values(axis=0, key=lambda x: x + 5)\n expected = series.iloc[[0, 4, 3, 1, 2, 5]]\n tm.assert_series_equal(result, expected)\n\n result = series.sort_values(axis=0, key=lambda x: -x, ascending=False)\n expected = series.iloc[[0, 4, 3, 1, 2, 5]]\n tm.assert_series_equal(result, expected)\n | .venv\Lib\site-packages\pandas\tests\series\methods\test_sort_values.py | test_sort_values.py | Python | 9,372 | 0.95 | 0.065041 | 0.098039 | node-utils | 675 | 2024-03-30T10:34:47.004101 | GPL-3.0 | true | 40b89b68491b85e83b9fe9cc68796867 |
import pytest\n\nimport pandas.util._test_decorators as td\n\nfrom pandas import (\n Interval,\n Period,\n Series,\n Timedelta,\n Timestamp,\n)\n\n\n@pytest.mark.parametrize(\n "values, dtype, expected_dtype",\n (\n ([1], "int64", int),\n ([1], "Int64", int),\n ([1.0], "float64", float),\n ([1.0], "Float64", float),\n (["abc"], "object", str),\n (["abc"], "string", str),\n ([Interval(1, 3)], "interval", Interval),\n ([Period("2000-01-01", "D")], "period[D]", Period),\n ([Timedelta(days=1)], "timedelta64[ns]", Timedelta),\n ([Timestamp("2000-01-01")], "datetime64[ns]", Timestamp),\n pytest.param([1], "int64[pyarrow]", int, marks=td.skip_if_no("pyarrow")),\n pytest.param([1.0], "float64[pyarrow]", float, marks=td.skip_if_no("pyarrow")),\n pytest.param(["abc"], "string[pyarrow]", str, marks=td.skip_if_no("pyarrow")),\n ),\n)\ndef test_tolist_scalar_dtype(values, dtype, expected_dtype):\n # GH49890\n ser = Series(values, dtype=dtype)\n result_dtype = type(ser.tolist()[0])\n assert result_dtype == expected_dtype\n | .venv\Lib\site-packages\pandas\tests\series\methods\test_tolist.py | test_tolist.py | Python | 1,115 | 0.95 | 0.027778 | 0.03125 | python-kit | 471 | 2025-04-21T00:13:49.905504 | Apache-2.0 | true | 2e3dbf5faa7e843e03b12aec9613a086 |
from datetime import datetime\nfrom io import StringIO\n\nimport numpy as np\nimport pytest\n\nimport pandas as pd\nfrom pandas import Series\nimport pandas._testing as tm\n\nfrom pandas.io.common import get_handle\n\n\nclass TestSeriesToCSV:\n def read_csv(self, path, **kwargs):\n params = {"index_col": 0, "header": None}\n params.update(**kwargs)\n\n header = params.get("header")\n out = pd.read_csv(path, **params).squeeze("columns")\n\n if header is None:\n out.name = out.index.name = None\n\n return out\n\n def test_from_csv(self, datetime_series, string_series):\n # freq doesn't round-trip\n datetime_series.index = datetime_series.index._with_freq(None)\n\n with tm.ensure_clean() as path:\n datetime_series.to_csv(path, header=False)\n ts = self.read_csv(path, parse_dates=True)\n tm.assert_series_equal(datetime_series, ts, check_names=False)\n\n assert ts.name is None\n assert ts.index.name is None\n\n # see gh-10483\n datetime_series.to_csv(path, header=True)\n ts_h = self.read_csv(path, header=0)\n assert ts_h.name == "ts"\n\n string_series.to_csv(path, header=False)\n series = self.read_csv(path)\n tm.assert_series_equal(string_series, series, check_names=False)\n\n assert series.name is None\n assert series.index.name is None\n\n string_series.to_csv(path, header=True)\n series_h = self.read_csv(path, header=0)\n assert series_h.name == "series"\n\n with open(path, "w", encoding="utf-8") as outfile:\n outfile.write("1998-01-01|1.0\n1999-01-01|2.0")\n\n series = self.read_csv(path, sep="|", parse_dates=True)\n check_series = Series(\n {datetime(1998, 1, 1): 1.0, datetime(1999, 1, 1): 2.0}\n )\n tm.assert_series_equal(check_series, series)\n\n series = self.read_csv(path, sep="|", parse_dates=False)\n check_series = Series({"1998-01-01": 1.0, "1999-01-01": 2.0})\n tm.assert_series_equal(check_series, series)\n\n def test_to_csv(self, datetime_series):\n with tm.ensure_clean() as path:\n datetime_series.to_csv(path, header=False)\n\n with open(path, newline=None, encoding="utf-8") as f:\n lines = f.readlines()\n assert lines[1] != "\n"\n\n datetime_series.to_csv(path, index=False, header=False)\n arr = np.loadtxt(path)\n tm.assert_almost_equal(arr, datetime_series.values)\n\n def test_to_csv_unicode_index(self):\n buf = StringIO()\n s = Series(["\u05d0", "d2"], index=["\u05d0", "\u05d1"])\n\n s.to_csv(buf, encoding="UTF-8", header=False)\n buf.seek(0)\n\n s2 = self.read_csv(buf, index_col=0, encoding="UTF-8")\n tm.assert_series_equal(s, s2)\n\n def test_to_csv_float_format(self):\n with tm.ensure_clean() as filename:\n ser = Series([0.123456, 0.234567, 0.567567])\n ser.to_csv(filename, float_format="%.2f", header=False)\n\n rs = self.read_csv(filename)\n xp = Series([0.12, 0.23, 0.57])\n tm.assert_series_equal(rs, xp)\n\n def test_to_csv_list_entries(self):\n s = Series(["jack and jill", "jesse and frank"])\n\n split = s.str.split(r"\s+and\s+")\n\n buf = StringIO()\n split.to_csv(buf, header=False)\n\n def test_to_csv_path_is_none(self):\n # GH 8215\n # Series.to_csv() was returning None, inconsistent with\n # DataFrame.to_csv() which returned string\n s = Series([1, 2, 3])\n csv_str = s.to_csv(path_or_buf=None, header=False)\n assert isinstance(csv_str, str)\n\n @pytest.mark.parametrize(\n "s,encoding",\n [\n (\n Series([0.123456, 0.234567, 0.567567], index=["A", "B", "C"], name="X"),\n None,\n ),\n # GH 21241, 21118\n (Series(["abc", "def", "ghi"], name="X"), "ascii"),\n (Series(["123", "你好", "世界"], name="中文"), "gb2312"),\n (\n Series(["123", "Γειά σου", "Κόσμε"], name="Ελληνικά"), # noqa: RUF001\n "cp737",\n ),\n ],\n )\n def test_to_csv_compression(self, s, encoding, compression):\n with tm.ensure_clean() as filename:\n s.to_csv(filename, compression=compression, encoding=encoding, header=True)\n # test the round trip - to_csv -> read_csv\n result = pd.read_csv(\n filename,\n compression=compression,\n encoding=encoding,\n index_col=0,\n ).squeeze("columns")\n tm.assert_series_equal(s, result)\n\n # test the round trip using file handle - to_csv -> read_csv\n with get_handle(\n filename, "w", compression=compression, encoding=encoding\n ) as handles:\n s.to_csv(handles.handle, encoding=encoding, header=True)\n\n result = pd.read_csv(\n filename,\n compression=compression,\n encoding=encoding,\n index_col=0,\n ).squeeze("columns")\n tm.assert_series_equal(s, result)\n\n # explicitly ensure file was compressed\n with tm.decompress_file(filename, compression) as fh:\n text = fh.read().decode(encoding or "utf8")\n assert s.name in text\n\n with tm.decompress_file(filename, compression) as fh:\n tm.assert_series_equal(\n s,\n pd.read_csv(fh, index_col=0, encoding=encoding).squeeze("columns"),\n )\n\n def test_to_csv_interval_index(self, using_infer_string):\n # GH 28210\n s = Series(["foo", "bar", "baz"], index=pd.interval_range(0, 3))\n\n with tm.ensure_clean("__tmp_to_csv_interval_index__.csv") as path:\n s.to_csv(path, header=False)\n result = self.read_csv(path, index_col=0)\n\n # can't roundtrip intervalindex via read_csv so check string repr (GH 23595)\n expected = s\n expected.index = expected.index.astype("str")\n tm.assert_series_equal(result, expected)\n | .venv\Lib\site-packages\pandas\tests\series\methods\test_to_csv.py | test_to_csv.py | Python | 6,346 | 0.95 | 0.067039 | 0.078014 | react-lib | 663 | 2023-08-01T01:22:22.149248 | Apache-2.0 | true | 3a64f5788c444f866a552105dbcc26e7 |
import collections\n\nimport numpy as np\nimport pytest\n\nfrom pandas import Series\nimport pandas._testing as tm\n\n\nclass TestSeriesToDict:\n @pytest.mark.parametrize(\n "mapping", (dict, collections.defaultdict(list), collections.OrderedDict)\n )\n def test_to_dict(self, mapping, datetime_series):\n # GH#16122\n result = Series(datetime_series.to_dict(into=mapping), name="ts")\n expected = datetime_series.copy()\n expected.index = expected.index._with_freq(None)\n tm.assert_series_equal(result, expected)\n\n from_method = Series(datetime_series.to_dict(into=collections.Counter))\n from_constructor = Series(collections.Counter(datetime_series.items()))\n tm.assert_series_equal(from_method, from_constructor)\n\n @pytest.mark.parametrize(\n "input",\n (\n {"a": np.int64(64), "b": 10},\n {"a": np.int64(64), "b": 10, "c": "ABC"},\n {"a": np.uint64(64), "b": 10, "c": "ABC"},\n ),\n )\n def test_to_dict_return_types(self, input):\n # GH25969\n\n d = Series(input).to_dict()\n assert isinstance(d["a"], int)\n assert isinstance(d["b"], int)\n | .venv\Lib\site-packages\pandas\tests\series\methods\test_to_dict.py | test_to_dict.py | Python | 1,178 | 0.95 | 0.078947 | 0.064516 | awesome-app | 479 | 2024-09-04T19:12:36.386386 | GPL-3.0 | true | f75939e07ec5ebcf9a780376fc8d0371 |
import pytest\n\nfrom pandas import (\n DataFrame,\n Index,\n Series,\n)\nimport pandas._testing as tm\n\n\nclass TestToFrame:\n def test_to_frame_respects_name_none(self):\n # GH#44212 if we explicitly pass name=None, then that should be respected,\n # not changed to 0\n # GH-45448 this is first deprecated & enforced in 2.0\n ser = Series(range(3))\n result = ser.to_frame(None)\n\n exp_index = Index([None], dtype=object)\n tm.assert_index_equal(result.columns, exp_index)\n\n result = ser.rename("foo").to_frame(None)\n exp_index = Index([None], dtype=object)\n tm.assert_index_equal(result.columns, exp_index)\n\n def test_to_frame(self, datetime_series):\n datetime_series.name = None\n rs = datetime_series.to_frame()\n xp = DataFrame(datetime_series.values, index=datetime_series.index)\n tm.assert_frame_equal(rs, xp)\n\n datetime_series.name = "testname"\n rs = datetime_series.to_frame()\n xp = DataFrame(\n {"testname": datetime_series.values}, index=datetime_series.index\n )\n tm.assert_frame_equal(rs, xp)\n\n rs = datetime_series.to_frame(name="testdifferent")\n xp = DataFrame(\n {"testdifferent": datetime_series.values}, index=datetime_series.index\n )\n tm.assert_frame_equal(rs, xp)\n\n @pytest.mark.filterwarnings(\n "ignore:Passing a BlockManager|Passing a SingleBlockManager:DeprecationWarning"\n )\n def test_to_frame_expanddim(self):\n # GH#9762\n\n class SubclassedSeries(Series):\n @property\n def _constructor_expanddim(self):\n return SubclassedFrame\n\n class SubclassedFrame(DataFrame):\n pass\n\n ser = SubclassedSeries([1, 2, 3], name="X")\n result = ser.to_frame()\n assert isinstance(result, SubclassedFrame)\n expected = SubclassedFrame({"X": [1, 2, 3]})\n tm.assert_frame_equal(result, expected)\n | .venv\Lib\site-packages\pandas\tests\series\methods\test_to_frame.py | test_to_frame.py | Python | 1,992 | 0.95 | 0.126984 | 0.078431 | node-utils | 319 | 2023-12-14T05:34:08.264657 | MIT | true | 8369061ab1a0020d95284bf716172f10 |
import numpy as np\nimport pytest\n\nimport pandas.util._test_decorators as td\n\nfrom pandas import (\n NA,\n Series,\n Timedelta,\n)\nimport pandas._testing as tm\n\n\n@pytest.mark.parametrize("dtype", ["int64", "float64"])\ndef test_to_numpy_na_value(dtype):\n # GH#48951\n ser = Series([1, 2, NA, 4])\n result = ser.to_numpy(dtype=dtype, na_value=0)\n expected = np.array([1, 2, 0, 4], dtype=dtype)\n tm.assert_numpy_array_equal(result, expected)\n\n\ndef test_to_numpy_cast_before_setting_na():\n # GH#50600\n ser = Series([1])\n result = ser.to_numpy(dtype=np.float64, na_value=np.nan)\n expected = np.array([1.0])\n tm.assert_numpy_array_equal(result, expected)\n\n\n@td.skip_if_no("pyarrow")\ndef test_to_numpy_arrow_dtype_given():\n # GH#57121\n ser = Series([1, NA], dtype="int64[pyarrow]")\n result = ser.to_numpy(dtype="float64")\n expected = np.array([1.0, np.nan])\n tm.assert_numpy_array_equal(result, expected)\n\n\ndef test_astype_ea_int_to_td_ts():\n # GH#57093\n ser = Series([1, None], dtype="Int64")\n result = ser.astype("m8[ns]")\n expected = Series([1, Timedelta("nat")], dtype="m8[ns]")\n tm.assert_series_equal(result, expected)\n\n result = ser.astype("M8[ns]")\n expected = Series([1, Timedelta("nat")], dtype="M8[ns]")\n tm.assert_series_equal(result, expected)\n | .venv\Lib\site-packages\pandas\tests\series\methods\test_to_numpy.py | test_to_numpy.py | Python | 1,321 | 0.95 | 0.081633 | 0.105263 | python-kit | 519 | 2025-02-17T09:45:28.129643 | GPL-3.0 | true | 2cc1d37eba0131598b5ce3a5f870b582 |
from datetime import datetime\n\nimport pytest\n\nimport pandas as pd\nfrom pandas import (\n Series,\n date_range,\n)\nimport pandas._testing as tm\n\n\nclass TestTruncate:\n def test_truncate_datetimeindex_tz(self):\n # GH 9243\n idx = date_range("4/1/2005", "4/30/2005", freq="D", tz="US/Pacific")\n s = Series(range(len(idx)), index=idx)\n with pytest.raises(TypeError, match="Cannot compare tz-naive"):\n # GH#36148 as of 2.0 we require tzawareness compat\n s.truncate(datetime(2005, 4, 2), datetime(2005, 4, 4))\n\n lb = idx[1]\n ub = idx[3]\n result = s.truncate(lb.to_pydatetime(), ub.to_pydatetime())\n expected = Series([1, 2, 3], index=idx[1:4])\n tm.assert_series_equal(result, expected)\n\n def test_truncate_periodindex(self):\n # GH 17717\n idx1 = pd.PeriodIndex(\n [pd.Period("2017-09-02"), pd.Period("2017-09-02"), pd.Period("2017-09-03")]\n )\n series1 = Series([1, 2, 3], index=idx1)\n result1 = series1.truncate(after="2017-09-02")\n\n expected_idx1 = pd.PeriodIndex(\n [pd.Period("2017-09-02"), pd.Period("2017-09-02")]\n )\n tm.assert_series_equal(result1, Series([1, 2], index=expected_idx1))\n\n idx2 = pd.PeriodIndex(\n [pd.Period("2017-09-03"), pd.Period("2017-09-02"), pd.Period("2017-09-03")]\n )\n series2 = Series([1, 2, 3], index=idx2)\n result2 = series2.sort_index().truncate(after="2017-09-02")\n\n expected_idx2 = pd.PeriodIndex([pd.Period("2017-09-02")])\n tm.assert_series_equal(result2, Series([2], index=expected_idx2))\n\n def test_truncate_one_element_series(self):\n # GH 35544\n series = Series([0.1], index=pd.DatetimeIndex(["2020-08-04"]))\n before = pd.Timestamp("2020-08-02")\n after = pd.Timestamp("2020-08-04")\n\n result = series.truncate(before=before, after=after)\n\n # the input Series and the expected Series are the same\n tm.assert_series_equal(result, series)\n\n def test_truncate_index_only_one_unique_value(self):\n # GH 42365\n obj = Series(0, index=date_range("2021-06-30", "2021-06-30")).repeat(5)\n\n truncated = obj.truncate("2021-06-28", "2021-07-01")\n\n tm.assert_series_equal(truncated, obj)\n | .venv\Lib\site-packages\pandas\tests\series\methods\test_truncate.py | test_truncate.py | Python | 2,307 | 0.95 | 0.074627 | 0.115385 | awesome-app | 607 | 2023-09-23T14:48:14.399000 | BSD-3-Clause | true | 8e14b646f9739c8fe829e9dfc9baf1e9 |
from datetime import timezone\n\nimport pytest\nimport pytz\n\nfrom pandas._libs.tslibs import timezones\n\nfrom pandas import (\n DatetimeIndex,\n NaT,\n Series,\n Timestamp,\n date_range,\n)\nimport pandas._testing as tm\n\n\nclass TestTZLocalize:\n def test_series_tz_localize_ambiguous_bool(self):\n # make sure that we are correctly accepting bool values as ambiguous\n\n # GH#14402\n ts = Timestamp("2015-11-01 01:00:03")\n expected0 = Timestamp("2015-11-01 01:00:03-0500", tz="US/Central")\n expected1 = Timestamp("2015-11-01 01:00:03-0600", tz="US/Central")\n\n ser = Series([ts])\n expected0 = Series([expected0])\n expected1 = Series([expected1])\n\n with tm.external_error_raised(pytz.AmbiguousTimeError):\n ser.dt.tz_localize("US/Central")\n\n result = ser.dt.tz_localize("US/Central", ambiguous=True)\n tm.assert_series_equal(result, expected0)\n\n result = ser.dt.tz_localize("US/Central", ambiguous=[True])\n tm.assert_series_equal(result, expected0)\n\n result = ser.dt.tz_localize("US/Central", ambiguous=False)\n tm.assert_series_equal(result, expected1)\n\n result = ser.dt.tz_localize("US/Central", ambiguous=[False])\n tm.assert_series_equal(result, expected1)\n\n def test_series_tz_localize_matching_index(self):\n # Matching the index of the result with that of the original series\n # GH 43080\n dt_series = Series(\n date_range(start="2021-01-01T02:00:00", periods=5, freq="1D"),\n index=[2, 6, 7, 8, 11],\n dtype="category",\n )\n result = dt_series.dt.tz_localize("Europe/Berlin")\n expected = Series(\n date_range(\n start="2021-01-01T02:00:00", periods=5, freq="1D", tz="Europe/Berlin"\n ),\n index=[2, 6, 7, 8, 11],\n )\n tm.assert_series_equal(result, expected)\n\n @pytest.mark.parametrize(\n "method, exp",\n [\n ["shift_forward", "2015-03-29 03:00:00"],\n ["shift_backward", "2015-03-29 01:59:59.999999999"],\n ["NaT", NaT],\n ["raise", None],\n ["foo", "invalid"],\n ],\n )\n def test_tz_localize_nonexistent(self, warsaw, method, exp, unit):\n # GH 8917\n tz = warsaw\n n = 60\n dti = date_range(start="2015-03-29 02:00:00", periods=n, freq="min", unit=unit)\n ser = Series(1, index=dti)\n df = ser.to_frame()\n\n if method == "raise":\n with tm.external_error_raised(pytz.NonExistentTimeError):\n dti.tz_localize(tz, nonexistent=method)\n with tm.external_error_raised(pytz.NonExistentTimeError):\n ser.tz_localize(tz, nonexistent=method)\n with tm.external_error_raised(pytz.NonExistentTimeError):\n df.tz_localize(tz, nonexistent=method)\n\n elif exp == "invalid":\n msg = (\n "The nonexistent argument must be one of "\n "'raise', 'NaT', 'shift_forward', 'shift_backward' "\n "or a timedelta object"\n )\n with pytest.raises(ValueError, match=msg):\n dti.tz_localize(tz, nonexistent=method)\n with pytest.raises(ValueError, match=msg):\n ser.tz_localize(tz, nonexistent=method)\n with pytest.raises(ValueError, match=msg):\n df.tz_localize(tz, nonexistent=method)\n\n else:\n result = ser.tz_localize(tz, nonexistent=method)\n expected = Series(1, index=DatetimeIndex([exp] * n, tz=tz).as_unit(unit))\n tm.assert_series_equal(result, expected)\n\n result = df.tz_localize(tz, nonexistent=method)\n expected = expected.to_frame()\n tm.assert_frame_equal(result, expected)\n\n res_index = dti.tz_localize(tz, nonexistent=method)\n tm.assert_index_equal(res_index, expected.index)\n\n @pytest.mark.parametrize("tzstr", ["US/Eastern", "dateutil/US/Eastern"])\n def test_series_tz_localize_empty(self, tzstr):\n # GH#2248\n ser = Series(dtype=object)\n\n ser2 = ser.tz_localize("utc")\n assert ser2.index.tz == timezone.utc\n\n ser2 = ser.tz_localize(tzstr)\n timezones.tz_compare(ser2.index.tz, timezones.maybe_get_tz(tzstr))\n | .venv\Lib\site-packages\pandas\tests\series\methods\test_tz_localize.py | test_tz_localize.py | Python | 4,336 | 0.95 | 0.04878 | 0.059406 | vue-tools | 402 | 2025-07-07T22:41:15.462475 | BSD-3-Clause | true | cf6f87f146bae1bdbe4998e94dd8d32c |
import numpy as np\n\nfrom pandas import (\n Categorical,\n IntervalIndex,\n Series,\n date_range,\n)\nimport pandas._testing as tm\n\n\nclass TestUnique:\n def test_unique_uint64(self):\n ser = Series([1, 2, 2**63, 2**63], dtype=np.uint64)\n res = ser.unique()\n exp = np.array([1, 2, 2**63], dtype=np.uint64)\n tm.assert_numpy_array_equal(res, exp)\n\n def test_unique_data_ownership(self):\n # it works! GH#1807\n Series(Series(["a", "c", "b"]).unique()).sort_values()\n\n def test_unique(self):\n # GH#714 also, dtype=float\n ser = Series([1.2345] * 100)\n ser[::2] = np.nan\n result = ser.unique()\n assert len(result) == 2\n\n # explicit f4 dtype\n ser = Series([1.2345] * 100, dtype="f4")\n ser[::2] = np.nan\n result = ser.unique()\n assert len(result) == 2\n\n def test_unique_nan_object_dtype(self):\n # NAs in object arrays GH#714\n ser = Series(["foo"] * 100, dtype="O")\n ser[::2] = np.nan\n result = ser.unique()\n assert len(result) == 2\n\n def test_unique_none(self):\n # decision about None\n ser = Series([1, 2, 3, None, None, None], dtype=object)\n result = ser.unique()\n expected = np.array([1, 2, 3, None], dtype=object)\n tm.assert_numpy_array_equal(result, expected)\n\n def test_unique_categorical(self):\n # GH#18051\n cat = Categorical([])\n ser = Series(cat)\n result = ser.unique()\n tm.assert_categorical_equal(result, cat)\n\n cat = Categorical([np.nan])\n ser = Series(cat)\n result = ser.unique()\n tm.assert_categorical_equal(result, cat)\n\n def test_tz_unique(self):\n # GH 46128\n dti1 = date_range("2016-01-01", periods=3)\n ii1 = IntervalIndex.from_breaks(dti1)\n ser1 = Series(ii1)\n uni1 = ser1.unique()\n tm.assert_interval_array_equal(ser1.array, uni1)\n\n dti2 = date_range("2016-01-01", periods=3, tz="US/Eastern")\n ii2 = IntervalIndex.from_breaks(dti2)\n ser2 = Series(ii2)\n uni2 = ser2.unique()\n tm.assert_interval_array_equal(ser2.array, uni2)\n\n assert uni1.dtype != uni2.dtype\n | .venv\Lib\site-packages\pandas\tests\series\methods\test_unique.py | test_unique.py | Python | 2,219 | 0.95 | 0.105263 | 0.111111 | python-kit | 635 | 2024-01-18T20:53:55.101295 | BSD-3-Clause | true | 7e5ef68087703930ab80357a3aaa2fac |
import numpy as np\nimport pytest\n\nimport pandas as pd\nfrom pandas import (\n DataFrame,\n Index,\n MultiIndex,\n Series,\n date_range,\n)\nimport pandas._testing as tm\n\n\ndef test_unstack_preserves_object():\n mi = MultiIndex.from_product([["bar", "foo"], ["one", "two"]])\n\n ser = Series(np.arange(4.0), index=mi, dtype=object)\n\n res1 = ser.unstack()\n assert (res1.dtypes == object).all()\n\n res2 = ser.unstack(level=0)\n assert (res2.dtypes == object).all()\n\n\ndef test_unstack():\n index = MultiIndex(\n levels=[["bar", "foo"], ["one", "three", "two"]],\n codes=[[1, 1, 0, 0], [0, 1, 0, 2]],\n )\n\n s = Series(np.arange(4.0), index=index)\n unstacked = s.unstack()\n\n expected = DataFrame(\n [[2.0, np.nan, 3.0], [0.0, 1.0, np.nan]],\n index=["bar", "foo"],\n columns=["one", "three", "two"],\n )\n\n tm.assert_frame_equal(unstacked, expected)\n\n unstacked = s.unstack(level=0)\n tm.assert_frame_equal(unstacked, expected.T)\n\n index = MultiIndex(\n levels=[["bar"], ["one", "two", "three"], [0, 1]],\n codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]],\n )\n s = Series(np.random.default_rng(2).standard_normal(6), index=index)\n exp_index = MultiIndex(\n levels=[["one", "two", "three"], [0, 1]],\n codes=[[0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]],\n )\n expected = DataFrame({"bar": s.values}, index=exp_index).sort_index(level=0)\n unstacked = s.unstack(0).sort_index()\n tm.assert_frame_equal(unstacked, expected)\n\n # GH5873\n idx = MultiIndex.from_arrays([[101, 102], [3.5, np.nan]])\n ts = Series([1, 2], index=idx)\n left = ts.unstack()\n right = DataFrame(\n [[np.nan, 1], [2, np.nan]], index=[101, 102], columns=[np.nan, 3.5]\n )\n tm.assert_frame_equal(left, right)\n\n idx = MultiIndex.from_arrays(\n [\n ["cat", "cat", "cat", "dog", "dog"],\n ["a", "a", "b", "a", "b"],\n [1, 2, 1, 1, np.nan],\n ]\n )\n ts = Series([1.0, 1.1, 1.2, 1.3, 1.4], index=idx)\n right = DataFrame(\n [[1.0, 1.3], [1.1, np.nan], [np.nan, 1.4], [1.2, np.nan]],\n columns=["cat", "dog"],\n )\n tpls = [("a", 1), ("a", 2), ("b", np.nan), ("b", 1)]\n right.index = MultiIndex.from_tuples(tpls)\n tm.assert_frame_equal(ts.unstack(level=0), right)\n\n\ndef test_unstack_tuplename_in_multiindex():\n # GH 19966\n idx = MultiIndex.from_product(\n [["a", "b", "c"], [1, 2, 3]], names=[("A", "a"), ("B", "b")]\n )\n ser = Series(1, index=idx)\n result = ser.unstack(("A", "a"))\n\n expected = DataFrame(\n [[1, 1, 1], [1, 1, 1], [1, 1, 1]],\n columns=MultiIndex.from_tuples([("a",), ("b",), ("c",)], names=[("A", "a")]),\n index=Index([1, 2, 3], name=("B", "b")),\n )\n tm.assert_frame_equal(result, expected)\n\n\n@pytest.mark.parametrize(\n "unstack_idx, expected_values, expected_index, expected_columns",\n [\n (\n ("A", "a"),\n [[1, 1], [1, 1], [1, 1], [1, 1]],\n MultiIndex.from_tuples([(1, 3), (1, 4), (2, 3), (2, 4)], names=["B", "C"]),\n MultiIndex.from_tuples([("a",), ("b",)], names=[("A", "a")]),\n ),\n (\n (("A", "a"), "B"),\n [[1, 1, 1, 1], [1, 1, 1, 1]],\n Index([3, 4], name="C"),\n MultiIndex.from_tuples(\n [("a", 1), ("a", 2), ("b", 1), ("b", 2)], names=[("A", "a"), "B"]\n ),\n ),\n ],\n)\ndef test_unstack_mixed_type_name_in_multiindex(\n unstack_idx, expected_values, expected_index, expected_columns\n):\n # GH 19966\n idx = MultiIndex.from_product(\n [["a", "b"], [1, 2], [3, 4]], names=[("A", "a"), "B", "C"]\n )\n ser = Series(1, index=idx)\n result = ser.unstack(unstack_idx)\n\n expected = DataFrame(\n expected_values, columns=expected_columns, index=expected_index\n )\n tm.assert_frame_equal(result, expected)\n\n\ndef test_unstack_multi_index_categorical_values():\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n mi = df.stack(future_stack=True).index.rename(["major", "minor"])\n ser = Series(["foo"] * len(mi), index=mi, name="category", dtype="category")\n\n result = ser.unstack()\n\n dti = ser.index.levels[0]\n c = pd.Categorical(["foo"] * len(dti))\n expected = DataFrame(\n {"A": c.copy(), "B": c.copy(), "C": c.copy(), "D": c.copy()},\n columns=Index(list("ABCD"), name="minor"),\n index=dti.rename("major"),\n )\n tm.assert_frame_equal(result, expected)\n\n\ndef test_unstack_mixed_level_names():\n # GH#48763\n arrays = [["a", "a"], [1, 2], ["red", "blue"]]\n idx = MultiIndex.from_arrays(arrays, names=("x", 0, "y"))\n ser = Series([1, 2], index=idx)\n result = ser.unstack("x")\n expected = DataFrame(\n [[1], [2]],\n columns=Index(["a"], name="x"),\n index=MultiIndex.from_tuples([(1, "red"), (2, "blue")], names=[0, "y"]),\n )\n tm.assert_frame_equal(result, expected)\n | .venv\Lib\site-packages\pandas\tests\series\methods\test_unstack.py | test_unstack.py | Python | 5,102 | 0.95 | 0.035503 | 0.028169 | react-lib | 57 | 2024-02-21T11:43:25.992463 | GPL-3.0 | true | bb8c6641c4d5f240a57f12460ef7dadb |
import numpy as np\nimport pytest\n\nimport pandas.util._test_decorators as td\n\nfrom pandas import (\n CategoricalDtype,\n DataFrame,\n NaT,\n Series,\n Timestamp,\n)\nimport pandas._testing as tm\n\n\nclass TestUpdate:\n def test_update(self, using_copy_on_write):\n s = Series([1.5, np.nan, 3.0, 4.0, np.nan])\n s2 = Series([np.nan, 3.5, np.nan, 5.0])\n s.update(s2)\n\n expected = Series([1.5, 3.5, 3.0, 5.0, np.nan])\n tm.assert_series_equal(s, expected)\n\n # GH 3217\n df = DataFrame([{"a": 1}, {"a": 3, "b": 2}])\n df["c"] = np.nan\n # Cast to object to avoid upcast when setting "foo"\n df["c"] = df["c"].astype(object)\n df_orig = df.copy()\n\n if using_copy_on_write:\n with tm.raises_chained_assignment_error():\n df["c"].update(Series(["foo"], index=[0]))\n expected = df_orig\n else:\n with tm.assert_produces_warning(FutureWarning, match="inplace method"):\n df["c"].update(Series(["foo"], index=[0]))\n expected = DataFrame(\n [[1, np.nan, "foo"], [3, 2.0, np.nan]], columns=["a", "b", "c"]\n )\n expected["c"] = expected["c"].astype(object)\n tm.assert_frame_equal(df, expected)\n\n @pytest.mark.parametrize(\n "other, dtype, expected, warn",\n [\n # other is int\n ([61, 63], "int32", Series([10, 61, 12], dtype="int32"), None),\n ([61, 63], "int64", Series([10, 61, 12]), None),\n ([61, 63], float, Series([10.0, 61.0, 12.0]), None),\n ([61, 63], object, Series([10, 61, 12], dtype=object), None),\n # other is float, but can be cast to int\n ([61.0, 63.0], "int32", Series([10, 61, 12], dtype="int32"), None),\n ([61.0, 63.0], "int64", Series([10, 61, 12]), None),\n ([61.0, 63.0], float, Series([10.0, 61.0, 12.0]), None),\n ([61.0, 63.0], object, Series([10, 61.0, 12], dtype=object), None),\n # others is float, cannot be cast to int\n ([61.1, 63.1], "int32", Series([10.0, 61.1, 12.0]), FutureWarning),\n ([61.1, 63.1], "int64", Series([10.0, 61.1, 12.0]), FutureWarning),\n ([61.1, 63.1], float, Series([10.0, 61.1, 12.0]), None),\n ([61.1, 63.1], object, Series([10, 61.1, 12], dtype=object), None),\n # other is object, cannot be cast\n ([(61,), (63,)], "int32", Series([10, (61,), 12]), FutureWarning),\n ([(61,), (63,)], "int64", Series([10, (61,), 12]), FutureWarning),\n ([(61,), (63,)], float, Series([10.0, (61,), 12.0]), FutureWarning),\n ([(61,), (63,)], object, Series([10, (61,), 12]), None),\n ],\n )\n def test_update_dtypes(self, other, dtype, expected, warn):\n ser = Series([10, 11, 12], dtype=dtype)\n other = Series(other, index=[1, 3])\n with tm.assert_produces_warning(warn, match="item of incompatible dtype"):\n ser.update(other)\n\n tm.assert_series_equal(ser, expected)\n\n @pytest.mark.parametrize(\n "series, other, expected",\n [\n # update by key\n (\n Series({"a": 1, "b": 2, "c": 3, "d": 4}),\n {"b": 5, "c": np.nan},\n Series({"a": 1, "b": 5, "c": 3, "d": 4}),\n ),\n # update by position\n (Series([1, 2, 3, 4]), [np.nan, 5, 1], Series([1, 5, 1, 4])),\n ],\n )\n def test_update_from_non_series(self, series, other, expected):\n # GH 33215\n series.update(other)\n tm.assert_series_equal(series, expected)\n\n @pytest.mark.parametrize(\n "data, other, expected, dtype",\n [\n (["a", None], [None, "b"], ["a", "b"], "string[python]"),\n pytest.param(\n ["a", None],\n [None, "b"],\n ["a", "b"],\n "string[pyarrow]",\n marks=td.skip_if_no("pyarrow"),\n ),\n ([1, None], [None, 2], [1, 2], "Int64"),\n ([True, None], [None, False], [True, False], "boolean"),\n (\n ["a", None],\n [None, "b"],\n ["a", "b"],\n CategoricalDtype(categories=["a", "b"]),\n ),\n (\n [Timestamp(year=2020, month=1, day=1, tz="Europe/London"), NaT],\n [NaT, Timestamp(year=2020, month=1, day=1, tz="Europe/London")],\n [Timestamp(year=2020, month=1, day=1, tz="Europe/London")] * 2,\n "datetime64[ns, Europe/London]",\n ),\n ],\n )\n def test_update_extension_array_series(self, data, other, expected, dtype):\n result = Series(data, dtype=dtype)\n other = Series(other, dtype=dtype)\n expected = Series(expected, dtype=dtype)\n\n result.update(other)\n tm.assert_series_equal(result, expected)\n\n def test_update_with_categorical_type(self):\n # GH 25744\n dtype = CategoricalDtype(["a", "b", "c", "d"])\n s1 = Series(["a", "b", "c"], index=[1, 2, 3], dtype=dtype)\n s2 = Series(["b", "a"], index=[1, 2], dtype=dtype)\n s1.update(s2)\n result = s1\n expected = Series(["b", "a", "c"], index=[1, 2, 3], dtype=dtype)\n tm.assert_series_equal(result, expected)\n | .venv\Lib\site-packages\pandas\tests\series\methods\test_update.py | test_update.py | Python | 5,339 | 0.95 | 0.05036 | 0.079365 | node-utils | 615 | 2025-02-02T10:43:51.846089 | GPL-3.0 | true | 83468ef671e2f7d56f1d3624d496f570 |
import numpy as np\nimport pytest\n\nfrom pandas import (\n IntervalIndex,\n Series,\n period_range,\n)\nimport pandas._testing as tm\n\n\nclass TestValues:\n @pytest.mark.parametrize(\n "data",\n [\n period_range("2000", periods=4),\n IntervalIndex.from_breaks([1, 2, 3, 4]),\n ],\n )\n def test_values_object_extension_dtypes(self, data):\n # https://github.com/pandas-dev/pandas/issues/23995\n result = Series(data).values\n expected = np.array(data.astype(object))\n tm.assert_numpy_array_equal(result, expected)\n\n def test_values(self, datetime_series):\n tm.assert_almost_equal(\n datetime_series.values, list(datetime_series), check_dtype=False\n )\n | .venv\Lib\site-packages\pandas\tests\series\methods\test_values.py | test_values.py | Python | 747 | 0.95 | 0.103448 | 0.04 | vue-tools | 193 | 2025-01-11T05:21:38.670208 | BSD-3-Clause | true | 5b34e719b0697c58159830085cbba680 |
import numpy as np\nimport pytest\n\nimport pandas as pd\nfrom pandas import (\n Categorical,\n CategoricalIndex,\n Index,\n Series,\n)\nimport pandas._testing as tm\n\n\nclass TestSeriesValueCounts:\n def test_value_counts_datetime(self, unit):\n # most dtypes are tested in tests/base\n values = [\n pd.Timestamp("2011-01-01 09:00"),\n pd.Timestamp("2011-01-01 10:00"),\n pd.Timestamp("2011-01-01 11:00"),\n pd.Timestamp("2011-01-01 09:00"),\n pd.Timestamp("2011-01-01 09:00"),\n pd.Timestamp("2011-01-01 11:00"),\n ]\n\n exp_idx = pd.DatetimeIndex(\n ["2011-01-01 09:00", "2011-01-01 11:00", "2011-01-01 10:00"],\n name="xxx",\n ).as_unit(unit)\n exp = Series([3, 2, 1], index=exp_idx, name="count")\n\n ser = Series(values, name="xxx").dt.as_unit(unit)\n tm.assert_series_equal(ser.value_counts(), exp)\n # check DatetimeIndex outputs the same result\n idx = pd.DatetimeIndex(values, name="xxx").as_unit(unit)\n tm.assert_series_equal(idx.value_counts(), exp)\n\n # normalize\n exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="proportion")\n tm.assert_series_equal(ser.value_counts(normalize=True), exp)\n tm.assert_series_equal(idx.value_counts(normalize=True), exp)\n\n def test_value_counts_datetime_tz(self, unit):\n values = [\n pd.Timestamp("2011-01-01 09:00", tz="US/Eastern"),\n pd.Timestamp("2011-01-01 10:00", tz="US/Eastern"),\n pd.Timestamp("2011-01-01 11:00", tz="US/Eastern"),\n pd.Timestamp("2011-01-01 09:00", tz="US/Eastern"),\n pd.Timestamp("2011-01-01 09:00", tz="US/Eastern"),\n pd.Timestamp("2011-01-01 11:00", tz="US/Eastern"),\n ]\n\n exp_idx = pd.DatetimeIndex(\n ["2011-01-01 09:00", "2011-01-01 11:00", "2011-01-01 10:00"],\n tz="US/Eastern",\n name="xxx",\n ).as_unit(unit)\n exp = Series([3, 2, 1], index=exp_idx, name="count")\n\n ser = Series(values, name="xxx").dt.as_unit(unit)\n tm.assert_series_equal(ser.value_counts(), exp)\n idx = pd.DatetimeIndex(values, name="xxx").as_unit(unit)\n tm.assert_series_equal(idx.value_counts(), exp)\n\n exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="proportion")\n tm.assert_series_equal(ser.value_counts(normalize=True), exp)\n tm.assert_series_equal(idx.value_counts(normalize=True), exp)\n\n def test_value_counts_period(self):\n values = [\n pd.Period("2011-01", freq="M"),\n pd.Period("2011-02", freq="M"),\n pd.Period("2011-03", freq="M"),\n pd.Period("2011-01", freq="M"),\n pd.Period("2011-01", freq="M"),\n pd.Period("2011-03", freq="M"),\n ]\n\n exp_idx = pd.PeriodIndex(\n ["2011-01", "2011-03", "2011-02"], freq="M", name="xxx"\n )\n exp = Series([3, 2, 1], index=exp_idx, name="count")\n\n ser = Series(values, name="xxx")\n tm.assert_series_equal(ser.value_counts(), exp)\n # check DatetimeIndex outputs the same result\n idx = pd.PeriodIndex(values, name="xxx")\n tm.assert_series_equal(idx.value_counts(), exp)\n\n # normalize\n exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="proportion")\n tm.assert_series_equal(ser.value_counts(normalize=True), exp)\n tm.assert_series_equal(idx.value_counts(normalize=True), exp)\n\n def test_value_counts_categorical_ordered(self):\n # most dtypes are tested in tests/base\n values = Categorical([1, 2, 3, 1, 1, 3], ordered=True)\n\n exp_idx = CategoricalIndex(\n [1, 3, 2], categories=[1, 2, 3], ordered=True, name="xxx"\n )\n exp = Series([3, 2, 1], index=exp_idx, name="count")\n\n ser = Series(values, name="xxx")\n tm.assert_series_equal(ser.value_counts(), exp)\n # check CategoricalIndex outputs the same result\n idx = CategoricalIndex(values, name="xxx")\n tm.assert_series_equal(idx.value_counts(), exp)\n\n # normalize\n exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="proportion")\n tm.assert_series_equal(ser.value_counts(normalize=True), exp)\n tm.assert_series_equal(idx.value_counts(normalize=True), exp)\n\n def test_value_counts_categorical_not_ordered(self):\n values = Categorical([1, 2, 3, 1, 1, 3], ordered=False)\n\n exp_idx = CategoricalIndex(\n [1, 3, 2], categories=[1, 2, 3], ordered=False, name="xxx"\n )\n exp = Series([3, 2, 1], index=exp_idx, name="count")\n\n ser = Series(values, name="xxx")\n tm.assert_series_equal(ser.value_counts(), exp)\n # check CategoricalIndex outputs the same result\n idx = CategoricalIndex(values, name="xxx")\n tm.assert_series_equal(idx.value_counts(), exp)\n\n # normalize\n exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="proportion")\n tm.assert_series_equal(ser.value_counts(normalize=True), exp)\n tm.assert_series_equal(idx.value_counts(normalize=True), exp)\n\n def test_value_counts_categorical(self):\n # GH#12835\n cats = Categorical(list("abcccb"), categories=list("cabd"))\n ser = Series(cats, name="xxx")\n res = ser.value_counts(sort=False)\n\n exp_index = CategoricalIndex(\n list("cabd"), categories=cats.categories, name="xxx"\n )\n exp = Series([3, 1, 2, 0], name="count", index=exp_index)\n tm.assert_series_equal(res, exp)\n\n res = ser.value_counts(sort=True)\n\n exp_index = CategoricalIndex(\n list("cbad"), categories=cats.categories, name="xxx"\n )\n exp = Series([3, 2, 1, 0], name="count", index=exp_index)\n tm.assert_series_equal(res, exp)\n\n # check object dtype handles the Series.name as the same\n # (tested in tests/base)\n ser = Series(["a", "b", "c", "c", "c", "b"], name="xxx")\n res = ser.value_counts()\n exp = Series([3, 2, 1], name="count", index=Index(["c", "b", "a"], name="xxx"))\n tm.assert_series_equal(res, exp)\n\n def test_value_counts_categorical_with_nan(self):\n # see GH#9443\n\n # sanity check\n ser = Series(["a", "b", "a"], dtype="category")\n exp = Series([2, 1], index=CategoricalIndex(["a", "b"]), name="count")\n\n res = ser.value_counts(dropna=True)\n tm.assert_series_equal(res, exp)\n\n res = ser.value_counts(dropna=True)\n tm.assert_series_equal(res, exp)\n\n # same Series via two different constructions --> same behaviour\n series = [\n Series(["a", "b", None, "a", None, None], dtype="category"),\n Series(\n Categorical(["a", "b", None, "a", None, None], categories=["a", "b"])\n ),\n ]\n\n for ser in series:\n # None is a NaN value, so we exclude its count here\n exp = Series([2, 1], index=CategoricalIndex(["a", "b"]), name="count")\n res = ser.value_counts(dropna=True)\n tm.assert_series_equal(res, exp)\n\n # we don't exclude the count of None and sort by counts\n exp = Series(\n [3, 2, 1], index=CategoricalIndex([np.nan, "a", "b"]), name="count"\n )\n res = ser.value_counts(dropna=False)\n tm.assert_series_equal(res, exp)\n\n # When we aren't sorting by counts, and np.nan isn't a\n # category, it should be last.\n exp = Series(\n [2, 1, 3], index=CategoricalIndex(["a", "b", np.nan]), name="count"\n )\n res = ser.value_counts(dropna=False, sort=False)\n tm.assert_series_equal(res, exp)\n\n @pytest.mark.parametrize(\n "ser, dropna, exp",\n [\n (\n Series([False, True, True, pd.NA]),\n False,\n Series([2, 1, 1], index=[True, False, pd.NA], name="count"),\n ),\n (\n Series([False, True, True, pd.NA]),\n True,\n Series([2, 1], index=Index([True, False], dtype=object), name="count"),\n ),\n (\n Series(range(3), index=[True, False, np.nan]).index,\n False,\n Series([1, 1, 1], index=[True, False, np.nan], name="count"),\n ),\n ],\n )\n def test_value_counts_bool_with_nan(self, ser, dropna, exp):\n # GH32146\n out = ser.value_counts(dropna=dropna)\n tm.assert_series_equal(out, exp)\n\n @pytest.mark.parametrize(\n "input_array,expected",\n [\n (\n [1 + 1j, 1 + 1j, 1, 3j, 3j, 3j],\n Series(\n [3, 2, 1],\n index=Index([3j, 1 + 1j, 1], dtype=np.complex128),\n name="count",\n ),\n ),\n (\n np.array([1 + 1j, 1 + 1j, 1, 3j, 3j, 3j], dtype=np.complex64),\n Series(\n [3, 2, 1],\n index=Index([3j, 1 + 1j, 1], dtype=np.complex64),\n name="count",\n ),\n ),\n ],\n )\n def test_value_counts_complex_numbers(self, input_array, expected):\n # GH 17927\n result = Series(input_array).value_counts()\n tm.assert_series_equal(result, expected)\n\n def test_value_counts_masked(self):\n # GH#54984\n dtype = "Int64"\n ser = Series([1, 2, None, 2, None, 3], dtype=dtype)\n result = ser.value_counts(dropna=False)\n expected = Series(\n [2, 2, 1, 1],\n index=Index([2, None, 1, 3], dtype=dtype),\n dtype=dtype,\n name="count",\n )\n tm.assert_series_equal(result, expected)\n\n result = ser.value_counts(dropna=True)\n expected = Series(\n [2, 1, 1], index=Index([2, 1, 3], dtype=dtype), dtype=dtype, name="count"\n )\n tm.assert_series_equal(result, expected)\n | .venv\Lib\site-packages\pandas\tests\series\methods\test_value_counts.py | test_value_counts.py | Python | 10,109 | 0.95 | 0.04428 | 0.099138 | awesome-app | 964 | 2024-06-24T20:54:26.277379 | BSD-3-Clause | true | 6230360c9831da6761acf7bcea83edbe |
import numpy as np\nimport pytest\n\nfrom pandas import (\n Index,\n Series,\n array,\n date_range,\n)\nimport pandas._testing as tm\n\npytestmark = pytest.mark.filterwarnings(\n "ignore:Series.view is deprecated and will be removed in a future version.:FutureWarning" # noqa: E501\n)\n\n\nclass TestView:\n def test_view_i8_to_datetimelike(self):\n dti = date_range("2000", periods=4, tz="US/Central")\n ser = Series(dti.asi8)\n\n result = ser.view(dti.dtype)\n tm.assert_datetime_array_equal(result._values, dti._data._with_freq(None))\n\n pi = dti.tz_localize(None).to_period("D")\n ser = Series(pi.asi8)\n result = ser.view(pi.dtype)\n tm.assert_period_array_equal(result._values, pi._data)\n\n def test_view_tz(self):\n # GH#24024\n ser = Series(date_range("2000", periods=4, tz="US/Central"))\n result = ser.view("i8")\n expected = Series(\n [\n 946706400000000000,\n 946792800000000000,\n 946879200000000000,\n 946965600000000000,\n ]\n )\n tm.assert_series_equal(result, expected)\n\n @pytest.mark.parametrize(\n "first", ["m8[ns]", "M8[ns]", "M8[ns, US/Central]", "period[D]"]\n )\n @pytest.mark.parametrize(\n "second", ["m8[ns]", "M8[ns]", "M8[ns, US/Central]", "period[D]"]\n )\n @pytest.mark.parametrize("box", [Series, Index, array])\n def test_view_between_datetimelike(self, first, second, box):\n dti = date_range("2016-01-01", periods=3)\n\n orig = box(dti)\n obj = orig.view(first)\n assert obj.dtype == first\n tm.assert_numpy_array_equal(np.asarray(obj.view("i8")), dti.asi8)\n\n res = obj.view(second)\n assert res.dtype == second\n tm.assert_numpy_array_equal(np.asarray(obj.view("i8")), dti.asi8)\n | .venv\Lib\site-packages\pandas\tests\series\methods\test_view.py | test_view.py | Python | 1,851 | 0.95 | 0.065574 | 0.019608 | python-kit | 110 | 2024-06-28T01:16:57.796511 | Apache-2.0 | true | b7f48aad7467bdfa62ad3ae7b73058ab |
"""\nTest files dedicated to individual (stand-alone) Series methods\n\nIdeally these files/tests should correspond 1-to-1 with tests.frame.methods\n\nThese may also present opportunities for sharing/de-duplicating test code.\n"""\n | .venv\Lib\site-packages\pandas\tests\series\methods\__init__.py | __init__.py | Python | 225 | 0.7 | 0.142857 | 0 | node-utils | 314 | 2025-06-22T15:32:21.378692 | BSD-3-Clause | true | a5dc39777b5d66d721e7a428fc11845f |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_add_prefix_suffix.cpython-313.pyc | test_add_prefix_suffix.cpython-313.pyc | Other | 3,062 | 0.8 | 0.055556 | 0 | node-utils | 809 | 2024-07-03T16:33:56.700273 | BSD-3-Clause | true | 3b964e88cdafdc316dba3306a73d975d |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_align.cpython-313.pyc | test_align.cpython-313.pyc | Other | 13,793 | 0.8 | 0 | 0.022059 | awesome-app | 892 | 2024-02-17T13:46:50.441030 | GPL-3.0 | true | be048115c98008d366cbc6c5dd5bea19 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_argsort.cpython-313.pyc | test_argsort.cpython-313.pyc | Other | 5,834 | 0.95 | 0.045455 | 0 | react-lib | 818 | 2024-09-14T11:50:31.577376 | BSD-3-Clause | true | 512bf502dbeee3ce3d98d1647ddae10b |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_asof.cpython-313.pyc | test_asof.cpython-313.pyc | Other | 11,262 | 0.95 | 0.007937 | 0.016 | node-utils | 844 | 2025-06-03T23:43:31.477491 | MIT | true | cc767f34dec1464a49fe64ca73cafedb |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_astype.cpython-313.pyc | test_astype.cpython-313.pyc | Other | 40,736 | 0.95 | 0.007389 | 0.007576 | awesome-app | 434 | 2025-06-19T17:13:02.916307 | MIT | true | 87dc82439d7834df9b87c905528ecf5c |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_autocorr.cpython-313.pyc | test_autocorr.cpython-313.pyc | Other | 1,604 | 0.8 | 0 | 0 | awesome-app | 818 | 2025-02-23T03:57:18.580641 | GPL-3.0 | true | 640bad36ed56ebb5fb4f198a87201e97 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_between.cpython-313.pyc | test_between.cpython-313.pyc | Other | 4,121 | 0.8 | 0 | 0 | python-kit | 185 | 2024-09-26T08:42:55.640636 | BSD-3-Clause | true | 986876cae668ad20a702c7e52f4e6a65 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_case_when.cpython-313.pyc | test_case_when.cpython-313.pyc | Other | 8,919 | 0.8 | 0.042373 | 0.017391 | python-kit | 505 | 2025-01-25T03:36:50.565646 | MIT | true | 3a3b78c7234a10cef60aa7842c5a3c61 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_clip.cpython-313.pyc | test_clip.cpython-313.pyc | Other | 8,401 | 0.8 | 0 | 0 | python-kit | 881 | 2024-08-14T00:54:59.776695 | BSD-3-Clause | true | cc2cec88ef95927454759a17ffe6f5bc |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_combine.cpython-313.pyc | test_combine.cpython-313.pyc | Other | 1,676 | 0.7 | 0 | 0 | awesome-app | 445 | 2024-09-19T06:24:10.907085 | BSD-3-Clause | true | d80e97cf4d6ef8d3d3691976b688292e |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_combine_first.cpython-313.pyc | test_combine_first.cpython-313.pyc | Other | 9,059 | 0.8 | 0 | 0 | python-kit | 460 | 2024-07-21T23:30:58.148144 | GPL-3.0 | true | 6575648871a9f37a9f0f46193effaa54 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_compare.cpython-313.pyc | test_compare.cpython-313.pyc | Other | 7,152 | 0.8 | 0 | 0 | react-lib | 840 | 2025-03-27T01:23:55.871437 | MIT | true | 65a0950a0603c659a9c311ea012d776a |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_convert_dtypes.cpython-313.pyc | test_convert_dtypes.cpython-313.pyc | Other | 12,578 | 0.95 | 0 | 0 | python-kit | 901 | 2024-07-15T11:47:38.757957 | MIT | true | 2676b0543e5c1a1bb985df8b1242b8a9 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_copy.cpython-313.pyc | test_copy.cpython-313.pyc | Other | 4,426 | 0.8 | 0 | 0.027027 | vue-tools | 209 | 2025-03-24T14:28:43.179499 | BSD-3-Clause | true | 294beaf517d9a69294c7a7cd58d9e1ff |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_count.cpython-313.pyc | test_count.cpython-313.pyc | Other | 2,332 | 0.8 | 0 | 0 | awesome-app | 595 | 2024-09-20T21:15:59.089042 | MIT | true | 311f903bc5403dde32c8fe353ab59906 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_cov_corr.cpython-313.pyc | test_cov_corr.cpython-313.pyc | Other | 9,093 | 0.95 | 0 | 0.009091 | awesome-app | 162 | 2024-01-06T00:33:35.036302 | BSD-3-Clause | true | 62fdf0a850516a351512848f3d5d27d8 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_describe.cpython-313.pyc | test_describe.cpython-313.pyc | Other | 8,748 | 0.8 | 0 | 0.011236 | awesome-app | 137 | 2025-06-24T17:55:28.979991 | MIT | true | d9684360b8001f90a1a1d9225ff0647c |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_diff.cpython-313.pyc | test_diff.cpython-313.pyc | Other | 4,300 | 0.8 | 0 | 0 | vue-tools | 96 | 2024-07-22T00:01:49.620202 | MIT | true | 149c2baec0b1129da129cc5fd8b3132d |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_drop.cpython-313.pyc | test_drop.cpython-313.pyc | Other | 5,143 | 0.8 | 0 | 0.014925 | python-kit | 17 | 2025-05-01T18:37:42.223036 | Apache-2.0 | true | daab523b8997e76e2a175f7f045040f6 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_dropna.cpython-313.pyc | test_dropna.cpython-313.pyc | Other | 5,671 | 0.8 | 0.015385 | 0.032787 | vue-tools | 636 | 2023-10-17T11:50:11.161438 | GPL-3.0 | true | f7235e0d16a5131050efcec3502417c7 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_drop_duplicates.cpython-313.pyc | test_drop_duplicates.cpython-313.pyc | Other | 13,317 | 0.95 | 0 | 0 | awesome-app | 604 | 2025-05-18T02:56:28.198873 | Apache-2.0 | true | 9f116cea0d3bf47f699fa40dcc3edd6f |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_dtypes.cpython-313.pyc | test_dtypes.cpython-313.pyc | Other | 849 | 0.7 | 0 | 0 | vue-tools | 471 | 2024-06-14T17:05:55.943651 | MIT | true | df66f9accf63863daca4f59845a7d368 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_duplicated.cpython-313.pyc | test_duplicated.cpython-313.pyc | Other | 3,077 | 0.8 | 0 | 0 | vue-tools | 525 | 2024-09-29T07:05:58.365607 | BSD-3-Clause | true | 1fa0adc6bfb0a8f93fb85248ddc2b1bb |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_equals.cpython-313.pyc | test_equals.cpython-313.pyc | Other | 8,204 | 0.8 | 0 | 0 | python-kit | 85 | 2025-05-26T16:15:14.545959 | GPL-3.0 | true | e415e3860f77b65e05fb00c866bb038c |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_explode.cpython-313.pyc | test_explode.cpython-313.pyc | Other | 10,191 | 0.95 | 0 | 0 | python-kit | 443 | 2025-06-02T14:10:04.419213 | BSD-3-Clause | true | 240aee1b703557cd260b2ebafafd0ae8 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_fillna.cpython-313.pyc | test_fillna.cpython-313.pyc | Other | 54,468 | 0.8 | 0 | 0.001623 | awesome-app | 611 | 2025-01-19T12:13:49.309714 | BSD-3-Clause | true | e747c0b661308fb7e901efb0b0e46726 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_get_numeric_data.cpython-313.pyc | test_get_numeric_data.cpython-313.pyc | Other | 2,092 | 0.8 | 0 | 0 | awesome-app | 52 | 2025-06-25T23:53:19.239914 | Apache-2.0 | true | 3606ecf5a05adeca861e02a9be1d5ecb |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_head_tail.cpython-313.pyc | test_head_tail.cpython-313.pyc | Other | 882 | 0.7 | 0 | 0 | python-kit | 794 | 2025-03-06T01:35:45.978691 | BSD-3-Clause | true | 8437f960f53c74ab286a9c1515146e3c |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_infer_objects.cpython-313.pyc | test_infer_objects.cpython-313.pyc | Other | 3,199 | 0.8 | 0 | 0 | python-kit | 64 | 2024-04-15T12:56:14.044053 | GPL-3.0 | true | 051b5765d5ecde5e424eb9f5a9a2b5f3 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_info.cpython-313.pyc | test_info.cpython-313.pyc | Other | 8,831 | 0.95 | 0.024691 | 0.0125 | node-utils | 508 | 2024-07-04T08:14:04.452195 | GPL-3.0 | true | 09e7e6f4ea8a2bd4f3f5beb73f4836ac |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_interpolate.cpython-313.pyc | test_interpolate.cpython-313.pyc | Other | 53,512 | 0.95 | 0.009452 | 0.001923 | react-lib | 786 | 2024-12-26T09:19:58.774819 | BSD-3-Clause | true | 043f020698e324deef5788c462bd5357 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_isin.cpython-313.pyc | test_isin.cpython-313.pyc | Other | 13,654 | 0.8 | 0 | 0 | vue-tools | 701 | 2023-10-05T19:47:51.396588 | MIT | true | e74b642ab8c693854b6218595c0cbe79 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_isna.cpython-313.pyc | test_isna.cpython-313.pyc | Other | 2,038 | 0.8 | 0 | 0 | python-kit | 673 | 2025-05-20T09:26:18.516021 | BSD-3-Clause | true | 1319e36e8dfbe8e82a6342c0334a6665 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_is_monotonic.cpython-313.pyc | test_is_monotonic.cpython-313.pyc | Other | 1,923 | 0.7 | 0 | 0 | awesome-app | 497 | 2023-10-03T11:19:10.876637 | MIT | true | f0b5eec6b37aa59cbff84838bce651ab |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_is_unique.cpython-313.pyc | test_is_unique.cpython-313.pyc | Other | 2,495 | 0.8 | 0 | 0 | awesome-app | 46 | 2024-03-02T03:58:03.571965 | MIT | true | df29658710cd3ee5383d28917af2cc66 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_item.cpython-313.pyc | test_item.cpython-313.pyc | Other | 3,191 | 0.8 | 0 | 0 | react-lib | 18 | 2025-06-26T11:20:54.119470 | GPL-3.0 | true | c5cc8d7ddcafd1acbf5f253e79c2f85a |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_map.cpython-313.pyc | test_map.cpython-313.pyc | Other | 36,409 | 0.95 | 0 | 0.022951 | node-utils | 70 | 2024-07-01T01:40:01.621041 | MIT | true | d2ced6d43e55c5498c45bb3d2822623c |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_matmul.cpython-313.pyc | test_matmul.cpython-313.pyc | Other | 4,714 | 0.8 | 0 | 0 | react-lib | 306 | 2024-03-30T01:18:32.372384 | GPL-3.0 | true | 2e2b42174367e686b3a4e0a6477741b9 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_nlargest.cpython-313.pyc | test_nlargest.cpython-313.pyc | Other | 12,916 | 0.8 | 0.007576 | 0.046512 | vue-tools | 93 | 2025-02-06T11:20:30.104523 | GPL-3.0 | true | 503c24492c844ef25bab3adca997a611 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_nunique.cpython-313.pyc | test_nunique.cpython-313.pyc | Other | 1,211 | 0.7 | 0 | 0 | react-lib | 588 | 2024-01-16T22:33:56.381796 | GPL-3.0 | true | d39f072f5fd80333a9be134d282bbf4b |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_pct_change.cpython-313.pyc | test_pct_change.cpython-313.pyc | Other | 6,990 | 0.8 | 0 | 0.017094 | vue-tools | 661 | 2024-02-27T02:53:28.626304 | BSD-3-Clause | true | 29045b1594462c4db3eafae928d4ee10 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_pop.cpython-313.pyc | test_pop.cpython-313.pyc | Other | 741 | 0.8 | 0 | 0 | react-lib | 211 | 2024-05-03T07:18:24.388270 | MIT | true | dd1f5e6760489d90449d41df9fb41e9f |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_quantile.cpython-313.pyc | test_quantile.cpython-313.pyc | Other | 14,094 | 0.8 | 0 | 0.017857 | react-lib | 872 | 2024-05-25T18:14:24.596884 | GPL-3.0 | true | 5f138b3967178bc89bccc2524f50907e |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_rank.cpython-313.pyc | test_rank.cpython-313.pyc | Other | 27,852 | 0.95 | 0.003322 | 0.003425 | awesome-app | 261 | 2025-04-27T09:58:18.450919 | MIT | true | 31494bce2ed9c7d0b5dcf6053bb0b52f |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_reindex.cpython-313.pyc | test_reindex.cpython-313.pyc | Other | 23,264 | 0.8 | 0.003774 | 0.023346 | python-kit | 627 | 2025-03-27T13:09:06.933512 | GPL-3.0 | true | 014c3142507d431ae70dec09eafc944f |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_reindex_like.cpython-313.pyc | test_reindex_like.cpython-313.pyc | Other | 2,211 | 0.8 | 0 | 0 | node-utils | 169 | 2024-03-28T20:36:16.395139 | BSD-3-Clause | true | 126523e0211c328941bc7abec36d4cf8 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_rename.cpython-313.pyc | test_rename.cpython-313.pyc | Other | 11,697 | 0.8 | 0 | 0.024691 | vue-tools | 106 | 2024-03-08T22:26:44.950355 | BSD-3-Clause | true | a9499d02602c10ff737fabab276d14df |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_rename_axis.cpython-313.pyc | test_rename_axis.cpython-313.pyc | Other | 3,033 | 0.7 | 0 | 0 | node-utils | 553 | 2024-10-05T22:09:02.411126 | Apache-2.0 | true | 301676b93000554ade1a75ae9a272621 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_repeat.cpython-313.pyc | test_repeat.cpython-313.pyc | Other | 3,088 | 0.8 | 0 | 0 | react-lib | 212 | 2023-08-09T18:28:21.547159 | Apache-2.0 | true | ba8adce41685f9d8de4da7012e7c0681 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_replace.cpython-313.pyc | test_replace.cpython-313.pyc | Other | 54,272 | 0.8 | 0.001919 | 0.005814 | awesome-app | 691 | 2024-09-04T12:37:47.504780 | GPL-3.0 | true | 3a869bf1851c7ed51feeeba3a23e1967 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_reset_index.cpython-313.pyc | test_reset_index.cpython-313.pyc | Other | 13,401 | 0.95 | 0 | 0.006579 | python-kit | 846 | 2024-01-08T13:09:10.280311 | BSD-3-Clause | true | d27b9b9baca394dd6d8533d730bfaddd |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_round.cpython-313.pyc | test_round.cpython-313.pyc | Other | 5,202 | 0.8 | 0 | 0 | awesome-app | 0 | 2024-07-08T19:10:14.921730 | BSD-3-Clause | true | c6e297b15581d6cea3ef7e571ccea68f |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_searchsorted.cpython-313.pyc | test_searchsorted.cpython-313.pyc | Other | 5,308 | 0.8 | 0 | 0 | awesome-app | 178 | 2025-03-18T12:03:21.256585 | BSD-3-Clause | true | 5e2e98fcb29283a4a5262e7f64a65e8d |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_set_name.cpython-313.pyc | test_set_name.cpython-313.pyc | Other | 1,440 | 0.8 | 0 | 0 | node-utils | 165 | 2024-06-13T20:30:09.994405 | BSD-3-Clause | true | ca399300c3e43bd233aa5ad093a3f1ea |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_size.cpython-313.pyc | test_size.cpython-313.pyc | Other | 1,082 | 0.8 | 0 | 0.1 | awesome-app | 30 | 2024-08-23T11:11:57.235265 | MIT | true | 727ae29d98a62f94219ffa92be0d16ae |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_sort_index.cpython-313.pyc | test_sort_index.cpython-313.pyc | Other | 20,341 | 0.8 | 0.004762 | 0.048309 | react-lib | 491 | 2024-01-24T19:35:13.636868 | Apache-2.0 | true | 625ea2ae74506b3a4dac2ec28a0b9b24 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_sort_values.cpython-313.pyc | test_sort_values.cpython-313.pyc | Other | 14,050 | 0.95 | 0.015385 | 0.010417 | vue-tools | 261 | 2025-02-18T09:19:48.467750 | GPL-3.0 | true | 3ae94323bcc4802dae9a51853368456e |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_tolist.cpython-313.pyc | test_tolist.cpython-313.pyc | Other | 1,758 | 0.8 | 0 | 0.032258 | react-lib | 578 | 2024-07-31T08:18:29.154058 | GPL-3.0 | true | 1d193086c22246623cf1b0ed821d6f17 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_to_csv.cpython-313.pyc | test_to_csv.cpython-313.pyc | Other | 9,402 | 0.8 | 0.009804 | 0 | awesome-app | 417 | 2024-12-08T04:14:57.078941 | Apache-2.0 | true | aaadb1c5c1984f7d61487e5e1d260420 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_to_dict.cpython-313.pyc | test_to_dict.cpython-313.pyc | Other | 2,468 | 0.8 | 0 | 0 | python-kit | 967 | 2024-08-11T20:31:07.355244 | MIT | true | 1ba0cb408b972f5933a35ae80061afd3 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_to_frame.cpython-313.pyc | test_to_frame.cpython-313.pyc | Other | 3,661 | 0.8 | 0 | 0 | node-utils | 305 | 2023-10-17T20:28:13.864555 | BSD-3-Clause | true | 6a049c1a2cc60300a5f24f90634d3b82 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_to_numpy.cpython-313.pyc | test_to_numpy.cpython-313.pyc | Other | 2,687 | 0.8 | 0 | 0.060606 | python-kit | 581 | 2025-04-23T12:26:38.128843 | GPL-3.0 | true | f6fa030d451b26c1d4eaaaae6bcf6e25 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_truncate.cpython-313.pyc | test_truncate.cpython-313.pyc | Other | 4,272 | 0.8 | 0 | 0 | awesome-app | 578 | 2023-12-07T02:43:08.261219 | Apache-2.0 | true | 50102aadce2541bf8d71eefd56e7aba0 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_tz_localize.cpython-313.pyc | test_tz_localize.cpython-313.pyc | Other | 6,516 | 0.8 | 0 | 0 | react-lib | 248 | 2025-03-28T22:08:38.505655 | BSD-3-Clause | true | e9f39b2688c6df33d7b31c8c34449820 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_unique.cpython-313.pyc | test_unique.cpython-313.pyc | Other | 4,390 | 0.8 | 0 | 0 | vue-tools | 414 | 2024-05-03T17:38:44.275260 | BSD-3-Clause | true | a5be8935e1aa52bf3da16dcd8035d304 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_unstack.cpython-313.pyc | test_unstack.cpython-313.pyc | Other | 8,248 | 0.8 | 0 | 0.009615 | node-utils | 837 | 2024-07-09T23:17:46.282611 | Apache-2.0 | true | 2220ba3b3ae2f21c9cc69dce96f51fae |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_update.cpython-313.pyc | test_update.cpython-313.pyc | Other | 7,026 | 0.8 | 0 | 0 | vue-tools | 897 | 2024-11-01T22:44:51.724727 | Apache-2.0 | true | 9515048e443f836c8cbcdde086dc619a |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_values.cpython-313.pyc | test_values.cpython-313.pyc | Other | 1,685 | 0.8 | 0 | 0 | node-utils | 893 | 2024-12-08T21:22:50.338022 | GPL-3.0 | true | 9d84770c03902c888465c5db1ecbffec |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_value_counts.cpython-313.pyc | test_value_counts.cpython-313.pyc | Other | 12,631 | 0.8 | 0 | 0 | node-utils | 67 | 2025-03-24T21:32:00.683973 | MIT | true | 05d623a24d7498e11ec9ac91711df582 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\test_view.cpython-313.pyc | test_view.cpython-313.pyc | Other | 3,612 | 0.8 | 0 | 0 | node-utils | 548 | 2024-12-24T05:20:43.799721 | GPL-3.0 | true | d332177d0ed588a08e5a898763c5f6ee |
\n\n | .venv\Lib\site-packages\pandas\tests\series\methods\__pycache__\__init__.cpython-313.pyc | __init__.cpython-313.pyc | Other | 437 | 0.7 | 0.111111 | 0 | node-utils | 522 | 2024-06-29T17:14:12.965487 | BSD-3-Clause | true | a9399b24f4500add5cf73c4ccfccc892 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\__pycache__\test_api.cpython-313.pyc | test_api.cpython-313.pyc | Other | 16,375 | 0.8 | 0 | 0.008065 | awesome-app | 399 | 2025-01-10T04:32:22.817671 | MIT | true | 11fecca0f3d513a4761ea7eb8a494de3 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\__pycache__\test_arithmetic.cpython-313.pyc | test_arithmetic.cpython-313.pyc | Other | 52,822 | 0.8 | 0.001957 | 0.003968 | react-lib | 288 | 2024-08-01T03:00:35.572636 | MIT | true | 9682d76aa541824092aa7226f71702a3 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\__pycache__\test_cumulative.cpython-313.pyc | test_cumulative.cpython-313.pyc | Other | 12,679 | 0.8 | 0.018072 | 0.012346 | python-kit | 520 | 2023-09-28T09:03:18.251871 | BSD-3-Clause | true | 3bc2a4dee50fc5fc4ba2927ab19eae45 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\__pycache__\test_formats.cpython-313.pyc | test_formats.cpython-313.pyc | Other | 25,347 | 0.95 | 0 | 0 | awesome-app | 867 | 2024-01-30T03:04:20.233049 | GPL-3.0 | true | 75a590d557d68cb68b8f58ad714ad2df |
\n\n | .venv\Lib\site-packages\pandas\tests\series\__pycache__\test_iteration.cpython-313.pyc | test_iteration.cpython-313.pyc | Other | 2,437 | 0.8 | 0 | 0 | python-kit | 571 | 2025-01-20T16:57:50.451254 | GPL-3.0 | true | d224b44821c451dc3dc649a2fc9e9660 |
\n\n | .venv\Lib\site-packages\pandas\tests\series\__pycache__\test_logical_ops.cpython-313.pyc | test_logical_ops.cpython-313.pyc | Other | 29,410 | 0.95 | 0.008043 | 0.013812 | vue-tools | 886 | 2024-06-12T21:01:39.927655 | Apache-2.0 | true | 5a190575e621635f32f58712e17311be |
\n\n | .venv\Lib\site-packages\pandas\tests\series\__pycache__\test_missing.cpython-313.pyc | test_missing.cpython-313.pyc | Other | 6,493 | 0.8 | 0 | 0.014085 | python-kit | 97 | 2023-10-05T04:53:56.246455 | Apache-2.0 | true | f1c9f5184c33ab4e92feccefb43ea72b |
\n\n | .venv\Lib\site-packages\pandas\tests\series\__pycache__\test_npfuncs.cpython-313.pyc | test_npfuncs.cpython-313.pyc | Other | 2,745 | 0.8 | 0.047619 | 0 | vue-tools | 840 | 2024-03-01T01:52:05.316567 | BSD-3-Clause | true | 4b41b9b5dab9a585f0b6bc6733e937ed |
\n\n | .venv\Lib\site-packages\pandas\tests\series\__pycache__\test_reductions.cpython-313.pyc | test_reductions.cpython-313.pyc | Other | 12,123 | 0.95 | 0 | 0.039735 | awesome-app | 761 | 2024-08-08T23:56:01.907489 | GPL-3.0 | true | 6cd16173d208e64dc5fca7705cc92d16 |
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