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functions/test_rename_columns.py\u0000tests/functions/test_reorder_columns.py\u0000tests/functions/test_round_to_fraction.py\u0000tests/functions/test_row_to_names.py\u0000tests/functions/test_select_columns.py\u0000tests/functions/test_shuffle.py\u0000tests/functions/test_sort_naturally.py\u0000tests/functions/test_take_first.py\u0000tests/functions/test_then.py\u0000tests/functions/test_to_datetime.py\u0000tests/functions/test_toset.py\u0000tests/functions/test_transform_column.py\u0000tests/functions/test_transform_columns.py\u0000tests/functions/test_unionize_dataframe_categories.py\u0000tests/functions/test_update_where.py\u0000tests/helpers.py\u0000tests/io/test_read_csvs.py\u0000tests/math/test_ecdf.py\u0000tests/math/test_exp.py\u0000tests/math/test_log.py\u0000tests/math/test_logit.py\u0000tests/math/test_normal_cdf.py\u0000tests/math/test_probit.py\u0000tests/math/test_sigmoid.py\u0000tests/math/test_z_score.py\u0000tests/ml/test_get_features_targets.py\u0000tests/spark/conftest.py\u0000tests/spark/functions/test_clean_names_spark.py\u0000tests/spark/functions/test_update_where_spark.py\u0000tests/test_data/corrected_smiles.txt\u0000tests/test_data/sequences.fasta\u0000tests/test_data/sequences.tsv\u0000tests/test_df_registration.py\u0000tests/test_helpers.py\u0000tests/timeseries/test_fill_missing_timestamps.py\u0000tests/timeseries/test_sort_timestamps_monotonically.py\u0000tests/utils/test_check_column.py\u0000tests/utils/test_clean_accounting_column.py\u0000tests/utils/test_currency_column_to_numeric.py\u0000tests/utils/test_deprecated_alias.py\u0000tests/utils/test_idempotent.py\u0000tests/utils/test_import_message.py\u0000tests/utils/test_replace_empty_string_with_none.py\u0000tests/utils/test_skiperror.py\u0000tests/utils/test_skipna.py\u0000tests/xarray/conftest.py\u0000tests/xarray/test_clone_using.py\u0000tests/xarray/test_convert_datetime_to_number.py\u0000","stderr_tail":"","stdout_sha256":"b86e499b31b6f859452fcb06a202f6cee29ba278eef5fba35a77fec23fecc0e6","log_path":"~/dev/nebius-hackathon/pilot/v2-P/ericmjl__pyjanitor-728/runs/pilot-ericmjl__pyjanitor-728-65b2a91a53/logs/agent1/agent1-1.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T20:14:46.819045Z","duration_s":0.003938,"timed_out":false,"image_before":"d93b3180-8441-4b72-b2a0-ae2f6dd551d3","image_after":"44024e91-ee50-4202-ac04-cb5b3744ff4f","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null} 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{"receipt_id":"agent1-4","seq":4,"branch_id":"agent1","kind":"command","cmd":"/opt/receipts-81643ac64f52464aa9abec53008b1396/bin/busybox mkdir -p /opt/receipts/reports && python -I /opt/receipts-81643ac64f52464aa9abec53008b1396/receipts_pytest.py tests/timeseries/test_sort_timestamps_monotonically.py -v -o junit_family=xunit1 -p no:cacheprovider -rA --continue-on-collection-errors --junitxml=/opt/receipts/reports/agent1-4.xml","path":null,"exit_code":1,"stdout_tail":" _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = v1\n2019-01-09 22:00:00 620\n2019-01-21 03:00:00 523\n2019-01-28 13:00:00 572\n2019-02-01 15:...3 14:00:00 1173\n2019-01-24 18:00:00 1401\n2019-01-01 07:00:00 353\n2019-01-21 11:00:00 993\n\n[1000 rows x 1 columns]\nname = 'append'\n\n @final\n def __getattr__(self, name: str):\n \"\"\"\n After regular attribute access, try looking up the name\n This allows simpler access to columns for interactive use.\n \"\"\"\n # Note: obj.x will always call obj.__getattribute__('x') prior to\n # calling obj.__getattr__('x').\n if (\n name not in self._internal_names_set\n and name not in self._metadata\n and name not in self._accessors\n and self._info_axis._can_hold_identifiers_and_holds_name(name)\n ):\n return self[name]\n> return object.__getattribute__(self, name)\nE AttributeError: 'DataFrame' object has no attribute 'append'. Did you mean: '_append'?\n\n/usr/local/lib/python3.10/site-packages/pandas/core/generic.py:6321: AttributeError\n=============================== warnings summary ===============================\ntests/timeseries/test_sort_timestamps_monotonically.py:27\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:27: PytestUnknownMarkWarning: Unknown pytest.mark.timeseries - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html\n @pytest.mark.timeseries\n\ntests/timeseries/test_sort_timestamps_monotonically.py:36\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:36: PytestUnknownMarkWarning: Unknown pytest.mark.timeseries - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html\n @pytest.mark.timeseries\n\ntests/timeseries/test_sort_timestamps_monotonically.py:44\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:44: PytestUnknownMarkWarning: Unknown pytest.mark.timeseries - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html\n @pytest.mark.timeseries\n\ntests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically\ntests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_decreasing\ntests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_strict\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:13: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n ts_index = pd.date_range(\"1/1/2019\", periods=1000, freq=\"1H\")\n\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n------------ generated xml file: /opt/receipts/reports/agent1-4.xml ------------\n================================ tests coverage ================================\n_______________ coverage: platform linux, python 3.10.19-final-0 _______________\n\nName Stmts Miss Cover Missing\n-------------------------------------------------------------------\njanitor/__init__.py 12 3 75% 4-5, 21\njanitor/biology.py 14 14 0% 5-84\njanitor/chemistry.py 73 73 0% 5-461\njanitor/engineering.py 21 21 0% 5-106\njanitor/errors.py 2 0 100%\njanitor/finance.py 85 85 0% 5-654\njanitor/functions.py 718 535 25% 90-134, 180-223, 287-308, 313-325, 330, 346-347, 355-358, 366, 404-410, 445-446, 480-495, 534-548, 583-585, 614-616, 657-667, 717-727, 761-766, 800-806, 838-842, 878-890, 929-934, 975-985, 1076-1124, 1189-1193, 1252-1255, 1432-1469, 1515-1524, 1548, 1585-1600, 1725-1772, 1809-1812, 1897-1929, 2044-2055, 2167-2177, 2272-2281, 2361-2385, 2461-2489, 2544-2555, 2745-2779, 2814-2844, 2909-2944, 2960-2961, 2981, 3067-3069, 3095-3109, 3164-3170, 3201-3203, 3250-3270, 3286-3295, 3300, 3304, 3308, 3315-3326, 3367-3379, 3418-3433, 3466-3470, 3496, 3544-3545, 3598-3617, 3681-3698, 3733, 3818-3840, 3912-3913, 4006-4027, 4116-4127, 4224-4268, 4369-4387\njanitor/io.py 24 24 0% 1-63\njanitor/math.py 79 55 30% 31-42, 48, 57, 76-89, 95, 115-128, 153-160, 191-200\njanitor/ml.py 15 8 47% 60-71\njanitor/spark/__init__.py 1 1 0% 1\njanitor/spark/backend.py 37 37 0% 3-102\njanitor/spark/functions.py 30 30 0% 5-182\njanitor/testing_utils/__init__.py 0 0 100%\njanitor/testing_utils/date_data.py 1 0 100%\njanitor/testing_utils/strategies.py 12 12 0% 5-69\njanitor/timeseries.py 41 31 24% 46-60, 83-98, 119-128, 151-157\njanitor/utils.py 164 129 21% 29-36, 54-60, 73-101, 105-106, 110-111, 133-136, 143-155, 177-202, 217-220, 250-251, 282-283, 304-314, 335-343, 366-372, 400-408, 423-470, 490-539\njanitor/xarray/__init__.py 1 0 100%\njanitor/xarray/functions.py 16 7 56% 71-86, 128-130\n-------------------------------------------------------------------\nTOTAL 1346 1065 21%\n=========================== short test summary info ============================\nFAILED tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically\nFAILED tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_decreasing\nFAILED tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_strict\n======================== 3 failed, 6 warnings in 1.53s =========================\n","stderr_tail":"","stdout_sha256":"4517121fa5a9c647d03b754c35a6c2952b549cc7eac619f1408fb60b23e13d72","log_path":"~/dev/nebius-hackathon/pilot/v2-P/ericmjl__pyjanitor-728/runs/pilot-ericmjl__pyjanitor-728-65b2a91a53/logs/agent1/agent1-4.log","tree_digest":"abd99a532a9677fbd9b6e76310710884d7810c0ddaad64348ffaffcdd304f6fe","tree_download_s":6.347436542157084,"started_at":"2026-10-03T20:15:05.634338Z","duration_s":5.43921,"timed_out":false,"image_before":"2e1a132f-e213-415d-b82e-55558a470341","image_after":"408b4d43-0fb5-42cb-bb45-5185ca872700","junit":{"passed":[],"failed":["tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically","tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_decreasing","tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_strict"],"errors":[],"skipped":[],"collection_errors":[],"failure_text":{"tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically":"AttributeError: 'DataFrame' object has no attribute 'sort_timestamps_monotonically'\ntimeseries_dataframe = v1\n2019-01-01 00:00:00 731\n2019-01-01 01:00:00 1331\n2019-01-01 02:00:00 1904\n2019-01-01 03:...1 12:00:00 103\n2019-02-11 13:00:00 917\n2019-02-11 14:00:00 1623\n2019-02-11 15:00:00 1063\n\n[1000 rows x 1 columns]\n\n @pytest.mark.timeseries\n def test_sort_timestamps_monotonically(timeseries_dataframe):\n \"\"\"Test sort_timestamps_monotonically for ascending order\"\"\"\n df = timeseries_dataframe.shuffle(\n reset_index=False\n> ).sort_timestamps_monotonically()\n\ntests/timeseries/test_sort_timestamps_monotonically.py:32: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = v1\n2019-01-27 12:00:00 1915\n2019-01-25 18:00:00 1689\n2019-01-02 10:00:00 1833\n2019-01-01 18:...2 00:00:00 1501\n2019-02-04 20:00:00 1148\n2019-01-30 17:00:00 237\n2019-01-05 20:00:00 1902\n\n[1000 rows x 1 columns]\nname = 'sort_timestamps_monotonically'\n\n @final\n def __getattr__(self, name: str):\n \"\"\"\n After regular attribute access, try looking up the name\n This allows simpler access to columns for interactive use.\n \"\"\"\n # Note: obj.x will always call obj.__getattribute__('x') prior to\n # calling obj.__getattr__('x').\n if (\n name not in self._internal_names_set\n and name not in self._metadata\n and name not in self._accessors\n and self._info_axis._can_hold_identifiers_and_holds_name(name)\n ):\n return self[name]\n> return object.__getattribute__(self, name)\nE AttributeError: 'DataFrame' object has no attribute 'sort_timestamps_monotonically'\n\n/usr/local/lib/python3.10/site-packages/pandas/core/generic.py:6321: AttributeError","tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_decreasing":"AttributeError: 'DataFrame' object has no attribute 'sort_timestamps_monotonically'\ntimeseries_dataframe = v1\n2019-01-01 00:00:00 207\n2019-01-01 01:00:00 215\n2019-01-01 02:00:00 1881\n2019-01-01 03:...1 12:00:00 732\n2019-02-11 13:00:00 900\n2019-02-11 14:00:00 299\n2019-02-11 15:00:00 534\n\n[1000 rows x 1 columns]\n\n @pytest.mark.timeseries\n def test_sort_timestamps_monotonically_decreasing(timeseries_dataframe):\n \"\"\"Test sort_timestamps_monotonically for descending order\"\"\"\n df2 = timeseries_dataframe.sort_index(ascending=False)\n> df3 = df2.sort_timestamps_monotonically(\"decreasing\")\n\ntests/timeseries/test_sort_timestamps_monotonically.py:40: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = v1\n2019-02-11 15:00:00 534\n2019-02-11 14:00:00 299\n2019-02-11 13:00:00 900\n2019-02-11 12:...1 03:00:00 858\n2019-01-01 02:00:00 1881\n2019-01-01 01:00:00 215\n2019-01-01 00:00:00 207\n\n[1000 rows x 1 columns]\nname = 'sort_timestamps_monotonically'\n\n @final\n def __getattr__(self, name: str):\n \"\"\"\n After regular attribute access, try looking up the name\n This allows simpler access to columns for interactive use.\n \"\"\"\n # Note: obj.x will always call obj.__getattribute__('x') prior to\n # calling obj.__getattr__('x').\n if (\n name not in self._internal_names_set\n and name not in self._metadata\n and name not in self._accessors\n and self._info_axis._can_hold_identifiers_and_holds_name(name)\n ):\n return self[name]\n> return object.__getattribute__(self, name)\nE AttributeError: 'DataFrame' object has no attribute 'sort_timestamps_monotonically'\n\n/usr/local/lib/python3.10/site-packages/pandas/core/generic.py:6321: AttributeError","tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_strict":"[...]ject has no attribute 'append'. Did you mean: '_append'?\ntimeseries_dataframe = v1\n2019-01-01 00:00:00 1819\n2019-01-01 01:00:00 984\n2019-01-01 02:00:00 1354\n2019-01-01 03:...1 12:00:00 299\n2019-02-11 13:00:00 519\n2019-02-11 14:00:00 1494\n2019-02-11 15:00:00 279\n\n[1000 rows x 1 columns]\n\n @pytest.mark.timeseries\n def test_sort_timestamps_monotonically_strict(timeseries_dataframe):\n \"\"\"Test sort_timestamps_monotonically for index duplication handling\"\"\"\n df = timeseries_dataframe.shuffle(reset_index=False)\n random_number = randint(1, len(timeseries_dataframe))\n> df = df.append(\n df.loc[df.index[random_number], :]\n ).sort_timestamps_monotonically(direction=\"increasing\", strict=True)\n\ntests/timeseries/test_sort_timestamps_monotonically.py:49: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = v1\n2019-01-09 22:00:00 620\n2019-01-21 03:00:00 523\n2019-01-28 13:00:00 572\n2019-02-01 15:...3 14:00:00 1173\n2019-01-24 18:00:00 1401\n2019-01-01 07:00:00 353\n2019-01-21 11:00:00 993\n\n[1000 rows x 1 columns]\nname = 'append'\n\n @final\n def __getattr__(self, name: str):\n \"\"\"\n After regular attribute access, try looking up the name\n This allows simpler access to columns for interactive use.\n \"\"\"\n # Note: obj.x will always call obj.__getattribute__('x') prior to\n # calling obj.__getattr__('x').\n if (\n name not in self._internal_names_set\n and name not in self._metadata\n and name not in self._accessors\n and self._info_axis._can_hold_identifiers_and_holds_name(name)\n ):\n return self[name]\n> return object.__getattribute__(self, name)\nE AttributeError: 'DataFrame' object has no attribute 'append'. Did you mean: '_append'?\n\n/usr/local/lib/python3.10/site-packages/pandas/core/generic.py:6321: AttributeError"}},"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null} {"receipt_id":"agent1-5","seq":5,"branch_id":"agent1","kind":"write","cmd":"/opt/receipts-18faa69e317543f689c32b0704853e21/bin/busybox sh -c 'mkdir -p /pyjanitor/janitor && cp /opt/receipts-18faa69e317543f689c32b0704853e21/incoming/47d14fa61569df09da803cbe399a1180791074b5f741c075c8aef6a27038271b /pyjanitor/janitor/timeseries.py'","path":"janitor/timeseries.py","exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"47d14fa61569df09da803cbe399a1180791074b5f741c075c8aef6a27038271b","log_path":"~/dev/nebius-hackathon/pilot/v2-P/ericmjl__pyjanitor-728/runs/pilot-ericmjl__pyjanitor-728-65b2a91a53/logs/agent1/agent1-5.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T20:15:28.406648Z","duration_s":0.005115,"timed_out":false,"image_before":"408b4d43-0fb5-42cb-bb45-5185ca872700","image_after":"fd25f4b8-8553-4382-baf5-4bc2f27264df","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null} {"receipt_id":"agent1-6","seq":6,"branch_id":"agent1","kind":"write","cmd":"/opt/receipts-e19f3051fa0e48d09de768410451043d/bin/busybox sh -c 'mkdir -p /pyjanitor/janitor && cp /opt/receipts-e19f3051fa0e48d09de768410451043d/incoming/47d14fa61569df09da803cbe399a1180791074b5f741c075c8aef6a27038271b /pyjanitor/janitor/timeseries.py'","path":"janitor/timeseries.py","exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"47d14fa61569df09da803cbe399a1180791074b5f741c075c8aef6a27038271b","log_path":"~/dev/nebius-hackathon/pilot/v2-P/ericmjl__pyjanitor-728/runs/pilot-ericmjl__pyjanitor-728-65b2a91a53/logs/agent1/agent1-6.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T20:15:32.281173Z","duration_s":0.005272,"timed_out":false,"image_before":"fd25f4b8-8553-4382-baf5-4bc2f27264df","image_after":"f1e1909f-49f6-4f80-942c-4e39f64e781f","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null} {"receipt_id":"agent1-7","seq":7,"branch_id":"agent1","kind":"command","cmd":"/opt/receipts-b6a066d523c84e848c58677410508e44/bin/busybox mkdir -p /opt/receipts/reports && python -I /opt/receipts-b6a066d523c84e848c58677410508e44/receipts_pytest.py tests/timeseries/test_sort_timestamps_monotonically.py -v -o junit_family=xunit1 -p no:cacheprovider -rA --continue-on-collection-errors --junitxml=/opt/receipts/reports/agent1-7.xml","path":null,"exit_code":1,"stdout_tail":"4:00:00 279\n2019-02-11 01:00:00 1391\n2019-01-05 07:00:00 1588\n2019-02-05 00:...3 01:00:00 33\n2019-01-15 07:00:00 746\n2019-01-06 05:00:00 1757\n2019-02-06 17:00:00 244\n\n[1000 rows x 1 columns]\nname = 'append'\n\n @final\n def __getattr__(self, name: str):\n \"\"\"\n After regular attribute access, try looking up the name\n This allows simpler access to columns for interactive use.\n \"\"\"\n # Note: obj.x will always call obj.__getattribute__('x') prior to\n # calling obj.__getattr__('x').\n if (\n name not in self._internal_names_set\n and name not in self._metadata\n and name not in self._accessors\n and self._info_axis._can_hold_identifiers_and_holds_name(name)\n ):\n return self[name]\n> return object.__getattribute__(self, name)\nE AttributeError: 'DataFrame' object has no attribute 'append'. Did you mean: '_append'?\n\n/usr/local/lib/python3.10/site-packages/pandas/core/generic.py:6321: AttributeError\n=============================== warnings summary ===============================\ntests/timeseries/test_sort_timestamps_monotonically.py:27\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:27: PytestUnknownMarkWarning: Unknown pytest.mark.timeseries - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html\n @pytest.mark.timeseries\n\ntests/timeseries/test_sort_timestamps_monotonically.py:36\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:36: PytestUnknownMarkWarning: Unknown pytest.mark.timeseries - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html\n @pytest.mark.timeseries\n\ntests/timeseries/test_sort_timestamps_monotonically.py:44\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:44: PytestUnknownMarkWarning: Unknown pytest.mark.timeseries - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html\n @pytest.mark.timeseries\n\ntests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically\ntests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_decreasing\ntests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_strict\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:13: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n ts_index = pd.date_range(\"1/1/2019\", periods=1000, freq=\"1H\")\n\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n==================================== PASSES ====================================\n------------ generated xml file: /opt/receipts/reports/agent1-7.xml ------------\n================================ tests coverage ================================\n_______________ coverage: platform linux, python 3.10.19-final-0 _______________\n\nName Stmts Miss Cover Missing\n-------------------------------------------------------------------\njanitor/__init__.py 12 3 75% 4-5, 21\njanitor/biology.py 14 14 0% 5-84\njanitor/chemistry.py 73 73 0% 5-461\njanitor/engineering.py 21 21 0% 5-106\njanitor/errors.py 2 0 100%\njanitor/finance.py 85 85 0% 5-654\njanitor/functions.py 718 535 25% 90-134, 180-223, 287-308, 313-325, 330, 346-347, 355-358, 366, 404-410, 445-446, 480-495, 534-548, 583-585, 614-616, 657-667, 717-727, 761-766, 800-806, 838-842, 878-890, 929-934, 975-985, 1076-1124, 1189-1193, 1252-1255, 1432-1469, 1515-1524, 1548, 1585-1600, 1725-1772, 1809-1812, 1897-1929, 2044-2055, 2167-2177, 2272-2281, 2361-2385, 2461-2489, 2544-2555, 2745-2779, 2814-2844, 2909-2944, 2960-2961, 2981, 3067-3069, 3095-3109, 3164-3170, 3201-3203, 3250-3270, 3286-3295, 3300, 3304, 3308, 3315-3326, 3367-3379, 3418-3433, 3466-3470, 3496, 3544-3545, 3598-3617, 3681-3698, 3733, 3818-3840, 3912-3913, 4006-4027, 4116-4127, 4224-4268, 4369-4387\njanitor/io.py 24 24 0% 1-63\njanitor/math.py 79 55 30% 31-42, 48, 57, 76-89, 95, 115-128, 153-160, 191-200\njanitor/ml.py 15 8 47% 60-71\njanitor/spark/__init__.py 1 1 0% 1\njanitor/spark/backend.py 37 37 0% 3-102\njanitor/spark/functions.py 30 30 0% 5-182\njanitor/testing_utils/__init__.py 0 0 100%\njanitor/testing_utils/date_data.py 1 0 100%\njanitor/testing_utils/strategies.py 12 12 0% 5-69\njanitor/timeseries.py 46 29 37% 46-60, 87-88, 95-98, 123-138, 161-167\njanitor/utils.py 164 129 21% 29-36, 54-60, 73-101, 105-106, 110-111, 133-136, 143-155, 177-202, 217-220, 250-251, 282-283, 304-314, 335-343, 366-372, 400-408, 423-470, 490-539\njanitor/xarray/__init__.py 1 0 100%\njanitor/xarray/functions.py 16 7 56% 71-86, 128-130\n-------------------------------------------------------------------\nTOTAL 1351 1063 21%\n=========================== short test summary info ============================\nPASSED tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically\nFAILED tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_decreasing\nFAILED tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_strict\n=================== 2 failed, 1 passed, 6 warnings in 1.31s ====================\n","stderr_tail":"","stdout_sha256":"5816f8ac8ac2fdec39c7858cafa388116a3783b9149babe4dc9a7ff0f1074288","log_path":"~/dev/nebius-hackathon/pilot/v2-P/ericmjl__pyjanitor-728/runs/pilot-ericmjl__pyjanitor-728-65b2a91a53/logs/agent1/agent1-7.log","tree_digest":"bceb55006082ee50ffa1e8a9223726572268996e81097138f6ce2a81b0b55747","tree_download_s":4.580626958049834,"started_at":"2026-10-03T20:15:36.814327Z","duration_s":5.248882999999999,"timed_out":false,"image_before":"f1e1909f-49f6-4f80-942c-4e39f64e781f","image_after":"913337a3-1e99-4a27-80de-a9ff2b5db206","junit":{"passed":["tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically"],"failed":["tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_decreasing","tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_strict"],"errors":[],"skipped":[],"collection_errors":[],"failure_text":{"tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_decreasing":"[...]nically for descending order\"\"\"\n df2 = timeseries_dataframe.sort_index(ascending=False)\n> df3 = df2.sort_timestamps_monotonically(\"decreasing\")\n\ntests/timeseries/test_sort_timestamps_monotonically.py:40: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n/usr/local/lib/python3.10/site-packages/pandas_flavor/register.py:29: in __call__\n return method(self._obj, *args, **kwargs)\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\ndf = v1\n2019-02-11 15:00:00 1631\n2019-02-11 14:00:00 988\n2019-02-11 13:00:00 973\n2019-02-11 12:...1 03:00:00 1426\n2019-01-01 02:00:00 1554\n2019-01-01 01:00:00 74\n2019-01-01 00:00:00 649\n\n[1000 rows x 1 columns]\ncolumn_name = 'decreasing', direction = 'increasing', strict = False\n\n @pf.register_dataframe_method\n def sort_timestamps_monotonically(\n df: pd.DataFrame,\n column_name: str = None,\n direction: str = \"increasing\",\n strict: bool = False,\n ) -> pd.DataFrame:\n \"\"\"\n Sorts data frame monotonically.\n It assumes the data frame has an index of type pd.DateTimeIndex when index is datetime.\n If datetime is in a column, then the column is expected to be of type pd.Timestamp\n \n :param df: data frame to sort monotonically\n :param column_name: needs to be specified if and only if the date time is not in index.\n Defaults to None\n :param direction: specifies the direction in which monotonicity is being tested for.\n Defaults to 'increasing'\n :param strict: whether to drop duplicates after sorting\n :return: data frame with its index sorted\n \"\"\"\n if column_name is not None:\n if column_name not in df.columns:\n> raise KeyError(f\"Column '{column_name}' not found in DataFrame\")\nE KeyError: \"Column 'decreasing' not found in DataFrame\"\n\njanitor/timeseries.py:85: KeyError","tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_strict":"[...]ject has no attribute 'append'. Did you mean: '_append'?\ntimeseries_dataframe = v1\n2019-01-01 00:00:00 1255\n2019-01-01 01:00:00 1702\n2019-01-01 02:00:00 473\n2019-01-01 03:...1 12:00:00 1160\n2019-02-11 13:00:00 1257\n2019-02-11 14:00:00 189\n2019-02-11 15:00:00 1616\n\n[1000 rows x 1 columns]\n\n @pytest.mark.timeseries\n def test_sort_timestamps_monotonically_strict(timeseries_dataframe):\n \"\"\"Test sort_timestamps_monotonically for index duplication handling\"\"\"\n df = timeseries_dataframe.shuffle(reset_index=False)\n random_number = randint(1, len(timeseries_dataframe))\n> df = df.append(\n df.loc[df.index[random_number], :]\n ).sort_timestamps_monotonically(direction=\"increasing\", strict=True)\n\ntests/timeseries/test_sort_timestamps_monotonically.py:49: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = v1\n2019-01-22 04:00:00 279\n2019-02-11 01:00:00 1391\n2019-01-05 07:00:00 1588\n2019-02-05 00:...3 01:00:00 33\n2019-01-15 07:00:00 746\n2019-01-06 05:00:00 1757\n2019-02-06 17:00:00 244\n\n[1000 rows x 1 columns]\nname = 'append'\n\n @final\n def __getattr__(self, name: str):\n \"\"\"\n After regular attribute access, try looking up the name\n This allows simpler access to columns for interactive use.\n \"\"\"\n # Note: obj.x will always call obj.__getattribute__('x') prior to\n # calling obj.__getattr__('x').\n if (\n name not in self._internal_names_set\n and name not in self._metadata\n and name not in self._accessors\n and self._info_axis._can_hold_identifiers_and_holds_name(name)\n ):\n return self[name]\n> return object.__getattribute__(self, name)\nE AttributeError: 'DataFrame' object has no attribute 'append'. Did you mean: '_append'?\n\n/usr/local/lib/python3.10/site-packages/pandas/core/generic.py:6321: AttributeError"}},"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null} {"receipt_id":"agent1-8","seq":8,"branch_id":"agent1","kind":"write","cmd":"/opt/receipts-46985fcca1db4d2aa37dd6938dd7ae8a/bin/busybox sh -c 'mkdir -p /pyjanitor/janitor && cp /opt/receipts-46985fcca1db4d2aa37dd6938dd7ae8a/incoming/47d14fa61569df09da803cbe399a1180791074b5f741c075c8aef6a27038271b /pyjanitor/janitor/timeseries.py'","path":"janitor/timeseries.py","exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"47d14fa61569df09da803cbe399a1180791074b5f741c075c8aef6a27038271b","log_path":"~/dev/nebius-hackathon/pilot/v2-P/ericmjl__pyjanitor-728/runs/pilot-ericmjl__pyjanitor-728-65b2a91a53/logs/agent1/agent1-8.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T20:15:59.053219Z","duration_s":0.004682,"timed_out":false,"image_before":"913337a3-1e99-4a27-80de-a9ff2b5db206","image_after":"bfdd313c-9598-4db3-9164-7fc40f821c51","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null} {"receipt_id":"agent1-9","seq":9,"branch_id":"agent1","kind":"command","cmd":"/opt/receipts-0427ff8b76d048f3acd4236d11ca0bfd/bin/busybox mkdir -p /opt/receipts/reports && python -I /opt/receipts-0427ff8b76d048f3acd4236d11ca0bfd/receipts_pytest.py tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_decreasing -v -s -o junit_family=xunit1 -p no:cacheprovider -rA --continue-on-collection-errors --junitxml=/opt/receipts/reports/agent1-9.xml","path":null,"exit_code":1,"stdout_tail":"_ _ _ _ \n\ndf = v1\n2019-02-11 15:00:00 240\n2019-02-11 14:00:00 532\n2019-02-11 13:00:00 1224\n2019-02-11 12:...1 03:00:00 519\n2019-01-01 02:00:00 1844\n2019-01-01 01:00:00 24\n2019-01-01 00:00:00 1173\n\n[1000 rows x 1 columns]\ncolumn_name = 'decreasing', direction = 'increasing', strict = False\n\n @pf.register_dataframe_method\n def sort_timestamps_monotonically(\n df: pd.DataFrame,\n column_name: str = None,\n direction: str = \"increasing\",\n strict: bool = False,\n ) -> pd.DataFrame:\n \"\"\"\n Sorts data frame monotonically.\n It assumes the data frame has an index of type pd.DateTimeIndex when index is datetime.\n If datetime is in a column, then the column is expected to be of type pd.Timestamp\n \n :param df: data frame to sort monotonically\n :param column_name: needs to be specified if and only if the date time is not in index.\n Defaults to None\n :param direction: specifies the direction in which monotonicity is being tested for.\n Defaults to 'increasing'\n :param strict: whether to drop duplicates after sorting\n :return: data frame with its index sorted\n \"\"\"\n if column_name is not None:\n if column_name not in df.columns:\n> raise KeyError(f\"Column '{column_name}' not found in DataFrame\")\nE KeyError: \"Column 'decreasing' not found in DataFrame\"\n\njanitor/timeseries.py:85: KeyError\n=============================== warnings summary ===============================\ntests/timeseries/test_sort_timestamps_monotonically.py:27\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:27: PytestUnknownMarkWarning: Unknown pytest.mark.timeseries - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html\n @pytest.mark.timeseries\n\ntests/timeseries/test_sort_timestamps_monotonically.py:36\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:36: PytestUnknownMarkWarning: Unknown pytest.mark.timeseries - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html\n @pytest.mark.timeseries\n\ntests/timeseries/test_sort_timestamps_monotonically.py:44\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:44: PytestUnknownMarkWarning: Unknown pytest.mark.timeseries - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html\n @pytest.mark.timeseries\n\ntests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_decreasing\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:13: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n ts_index = pd.date_range(\"1/1/2019\", periods=1000, freq=\"1H\")\n\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n------------ generated xml file: /opt/receipts/reports/agent1-9.xml ------------\n================================ tests coverage ================================\n_______________ coverage: platform linux, python 3.10.19-final-0 _______________\n\nName Stmts Miss Cover Missing\n-------------------------------------------------------------------\njanitor/__init__.py 12 3 75% 4-5, 21\njanitor/biology.py 14 14 0% 5-84\njanitor/chemistry.py 73 73 0% 5-461\njanitor/engineering.py 21 21 0% 5-106\njanitor/errors.py 2 0 100%\njanitor/finance.py 85 85 0% 5-654\njanitor/functions.py 718 540 25% 90-134, 180-223, 287-308, 313-325, 330, 346-347, 355-358, 366, 404-410, 445-446, 480-495, 534-548, 583-585, 614-616, 657-667, 717-727, 761-766, 800-806, 838-842, 878-890, 929-934, 975-985, 1076-1124, 1189-1193, 1252-1255, 1432-1469, 1515-1524, 1548, 1585-1600, 1725-1772, 1809-1812, 1897-1929, 2044-2055, 2167-2177, 2272-2281, 2361-2385, 2461-2489, 2544-2555, 2745-2779, 2814-2844, 2909-2944, 2960-2961, 2981, 3067-3069, 3095-3109, 3164-3170, 3201-3203, 3250-3270, 3282-3283, 3286-3295, 3300, 3304, 3308, 3315-3326, 3367-3379, 3418-3433, 3466-3470, 3494-3497, 3544-3545, 3598-3617, 3681-3698, 3733, 3818-3840, 3912-3913, 4006-4027, 4116-4127, 4224-4268, 4369-4387\njanitor/io.py 24 24 0% 1-63\njanitor/math.py 79 55 30% 31-42, 48, 57, 76-89, 95, 115-128, 153-160, 191-200\njanitor/ml.py 15 8 47% 60-71\njanitor/spark/__init__.py 1 1 0% 1\njanitor/spark/backend.py 37 37 0% 3-102\njanitor/spark/functions.py 30 30 0% 5-182\njanitor/testing_utils/__init__.py 0 0 100%\njanitor/testing_utils/date_data.py 1 0 100%\njanitor/testing_utils/strategies.py 12 12 0% 5-69\njanitor/timeseries.py 46 33 28% 46-60, 87-100, 123-138, 161-167\njanitor/utils.py 164 129 21% 29-36, 54-60, 73-101, 105-106, 110-111, 133-136, 143-155, 177-202, 217-220, 250-251, 282-283, 304-314, 335-343, 366-372, 400-408, 423-470, 490-539\njanitor/xarray/__init__.py 1 0 100%\njanitor/xarray/functions.py 16 7 56% 71-86, 128-130\n-------------------------------------------------------------------\nTOTAL 1351 1072 21%\n=========================== short test summary info ============================\nFAILED tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_decreasing\n======================== 1 failed, 4 warnings in 1.12s =========================\n","stderr_tail":"","stdout_sha256":"13286059b8ad5f540e1ae0fe3cd0a00c0c27115b144799202374bbb72bbe2fe8","log_path":"~/dev/nebius-hackathon/pilot/v2-P/ericmjl__pyjanitor-728/runs/pilot-ericmjl__pyjanitor-728-65b2a91a53/logs/agent1/agent1-9.log","tree_digest":"f3fc727d101d3f5f4b1cbc17ce135e97aaee2de8dadb3b70b5d0eec006b7e4e2","tree_download_s":4.953356208978221,"started_at":"2026-10-03T20:16:01.989288Z","duration_s":4.957133000000001,"timed_out":false,"image_before":"bfdd313c-9598-4db3-9164-7fc40f821c51","image_after":"e30a6dec-46d7-4d38-bf38-514329999768","junit":{"passed":[],"failed":["tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_decreasing"],"errors":[],"skipped":[],"collection_errors":[],"failure_text":{"tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_decreasing":"[...]nically for descending order\"\"\"\n df2 = timeseries_dataframe.sort_index(ascending=False)\n> df3 = df2.sort_timestamps_monotonically(\"decreasing\")\n\ntests/timeseries/test_sort_timestamps_monotonically.py:40: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n/usr/local/lib/python3.10/site-packages/pandas_flavor/register.py:29: in __call__\n return method(self._obj, *args, **kwargs)\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\ndf = v1\n2019-02-11 15:00:00 240\n2019-02-11 14:00:00 532\n2019-02-11 13:00:00 1224\n2019-02-11 12:...1 03:00:00 519\n2019-01-01 02:00:00 1844\n2019-01-01 01:00:00 24\n2019-01-01 00:00:00 1173\n\n[1000 rows x 1 columns]\ncolumn_name = 'decreasing', direction = 'increasing', strict = False\n\n @pf.register_dataframe_method\n def sort_timestamps_monotonically(\n df: pd.DataFrame,\n column_name: str = None,\n direction: str = \"increasing\",\n strict: bool = False,\n ) -> pd.DataFrame:\n \"\"\"\n Sorts data frame monotonically.\n It assumes the data frame has an index of type pd.DateTimeIndex when index is datetime.\n If datetime is in a column, then the column is expected to be of type pd.Timestamp\n \n :param df: data frame to sort monotonically\n :param column_name: needs to be specified if and only if the date time is not in index.\n Defaults to None\n :param direction: specifies the direction in which monotonicity is being tested for.\n Defaults to 'increasing'\n :param strict: whether to drop duplicates after sorting\n :return: data frame with its index sorted\n \"\"\"\n if column_name is not None:\n if column_name not in df.columns:\n> raise KeyError(f\"Column '{column_name}' not found in DataFrame\")\nE KeyError: \"Column 'decreasing' not found in DataFrame\"\n\njanitor/timeseries.py:85: KeyError"}},"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null} {"receipt_id":"agent1-10","seq":10,"branch_id":"agent1","kind":"command","cmd":"grep -n \"\\.\" tests/timeseries/test_fill_missing_timestamps.py | head -10","path":null,"exit_code":0,"stdout_tail":"4:from janitor.timeseries import fill_missing_timestamps, _get_missing_timestamps\n8:@pytest.fixture\n9:def timeseries_dataframe() -> pd.DataFrame:\n13: ts_index = pd.date_range(\"1/1/2019\", periods=1000, freq=\"1H\")\n15: test_df = pd.DataFrame({\"v1\": v1}, index=ts_index)\n19:@pytest.mark.timeseries\n21: \"\"\"Test that filling missing timestamps works as expected.\"\"\"\n24: df1 = timeseries_dataframe.drop(timeseries_dataframe.index[random_number])\n33: original_index = timeseries_dataframe.index\n34: new_index = result.index\n","stderr_tail":"","stdout_sha256":"80c1ec1149778d182b00c2a99a7f83ef40a16269590493b15bfd3e4af4704130","log_path":"~/dev/nebius-hackathon/pilot/v2-P/ericmjl__pyjanitor-728/runs/pilot-ericmjl__pyjanitor-728-65b2a91a53/logs/agent1/agent1-10.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T20:16:17.708419Z","duration_s":0.004679,"timed_out":false,"image_before":"e30a6dec-46d7-4d38-bf38-514329999768","image_after":"c0956661-3790-40d9-8922-7e6cb534d528","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null} {"receipt_id":"agent1-11","seq":11,"branch_id":"agent1","kind":"command","cmd":"grep -A5 -B5 \"\\..*\" tests/timeseries/test_fill_missing_timestamps.py","path":null,"exit_code":0,"stdout_tail":"import pytest\nimport pandas as pd\nfrom random import randint\nfrom janitor.timeseries import fill_missing_timestamps, _get_missing_timestamps\n\n\n# Random data for testing\n@pytest.fixture\ndef timeseries_dataframe() -> pd.DataFrame:\n \"\"\"\n Returns a time series dataframe\n \"\"\"\n ts_index = pd.date_range(\"1/1/2019\", periods=1000, freq=\"1H\")\n v1 = [randint(1, 2000) for i in range(1000)]\n test_df = pd.DataFrame({\"v1\": v1}, index=ts_index)\n return test_df\n\n\n@pytest.mark.timeseries\ndef test_fill_missing_timestamps(timeseries_dataframe):\n \"\"\"Test that filling missing timestamps works as expected.\"\"\"\n # Remove random row from the data frame\n random_number = randint(1, len(timeseries_dataframe))\n df1 = timeseries_dataframe.drop(timeseries_dataframe.index[random_number])\n\n # Fill missing timestamps\n result = fill_missing_timestamps(df1, frequency=\"1H\")\n\n # Testing if the missing timestamp has been filled\n assert len(result) == len(timeseries_dataframe)\n\n # Testing if indices are exactly the same after filling\n original_index = timeseries_dataframe.index\n new_index = result.index\n delta = original_index.difference(new_index)\n\n assert delta.empty is True\n\n\n@pytest.mark.timeseries\ndef test__get_missing_timestamps(timeseries_dataframe):\n \"\"\"Test utility function for identifying the missing timestamps.\"\"\"\n from random import sample\n\n timestamps_to_drop = sample(timeseries_dataframe.index.tolist(), 3)\n df = timeseries_dataframe.drop(index=timestamps_to_drop)\n missing_timestamps = _get_missing_timestamps(df, \"1H\")\n assert set(missing_timestamps.index) == set(timestamps_to_drop)\n","stderr_tail":"","stdout_sha256":"7a9895880015ac862884d09a5eeea29fb7d30eebccbf2122f76dbdccadac3f2d","log_path":"~/dev/nebius-hackathon/pilot/v2-P/ericmjl__pyjanitor-728/runs/pilot-ericmjl__pyjanitor-728-65b2a91a53/logs/agent1/agent1-11.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T20:16:20.801155Z","duration_s":0.003056,"timed_out":false,"image_before":"c0956661-3790-40d9-8922-7e6cb534d528","image_after":"48c4110c-4790-4e1b-82fd-bce56538c9a6","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null} {"receipt_id":"agent1-12","seq":12,"branch_id":"agent1","kind":"command","cmd":"find tests/ -name \"*.py\" -exec grep -l \"\\.sort_index\\|\\.sort_values\" {} \\;","path":null,"exit_code":0,"stdout_tail":"tests/functions/test_groupby_topk.py\ntests/functions/test_sort_naturally.py\ntests/timeseries/test_sort_timestamps_monotonically.py\n","stderr_tail":"","stdout_sha256":"d8b47a02a9a7084c1f4441972cd7521661fcb3b112476ee9fe7d7dde58573127","log_path":"~/dev/nebius-hackathon/pilot/v2-P/ericmjl__pyjanitor-728/runs/pilot-ericmjl__pyjanitor-728-65b2a91a53/logs/agent1/agent1-12.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T20:16:23.912094Z","duration_s":0.11285999999999999,"timed_out":false,"image_before":"48c4110c-4790-4e1b-82fd-bce56538c9a6","image_after":"1dd5932d-dd07-4456-8147-f92021d6c34f","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null} {"receipt_id":"agent1-13","seq":13,"branch_id":"agent1","kind":"write","cmd":"/opt/receipts-fd0bdd0fb83d4821978f2f7e793db5c4/bin/busybox sh -c 'mkdir -p /pyjanitor/janitor && cp /opt/receipts-fd0bdd0fb83d4821978f2f7e793db5c4/incoming/545c58e316638a3b5119f74f0fee89c47525fbbf618266eb685b5705e5c6b07a /pyjanitor/janitor/timeseries.py'","path":"janitor/timeseries.py","exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"545c58e316638a3b5119f74f0fee89c47525fbbf618266eb685b5705e5c6b07a","log_path":"~/dev/nebius-hackathon/pilot/v2-P/ericmjl__pyjanitor-728/runs/pilot-ericmjl__pyjanitor-728-65b2a91a53/logs/agent1/agent1-13.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T20:16:41.380437Z","duration_s":0.004551,"timed_out":false,"image_before":"1dd5932d-dd07-4456-8147-f92021d6c34f","image_after":"cd09b158-fc56-4d8a-9b46-d13f24992a44","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null} {"receipt_id":"agent1-14","seq":14,"branch_id":"agent1","kind":"command","cmd":"/opt/receipts-bcd3179b26b54992b8dec005fd434ede/bin/busybox mkdir -p /opt/receipts/reports && python -I /opt/receipts-bcd3179b26b54992b8dec005fd434ede/receipts_pytest.py tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_decreasing -v -o junit_family=xunit1 -p no:cacheprovider -rA --continue-on-collection-errors --junitxml=/opt/receipts/reports/agent1-14.xml","path":null,"exit_code":0,"stdout_tail":"============================= test session starts ==============================\nplatform linux -- Python 3.10.19, pytest-9.0.2, pluggy-1.6.0 -- /usr/local/bin/python\nhypothesis profile 'default'\nrootdir: /pyjanitor\nconfigfile: pytest.ini\nplugins: hypothesis-6.151.9, cov-7.0.0\ncollecting ... collected 1 item\n\ntests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_decreasing PASSED [100%]\n\n=============================== warnings summary ===============================\ntests/timeseries/test_sort_timestamps_monotonically.py:27\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:27: PytestUnknownMarkWarning: Unknown pytest.mark.timeseries - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html\n @pytest.mark.timeseries\n\ntests/timeseries/test_sort_timestamps_monotonically.py:36\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:36: PytestUnknownMarkWarning: Unknown pytest.mark.timeseries - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html\n @pytest.mark.timeseries\n\ntests/timeseries/test_sort_timestamps_monotonically.py:44\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:44: PytestUnknownMarkWarning: Unknown pytest.mark.timeseries - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html\n @pytest.mark.timeseries\n\ntests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_decreasing\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:13: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n ts_index = pd.date_range(\"1/1/2019\", periods=1000, freq=\"1H\")\n\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n==================================== PASSES ====================================\n----------- generated xml file: /opt/receipts/reports/agent1-14.xml ------------\n================================ tests coverage ================================\n_______________ coverage: platform linux, python 3.10.19-final-0 _______________\n\nName Stmts Miss Cover Missing\n-------------------------------------------------------------------\njanitor/__init__.py 12 3 75% 4-5, 21\njanitor/biology.py 14 14 0% 5-84\njanitor/chemistry.py 73 73 0% 5-461\njanitor/engineering.py 21 21 0% 5-106\njanitor/errors.py 2 0 100%\njanitor/finance.py 85 85 0% 5-654\njanitor/functions.py 718 540 25% 90-134, 180-223, 287-308, 313-325, 330, 346-347, 355-358, 366, 404-410, 445-446, 480-495, 534-548, 583-585, 614-616, 657-667, 717-727, 761-766, 800-806, 838-842, 878-890, 929-934, 975-985, 1076-1124, 1189-1193, 1252-1255, 1432-1469, 1515-1524, 1548, 1585-1600, 1725-1772, 1809-1812, 1897-1929, 2044-2055, 2167-2177, 2272-2281, 2361-2385, 2461-2489, 2544-2555, 2745-2779, 2814-2844, 2909-2944, 2960-2961, 2981, 3067-3069, 3095-3109, 3164-3170, 3201-3203, 3250-3270, 3282-3283, 3286-3295, 3300, 3304, 3308, 3315-3326, 3367-3379, 3418-3433, 3466-3470, 3494-3497, 3544-3545, 3598-3617, 3681-3698, 3733, 3818-3840, 3912-3913, 4006-4027, 4116-4127, 4224-4268, 4369-4387\njanitor/io.py 24 24 0% 1-63\njanitor/math.py 79 55 30% 31-42, 48, 57, 76-89, 95, 115-128, 153-160, 191-200\njanitor/ml.py 15 8 47% 60-71\njanitor/spark/__init__.py 1 1 0% 1\njanitor/spark/backend.py 37 37 0% 3-102\njanitor/spark/functions.py 30 30 0% 5-182\njanitor/testing_utils/__init__.py 0 0 100%\njanitor/testing_utils/date_data.py 1 0 100%\njanitor/testing_utils/strategies.py 12 12 0% 5-69\njanitor/timeseries.py 49 30 39% 46-60, 92, 96-97, 104-107, 132-147, 170-176\njanitor/utils.py 164 129 21% 29-36, 54-60, 73-101, 105-106, 110-111, 133-136, 143-155, 177-202, 217-220, 250-251, 282-283, 304-314, 335-343, 366-372, 400-408, 423-470, 490-539\njanitor/xarray/__init__.py 1 0 100%\njanitor/xarray/functions.py 16 7 56% 71-86, 128-130\n-------------------------------------------------------------------\nTOTAL 1354 1069 21%\n=========================== short test summary info ============================\nPASSED tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_decreasing\n======================== 1 passed, 4 warnings in 1.06s =========================\n","stderr_tail":"","stdout_sha256":"f3cb63f9695248893f424bb2f682e865bd81f032b83589c0868f4fd2dd9b973c","log_path":"~/dev/nebius-hackathon/pilot/v2-P/ericmjl__pyjanitor-728/runs/pilot-ericmjl__pyjanitor-728-65b2a91a53/logs/agent1/agent1-14.log","tree_digest":"74b6e1aad60e40b948cb1a07819cf68782fe6b9554c0935a5f7d2801205db2cb","tree_download_s":4.788963542087004,"started_at":"2026-10-03T20:16:44.613679Z","duration_s":4.888966,"timed_out":false,"image_before":"cd09b158-fc56-4d8a-9b46-d13f24992a44","image_after":"42afca67-f0cc-4d44-a8f3-a160e22cff3d","junit":{"passed":["tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_decreasing"],"failed":[],"errors":[],"skipped":[],"collection_errors":[],"failure_text":{}},"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null} {"receipt_id":"agent1-15","seq":15,"branch_id":"agent1","kind":"command","cmd":"/opt/receipts-2b2406fffc844d6892b10ecbd453398f/bin/busybox mkdir -p /opt/receipts/reports && python -I /opt/receipts-2b2406fffc844d6892b10ecbd453398f/receipts_pytest.py tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_strict -v -o junit_family=xunit1 -p no:cacheprovider -rA --continue-on-collection-errors --junitxml=/opt/receipts/reports/agent1-15.xml","path":null,"exit_code":1,"stdout_tail":"dex duplication handling\"\"\"\n df = timeseries_dataframe.shuffle(reset_index=False)\n random_number = randint(1, len(timeseries_dataframe))\n> df = df.append(\n df.loc[df.index[random_number], :]\n ).sort_timestamps_monotonically(direction=\"increasing\", strict=True)\n\ntests/timeseries/test_sort_timestamps_monotonically.py:49: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = v1\n2019-01-14 13:00:00 1967\n2019-01-13 08:00:00 28\n2019-01-14 10:00:00 715\n2019-02-09 12:...1 06:00:00 1057\n2019-01-19 02:00:00 1404\n2019-02-09 01:00:00 1854\n2019-01-07 17:00:00 632\n\n[1000 rows x 1 columns]\nname = 'append'\n\n @final\n def __getattr__(self, name: str):\n \"\"\"\n After regular attribute access, try looking up the name\n This allows simpler access to columns for interactive use.\n \"\"\"\n # Note: obj.x will always call obj.__getattribute__('x') prior to\n # calling obj.__getattr__('x').\n if (\n name not in self._internal_names_set\n and name not in self._metadata\n and name not in self._accessors\n and self._info_axis._can_hold_identifiers_and_holds_name(name)\n ):\n return self[name]\n> return object.__getattribute__(self, name)\nE AttributeError: 'DataFrame' object has no attribute 'append'. Did you mean: '_append'?\n\n/usr/local/lib/python3.10/site-packages/pandas/core/generic.py:6321: AttributeError\n=============================== warnings summary ===============================\ntests/timeseries/test_sort_timestamps_monotonically.py:27\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:27: PytestUnknownMarkWarning: Unknown pytest.mark.timeseries - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html\n @pytest.mark.timeseries\n\ntests/timeseries/test_sort_timestamps_monotonically.py:36\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:36: PytestUnknownMarkWarning: Unknown pytest.mark.timeseries - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html\n @pytest.mark.timeseries\n\ntests/timeseries/test_sort_timestamps_monotonically.py:44\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:44: PytestUnknownMarkWarning: Unknown pytest.mark.timeseries - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html\n @pytest.mark.timeseries\n\ntests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_strict\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:13: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n ts_index = pd.date_range(\"1/1/2019\", periods=1000, freq=\"1H\")\n\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n----------- generated xml file: /opt/receipts/reports/agent1-15.xml ------------\n================================ tests coverage ================================\n_______________ coverage: platform linux, python 3.10.19-final-0 _______________\n\nName Stmts Miss Cover Missing\n-------------------------------------------------------------------\njanitor/__init__.py 12 3 75% 4-5, 21\njanitor/biology.py 14 14 0% 5-84\njanitor/chemistry.py 73 73 0% 5-461\njanitor/engineering.py 21 21 0% 5-106\njanitor/errors.py 2 0 100%\njanitor/finance.py 85 85 0% 5-654\njanitor/functions.py 718 535 25% 90-134, 180-223, 287-308, 313-325, 330, 346-347, 355-358, 366, 404-410, 445-446, 480-495, 534-548, 583-585, 614-616, 657-667, 717-727, 761-766, 800-806, 838-842, 878-890, 929-934, 975-985, 1076-1124, 1189-1193, 1252-1255, 1432-1469, 1515-1524, 1548, 1585-1600, 1725-1772, 1809-1812, 1897-1929, 2044-2055, 2167-2177, 2272-2281, 2361-2385, 2461-2489, 2544-2555, 2745-2779, 2814-2844, 2909-2944, 2960-2961, 2981, 3067-3069, 3095-3109, 3164-3170, 3201-3203, 3250-3270, 3286-3295, 3300, 3304, 3308, 3315-3326, 3367-3379, 3418-3433, 3466-3470, 3496, 3544-3545, 3598-3617, 3681-3698, 3733, 3818-3840, 3912-3913, 4006-4027, 4116-4127, 4224-4268, 4369-4387\njanitor/io.py 24 24 0% 1-63\njanitor/math.py 79 55 30% 31-42, 48, 57, 76-89, 95, 115-128, 153-160, 191-200\njanitor/ml.py 15 8 47% 60-71\njanitor/spark/__init__.py 1 1 0% 1\njanitor/spark/backend.py 37 37 0% 3-102\njanitor/spark/functions.py 30 30 0% 5-182\njanitor/testing_utils/__init__.py 0 0 100%\njanitor/testing_utils/date_data.py 1 0 100%\njanitor/testing_utils/strategies.py 12 12 0% 5-69\njanitor/timeseries.py 49 39 20% 46-60, 86-109, 132-147, 170-176\njanitor/utils.py 164 129 21% 29-36, 54-60, 73-101, 105-106, 110-111, 133-136, 143-155, 177-202, 217-220, 250-251, 282-283, 304-314, 335-343, 366-372, 400-408, 423-470, 490-539\njanitor/xarray/__init__.py 1 0 100%\njanitor/xarray/functions.py 16 7 56% 71-86, 128-130\n-------------------------------------------------------------------\nTOTAL 1354 1073 21%\n=========================== short test summary info ============================\nFAILED tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_strict\n======================== 1 failed, 4 warnings in 1.26s =========================\n","stderr_tail":"","stdout_sha256":"787b7f337f3e8f2713813e6eaab6360462c9c069a3aece3ce2e55c578716df08","log_path":"~/dev/nebius-hackathon/pilot/v2-P/ericmjl__pyjanitor-728/runs/pilot-ericmjl__pyjanitor-728-65b2a91a53/logs/agent1/agent1-15.log","tree_digest":"7f85905d3b2ba7c03096c80db26d16c86a6baf1206e6364d5b017d097b796627","tree_download_s":7.341742125106975,"started_at":"2026-10-03T20:17:00.262630Z","duration_s":5.134104000000001,"timed_out":false,"image_before":"42afca67-f0cc-4d44-a8f3-a160e22cff3d","image_after":"e9f4ef08-99a3-4a87-b0a9-775338b54058","junit":{"passed":[],"failed":["tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_strict"],"errors":[],"skipped":[],"collection_errors":[],"failure_text":{"tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_strict":"[...]ject has no attribute 'append'. Did you mean: '_append'?\ntimeseries_dataframe = v1\n2019-01-01 00:00:00 1690\n2019-01-01 01:00:00 743\n2019-01-01 02:00:00 1337\n2019-01-01 03:...1 12:00:00 1271\n2019-02-11 13:00:00 1320\n2019-02-11 14:00:00 1051\n2019-02-11 15:00:00 1642\n\n[1000 rows x 1 columns]\n\n @pytest.mark.timeseries\n def test_sort_timestamps_monotonically_strict(timeseries_dataframe):\n \"\"\"Test sort_timestamps_monotonically for index duplication handling\"\"\"\n df = timeseries_dataframe.shuffle(reset_index=False)\n random_number = randint(1, len(timeseries_dataframe))\n> df = df.append(\n df.loc[df.index[random_number], :]\n ).sort_timestamps_monotonically(direction=\"increasing\", strict=True)\n\ntests/timeseries/test_sort_timestamps_monotonically.py:49: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = v1\n2019-01-14 13:00:00 1967\n2019-01-13 08:00:00 28\n2019-01-14 10:00:00 715\n2019-02-09 12:...1 06:00:00 1057\n2019-01-19 02:00:00 1404\n2019-02-09 01:00:00 1854\n2019-01-07 17:00:00 632\n\n[1000 rows x 1 columns]\nname = 'append'\n\n @final\n def __getattr__(self, name: str):\n \"\"\"\n After regular attribute access, try looking up the name\n This allows simpler access to columns for interactive use.\n \"\"\"\n # Note: obj.x will always call obj.__getattribute__('x') prior to\n # calling obj.__getattr__('x').\n if (\n name not in self._internal_names_set\n and name not in self._metadata\n and name not in self._accessors\n and self._info_axis._can_hold_identifiers_and_holds_name(name)\n ):\n return self[name]\n> return object.__getattribute__(self, name)\nE AttributeError: 'DataFrame' object has no attribute 'append'. Did you mean: '_append'?\n\n/usr/local/lib/python3.10/site-packages/pandas/core/generic.py:6321: AttributeError"}},"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null} {"receipt_id":"agent1-16","seq":16,"branch_id":"agent1","kind":"command","cmd":"python -c \"import pandas as pd; print(pd.__version__); df = pd.DataFrame({'a': [1]}); print(hasattr(df, 'append'))\"","path":null,"exit_code":0,"stdout_tail":"2.3.3\nFalse\n","stderr_tail":"","stdout_sha256":"b3b53d4c00c093feeb0780c74a25d2f874fb44c7bcdb99413937c1b7c9ec399a","log_path":"~/dev/nebius-hackathon/pilot/v2-P/ericmjl__pyjanitor-728/runs/pilot-ericmjl__pyjanitor-728-65b2a91a53/logs/agent1/agent1-16.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T20:17:19.618517Z","duration_s":0.587378,"timed_out":false,"image_before":"e9f4ef08-99a3-4a87-b0a9-775338b54058","image_after":"56254e6c-b9b4-4403-96ae-39f7b15c3502","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null} {"receipt_id":"agent1-17","seq":17,"branch_id":"agent1","kind":"command","cmd":"python -c \"import pandas as pd; df = pd.DataFrame({'a': [1]}); print(hasattr(df, '_append')); result = df._append(df.iloc[0]); print(len(result))\"","path":null,"exit_code":0,"stdout_tail":"True\n2\n","stderr_tail":"","stdout_sha256":"e203152486f2e180b5a318850ef3202114d2dcc0dee1ba598153b7e61dad9ab7","log_path":"~/dev/nebius-hackathon/pilot/v2-P/ericmjl__pyjanitor-728/runs/pilot-ericmjl__pyjanitor-728-65b2a91a53/logs/agent1/agent1-17.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T20:17:25.548464Z","duration_s":0.583541,"timed_out":false,"image_before":"56254e6c-b9b4-4403-96ae-39f7b15c3502","image_after":"b5ef404e-b6b7-4d4f-80a1-331c4e2dd10f","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null} {"receipt_id":"agent1-18","seq":18,"branch_id":"agent1","kind":"command","cmd":"/opt/receipts-f39ce82f18b7451fad7c110e6a0c8220/bin/busybox mkdir -p /opt/receipts/reports && python -I /opt/receipts-f39ce82f18b7451fad7c110e6a0c8220/receipts_pytest.py tests/timeseries/test_sort_timestamps_monotonically.py -v -o junit_family=xunit1 -p no:cacheprovider -rA --continue-on-collection-errors --junitxml=/opt/receipts/reports/agent1-18.xml","path":null,"exit_code":1,"stdout_tail":"0 1273\n2019-01-27 07:00:00 1456\n2019-02-10 14:00:00 509\n2019-01-09 10:...9 15:00:00 395\n2019-01-07 09:00:00 128\n2019-01-24 02:00:00 1767\n2019-01-22 22:00:00 1967\n\n[1000 rows x 1 columns]\nname = 'append'\n\n @final\n def __getattr__(self, name: str):\n \"\"\"\n After regular attribute access, try looking up the name\n This allows simpler access to columns for interactive use.\n \"\"\"\n # Note: obj.x will always call obj.__getattribute__('x') prior to\n # calling obj.__getattr__('x').\n if (\n name not in self._internal_names_set\n and name not in self._metadata\n and name not in self._accessors\n and self._info_axis._can_hold_identifiers_and_holds_name(name)\n ):\n return self[name]\n> return object.__getattribute__(self, name)\nE AttributeError: 'DataFrame' object has no attribute 'append'. Did you mean: '_append'?\n\n/usr/local/lib/python3.10/site-packages/pandas/core/generic.py:6321: AttributeError\n=============================== warnings summary ===============================\ntests/timeseries/test_sort_timestamps_monotonically.py:27\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:27: PytestUnknownMarkWarning: Unknown pytest.mark.timeseries - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html\n @pytest.mark.timeseries\n\ntests/timeseries/test_sort_timestamps_monotonically.py:36\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:36: PytestUnknownMarkWarning: Unknown pytest.mark.timeseries - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html\n @pytest.mark.timeseries\n\ntests/timeseries/test_sort_timestamps_monotonically.py:44\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:44: PytestUnknownMarkWarning: Unknown pytest.mark.timeseries - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html\n @pytest.mark.timeseries\n\ntests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically\ntests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_decreasing\ntests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_strict\n /pyjanitor/tests/timeseries/test_sort_timestamps_monotonically.py:13: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n ts_index = pd.date_range(\"1/1/2019\", periods=1000, freq=\"1H\")\n\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n==================================== PASSES ====================================\n----------- generated xml file: /opt/receipts/reports/agent1-18.xml ------------\n================================ tests coverage ================================\n_______________ coverage: platform linux, python 3.10.19-final-0 _______________\n\nName Stmts Miss Cover Missing\n-------------------------------------------------------------------\njanitor/__init__.py 12 3 75% 4-5, 21\njanitor/biology.py 14 14 0% 5-84\njanitor/chemistry.py 73 73 0% 5-461\njanitor/engineering.py 21 21 0% 5-106\njanitor/errors.py 2 0 100%\njanitor/finance.py 85 85 0% 5-654\njanitor/functions.py 718 535 25% 90-134, 180-223, 287-308, 313-325, 330, 346-347, 355-358, 366, 404-410, 445-446, 480-495, 534-548, 583-585, 614-616, 657-667, 717-727, 761-766, 800-806, 838-842, 878-890, 929-934, 975-985, 1076-1124, 1189-1193, 1252-1255, 1432-1469, 1515-1524, 1548, 1585-1600, 1725-1772, 1809-1812, 1897-1929, 2044-2055, 2167-2177, 2272-2281, 2361-2385, 2461-2489, 2544-2555, 2745-2779, 2814-2844, 2909-2944, 2960-2961, 2981, 3067-3069, 3095-3109, 3164-3170, 3201-3203, 3250-3270, 3286-3295, 3300, 3304, 3308, 3315-3326, 3367-3379, 3418-3433, 3466-3470, 3496, 3544-3545, 3598-3617, 3681-3698, 3733, 3818-3840, 3912-3913, 4006-4027, 4116-4127, 4224-4268, 4369-4387\njanitor/io.py 24 24 0% 1-63\njanitor/math.py 79 55 30% 31-42, 48, 57, 76-89, 95, 115-128, 153-160, 191-200\njanitor/ml.py 15 8 47% 60-71\njanitor/spark/__init__.py 1 1 0% 1\njanitor/spark/backend.py 37 37 0% 3-102\njanitor/spark/functions.py 30 30 0% 5-182\njanitor/testing_utils/__init__.py 0 0 100%\njanitor/testing_utils/date_data.py 1 0 100%\njanitor/testing_utils/strategies.py 12 12 0% 5-69\njanitor/timeseries.py 49 30 39% 46-60, 92, 96-97, 104-107, 132-147, 170-176\njanitor/utils.py 164 129 21% 29-36, 54-60, 73-101, 105-106, 110-111, 133-136, 143-155, 177-202, 217-220, 250-251, 282-283, 304-314, 335-343, 366-372, 400-408, 423-470, 490-539\njanitor/xarray/__init__.py 1 0 100%\njanitor/xarray/functions.py 16 7 56% 71-86, 128-130\n-------------------------------------------------------------------\nTOTAL 1354 1064 21%\n=========================== short test summary info ============================\nPASSED tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically\nPASSED tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_decreasing\nFAILED tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_strict\n=================== 1 failed, 2 passed, 6 warnings in 1.26s ====================\n","stderr_tail":"","stdout_sha256":"21c3677409547fbac6ae72a059d9c1708edb7ca42e54b1e705e8a292fc11ccbd","log_path":"~/dev/nebius-hackathon/pilot/v2-P/ericmjl__pyjanitor-728/runs/pilot-ericmjl__pyjanitor-728-65b2a91a53/logs/agent1/agent1-18.log","tree_digest":"97c21e20971eb87c62a6f0fccd52de239f8324c82dea315dc382861e55a30770","tree_download_s":16.135632042074576,"started_at":"2026-10-03T20:17:29.497289Z","duration_s":5.1019890000000006,"timed_out":false,"image_before":"b5ef404e-b6b7-4d4f-80a1-331c4e2dd10f","image_after":"3aa4e618-99e5-40ca-b8bb-aeaa715b1fb9","junit":{"passed":["tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically","tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_decreasing"],"failed":["tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_strict"],"errors":[],"skipped":[],"collection_errors":[],"failure_text":{"tests/timeseries/test_sort_timestamps_monotonically.py::test_sort_timestamps_monotonically_strict":"[...]ject has no attribute 'append'. Did you mean: '_append'?\ntimeseries_dataframe = v1\n2019-01-01 00:00:00 644\n2019-01-01 01:00:00 768\n2019-01-01 02:00:00 1824\n2019-01-01 03:...1 12:00:00 448\n2019-02-11 13:00:00 944\n2019-02-11 14:00:00 1927\n2019-02-11 15:00:00 207\n\n[1000 rows x 1 columns]\n\n @pytest.mark.timeseries\n def test_sort_timestamps_monotonically_strict(timeseries_dataframe):\n \"\"\"Test sort_timestamps_monotonically for index duplication handling\"\"\"\n df = timeseries_dataframe.shuffle(reset_index=False)\n random_number = randint(1, len(timeseries_dataframe))\n> df = df.append(\n df.loc[df.index[random_number], :]\n ).sort_timestamps_monotonically(direction=\"increasing\", strict=True)\n\ntests/timeseries/test_sort_timestamps_monotonically.py:49: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = v1\n2019-01-26 05:00:00 1273\n2019-01-27 07:00:00 1456\n2019-02-10 14:00:00 509\n2019-01-09 10:...9 15:00:00 395\n2019-01-07 09:00:00 128\n2019-01-24 02:00:00 1767\n2019-01-22 22:00:00 1967\n\n[1000 rows x 1 columns]\nname = 'append'\n\n @final\n def __getattr__(self, name: str):\n \"\"\"\n After regular attribute access, try looking up the name\n This allows simpler access to columns for interactive use.\n \"\"\"\n # Note: obj.x will always call obj.__getattribute__('x') prior to\n # calling obj.__getattr__('x').\n if (\n name not in self._internal_names_set\n and name not in self._metadata\n and name not in self._accessors\n and self._info_axis._can_hold_identifiers_and_holds_name(name)\n ):\n return self[name]\n> return object.__getattribute__(self, name)\nE AttributeError: 'DataFrame' object has no attribute 'append'. Did you mean: '_append'?\n\n/usr/local/lib/python3.10/site-packages/pandas/core/generic.py:6321: AttributeError"}},"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null} {"receipt_id":"agent1-19","seq":19,"branch_id":"agent1","kind":"diff","cmd":"compute patch in executor staging agent1-42be9d087e5741c39352e5ba7e49ea00: /opt/homebrew/Cellar/git/2.53.0/bin/git -c core.hooksPath=/dev/null -c core.attributesFile=/dev/null -c diff.external= -c core.quotePath=false add -A && /opt/homebrew/Cellar/git/2.53.0/bin/git -c core.hooksPath=/dev/null -c core.attributesFile=/dev/null -c diff.external= -c core.quotePath=false diff --cached --binary --no-ext-diff --no-textconv --no-renames f1caf8acebd9a22ac11827c5dd717de1c5362283","path":null,"exit_code":0,"stdout_tail":"diff --git a/janitor/timeseries.py b/janitor/timeseries.py\nindex 5e1a99d..83836b3 100644\n--- a/janitor/timeseries.py\n+++ b/janitor/timeseries.py\n@@ -60,6 +60,93 @@ def fill_missing_timestamps(\n return df.reindex(expected_timestamps)\n \n \n+@pf.register_dataframe_method\n+def sort_timestamps_monotonically(\n+ df: pd.DataFrame,\n+ column_name: str = None,\n+ direction: str = \"increasing\",\n+ strict: bool = False,\n+) -> pd.DataFrame:\n+ \"\"\"\n+ Sorts data frame monotonically.\n+ It assumes the data frame has an index of type pd.DateTimeIndex when index is datetime.\n+ If datetime is in a column, then the column is expected to be of type pd.Timestamp\n+\n+ :param df: data frame to sort monotonically\n+ :param column_name: needs to be specified if and only if the date time is not in index.\n+ Defaults to None\n+ :param direction: specifies the direction in which monotonicity is being tested for.\n+ Defaults to 'increasing'\n+ :param strict: whether to drop duplicates after sorting\n+ :return: data frame with its index sorted\n+ \"\"\"\n+ # Handle case where column_name is actually a direction value\n+ # This supports calling df.sort_timestamps_monotonically(\"decreasing\") \n+ # as shorthand for df.sort_timestamps_monotonically(direction=\"decreasing\")\n+ if column_name is not None and column_name not in df.columns:\n+ if column_name in (\"increasing\", \"decreasing\"):\n+ # Treat as direction instead\n+ direction = column_name\n+ column_name = None\n+ else:\n+ raise KeyError(f\"Column '{column_name}' not found in DataFrame\")\n+ \n+ if column_name is not None:\n+ # Sort by the specified column\n+ ascending = direction == \"increasing\"\n+ result_df = df.sort_values(by=column_name, ascending=ascending)\n+ else:\n+ # Sort by index\n+ ascending = direction == \"increasing\"\n+ result_df = df.sort_index(ascending=ascending)\n+ \n+ if strict:\n+ if column_name is not None:\n+ result_df = result_df.drop_duplicates(subset=[column_name], keep='first')\n+ else:\n+ result_df = result_df[~result_df.index.duplicated(keep='first')]\n+ \n+ return result_df\n+\n+\n+@pf.register_dataframe_method\n+def test_for_monotonicity(\n+ df: pd.DataFrame,\n+ column_name: str = None,\n+ direction: str = \"increasing\",\n+) -> bool:\n+ \"\"\"\n+ Tests input data frame for monotonicity.\n+\n+ Check if the data is monotonically increasing or decreasing.\n+ Direction is dependent on user input.\n+ Defaults to increasing\n+\n+ :param df: data frame to be tested for monotonicity\n+ :param column_name: needs to be specified if and only if the date time is not in index.\n+ Defaults to None.\n+ :param direction: specifies the direction in which monotonicity is being tested for.\n+ Defaults to 'increasing'\n+ :return: single boolean flag indicating whether the test has passed or not\n+ \"\"\"\n+ if column_name is not None:\n+ if column_name not in df.columns:\n+ raise KeyError(f\"Column '{column_name}' not found in DataFrame\")\n+ series = df[column_name]\n+ else:\n+ series = df.index\n+\n+ if not isinstance(series, (pd.DatetimeIndex, pd.Series)):\n+ raise TypeError(\"Series must be DatetimeIndex or Series containing timestamps\")\n+\n+ if direction == \"increasing\":\n+ return series.is_monotonic_increasing\n+ elif direction == \"decreasing\":\n+ return series.is_monotonic_decreasing\n+ else:\n+ raise ValueError(\"Direction must be either 'increasing' or 'decreasing'\")\n+\n+\n def _get_missing_timestamps(\n df: pd.DataFrame,\n frequency: str,\n","stderr_tail":"","stdout_sha256":"57f20a0248f3cf794cdea9e26c8c65a3c1f25040293ba7d575f2aea739b9fb9f","log_path":null,"tree_digest":"97c21e20971eb87c62a6f0fccd52de239f8324c82dea315dc382861e55a30770","tree_download_s":4.121960499789566,"started_at":"2026-10-03T20:18:08.628977Z","duration_s":0.09422808303497732,"timed_out":false,"image_before":"3aa4e618-99e5-40ca-b8bb-aeaa715b1fb9","image_after":"3aa4e618-99e5-40ca-b8bb-aeaa715b1fb9","junit":null,"diff":{"files":[{"path":"janitor/timeseries.py","added":87,"deleted":0,"category":"source","status":"modified"}],"patch_sha256":"57f20a0248f3cf794cdea9e26c8c65a3c1f25040293ba7d575f2aea739b9fb9f","patch_path":"~/dev/nebius-hackathon/pilot/v2-P/ericmjl__pyjanitor-728/runs/pilot-ericmjl__pyjanitor-728-65b2a91a53/artifacts/agent1/agent1-19.patch"},"patch_sha256":"57f20a0248f3cf794cdea9e26c8c65a3c1f25040293ba7d575f2aea739b9fb9f","dropped_paths":[],"snapshot_sha":"f1caf8acebd9a22ac11827c5dd717de1c5362283"}