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ROLLOUT 7 · astanin/python-tabulate

astanin__python-tabulate-268

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454 stotal time

16tool calls

16model calls

7,624tokens generated

153,629prompt tokens read

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System instructions 4,822 characters

You are a principal Python software engineer resolving a repository issue in `/workspace`.
Your sole objective is to produce a minimal, root-cause source patch that passes every hidden fail-to-pass and pass-to-pass regression test on the first grading run.

## Critical Environment & Grader Rules
1. **Air-Gapped Sandbox & Available CLI Tools**: No network access. Never run `pip`, `uv`, `conda`, or `git fetch`. Note: `rg` (`ripgrep`) is NOT installed in the container — always use `grep -rn --include="*.py"` for fast exact searches so you never waste a tool call on `command not found`.
2. **Protected Grading Paths**: Before running official verification, the grading harness **strips and reverts all changes** to `tests/`, `test/`, `testing/`, `test_*.py`, `*_test.py`, `conftest.py`, `pyproject.toml`, `setup.cfg`, `pytest.ini`, and `tox.ini`. Every part of your fix MUST reside in non-test production source files.
3. **Untracked File Trap (`git add -N .`)**: `submit_patch` stages ALL untracked files inside `/workspace`! Never leave reproduction or scratch files inside `/workspace` — run reproductions inline via `python3 - <<'PY'` or inside `/tmp/repro.py` in a single `run_command`.
4. **Stdout Truncation Rule**: `run_command` truncates stdout after the first 5,000 characters from the TOP. Always run pytest quietly with short tracebacks:
`pytest path/to/test_file.py -k pattern -q --tb=short --no-header`
If a test file is large, redirect output to `/tmp/pytest.log` and inspect `tail -n 80 /tmp/pytest.log` so failure tracebacks at the bottom are never cut off.
5. **Strict Pacing Discipline**: `get_status` and `submit_patch` are free and do NOT count toward `max_tool_calls`. Target **16-22 counted tool calls total**, and **begin your first `edit_file` change no later than tool call 15**. Reserve at least 4 calls for verification, `git status --short && git diff`, and submission.

## Execution Protocol

### Phase 1 — Pinpoint Code & Tests Immediately (1-2 tool calls)
- The user message already contains `Workspace Layout` — do NOT waste a turn running `ls` on top-level directories.
- Run a single compound `run_command` with `grep -rn --include="*.py"` to locate:
(a) the exact symbols, error strings, or functions from the problem description,
(b) **all parallel or sibling implementations** (for example sync + async variants, `Client` + `AsyncClient`, serializer + deserializer, multiple backends/dialects, base + subclass methods, `__init__.py` / `__all__` exports), and
(c) the closest existing unit test files under `tests/`.

### Phase 2 — Deep Read & Spec Anchor (2-5 tool calls)
- Use `read_file` with generous `start_line` and `end_line` windows (180-300 lines per call so you read whole modules in fewer calls) on the target function/class, callers, type annotations, and neighboring tests.
- Before editing, verify every explicit and implicit requirement from the issue: exact parameter names, defaults, boundary/domain edge cases, exception types/messages, `None` vs empty collection vs falsy (`0`, `False`, `""`) semantics, mutability/copying, and return types.

### Phase 3 — Reproduce Bug in One Call (1 tool call)
- Run ONE concise command (`python3 - <<'PY' ... PY` or targeted `pytest ... -q --tb=short --no-header`) via `run_command` to observe the exact pre-fix failure. Do NOT spend multiple tool calls writing separate reproduction scripts.

### Phase 4 — Surgical Implementation Across All Parallel Paths (1-4 tool calls)
- Begin editing by tool call 15! Use `edit_file` with 4-6 lines of exact surrounding context copied from `read_file`.
- Update ALL parallel codepaths (sync/async, multiple classes/dialects, type hints) consistently so partial fixes do not fail sibling tests.
- **2-Strike Edit Rule**: If `edit_file` fails twice on the same block due to whitespace or escaping, do not retry `edit_file` a third time — apply an atomic Python transformation via `run_command` and immediately verify `python3 -m py_compile path/to/file.py`.

### Phase 5 — Edge-Case Verification, Diff Hygiene & Clean Exit (2-4 tool calls)
- Run a single compound `run_command` testing both your reproduction + boundary edge cases (`None`, empty input, leading/trailing delimiters, subdomain/prefix boundaries, subclass/async parity) AND the relevant existing `pytest` module(s) with `-q --tb=short --no-header`.
- Run `git status --short && git diff` via `run_command`. Confirm:
- ZERO untracked (`??`) files in `/workspace` (`rm -f` any untracked workspace files immediately),
- ZERO modifications to `tests/` or protected config files (`git checkout -- tests/` if touched),
- Minimal, clean diff with valid imports and indentation.
- Call `submit_patch` once verification passes, then respond with a brief 2-sentence plain-text summary and make NO further tool calls.

Task prompt 2,360 characters

You are evaluating a software engineering task for repository astanin/python-tabulate.

Problem Statement:
SEPARATING_LINE does not work with dataclasses
Using tabulate to print a list of dataclasses when one or more rows is SEPARATING_LINE, produces:

```
File "....\.venv\lib\site-packages\tabulate\__init__.py", line 1451, in <listcomp>
rows = [[getattr(row, f) for f in field_names] for row in rows ]
AttributeError: 'str' object has no attribute '<the first attribute of dataclass>'
```

Can be assigned to me, I have a fix.

## Task Budget (Session terminates when any budget is exhausted)
- Time allowance: 7.5 minutes
- Tool calls allowance: 30 calls
- Max loop iterations: 40 turns

## Execution Environment Rules
- Single command timeout: 90 seconds (commands exceeding this fail without ending the session)
- Command output limit: 5000 characters
- File view limit: 150 lines per read_file call
- File character limit: 10000 characters per read_file call
- Environment is offline (no network/PyPI access). All repository and test dependencies are ALREADY pre-installed. Do NOT attempt to run pip install or download packages.

## Instructions:
0. All source code is under `/workspace`. Do NOT search outside `/workspace` (e.g. `/usr/`, `/wheels/`, system site-packages). If imports fail, the issue is in the source code under `/workspace`, not in missing system packages.
1. Analyze the problem statement and any provided hints carefully to identify all requested script paths, CLI subcommands, or Python modules.
2. Inspect existing codebase conventions and test files before making edits.
3. Verify your implementation using targeted tests or inline assertions before submitting.
4. Call submit_patch only after your implementation is complete and verified.
5. As your final action, you must return a text-only response reporting your completion to terminate the session.
## Workspace Layout
The repository is located at `/workspace`. Here is the directory tree (up to 3 levels):
```
./CHANGELOG
./HOWTOPUBLISH
./LICENSE
./MANIFEST.in
./README
./README.md
./appveyor.yml
./benchmark.py
./pyproject.toml
./tabulate
./tabulate/__init__.py
./test
./test/common.py
./test/test_api.py
./test/test_cli.py
./test/test_input.py
./test/test_internal.py
./test/test_output.py
./test/test_regression.py
./test/test_textwrapper.py
./tox.ini
```

Turn 1at 19 s320 tokens out2,665 prompt tokens

The user is reporting an `AttributeError: 'str' object has no attribute '...'` when using `tabulate` with a list of dataclasses and `SEPARATING_LINE`.

Looking at the error message:
`File "....\.venv\lib\site-packages\tabulate/__init__.py", line 1451, in <listcomp>`
`rows = [[getattr(row, f) for f in field_names] for row in rows ]`

This suggests that when `tabulate` is processing a list of dataclasses, it expects every element in `rows` to be a dataclass (or have the attributes found in `field_names`). However, if `SEPARATING_LINE` (which seems to be a string or a special marker) is inserted into the list of rows, the code fails because it tries to call `getattr` on that string.

I need to:
1. Locate the code in `tabulate/__init__.py` around line 1451.
2. Create a reproduction script that uses dataclasses and `SEPARATING_LINE`.
3. Fix the logic to handle `SEPARATING_LINE` (or other non-dataclass objects) when the input is a list of dataclasses.

Let's start by exploring the code and reproducing the issue.

TOOL CALL · run_command

{
  "command": "grep -rn --include=\"*.py\" \"SEPARATING_LINE\" ."
}

Result of run_command 1,375 characters

{
  "status": "ok",
  "stdout": "./tabulate/__init__.py:51:SEPARATING_LINE = \"\\001\"\n./tabulate/__init__.py:108:        (len(row) >= 1 and row[0] == SEPARATING_LINE)\n./tabulate/__init__.py:109:        or (len(row) >= 2 and row[1] == SEPARATING_LINE)\n./tabulate/__init__.py:1290:            rows.insert(index, SEPARATING_LINE)\n./tabulate/__init__.py:2453:            # the SEPARATING_LINE flag\n./test/test_output.py:5:from tabulate import tabulate, simple_separated_format, SEPARATING_LINE\n./test/test_output.py:12:_test_table_with_sep_line = [[\"spam\", 41.9999], SEPARATING_LINE, [\"eggs\", \"451.0\"]]\n./test/test_output.py:118:        SEPARATING_LINE,\n./test/test_output.py:261:    table = [[\"Earth\", 6371], [\"Mars\", 3390], SEPARATING_LINE, [\"Moon\", 1737]]\n./test/test_output.py:307:        SEPARATING_LINE,\n./test/test_output.py:2676:        [[\"one\", \"two\"], SEPARATING_LINE, [\"three\", \"four\"]],\n./test/test_output.py:2921:    dd = [(0, 101), SEPARATING_LINE, (1, 102), (2, 103)]\n./test/test_internal.py:318:        T.SEPARATING_LINE,\n./test/test_internal.py:320:        T.SEPARATING_LINE,\n./test/test_internal.py:322:        T.SEPARATING_LINE,\n./test/test_internal.py:335:        T.SEPARATING_LINE,\n./test/test_internal.py:337:        T.SEPARATING_LINE,\n./test/test_internal.py:339:        T.SEPARATING_LINE,\n",
  "stderr": "",
  "exit_code": 0
}

Generated tokens 320 tokens · stopped: STOP

<|channel>thought The user is reporting an `AttributeError: 'str' object has no attribute '...'` when using `tabulate` with a list of dataclasses and `SEPARATING_LINE`. Looking at the error message: `File "....\.venv\lib\site-packages\tabulate/__init__.py", line 1451, in <listcomp>` `rows = [[getattr(row, f) for f in field_names] for row in rows ]` This suggests that when `tabulate` is processing a list of dataclasses, it expects every element in `rows` to be a dataclass (or have the attributes found in `field_names`). However, if `SEPARATING_LINE` (which seems to be a string or a special marker) is inserted into the list of rows, the code fails because it tries to call `getattr` on that string. I need to: 1. Locate the code in `tabulate/__init__.py` around line 1451. 2. Create a reproduction script that uses dataclasses and `SEPARATING_LINE`. 3. Fix the logic to handle `SEPARATING_LINE` (or other non-dataclass objects) when the input is a list of dataclasses. Let's start by exploring the code and reproducing the issue.<channel|><|tool_call>call:run_command{command:<|"|>grep -rn --include="*.py" "SEPARATING_LINE" .<|"|>}<tool_call|><|tool_response>

Turn 2at 26 s108 tokens out3,576 prompt tokens

`SEPARATING_LINE` is defined as `"\001"` in `tabulate/__init__.py`.

Now let's find the code that causes the `AttributeError`. The problem statement mentioned line 1451 in `tabulate/__init__.py`. Let's examine that part of the file.

TOOL CALL · read_file

{
  "end_line": 1470,
  "filepath": "tabulate/__init__.py",
  "start_line": 1430
}

Result of read_file 1,841 characters

{
  "status": "ok",
  "filepath": "tabulate/__init__.py",
  "content": "                # a dict of headers for a list of dicts\n                headers = [headers.get(k, k) for k in keys]\n                headers = list(map(str, headers))\n            elif headers == \"firstrow\":\n                if len(rows) > 0:\n                    headers = [firstdict.get(k, k) for k in keys]\n                    headers = list(map(str, headers))\n                else:\n                    headers = []\n            elif headers:\n                raise ValueError(\n                    \"headers for a list of dicts is not a dict or a keyword\"\n                )\n            rows = [[row.get(k) for k in keys] for row in rows]\n\n        elif (\n            headers == \"keys\"\n            and hasattr(tabular_data, \"description\")\n            and hasattr(tabular_data, \"fetchone\")\n            and hasattr(tabular_data, \"rowcount\")\n        ):\n            # Python Database API cursor object (PEP 0249)\n            # print tabulate(cursor, headers='keys')\n            headers = [column[0] for column in tabular_data.description]\n\n        elif (\n            dataclasses is not None\n            and len(rows) > 0\n            and dataclasses.is_dataclass(rows[0])\n        ):\n            # Python 3.7+'s dataclass\n            field_names = [field.name for field in dataclasses.fields(rows[0])]\n            if headers == \"keys\":\n                headers = field_names\n            rows = [[getattr(row, f) for f in field_names] for row in rows]\n\n        elif headers == \"keys\" and len(rows) > 0:\n            # keys are column indices\n            headers = list(map(str, range(len(rows[0]))))\n\n    # take headers from the first row if necessary",
  "start_line": 1430,
  "end_line": 1470,
  "total_lines": 2787,
  "is_truncated": false
}

Generated tokens 108 tokens · stopped: STOP

`SEPARATING_LINE` is defined as `"\001"` in `tabulate/__init__.py`. Now let's find the code that causes the `AttributeError`. The problem statement mentioned line 1451 in `tabulate/__init__.py`. Let's examine that part of the file.<channel|><|tool_call>call:read_file{end_line:1470,filepath:<|"|>tabulate/__init__.py<|"|>,start_line:1430}<tool_call|><|tool_response>

Turn 3at 49 s333 tokens out4,202 prompt tokens

The problematic code is here:

```python
        elif (
            dataclasses is not None
            and len(rows) > 0
            and dataclasses.is_dataclass(rows[0])
        ):
            # Python 3.7+'s dataclass
            field_names = [field.name for field in dataclasses.fields(rows[0])]\n            if headers == "keys":
                headers = field_names
            rows = [[getattr(row, f) for f in field_names] for row in rows]
```

When `rows[0]` is a dataclass, it assumes ALL subsequent `row` in `rows` are also dataclasses. If one of them is `SEPARATING_LINE` (which is a string), `getattr(row, f)` fails because strings don't have the attributes of the dataclass.

Let's reproduce this with a script.

TOOL CALL · run_command

{
  "command": "python3 - <<'PY'\nfrom dataclasses import dataclass\nfrom tabulate import tabulate, SEPARATING_LINE\n\n@dataclass\nclass Person:\n    name: str\n    age: int\n\ndata = [\n    Person(\"Alice\", 30),\n    SEPARATING_LINE,\n    Person(\"Bob\", 25)\n]\n\ntry:\n    print(tabulate(data, headers=\"keys\"))\nexcept Exception as e:\n    import traceback\n    traceback.print_exc()\nPY\n"
}

Result of run_command 758 characters

{
  "status": "ok",
  "stdout": "",
  "stderr": "Traceback (most recent call last):\n  File \"<stdin>\", line 16, in <module>\n  File \"/workspace/tabulate/__init__.py\", line 2088, in tabulate\n    list_of_lists, headers, headers_pad = _normalize_tabular_data(\n  File \"/workspace/tabulate/__init__.py\", line 1464, in _normalize_tabular_data\n    rows = [[getattr(row, f) for f in field_names] for row in rows]\n  File \"/workspace/tabulate/__init__.py\", line 1464, in <listcomp>\n    rows = [[getattr(row, f) for f in field_names] for row in rows]\n  File \"/workspace/tabulate/__init__.py\", line 1464, in <listcomp>\n    rows = [[getattr(row, f) for f in field_names] for row in rows]\nAttributeError: 'str' object has no attribute 'name'\n",
  "exit_code": 0
}

Generated tokens 333 tokens · stopped: STOP

The problematic code is here: ```python elif ( dataclasses is not None and len(rows) > 0 and dataclasses.is_dataclass(rows[0]) ): # Python 3.7+'s dataclass field_names = [field.name for field in dataclasses.fields(rows[0])]\n if headers == "keys": headers = field_names rows = [[getattr(row, f) for f in field_names] for row in rows] ``` When `rows[0]` is a dataclass, it assumes ALL subsequent `row` in `rows` are also dataclasses. If one of them is `SEPARATING_LINE` (which is a string), `getattr(row, f)` fails because strings don't have the attributes of the dataclass. Let's reproduce this with a script. <channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY' from dataclasses import dataclass from tabulate import tabulate, SEPARATING_LINE @dataclass class Person: name: str age: int data = [ Person("Alice", 30), SEPARATING_LINE, Person("Bob", 25) ] try: print(tabulate(data, headers="keys")) except Exception as e: import traceback traceback.print_exc() PY <|"|>}<tool_call|><|tool_response>

Turn 4at 113 s949 tokens out4,809 prompt tokens

The reproduction was successful.

Now I need to fix it. The fix should involve checking if the current `row` is a dataclass before calling `getattr`. If it's `SEPARATING_LINE`, it should probably be preserved as is.

Wait, the `rows` list in `_normalize_tabular_data` is used to create a list of lists.

If the row is `SEPARATING_LINE`, we should keep it as `SEPARATING_LINE` instead of trying to extract fields from it.

Let's see how other parts of `_normalize_tabular_data` handle `SEPARATING_LINE`.

Looking at the provided grep results:
`./tabulate/__init__.py:108:        (len(row) >= 1 and row[0] == SEPARATING_LINE)`
`./tabulate/__init__.py:109:        or (len(row) >= 2 and row[1] == SEPARATING_LINE)`

These are in different parts of the file. Let's check `_normalize_tabular_data` more broadly.

Actually, the code for dataclasses is:
```python
        elif (
            dataclasses is not None
            and len(rows) > 0
            and dataclasses.is_dataclass(rows[0])
        ):
            # Python 3.7+'s dataclass
            field_names = [field.name for field in dataclasses.fields(rows[0])]\n            if headers == "keys":
                headers = field_names
            rows = [[getattr(row, f) for f in field_names] for row in rows]
```

I should change `rows = [[getattr(row, f) for f in field_names] for row in rows]` to something that handles `SEPARATING_LINE`.

If `row == SEPARATING_LINE`, we should just keep `row` (the string `"\001"`).
Wait, if `rows` is intended to be a list of lists, and we just put `SEPARATING_LINE` in there, will it work downstream?

Let's see what happen if we have a list of lists and one of the lists is `SEPARATING_LINE`.
Wait, `SEPARATING_LINE` is a string.

If `rows` is `[[ "Alice", 30 ], SEPARATING_LINE, [ "Bob", 25 ]]`, that is a valid state for `rows` before it gets processed further.

So the fix should be:
```python
            rows = [
                [getattr(row, f) for f in field_names] if not (isinstance(row, str) and row == SEPARATING_LINE) else row
                for row in rows
            ]
```
Wait, is `SEPARATING_LINE` always a string? Yes, `SEPARATING_LINE = "\001"`.

However, what if the row is not a string but also not a dataclass?
The original code assumes if `rows[0]` is a dataclass, all other rows are too (or should be treated as such).

Let's see if there are other cases where `SEPARATING_LINE` is used.

If I use:
```python
            rows = [
                [getattr(row, f) for f in field_names] if not (isinstance(row, str) and row == SEPARATING_LINE) else row
                for row in rows
            ]
```
This looks correct for handling `SEPARATING_LINE`.

But wait, let's check if `row` can be something else.

If I have a list of dataclasses, and I accidentally put a string that isn't `SEPARATING_LINE`, it will still fail. That's probably acceptable.

Let's see where else `SEPARATING_LINE` is handled in `_normalize_tabular_data`.

Wait, `_normalize_tabular_data` is likely where the data is converted into a list of lists.

Let's look at the `_normalize_tabular_data` function.

TOOL CALL · read_file

{
  "end_line": 1470,
  "filepath": "tabulate/__init__.py",
  "start_line": 1350
}

Result of read_file 5,128 characters

{
  "status": "ok",
  "filepath": "tabulate/__init__.py",
  "content": "    If showindex is an iterable, show its values as row indices.\n\n    \"\"\"\n\n    try:\n        bool(headers)\n        is_headers2bool_broken = False  # noqa\n    except ValueError:  # numpy.ndarray, pandas.core.index.Index, ...\n        is_headers2bool_broken = True  # noqa\n        headers = list(headers)\n\n    index = None\n    if hasattr(tabular_data, \"keys\") and hasattr(tabular_data, \"values\"):\n        # dict-like and pandas.DataFrame?\n        if hasattr(tabular_data.values, \"__call__\"):\n            # likely a conventional dict\n            keys = tabular_data.keys()\n            rows = list(\n                izip_longest(*tabular_data.values())\n            )  # columns have to be transposed\n        elif hasattr(tabular_data, \"index\"):\n            # values is a property, has .index => it's likely a pandas.DataFrame (pandas 0.11.0)\n            keys = list(tabular_data)\n            if (\n                showindex in [\"default\", \"always\", True]\n                and tabular_data.index.name is not None\n            ):\n                if isinstance(tabular_data.index.name, list):\n                    keys[:0] = tabular_data.index.name\n                else:\n                    keys[:0] = [tabular_data.index.name]\n            vals = tabular_data.values  # values matrix doesn't need to be transposed\n            # for DataFrames add an index per default\n            index = list(tabular_data.index)\n            rows = [list(row) for row in vals]\n        else:\n            raise ValueError(\"tabular data doesn't appear to be a dict or a DataFrame\")\n\n        if headers == \"keys\":\n            headers = list(map(str, keys))  # headers should be strings\n\n    else:  # it's a usual iterable of iterables, or a NumPy array, or an iterable of dataclasses\n        rows = list(tabular_data)\n\n        if headers == \"keys\" and not rows:\n            # an empty table (issue #81)\n            headers = []\n        elif (\n            headers == \"keys\"\n            and hasattr(tabular_data, \"dtype\")\n            and getattr(tabular_data.dtype, \"names\")\n        ):\n            # numpy record array\n            headers = tabular_data.dtype.names\n        elif (\n            headers == \"keys\"\n            and len(rows) > 0\n            and isinstance(rows[0], tuple)\n            and hasattr(rows[0], \"_fields\")\n        ):\n            # namedtuple\n            headers = list(map(str, rows[0]._fields))\n        elif len(rows) > 0 and hasattr(rows[0], \"keys\") and hasattr(rows[0], \"values\"):\n            # dict-like object\n            uniq_keys = set()  # implements hashed lookup\n            keys = []  # storage for set\n            if headers == \"firstrow\":\n                firstdict = rows[0] if len(rows) > 0 else {}\n                keys.extend(firstdict.keys())\n                uniq_keys.update(keys)\n                rows = rows[1:]\n            for row in rows:\n                for k in row.keys():\n                    # Save unique items in input order\n                    if k not in uniq_keys:\n                        keys.append(k)\n                        uniq_keys.add(k)\n            if headers == \"keys\":\n                headers = keys\n            elif isinstance(headers, dict):\n                # a dict of headers for a list of dicts\n                headers = [headers.get(k, k) for k in keys]\n                headers = list(map(str, headers))\n            elif headers == \"firstrow\":\n                if len(rows) > 0:\n                    headers = [firstdict.get(k, k) for k in keys]\n                    headers = list(map(str, headers))\n                else:\n                    headers = []\n            elif headers:\n                raise ValueError(\n                    \"headers for a list of dicts is not a dict or a keyword\"\n                )\n            rows = [[row.get(k) for k in keys] for row in rows]\n\n        elif (\n            headers == \"keys\"\n            and hasattr(tabular_data, \"description\")\n            and hasattr(tabular_data, \"fetchone\")\n            and hasattr(tabular_data, \"rowcount\")\n        ):\n            # Python Database API cursor object (PEP 0249)\n            # print tabulate(cursor, headers='keys')\n            headers = [column[0] for column in tabular_data.description]\n\n        elif (\n            dataclasses is not None\n            and len(rows) > 0\n            and dataclasses.is_dataclass(rows[0])\n        ):\n            # Python 3.7+'s dataclass\n            field_names = [field.name for field in dataclasses.fields(rows[0])]\n            if headers == \"keys\":\n                headers = field_names\n            rows = [[getattr(row, f) for f in field_names] for row in rows]\n\n        elif headers == \"keys\" and len(rows) > 0:\n            # keys are column indices\n            headers = list(map(str, range(len(rows[0]))))\n\n    # take headers from the first row if necessary",
  "start_line": 1350,
  "end_line": 1470,
  "total_lines": 2787,
  "is_truncated": false
}

Generated tokens 949 tokens · stopped: STOP

The reproduction was successful. Now I need to fix it. The fix should involve checking if the current `row` is a dataclass before calling `getattr`. If it's `SEPARATING_LINE`, it should probably be preserved as is. Wait, the `rows` list in `_normalize_tabular_data` is used to create a list of lists. If the row is `SEPARATING_LINE`, we should keep it as `SEPARATING_LINE` instead of trying to extract fields from it. Let's see how other parts of `_normalize_tabular_data` handle `SEPARATING_LINE`. Looking at the provided grep results: `./tabulate/__init__.py:108: (len(row) >= 1 and row[0] == SEPARATING_LINE)` `./tabulate/__init__.py:109: or (len(row) >= 2 and row[1] == SEPARATING_LINE)` These are in different parts of the file. Let's check `_normalize_tabular_data` more broadly. Actually, the code for dataclasses is: ```python elif ( dataclasses is not None and len(rows) > 0 and dataclasses.is_dataclass(rows[0]) ): # Python 3.7+'s dataclass field_names = [field.name for field in dataclasses.fields(rows[0])]\n if headers == "keys": headers = field_names rows = [[getattr(row, f) for f in field_names] for row in rows] ``` I should change `rows = [[getattr(row, f) for f in field_names] for row in rows]` to something that handles `SEPARATING_LINE`. If `row == SEPARATING_LINE`, we should just keep `row` (the string `"\001"`). Wait, if `rows` is intended to be a list of lists, and we just put `SEPARATING_LINE` in there, will it work downstream? Let's see what happen if we have a list of lists and one of the lists is `SEPARATING_LINE`. Wait, `SEPARATING_LINE` is a string. If `rows` is `[[ "Alice", 30 ], SEPARATING_LINE, [ "Bob", 25 ]]`, that is a valid state for `rows` before it gets processed further. So the fix should be: ```python rows = [ [getattr(row, f) for f in field_names] if not (isinstance(row, str) and row == SEPARATING_LINE) else row for row in rows ] ``` Wait, is `SEPARATING_LINE` always a string? Yes, `SEPARATING_LINE = "\001"`. However, what if the row is not a string but also not a dataclass? The original code assumes if `rows[0]` is a dataclass, all other rows are too (or should be treated as such). Let's see if there are other cases where `SEPARATING_LINE` is used. If I use: ```python rows = [ [getattr(row, f) for f in field_names] if not (isinstance(row, str) and row == SEPARATING_LINE) else row for row in rows ] ``` This looks correct for handling `SEPARATING_LINE`. But wait, let's check if `row` can be something else. If I have a list of dataclasses, and I accidentally put a string that isn't `SEPARATING_LINE`, it will still fail. That's probably acceptable. Let's see where else `SEPARATING_LINE` is handled in `_normalize_tabular_data`. Wait, `_normalize_tabular_data` is likely where the data is converted into a list of lists. Let's look at the `_normalize_tabular_data` function. <channel|><|tool_call>call:read_file{end_line:1470,filepath:<|"|>tabulate/__init__.py<|"|>,start_line:1350}<tool_call|><|tool_response>

Turn 5at 126 s212 tokens out7,137 prompt tokens

I see that other data types also have similar transformations. For example, the dict-like objects:
```python
            rows = [[row.get(k) for k in keys] for row in rows]
```
If I have a list of dicts and one of them is `SEPARATING_LINE`, it would also fail with `AttributeError: 'str' object has no attribute 'get'`.

Let's test this.

TOOL CALL · run_command

{
  "command": "python3 - <<'PY'\nfrom tabulate import tabulate, SEPARATING_LINE\n\ndata = [\n    {\"name\": \"Alice\", \"age\": 30},\n    SEPARATING_LINE,\n    {\"name\": \"Bob\", \"age\": 25}\n]\n\ntry:\n    print(tabulate(data, headers=\"keys\"))\nexcept Exception as e:\n    import traceback\n    traceback.print_exc()\nPY\n"
}

Result of run_command 437 characters

{
  "status": "ok",
  "stdout": "",
  "stderr": "Traceback (most recent call last):\n  File \"<stdin>\", line 10, in <module>\n  File \"/workspace/tabulate/__init__.py\", line 2088, in tabulate\n    list_of_lists, headers, headers_pad = _normalize_tabular_data(\n  File \"/workspace/tabulate/__init__.py\", line 1422, in _normalize_tabular_data\n    for k in row.keys():\nAttributeError: 'str' object has no attribute 'keys'\n",
  "exit_code": 0
}

Generated tokens 212 tokens · stopped: STOP

I see that other data types also have similar transformations. For example, the dict-like objects: ```python rows = [[row.get(k) for k in keys] for row in rows] ``` If I have a list of dicts and one of them is `SEPARATING_LINE`, it would also fail with `AttributeError: 'str' object has no attribute 'get'`. Let's test this. <channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY' from tabulate import tabulate, SEPARATING_LINE data = [ {"name": "Alice", "age": 30}, SEPARATING_LINE, {"name": "Bob", "age": 25} ] try: print(tabulate(data, headers="keys")) except Exception as e: import traceback traceback.print_exc() PY <|"|>}<tool_call|><|tool_response>

Turn 6at 243 s2,121 tokens out7,512 prompt tokens

Wait, the failure happened even earlier for dicts:
```python
            for row in rows:
                for k in row.keys():
                    # Save unique items in input order
                    if k not in uniq_keys:
                        keys.append(k)
                        uniq_keys.add(k)
```
At line 1422, it calls `row.keys()` on `SEPARATING_LINE`.

Wait, the original issue report specifically mentioned dataclasses, but it seems that any structure that uses this optimization for headers/columns and is mixed with `SEPARATING_LINE` will fail.

Actually, let's look at the `tabulate` source code again.

In many other places, `SEPARATING_LINE` is checked.

If I have a list of lists, it works.
If I have a list of tuples, it works.

The issue is when the input is something that `tabulate` tries to "normalize" by treating as a collection of objects with the same structure (dicts, dataclasses, etc.).

Wait, if the user provides `SEPARATING_LINE` in a list of dataclasses, it's likely they want to use the `SEPARATING_LINE` feature of tabulate.

The `SEPARATING_LINE` is a special marker.

Let's check if there's a general way `tabulate` handles this.

Looking at `_normalize_tabular_data` again.

If `tabular_data` is a list:
```python
    else:  # it's a usual iterable of iterables, or a NumPy array, or an iterable of dataclasses
        rows = list(tabular_data)
```

Then it goes into several `if/elif` blocks to determine if it's a list of dicts, dataclasses, etc.

If it's a list of dicts, it tries to find all keys.

If the user intended to use `SEPARATING_LINE`, it should probably be ignored during this "structure discovery" phase and preserved in the final `rows`.

Let's see how to fix it for both dicts and dataclasses.

For dicts:
```python
            for row in rows:
                if row == SEPARATING_LINE:
                    continue
                for k in row.keys():
                    # Save unique items in input order
                    if k not in uniq_keys:
                        keys.append(k)
                        uniq_keys.add(k)
```
And later:
```python
            rows = [[row.get(k) for k in keys] if row != SEPARATING_LINE else row for row in rows]
```
Wait, if `row == SEPARATING_LINE`, we should just keep it as is.

For dataclasses:
```python
            rows = [[getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row for row in rows]
```

Let's check if `row == SEPARATING_LINE` is sufficient. `SEPARATING_LINE` is a string. If `row` is a dataclass, `row == SEPARATING_LINE` will be `False`.

Let's check if this is the correct approach.

Wait, what if the user has a list of lists, and one of the lists contains `SEPARATING_LINE` as its first element?
The code has this:
```python
./tabulate/__init__.py:108:        (len(row) >= 1 and row[0] == SEPARATING_LINE)
./tabulate/__init__.py:109:        or (len(row) >= 2 and row[1] == SEPARATING_LINE)
```
This is in a different function, probably `_print_row` or something.

If I change the normalization for dicts and dataclasses, I should make sure I don't break anything else.

Let's see where `SEPARATING_LINE` is used in `tabulate/__init__.py`.

The grep shows:
`./tabulate/__init__.py:51:SEPARATING_LINE = "\001"`
`./tabulate/__init__.py:108:        (len(row) >= 1 and row[0] == SEPARATING_LINE)`
`./tabulate/__init__.py:109:        or (len(row) >= 2 and row[1] == SEPARATING_LINE)`
`./tabulate/__init__.py:1290:            rows.insert(index, SEPARATING_LINE)`
`./tabulate/__init__.py:2453:            # the SEPARATING_LINE flag`

It seems `SEPARATING_LINE` is used as a sentinel.

Let's verify if `row == SEPARATING_LINE` is the right way.

If the input is `[ Person("Alice", 30), SEPARATING_LINE, Person("Bob", 25) ]`.
`rows` will be this list.
`rows[0]` is a `Person`.
`dataclasses.is_dataclass(rows[0])` is `True`.
`field_names` will be `['name', 'age']`.
Then we want `rows` to become `[ ['Alice', 30], SEPARATING_LINE, ['Bob', 25] ]`.

This seems correct.

Now let's apply this fix for both dataclasses and dicts (to be thorough).

Wait, the issue report specifically mentions dataclasses. Should I fix dicts too? Yes, because it's the same root cause: the normalization process assumes all rows have the same structure if the first row does.

Let's examine the dict-like part again.

```python
            for row in rows:
                for k in row.keys():
                    # Save unique items in input order
                    if k not in uniq_keys:
                        keys.append(k)
                        uniq_keys.add(k)
```

If I change it to:
```python
            for row in rows:
                if row == SEPARATING_LINE:
                    continue
                for k in row.keys():
                    # Save unique items in input order
                    if k not in uniq_keys:
                        keys.append(k)
                        uniq_keys.add(k)
```

Then later:
```python
            rows = [[row.get(k) for k in keys] if row != SEPARATING_LINE else row for row in rows]
```

And for dataclasses:
```python
            rows = [[getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row for row in rows]
```

Wait, there's a potential issue here. What if `rows` contains something else?
The original code for dataclasses was:
```python
        elif (
            dataclasses is not None
            and len(rows) > 0
            and dataclasses.is_dataclass(rows[0])
        ):
            # Python 3.7+'s dataclass
            field_names = [field.name for field in dataclasses.fields(rows[0])]
            if headers == "keys":
                headers = field_names
            rows = [[getattr(row, f) for f in field_names] for row in rows]
```

Wait, what if the first row is `SEPARATING_LINE`?
If `rows[0] == SEPARATING_LINE`, then `dataclasses.is_dataclass(rows[0])` will be `False`.
So it will skip the dataclass block.

But if `rows[0]` is a dataclass and `rows[1]` is `SEPARATING_LINE`, it enters the block and then fails.

Correct.

What if the user provides a list where only the first row is `SEPARATING_LINE`, and the rest are dataclasses?
`rows[0]` is `SEPARATING_LINE`.
The code will not enter the dataclass block.
It will skip to:
```python
        elif headers == "keys" and len(rows) > 0:
            # keys are column indices
            headers = list(map(str, range(len(rows[0]))))
```
`rows[0]` is `"\001"`, so `len(rows[0])` is 1. `headers` becomes `['0']`.
Then `tabulate` will probably treat the rest of the rows as iterables.

Wait, if the rows are dataclasses, they are not iterable.
So `tabulate` will probably fail later when it tries to iterate over the dataclass.

Actually, let's check if `tabulate` supports dataclasses if they are not the first element.

If I have `[SEPARATING_LINE, Person("Alice", 30)]`, will it work?

TOOL CALL · run_command

{
  "command": "python3 - <<'PY'\nfrom dataclasses import dataclass\nfrom tabulate import tabulate, SEPARATING_LINE\n\n@dataclass\nclass Person:\n    name: str\n    age: int\n\ndata = [\n    SEPARATING_LINE,\n    Person(\"Alice\", 30),\n    Person(\"Bob\", 25)\n]\n\ntry:\n    print(tabulate(data, headers=\"keys\"))\nexcept Exception as e:\n    import traceback\n    traceback.print_exc()\nPY\n"
}

Result of run_command 629 characters

{
  "status": "ok",
  "stdout": "",
  "stderr": "Traceback (most recent call last):\n  File \"<stdin>\", line 16, in <module>\n  File \"/workspace/tabulate/__init__.py\", line 2088, in tabulate\n    list_of_lists, headers, headers_pad = _normalize_tabular_data(\n  File \"/workspace/tabulate/__init__.py\", line 1484, in _normalize_tabular_data\n    rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))\n  File \"/workspace/tabulate/__init__.py\", line 1484, in <lambda>\n    rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))\nTypeError: 'Person' object is not iterable\n",
  "exit_code": 0
}

Generated tokens 2,121 tokens · stopped: STOP

Wait, the failure happened even earlier for dicts: ```python for row in rows: for k in row.keys(): # Save unique items in input order if k not in uniq_keys: keys.append(k) uniq_keys.add(k) ``` At line 1422, it calls `row.keys()` on `SEPARATING_LINE`. Wait, the original issue report specifically mentioned dataclasses, but it seems that any structure that uses this optimization for headers/columns and is mixed with `SEPARATING_LINE` will fail. Actually, let's look at the `tabulate` source code again. In many other places, `SEPARATING_LINE` is checked. If I have a list of lists, it works. If I have a list of tuples, it works. The issue is when the input is something that `tabulate` tries to "normalize" by treating as a collection of objects with the same structure (dicts, dataclasses, etc.). Wait, if the user provides `SEPARATING_LINE` in a list of dataclasses, it's likely they want to use the `SEPARATING_LINE` feature of tabulate. The `SEPARATING_LINE` is a special marker. Let's check if there's a general way `tabulate` handles this. Looking at `_normalize_tabular_data` again. If `tabular_data` is a list: ```python else: # it's a usual iterable of iterables, or a NumPy array, or an iterable of dataclasses rows = list(tabular_data) ``` Then it goes into several `if/elif` blocks to determine if it's a list of dicts, dataclasses, etc. If it's a list of dicts, it tries to find all keys. If the user intended to use `SEPARATING_LINE`, it should probably be ignored during this "structure discovery" phase and preserved in the final `rows`. Let's see how to fix it for both dicts and dataclasses. For dicts: ```python for row in rows: if row == SEPARATING_LINE: continue for k in row.keys(): # Save unique items in input order if k not in uniq_keys: keys.append(k) uniq_keys.add(k) ``` And later: ```python rows = [[row.get(k) for k in keys] if row != SEPARATING_LINE else row for row in rows] ``` Wait, if `row == SEPARATING_LINE`, we should just keep it as is. For dataclasses: ```python rows = [[getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row for row in rows] ``` Let's check if `row == SEPARATING_LINE` is sufficient. `SEPARATING_LINE` is a string. If `row` is a dataclass, `row == SEPARATING_LINE` will be `False`. Let's check if this is the correct approach. Wait, what if the user has a list of lists, and one of the lists contains `SEPARATING_LINE` as its first element? The code has this: ```python ./tabulate/__init__.py:108: (len(row) >= 1 and row[0] == SEPARATING_LINE) ./tabulate/__init__.py:109: or (len(row) >= 2 and row[1] == SEPARATING_LINE) ``` This is in a different function, probably `_print_row` or something. If I change the normalization for dicts and dataclasses, I should make sure I don't break anything else. Let's see where `SEPARATING_LINE` is used in `tabulate/__init__.py`. The grep shows: `./tabulate/__init__.py:51:SEPARATING_LINE = "\001"` `./tabulate/__init__.py:108: (len(row) >= 1 and row[0] == SEPARATING_LINE)` `./tabulate/__init__.py:109: or (len(row) >= 2 and row[1] == SEPARATING_LINE)` `./tabulate/__init__.py:1290: rows.insert(index, SEPARATING_LINE)` `./tabulate/__init__.py:2453: # the SEPARATING_LINE flag` It seems `SEPARATING_LINE` is used as a sentinel. Let's verify if `row == SEPARATING_LINE` is the right way. If the input is `[ Person("Alice", 30), SEPARATING_LINE, Person("Bob", 25) ]`. `rows` will be this list. `rows[0]` is a `Person`. `dataclasses.is_dataclass(rows[0])` is `True`. `field_names` will be `['name', 'age']`. Then we want `rows` to become `[ ['Alice', 30], SEPARATING_LINE, ['Bob', 25] ]`. This seems correct. Now let's apply this fix for both dataclasses and dicts (to be thorough). Wait, the issue report specifically mentions dataclasses. Should I fix dicts too? Yes, because it's the same root cause: the normalization process assumes all rows have the same structure if the first row does. Let's examine the dict-like part again. ```python for row in rows: for k in row.keys(): # Save unique items in input order if k not in uniq_keys: keys.append(k) uniq_keys.add(k) ``` If I change it to: ```python for row in rows: if row == SEPARATING_LINE: continue for k in row.keys(): # Save unique items in input order if k not in uniq_keys: keys.append(k) uniq_keys.add(k) ``` Then later: ```python rows = [[row.get(k) for k in keys] if row != SEPARATING_LINE else row for row in rows] ``` And for dataclasses: ```python rows = [[getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row for row in rows] ``` Wait, there's a potential issue here. What if `rows` contains something else? The original code for dataclasses was: ```python elif ( dataclasses is not None and len(rows) > 0 and dataclasses.is_dataclass(rows[0]) ): # Python 3.7+'s dataclass field_names = [field.name for field in dataclasses.fields(rows[0])] if headers == "keys": headers = field_names rows = [[getattr(row, f) for f in field_names] for row in rows] ``` Wait, what if the first row is `SEPARATING_LINE`? If `rows[0] == SEPARATING_LINE`, then `dataclasses.is_dataclass(rows[0])` will be `False`. So it will skip the dataclass block. But if `rows[0]` is a dataclass and `rows[1]` is `SEPARATING_LINE`, it enters the block and then fails. Correct. What if the user provides a list where only the first row is `SEPARATING_LINE`, and the rest are dataclasses? `rows[0]` is `SEPARATING_LINE`. The code will not enter the dataclass block. It will skip to: ```python elif headers == "keys" and len(rows) > 0: # keys are column indices headers = list(map(str, range(len(rows[0])))) ``` `rows[0]` is `"\001"`, so `len(rows[0])` is 1. `headers` becomes `['0']`. Then `tabulate` will probably treat the rest of the rows as iterables. Wait, if the rows are dataclasses, they are not iterable. So `tabulate` will probably fail later when it tries to iterate over the dataclass. Actually, let's check if `tabulate` supports dataclasses if they are not the first element. If I have `[SEPARATING_LINE, Person("Alice", 30)]`, will it work? <channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY' from dataclasses import dataclass from tabulate import tabulate, SEPARATING_LINE @dataclass class Person: name: str age: int data = [ SEPARATING_LINE, Person("Alice", 30), Person("Bob", 25) ] try: print(tabulate(data, headers="keys")) except Exception as e: import traceback traceback.print_exc() PY <|"|>}<tool_call|><|tool_response>

Turn 7at 275 s610 tokens out9,868 prompt tokens

Yes, it fails. But that's probably expected if the first element is not a dataclass. `tabulate` uses the first element to infer the structure.

So the primary fix is to handle `SEPARATING_LINE` when it occurs *after* the first element.

Now let's implement the fix.

I'll fix both the dataclass and the dict-like parts since they have the same flaw.

For dict-like objects:
1. In the loop that collects keys, skip `SEPARATING_LINE`.
2. In the list comprehension that builds `rows`, handle `SEPARATING_LINE`.

For dataclasses:
1. In the list comprehension that builds `rows`, handle `SEPARATING_LINE`.

Let's re-examine the dict-like section.
```python
            for row in rows:
                for k in row.keys():
                    # Save unique items in input order
                    if k not in uniq_keys:
                        keys.append(k)
                        uniq_keys.add(k)
```
Change to:
```python
            for row in rows:
                if row == SEPARATING_LINE:
                    continue
                for k in row.keys():
                    # Save unique items in input order
                    if k not in uniq_keys:
                        keys.append(k)
                        uniq_keys.add(k)
```

And then:
```python
            rows = [[row.get(k) for k in keys] for row in rows]
```
Change to:
```python
            rows = [[row.get(k) for k in keys] if row != SEPARATING_LINE else row for row in rows]
```

And for dataclasses:
```python
            rows = [[getattr(row, f) for f in field_names] for row in rows]
```
Change to:
```python
            rows = [[getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row for row in rows]
```

Wait, I should check if `row == SEPARATING_LINE` is the best way.
Since `SEPARATING_LINE` is `"\001"`, and `row` could be a dataclass or a dict, `row == "\001"` should be safe and correct.

Actually, let's check if there is a helper function for this.
The error message in the previous run mentioned `_is_separating_line(r)` on line 1484.
Let's see that function.

TOOL CALL · read_file

{
  "end_line": 1500,
  "filepath": "tabulate/__init__.py",
  "start_line": 1470
}

Result of read_file 1,476 characters

{
  "status": "ok",
  "filepath": "tabulate/__init__.py",
  "content": "    # take headers from the first row if necessary\n    if headers == \"firstrow\" and len(rows) > 0:\n        if index is not None:\n            headers = [index[0]] + list(rows[0])\n            index = index[1:]\n        else:\n            headers = rows[0]\n        headers = list(map(str, headers))  # headers should be strings\n        rows = rows[1:]\n    elif headers == \"firstrow\":\n        headers = []\n\n    headers = list(map(str, headers))\n    #    rows = list(map(list, rows))\n    rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))\n\n    # add or remove an index column\n    showindex_is_a_str = type(showindex) in [str, bytes]\n    if showindex == \"default\" and index is not None:\n        rows = _prepend_row_index(rows, index)\n    elif isinstance(showindex, Sized) and not showindex_is_a_str:\n        rows = _prepend_row_index(rows, list(showindex))\n    elif isinstance(showindex, Iterable) and not showindex_is_a_str:\n        rows = _prepend_row_index(rows, showindex)\n    elif showindex == \"always\" or (_bool(showindex) and not showindex_is_a_str):\n        if index is None:\n            index = list(range(len(rows)))\n        rows = _prepend_row_index(rows, index)\n    elif showindex == \"never\" or (not _bool(showindex) and not showindex_is_a_str):\n        pass\n",
  "start_line": 1470,
  "end_line": 1500,
  "total_lines": 2787,
  "is_truncated": false
}

Generated tokens 610 tokens · stopped: STOP

Yes, it fails. But that's probably expected if the first element is not a dataclass. `tabulate` uses the first element to infer the structure. So the primary fix is to handle `SEPARATING_LINE` when it occurs *after* the first element. Now let's implement the fix. I'll fix both the dataclass and the dict-like parts since they have the same flaw. For dict-like objects: 1. In the loop that collects keys, skip `SEPARATING_LINE`. 2. In the list comprehension that builds `rows`, handle `SEPARATING_LINE`. For dataclasses: 1. In the list comprehension that builds `rows`, handle `SEPARATING_LINE`. Let's re-examine the dict-like section. ```python for row in rows: for k in row.keys(): # Save unique items in input order if k not in uniq_keys: keys.append(k) uniq_keys.add(k) ``` Change to: ```python for row in rows: if row == SEPARATING_LINE: continue for k in row.keys(): # Save unique items in input order if k not in uniq_keys: keys.append(k) uniq_keys.add(k) ``` And then: ```python rows = [[row.get(k) for k in keys] for row in rows] ``` Change to: ```python rows = [[row.get(k) for k in keys] if row != SEPARATING_LINE else row for row in rows] ``` And for dataclasses: ```python rows = [[getattr(row, f) for f in field_names] for row in rows] ``` Change to: ```python rows = [[getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row for row in rows] ``` Wait, I should check if `row == SEPARATING_LINE` is the best way. Since `SEPARATING_LINE` is `"\001"`, and `row` could be a dataclass or a dict, `row == "\001"` should be safe and correct. Actually, let's check if there is a helper function for this. The error message in the previous run mentioned `_is_separating_line(r)` on line 1484. Let's see that function. <channel|><|tool_call>call:read_file{end_line:1500,filepath:<|"|>tabulate/__init__.py<|"|>,start_line:1470}<tool_call|><|tool_response>

Turn 8at 278 s45 tokens out10,969 prompt tokens

Let's find where `_is_separating_line` is defined.

TOOL CALL · run_command

{
  "command": "grep -rn \"_is_separating_line\" ."
}

Result of run_command 366 characters

{
  "status": "ok",
  "stdout": "./tabulate/__init__.py:105:def _is_separating_line(row):\n./tabulate/__init__.py:1278:            if _is_separating_line(row):\n./tabulate/__init__.py:1484:    rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))\n./tabulate/__init__.py:2454:            if _is_separating_line(row):\n",
  "stderr": "",
  "exit_code": 0
}

Generated tokens 45 tokens · stopped: STOP

Let's find where `_is_separating_line` is defined. <channel|><|tool_call>call:run_command{command:<|"|>grep -rn "_is_separating_line" .<|"|>}<tool_call|><|tool_response>

Turn 9at 280 s51 tokens out11,170 prompt tokens

Let's read `_is_separating_line`.

TOOL CALL · read_file

{
  "end_line": 112,
  "filepath": "tabulate/__init__.py",
  "start_line": 100
}

Result of read_file 445 characters

{
  "status": "ok",
  "filepath": "tabulate/__init__.py",
  "content": "        \"with_header_hide\",\n    ],\n)\n\n\ndef _is_separating_line(row):\n    row_type = type(row)\n    is_sl = (row_type == list or row_type == str) and (\n        (len(row) >= 1 and row[0] == SEPARATING_LINE)\n        or (len(row) >= 2 and row[1] == SEPARATING_LINE)\n    )\n    return is_sl\n",
  "start_line": 100,
  "end_line": 112,
  "total_lines": 2787,
  "is_truncated": false
}

Generated tokens 51 tokens · stopped: STOP

Let's read `_is_separating_line`. <channel|><|tool_call>call:read_file{end_line:112,filepath:<|"|>tabulate/__init__.py<|"|>,start_line:100}<tool_call|><|tool_response>

Turn 10at 333 s990 tokens out11,412 prompt tokens

Wait, `_is_separating_line` is more complex than I thought!

```python
def _is_separating_line(row):
    row_type = type(row)
    is_sl = (row_type == list or row_type == str) and (
        (len(row) >= 1 and row[0] == SEPARATING_LINE)
        or (len(row) >= 2 and row[1] == SEPARATING_LINE)
    )
    return is_sl
```

If `row` is just the string `"\001"`, then `row_type == str` is true. `len(row)` is 1. `row[0]` is `"\001"`, which is `SEPARATING_LINE`. So it returns `True`.

If `row` is a list `["\001"]` or `["any", "\001"]`, it also returns `True`.

So I should use `_is_separating_line(row)` instead of `row == SEPARATING_LINE`.

Let's review where to apply this.

First, the dict-like part.
```python
            for row in rows:
                for k in row.keys():
                    # Save unique items in input order
                    if k not in uniq_keys:
                        keys.append(k)
                        uniq_keys.add(k)
```
Update:
```python
            for row in rows:
                if _is_separating_line(row):
                    continue
                for k in row.keys():
                    # Save unique items in input order
                    if k not in uniq_keys:
                        keys.append(k)
                        uniq_keys.add(k)
```

Next, the dict-like transformation:
```python
            rows = [[row.get(k) for k in keys] for row in rows]
```
Update:
```python
            rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows]
```

And the dataclass transformation:
```python
            rows = [[getattr(row, f) for f in field_names] for row in rows]
```
Update:
```python
            rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]
```

Let's re-verify if these changes are correct.

If `row` is `SEPARATING_LINE` (which is `"\001"`), `_is_separating_line(row)` is `True`.
If `row` is a dataclass, `_is_separating_line(row)` will be `False` because `row_type` is not `list` or `str`.
If `row` is a dict, `_is_separating_line(row)` will be `False` because `row_type` is not `list` or `str`.

Wait, if `row` is a dict, `type(row)` is `dict`, not `list` or `str`. So it's `False`. Correct.

Wait, let's check one thing: `_is_separating_line` might be called with something that doesn't have `len()`.
But the `is_sl` calculation starts with `(row_type == list or row_type == str)`, and both have `len()`. So it's safe.

Now let's apply these changes.

I'll start with the dict-like part.

File `tabulate/__init__.py` around line 1419.

TOOL CALL · edit_file

{
  "filepath": "tabulate/__init__.py",
  "new_string": "            for row in rows:\n                if _is_separating_line(row):\n                    continue\n                for k in row.keys():\n                    # Save unique items in input order\n                    if k not in uniq_keys:\n                        keys.append(k)\n                        uniq_keys.add(k)",
  "old_string": "            for row in rows:\n                for k in row.keys():\n                    # Save unique items in input order\n                    if k not in uniq_keys:\n                        keys.append(k)\n                        uniq_keys.add(k)"
}

Result of edit_file 530 characters

{
  "status": "ok",
  "filepath": "tabulate/__init__.py",
  "occurrences": 1,
  "strategy": "exact",
  "diff": "--- a/tabulate/__init__.py\n+++ b/tabulate/__init__.py\n@@ -1419,6 +1419,8 @@\n                 uniq_keys.update(keys)\n                 rows = rows[1:]\n             for row in rows:\n+                if _is_separating_line(row):\n+                    continue\n                 for k in row.keys():\n                     # Save unique items in input order\n                     if k not in uniq_keys:\n",
  "is_truncated": false
}

Generated tokens 990 tokens · stopped: STOP

Wait, `_is_separating_line` is more complex than I thought! ```python def _is_separating_line(row): row_type = type(row) is_sl = (row_type == list or row_type == str) and ( (len(row) >= 1 and row[0] == SEPARATING_LINE) or (len(row) >= 2 and row[1] == SEPARATING_LINE) ) return is_sl ``` If `row` is just the string `"\001"`, then `row_type == str` is true. `len(row)` is 1. `row[0]` is `"\001"`, which is `SEPARATING_LINE`. So it returns `True`. If `row` is a list `["\001"]` or `["any", "\001"]`, it also returns `True`. So I should use `_is_separating_line(row)` instead of `row == SEPARATING_LINE`. Let's review where to apply this. First, the dict-like part. ```python for row in rows: for k in row.keys(): # Save unique items in input order if k not in uniq_keys: keys.append(k) uniq_keys.add(k) ``` Update: ```python for row in rows: if _is_separating_line(row): continue for k in row.keys(): # Save unique items in input order if k not in uniq_keys: keys.append(k) uniq_keys.add(k) ``` Next, the dict-like transformation: ```python rows = [[row.get(k) for k in keys] for row in rows] ``` Update: ```python rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows] ``` And the dataclass transformation: ```python rows = [[getattr(row, f) for f in field_names] for row in rows] ``` Update: ```python rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows] ``` Let's re-verify if these changes are correct. If `row` is `SEPARATING_LINE` (which is `"\001"`), `_is_separating_line(row)` is `True`. If `row` is a dataclass, `_is_separating_line(row)` will be `False` because `row_type` is not `list` or `str`. If `row` is a dict, `_is_separating_line(row)` will be `False` because `row_type` is not `list` or `str`. Wait, if `row` is a dict, `type(row)` is `dict`, not `list` or `str`. So it's `False`. Correct. Wait, let's check one thing: `_is_separating_line` might be called with something that doesn't have `len()`. But the `is_sl` calculation starts with `(row_type == list or row_type == str)`, and both have `len()`. So it's safe. Now let's apply these changes. I'll start with the dict-like part. File `tabulate/__init__.py` around line 1419.<channel|><|tool_call>call:edit_file{filepath:<|"|>tabulate/__init__.py<|"|>,new_string:<|"|> for row in rows: if _is_separating_line(row): continue for k in row.keys(): # Save unique items in input order if k not in uniq_keys: keys.append(k) uniq_keys.add(k)<|"|>,old_string:<|"|> for row in rows: for k in row.keys(): # Save unique items in input order if k not in uniq_keys: keys.append(k) uniq_keys.add(k)<|"|>}<tool_call|><|tool_response>

Turn 11at 337 s106 tokens out12,575 prompt tokens

Now the dict-like transformation.
Around line 1442.

TOOL CALL · edit_file

{
  "filepath": "tabulate/__init__.py",
  "new_string": "            rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows]",
  "old_string": "            rows = [[row.get(k) for k in keys] for row in rows]"
}

Result of edit_file 569 characters

{
  "status": "ok",
  "filepath": "tabulate/__init__.py",
  "occurrences": 1,
  "strategy": "exact",
  "diff": "--- a/tabulate/__init__.py\n+++ b/tabulate/__init__.py\n@@ -1442,7 +1442,7 @@\n                 raise ValueError(\n                     \"headers for a list of dicts is not a dict or a keyword\"\n                 )\n-            rows = [[row.get(k) for k in keys] for row in rows]\n+            rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows]\n \n         elif (\n             headers == \"keys\"\n",
  "is_truncated": false
}

Generated tokens 106 tokens · stopped: STOP

Now the dict-like transformation. Around line 1442. <channel|><|tool_call>call:edit_file{filepath:<|"|>tabulate/__init__.py<|"|>,new_string:<|"|> rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows]<|"|>,old_string:<|"|> rows = [[row.get(k) for k in keys] for row in rows]<|"|>}<tool_call|><|tool_response>

Turn 12at 341 s109 tokens out12,875 prompt tokens

Now the dataclass transformation.
Around line 1464.

TOOL CALL · edit_file

{
  "filepath": "tabulate/__init__.py",
  "new_string": "            rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]",
  "old_string": "            rows = [[getattr(row, f) for f in field_names] for row in rows]"
}

Result of edit_file 659 characters

{
  "status": "ok",
  "filepath": "tabulate/__init__.py",
  "occurrences": 1,
  "strategy": "exact",
  "diff": "--- a/tabulate/__init__.py\n+++ b/tabulate/__init__.py\n@@ -1463,7 +1463,7 @@\n             field_names = [field.name for field in dataclasses.fields(rows[0])]\n             if headers == \"keys\":\n                 headers = field_names\n-            rows = [[getattr(row, f) for f in field_names] for row in rows]\n+            rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]\n \n         elif headers == \"keys\" and len(rows) > 0:\n             # keys are column indices\n",
  "is_truncated": false
}

Generated tokens 109 tokens · stopped: STOP

Now the dataclass transformation. Around line 1464. <channel|><|tool_call>call:edit_file{filepath:<|"|>tabulate/__init__.py<|"|>,new_string:<|"|> rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]<|"|>,old_string:<|"|> rows = [[getattr(row, f) for f in field_names] for row in rows]<|"|>}<tool_call|><|tool_response>

Turn 13at 350 s262 tokens out13,210 prompt tokens

Now let's verify the fix with both dataclasses and dicts.

TOOL CALL · run_command

{
  "command": "python3 - <<'PY'\nfrom dataclasses import dataclass\nfrom tabulate import tabulate, SEPARATING_LINE\n\n@dataclass\nclass Person:\n    name: str\n    age: int\n\ndata_dc = [\n    Person(\"Alice\", 30),\n    SEPARATING_LINE,\n    Person(\"Bob\", 25)\n]\n\ndata_dict = [\n    {\"name\": \"Alice\", \"age\": 30},\n    SEPARATING_LINE,\n    {\"name\": \"Bob\", \"age\": 25}\n]\n\nprint(\"Testing dataclasses:\")\ntry:\n    print(tabulate(data_dc, headers=\"keys\"))\n    print(\"Success\")\nexcept Exception as e:\n    import traceback\n    traceback.print_exc()\n\nprint(\"\\nTesting dicts:\")\ntry:\n    print(tabulate(data_dict, headers=\"keys\"))\n    print(\"Success\")\nexcept Exception as e:\n    import traceback\n    traceback.print_exc()\nPY\n"
}

Result of run_command 268 characters

{
  "status": "ok",
  "stdout": "Testing dataclasses:\nname      age\n------  -----\nAlice      30\n------  -----\nBob        25\nSuccess\n\nTesting dicts:\nname      age\n------  -----\nAlice      30\n------  -----\nBob        25\nSuccess\n",
  "stderr": "",
  "exit_code": 0
}

Generated tokens 262 tokens · stopped: STOP

Now let's verify the fix with both dataclasses and dicts. <channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY' from dataclasses import dataclass from tabulate import tabulate, SEPARATING_LINE @dataclass class Person: name: str age: int data_dc = [ Person("Alice", 30), SEPARATING_LINE, Person("Bob", 25) ] data_dict = [ {"name": "Alice", "age": 30}, SEPARATING_LINE, {"name": "Bob", "age": 25} ] print("Testing dataclasses:") try: print(tabulate(data_dc, headers="keys")) print("Success") except Exception as e: import traceback traceback.print_exc() print("\nTesting dicts:") try: print(tabulate(data_dict, headers="keys")) print("Success") except Exception as e: import traceback traceback.print_exc() PY <|"|>}<tool_call|><|tool_response>

Turn 14at 362 s265 tokens out13,582 prompt tokens

The fix works for both dataclasses and dicts.

Wait, let me check the output of the dataclass test.
`name      age`
`------  -----`
`Alice      30`
`------  -----`
`Bob        25`

Wait, the separating line is rendered as a line of dashes. This is expected behavior of `tabulate` when it sees `SEPARATING_LINE`.

Now let's check if it works when the `SEPARATING_LINE` is a list like `[SEPARATING_LINE]`.

TOOL CALL · run_command

{
  "command": "python3 - <<'PY'\nfrom dataclasses import dataclass\nfrom tabulate import tabulate, SEPARATING_LINE\n\n@dataclass\nclass Person:\n    name: str\n    age: int\n\ndata_dc = [\n    Person(\"Alice\", 30),\n    [SEPARATING_LINE],\n    Person(\"Bob\", 25)\n]\n\ntry:\n    print(tabulate(data_dc, headers=\"keys\"))\n    print(\"Success\")\nexcept Exception as e:\n    import traceback\n    traceback.print_exc()\nPY\n"
}

Result of run_command 144 characters

{
  "status": "ok",
  "stdout": "name      age\n------  -----\nAlice      30\n------  -----\nBob        25\nSuccess\n",
  "stderr": "",
  "exit_code": 0
}

Generated tokens 265 tokens · stopped: STOP

The fix works for both dataclasses and dicts. Wait, let me check the output of the dataclass test. `name age` `------ -----` `Alice 30` `------ -----` `Bob 25` Wait, the separating line is rendered as a line of dashes. This is expected behavior of `tabulate` when it sees `SEPARATING_LINE`. Now let's check if it works when the `SEPARATING_LINE` is a list like `[SEPARATING_LINE]`. <channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY' from dataclasses import dataclass from tabulate import tabulate, SEPARATING_LINE @dataclass class Person: name: str age: int data_dc = [ Person("Alice", 30), [SEPARATING_LINE], Person("Bob", 25) ] try: print(tabulate(data_dc, headers="keys")) print("Success") except Exception as e: import traceback traceback.print_exc() PY <|"|>}<tool_call|><|tool_response>

Turn 15at 370 s183 tokens out13,915 prompt tokens

It also works!

One more thing: what if the data is a list of dicts and we have `SEPARATING_LINE` as the first element? We already saw it fails for dataclasses, let's see if it fails for dicts too.

TOOL CALL · run_command

{
  "command": "python3 - <<'PY'\nfrom tabulate import tabulate, SEPARATING_LINE\n\ndata_dict = [\n    SEPARATING_LINE,\n    {\"name\": \"Alice\", \"age\": 30},\n    {\"name\": \"Bob\", \"age\": 25}\n]\n\ntry:\n    print(tabulate(data_dict, headers=\"keys\"))\n    print(\"Success\")\nexcept Exception as e:\n    import traceback\n    traceback.print_exc()\nPY\n"
}

Result of run_command 96 characters

{
  "status": "ok",
  "stdout": "0\n----\n----\nname\nname\nSuccess\n",
  "stderr": "",
  "exit_code": 0
}

Generated tokens 183 tokens · stopped: STOP

It also works! One more thing: what if the data is a list of dicts and we have `SEPARATING_LINE` as the first element? We already saw it fails for dataclasses, let's see if it fails for dicts too. <channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY' from tabulate import tabulate, SEPARATING_LINE data_dict = [ SEPARATING_LINE, {"name": "Alice", "age": 30}, {"name": "Bob", "age": 25} ] try: print(tabulate(data_dict, headers="keys")) print("Success") except Exception as e: import traceback traceback.print_exc() PY <|"|>}<tool_call|><|tool_response>

Turn 16at 431 s960 tokens out14,152 prompt tokens

Wait, it "succeeded" but the output is weird.

```
0
----
----
name
name
```

This is because `rows[0]` is `SEPARATING_LINE` (`"\001"`).
It doesn't match `hasattr(rows[0], "keys")`, so it falls through to:

```python
        elif headers == "keys" and len(rows) > 0:
            # keys are column indices
            headers = list(map(str, range(len(rows[0]))))
```

`len(rows[0])` is 1. `headers` becomes `['0']`.

Then it treats the rest of the rows as iterables.
The dict `{"name": "Alice", "age": 30}` is iterable, and iterating over it gives its keys: `['name', 'age']`.

So the result is weird but it doesn't crash.

This is consistent with how `tabulate` worked before my changes and it's not part of the reported bug (which was about it crashing).

I should double check if there are any other parallel paths.
The `_normalize_tabular_data` function seems to cover the main paths for input data types.

Let's check if there are other places using `getattr` or `get` on `row` in `_normalize_tabular_data` without checking `_is_separating_line`.

Wait, I already checked the dict-like and dataclass paths.

Is there any other path?
Numpy record array:
```python
        elif (
            headers == "keys"
            and hasattr(tabular_data, "dtype")
            and getattr(tabular_data.dtype, "names")
        ):
            # numpy record array
            headers = tabular_data.dtype.names
```
Numpy arrays are homogeneous, so `SEPARATING_LINE` (a string) wouldn't be in a numpy array of dataclasses/dicts unless it's an object array. If it's an object array, `rows = list(tabular_data)` will just make it a list of objects. Then it will probably go into the `headers == "keys" and len(rows) > 0` block at the end.

Wait, if it's a numpy object array, `hasattr(tabular_data, "dtype")` is true.
But then it just sets `headers`. It doesn't transform `rows`.

The only transformations of `rows` in `_normalize_tabular_data` (excluding the index part) are:
1. For dict-like: `rows = [[row.get(k) for k in keys] ...]` (Fixed)
2. For dataclasses: `rows = [[getattr(row, f) for f in field_names] ...]` (Fixed)
3. For everything else: `rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))` (Already handled `SEPARATING_LINE`)

So I think I've covered all the critical paths.

One last check: should I check `_is_separating_line` for the `headers == "firstrow"` case for dicts?

```python
            if headers == "firstrow":
                firstdict = rows[0] if len(rows) > 0 else {}
                keys.extend(firstdict.keys())
                uniq_keys.update(keys)
                rows = rows[1:]
```

If `rows[0]` is `SEPARATING_LINE`, then `firstdict = SEPARATING_LINE`.
Then `firstdict.keys()` will fail because `SEPARATING_LINE` is a string.

Let's test this.

TOOL CALL · run_command

{
  "command": "python3 - <<'PY'\nfrom tabulate import tabulate, SEPARATING_LINE\n\ndata_dict = [\n    SEPARATING_LINE,\n    {\"name\": \"Alice\", \"age\": 30},\n    {\"name\": \"Bob\", \"age\": 25}\n]\n\ntry:\n    print(tabulate(data_dict, headers=\"firstrow\"))\n    print(\"Success\")\nexcept Exception as e:\n    import traceback\n    traceback.print_exc()\nPY\n"
}

Result of run_command 116 characters

{
  "status": "ok",
  "stdout": "      \u0001\n----  ---\nname  age\nname  age\nSuccess\n",
  "stderr": "",
  "exit_code": 0
}

Generated tokens 960 tokens · stopped: STOP

Wait, it "succeeded" but the output is weird. ``` 0 ---- ---- name name ``` This is because `rows[0]` is `SEPARATING_LINE` (`"\001"`). It doesn't match `hasattr(rows[0], "keys")`, so it falls through to: ```python elif headers == "keys" and len(rows) > 0: # keys are column indices headers = list(map(str, range(len(rows[0])))) ``` `len(rows[0])` is 1. `headers` becomes `['0']`. Then it treats the rest of the rows as iterables. The dict `{"name": "Alice", "age": 30}` is iterable, and iterating over it gives its keys: `['name', 'age']`. So the result is weird but it doesn't crash. This is consistent with how `tabulate` worked before my changes and it's not part of the reported bug (which was about it crashing). I should double check if there are any other parallel paths. The `_normalize_tabular_data` function seems to cover the main paths for input data types. Let's check if there are other places using `getattr` or `get` on `row` in `_normalize_tabular_data` without checking `_is_separating_line`. Wait, I already checked the dict-like and dataclass paths. Is there any other path? Numpy record array: ```python elif ( headers == "keys" and hasattr(tabular_data, "dtype") and getattr(tabular_data.dtype, "names") ): # numpy record array headers = tabular_data.dtype.names ``` Numpy arrays are homogeneous, so `SEPARATING_LINE` (a string) wouldn't be in a numpy array of dataclasses/dicts unless it's an object array. If it's an object array, `rows = list(tabular_data)` will just make it a list of objects. Then it will probably go into the `headers == "keys" and len(rows) > 0` block at the end. Wait, if it's a numpy object array, `hasattr(tabular_data, "dtype")` is true. But then it just sets `headers`. It doesn't transform `rows`. The only transformations of `rows` in `_normalize_tabular_data` (excluding the index part) are: 1. For dict-like: `rows = [[row.get(k) for k in keys] ...]` (Fixed) 2. For dataclasses: `rows = [[getattr(row, f) for f in field_names] ...]` (Fixed) 3. For everything else: `rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))` (Already handled `SEPARATING_LINE`) So I think I've covered all the critical paths. One last check: should I check `_is_separating_line` for the `headers == "firstrow"` case for dicts? ```python if headers == "firstrow": firstdict = rows[0] if len(rows) > 0 else {} keys.extend(firstdict.keys()) uniq_keys.update(keys) rows = rows[1:] ``` If `rows[0]` is `SEPARATING_LINE`, then `firstdict = SEPARATING_LINE`. Then `firstdict.keys()` will fail because `SEPARATING_LINE` is a string. Let's test this. <channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY' from tabulate import tabulate, SEPARATING_LINE data_dict = [ SEPARATING_LINE, {"name": "Alice", "age": 30}, {"name": "Bob", "age": 25} ] try: print(tabulate(data_dict, headers="firstrow")) print("Success") except Exception as e: import traceback traceback.print_exc() PY <|"|>}<tool_call|><|tool_response>

Patch, tests and logs

Final code change Included

diff --git a/tabulate/__init__.py b/tabulate/__init__.py
index 11bb865..11b6457 100644
--- a/tabulate/__init__.py
+++ b/tabulate/__init__.py
@@ -1419,6 +1419,8 @@ def _normalize_tabular_data(tabular_data, headers, showindex="default"):
                 uniq_keys.update(keys)
                 rows = rows[1:]
             for row in rows:
+                if _is_separating_line(row):
+                    continue
                 for k in row.keys():
                     # Save unique items in input order
                     if k not in uniq_keys:
@@ -1440,7 +1442,7 @@ def _normalize_tabular_data(tabular_data, headers, showindex="default"):
                 raise ValueError(
                     "headers for a list of dicts is not a dict or a keyword"
                 )
-            rows = [[row.get(k) for k in keys] for row in rows]
+            rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows]
 
         elif (
             headers == "keys"
@@ -1461,7 +1463,7 @@ def _normalize_tabular_data(tabular_data, headers, showindex="default"):
             field_names = [field.name for field in dataclasses.fields(rows[0])]
             if headers == "keys":
                 headers = field_names
-            rows = [[getattr(row, f) for f in field_names] for row in rows]
+            rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]
 
         elif headers == "keys" and len(rows) > 0:
             # keys are column indices

Test output Included

============================= test session starts ==============================
collected 33 items

test/test_input.py .................................                     [100%]

==================================== PASSES ====================================
__________________________ test_iterable_of_iterables __________________________
----------------------------- Captured stdout call -----------------------------
Expected:
-  -  -  -  -
0  1  2  3  4
5  4  3  2  1
-  -  -  -  -

Got:
-  -  -  -  -
0  1  2  3  4
5  4  3  2  1
-  -  -  -  -

______________________ test_iterable_of_iterables_headers ______________________
----------------------------- Captured stdout call -----------------------------
Expected:
  a    b    c    d    e
---  ---  ---  ---  ---
  0    1    2    3    4
  5    4    3    2    1

Got:
  a    b    c    d    e
---  ---  ---  ---  ---
  0    1    2    3    4
  5    4    3    2    1

_____________________ test_iterable_of_iterables_firstrow ______________________
----------------------------- Captured stdout call -----------------------------
Expected:
  a    b    c    d    e
---  ---  ---  ---  ---
  0    1    2    3    4
  5    4    3    2    1

Got:
  a    b    c    d    e
---  ---  ---  ---  ---
  0    1    2    3    4
  5    4    3    2    1

______________________________ test_list_of_lists ______________________________
----------------------------- Captured stdout call -----------------------------
Expected:
    string      number
--  --------  --------
a   one              1
b   two

Got:
    string      number
--  --------  --------
a   one              1
b   two

_________________________ test_list_of_lists_firstrow __________________________
----------------------------- Captured stdout call -----------------------------
Expected:
    string      number
--  --------  --------
a   one              1
b   two

Got:
    string      number
--  --------  --------
a   one              1
b   two

___________________________ test_list_of_lists_keys ____________________________
----------------------------- Captured stdout call -----------------------------
Expected:
0    1      2
---  ---  ---
a    one    1
b    two

Got:
0    1      2
---  ---  ---
a    one    1
b    two

________________________________ test_dict_like ________________________________
----------------------------- Captured stdout call -----------------------------
Keys' order: dict_keys(['a', 'b'])
Expected 1:
  a    b
---  ---
  0  101
  1  102
  2  103
     104

Expected 2:
  b    a
---  ---
101    0
102    1
103    2
104

Got:
  a    b
---  ---
  0  101
  1  102
  2  103
     104

________________________________ test_numpy_2d _________________________________
----------------------------- Captured stdout call -----------------------------
Expected:
    a      b      c
-----  -----  -----
  0.5    4     13.5
 32     62.5  108
171.5  256    364.5

Got:
    a      b      c
-----  -----  -----
  0.5    4     13.5
 32     62.5  108
171.5  256    364.5

____________________________ test_numpy_2d_firstrow ____________________________
----------------------------- Captured stdout call -----------------------------
Expected:
  1    8    27
---  ---  ----
 64  125   216
343  512   729

Got:
  1    8    27
---  ---  ----
 64  125   216
343  512   729

______________________________ test_numpy_2d_keys ______________________________
----------------------------- Captured stdout call -----------------------------
Expected:
    0      1      2
-----  -----  -----
  0.5    4     13.5
 32     62.5  108
171.5  256    364.5

Got:
    0      1      2
-----  -----  -----
  0.5    4     13.5
 32     62.5  108
171.5  256    364.5

___________________________ test_numpy_record_array ____________________________
----------------------------- Captured stdout call -----------------------------
Expected:
-----  --  -----
Alice  23  169.5
Bob    27  175
-----  --  -----

Got:
-----  --  -----
Alice  23  169.5
Bob    27  175
-----  --  -----

_________________________ test_numpy_record_array_keys _________________________
----------------------------- Captured stdout call -----------------------------
Expected:
name      age    height
------  -----  --------
Alice      23     169.5
Bob        27     175

Got:
name      age    height
------  -----  --------
Alice      23     169.5
Bob        27     175

_______________________ test_numpy_record_array_headers ________________________
----------------------------- Captured stdout call -----------------------------
Expected:
person      years     cm
--------  -------  -----
Alice          23  169.5
Bob            27  175

Got:
person      years     cm
--------  -------  -----
Alice          23  169.5
Bob            27  175

_________________________________ test_pandas __________________________________
----------------------------- Captured stdout call -----------------------------
Expected:
    string      number
--  --------  --------
a   one              1
b   two            nan

Got:
    string      number
--  --------  --------
a   one              1
b   two            nan

_____________________________ test_pandas_firstrow _____________________________
----------------------------- Captured stdout call -----------------------------
Expected:
a    one      1.0
---  -----  -----
b    two      nan

Got:
a    one      1.0
---  -----  -----
b    two      nan

_______________________________ test_pandas_keys _______________________________
----------------------------- Captured stdout call -----------------------------
Expected:
    string      number
--  --------  --------
a   one              1
b   two            nan

Got:
    string      number
--  --------  --------
a   one              1
b   two            nan

_________________________________ test_sqlite3 _________________________________
----------------------------- Captured stdout call -----------------------------
Expected:
whom      how old    how tall
------  ---------  ----------
Alice          23       169.5
Bob            27       175

Got:
whom      how old    how tall
------  ---------  ----------
Alice          23       169.5
Bob            27       175

______________________________ test_sqlite3_keys _______________________________
----------------------------- Captured stdout call -----------------------------
Expected:
whom      how old    how tall
------  ---------  ----------
Alice          23       169.5
Bob            27       175

Got:
whom      how old    how tall
------  ---------  ----------
Alice          23       169.5
Bob            27       175

___________________________ test_list_of_namedtuples ___________________________
----------------------------- Captured stdout call -----------------------------
Expected:
-  -
1  2
3  4
-  -

Got:
-  -
1  2
3  4
-  -

________________________ test_list_of_namedtuples_keys _________________________
----------------------------- Captured stdout call -----------------------------
Expected:
  foo    bar
-----  -----
    1      2
    3      4

Got:
  foo    bar
-----  -----
    1      2
    3      4

______________________________ test_list_of_dicts ______________________________
----------------------------- Captured stdout call -----------------------------
Expected 1:
-  -
1  2
3  4
-  -

Expected 2:
-  -
2  1
4  3
-  -

Got:
-  -
1  2
3  4
-  -

____________________________ test_list_of_userdicts ____________________________
----------------------------- Captured stdout call -----------------------------
Expected 1:
-  -
1  2
3  4
-  -

Expected 2:
-  -
2  1
4  3
-  -

Got:
-  -
1  2
3  4
-  -

___________________________ test_list_of_dicts_keys ____________________________
----------------------------- Captured stdout call -----------------------------
Expected 1:
  foo    bar
-----  -----
    1      2
    3      4

Expected 2:
  bar    foo
-----  -----
    2      1
    4      3

Got:
  foo    bar
-----  -----
    1      2
    3      4

_________________________ test_list_of_userdicts_keys __________________________
----------------------------- Captured stdout call -----------------------------
Expected 1:
  foo    bar
-----  -----
    1      2
    3      4

Expected 2:
  bar    foo
-----  -----
    2      1
    4      3

Got:
  foo    bar
-----  -----
    1      2
    3      4

_____________________ test_list_of_dicts_with_missing_keys _____________________
----------------------------- Captured stdout call -----------------------------
Expected:
  foo    bar    baz
-----  -----  -----
    1
           2
    4             3

Got:
  foo    bar    baz
-----  -----  -----
    1
           2
    4             3

_________________________ test_list_of_dicts_firstrow __________________________
----------------------------- Captured stdout call -----------------------------
Expected 1:
  FOO    BAR    baz
-----  -----  -----
    3      4      5

Expected 2:
  BAR    FOO    baz
-----  -----  -----
    4      3      5

Got:
  FOO    BAR    baz
-----  -----  -----
    3      4      5

___________________ test_list_of_dicts_with_dict_of_headers ____________________
----------------------------- Captured stdout call -----------------------------
Expected 1:
  DIGITS  LETTERS
--------  ---------
   12345  ABCDE

Expected 2:
LETTERS      DIGITS
---------  --------
ABCDE         12345

Got:
LETTERS      DIGITS
---------  --------
ABCDE         12345

__________________________ test_list_of_ordereddicts ___________________________
----------------------------- Captured stdout call -----------------------------
Expected:
  b    a
---  ---
  1    2
  1    2

Got:
  b    a
---  ---
  1    2
  1    2

__________________ test_py37orlater_list_of_dataclasses_keys ___________________
----------------------------- Captured stdout call -----------------------------
Expected:
name      age    height
------  -----  --------
Alice      23     169.5
Bob        27     175

Got:
name      age    height
------  -----  --------
Alice      23     169.5
Bob        27     175

_________________ test_py37orlater_list_of_dataclasses_headers _________________
----------------------------- Captured stdout call -----------------------------
Expected:
person      years     cm
--------  -------  -----
Alice          23  169.5
Bob            27  175

Got:
person      years     cm
--------  -------  -----
Alice          23  169.5
Bob            27  175

__________ test_py37orlater_list_of_dataclasses_with_separating_line ___________
----------------------------- Captured stdout call -----------------------------
Expected:
name      age    height
------  -----  --------
Alice      23     169.5
------  -----  --------
Bob        27     175

Got:
name      age    height
------  -----  --------
Alice      23     169.5
------  -----  --------
Bob        27     175

_______________________________ test_list_bytes ________________________________
----------------------------- Captured stdout call -----------------------------
Expected:
bytes
---------------------------
b'\xe4\xbd\xa0\xe5\xa5\xbd'
你好

Got:
bytes
---------------------------
b'\xe4\xbd\xa0\xe5\xa5\xbd'
你好

- generated xml file: /tmp/rebench-grade-07fc45e67e4d402f8f14f141d43f256a.xml --
=========================== short test summary info ============================
PASSED test/test_input.py::test_iterable_of_iterables
PASSED test/test_input.py::test_iterable_of_iterables_headers
PASSED test/test_input.py::test_iterable_of_iterables_firstrow
PASSED test/test_input.py::test_list_of_lists
PASSED test/test_input.py::test_list_of_lists_firstrow
PASSED test/test_input.py::test_list_of_lists_keys
PASSED test/test_input.py::test_dict_like
PASSED test/test_input.py::test_numpy_2d
PASSED test/test_input.py::test_numpy_2d_firstrow
PASSED test/test_input.py::test_numpy_2d_keys
PASSED test/test_input.py::test_numpy_record_array
PASSED test/test_input.py::test_numpy_record_array_keys
PASSED test/test_input.py::test_numpy_record_array_headers
PASSED test/test_input.py::test_pandas
PASSED test/test_input.py::test_pandas_firstrow
PASSED test/test_input.py::test_pandas_keys
PASSED test/test_input.py::test_sqlite3
PASSED test/test_input.py::test_sqlite3_keys
PASSED test/test_input.py::test_list_of_namedtuples
PASSED test/test_input.py::test_list_of_namedtuples_keys
PASSED test/test_input.py::test_list_of_dicts
PASSED test/test_input.py::test_list_of_userdicts
PASSED test/test_input.py::test_list_of_dicts_keys
PASSED test/test_input.py::test_list_of_userdicts_keys
PASSED test/test_input.py::test_list_of_dicts_with_missing_keys
PASSED test/test_input.py::test_list_of_dicts_firstrow
PASSED test/test_input.py::test_list_of_dicts_with_dict_of_headers
PASSED test/test_input.py::test_list_of_dicts_with_list_of_headers
PASSED test/test_input.py::test_list_of_ordereddicts
PASSED test/test_input.py::test_py37orlater_list_of_dataclasses_keys
PASSED test/test_input.py::test_py37orlater_list_of_dataclasses_headers
PASSED test/test_input.py::test_py37orlater_list_of_dataclasses_with_separating_line
PASSED test/test_input.py::test_list_bytes
============================== 33 passed in 0.68s ==============================

Test errors Included

Test report (JUnit) Included

<?xml version="1.0" encoding="utf-8"?><testsuites name="pytest tests"><testsuite name="pytest" errors="0" failures="0" skipped="0" tests="33" time="0.685" timestamp="2026-11-14T03:56:55.011686+00:00" hostname="60bd26e9a2d9"><testcase classname="test.test_input" name="test_iterable_of_iterables" time="0.001" /><testcase classname="test.test_input" name="test_iterable_of_iterables_headers" time="0.000" /><testcase classname="test.test_input" name="test_iterable_of_iterables_firstrow" time="0.000" /><testcase classname="test.test_input" name="test_list_of_lists" time="0.000" /><testcase classname="test.test_input" name="test_list_of_lists_firstrow" time="0.000" /><testcase classname="test.test_input" name="test_list_of_lists_keys" time="0.000" /><testcase classname="test.test_input" name="test_dict_like" time="0.000" /><testcase classname="test.test_input" name="test_numpy_2d" time="0.118" /><testcase classname="test.test_input" name="test_numpy_2d_firstrow" time="0.000" /><testcase classname="test.test_input" name="test_numpy_2d_keys" time="0.000" /><testcase classname="test.test_input" name="test_numpy_record_array" time="0.000" /><testcase classname="test.test_input" name="test_numpy_record_array_keys" time="0.000" /><testcase classname="test.test_input" name="test_numpy_record_array_headers" time="0.000" /><testcase classname="test.test_input" name="test_pandas" time="0.522" /><testcase classname="test.test_input" name="test_pandas_firstrow" time="0.001" /><testcase classname="test.test_input" name="test_pandas_keys" time="0.000" /><testcase classname="test.test_input" name="test_sqlite3" time="0.002" /><testcase classname="test.test_input" name="test_sqlite3_keys" time="0.000" /><testcase classname="test.test_input" name="test_list_of_namedtuples" time="0.000" /><testcase classname="test.test_input" name="test_list_of_namedtuples_keys" time="0.000" /><testcase classname="test.test_input" name="test_list_of_dicts" time="0.000" /><testcase classname="test.test_input" name="test_list_of_userdicts" time="0.000" /><testcase classname="test.test_input" name="test_list_of_dicts_keys" time="0.000" /><testcase classname="test.test_input" name="test_list_of_userdicts_keys" time="0.000" /><testcase classname="test.test_input" name="test_list_of_dicts_with_missing_keys" time="0.000" /><testcase classname="test.test_input" name="test_list_of_dicts_firstrow" time="0.000" /><testcase classname="test.test_input" name="test_list_of_dicts_with_dict_of_headers" time="0.000" /><testcase classname="test.test_input" name="test_list_of_dicts_with_list_of_headers" time="0.000" /><testcase classname="test.test_input" name="test_list_of_ordereddicts" time="0.000" /><testcase classname="test.test_input" name="test_py37orlater_list_of_dataclasses_keys" time="0.001" /><testcase classname="test.test_input" name="test_py37orlater_list_of_dataclasses_headers" time="0.001" /><testcase classname="test.test_input" name="test_py37orlater_list_of_dataclasses_with_separating_line" time="0.000" /><testcase classname="test.test_input" name="test_list_bytes" time="0.000" /></testsuite></testsuites>

Agent log Included

  [   0.0s] 📋 System Prompt: You are a principal Python software engineer resolving a repository issue in `/workspace`.
  [   0.0s] 🎯 Task Prompt: You are evaluating a software engineering task for repository astanin/python-tabulate.
  [  18.3s] 💭 (1021 chars) "The user is reporting an `AttributeError: 'str' object has no attribute '...'` when using 
`tabulate` with a list of data..."
  [  18.3s] 🔧 run_command grep -rn --include="*.py" "SEPARATING_LINE" .
  [  18.4s]   → ./tabulate/__init__.py:51:SEPARATING_LINE = "\001"
  [  25.6s] 💭 (232 chars) "`SEPARATING_LINE` is defined as `"\001"` in `tabulate/__init__.py`.

Now let's find the code that causes the `AttributeE..."
  [  25.6s] 📖 read_file (tabulate/__init__.py)
  [  25.6s]   ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'content': '                # a dict of headers for
a list of dicts…
  [  48.5s] 💭 (727 chars) "The problematic code is here:

```python
        elif (
            dataclasses is not None
            and len(rows) > ..."
  [  48.5s] 🔧 run_command (21 lines) python3 - <<'PY'
  [  48.6s]   🔧 
  [ 112.3s] 💭 (3100 chars) "The reproduction was successful.

Now I need to fix it. The fix should involve checking if the current `row` is a datacl..."
  [ 112.3s] 📖 read_file (tabulate/__init__.py)
  [ 112.4s]   ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'content': '    If showindex is an iterable, show 
its values as row…
  [ 125.9s] 💭 (337 chars) "I see that other data types also have similar transformations. For example, the dict-like 
objects:
```python
           ..."
  [ 125.9s] 🔧 run_command (15 lines) python3 - <<'PY'
  [ 125.9s]   🔧 
  [ 242.2s] 💭 (6861 chars) "Wait, the failure happened even earlier for dicts:
```python
            for row in rows:
                for k in row.k..."
  [ 242.2s] 🔧 run_command (21 lines) python3 - <<'PY'
  [ 242.2s]   🔧 
  [ 274.8s] 💭 (2066 chars) "Yes, it fails. But that's probably expected if the first element is not a dataclass. 
`tabulate` uses the first element t..."
  [ 274.8s] 📖 read_file (tabulate/__init__.py)
  [ 274.8s]   ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'content': '    # take headers from the first row 
if necessary\n   …
  [ 277.6s] 💭 (50 chars) "Let's find where `_is_separating_line` is defined."
  [ 277.6s] 🔧 run_command grep -rn "_is_separating_line" .
  [ 277.7s]   → ./tabulate/__init__.py:105:def _is_separating_line(row):
  [ 279.7s] 💭 (33 chars) "Let's read `_is_separating_line`."
  [ 279.7s] 📖 read_file (tabulate/__init__.py)
  [ 279.7s]   ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'content': '        "with_header_hide",\n    
],\n)\n\n\ndef _is_sep…
  [ 333.0s] 💭 (2599 chars) "Wait, `_is_separating_line` is more complex than I thought!

```python
def _is_separating_line(row):
    row_type = type..."
  [ 333.0s] ✏️ edit_file (tabulate/__init__.py)
  [ 333.1s]   ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'occurrences': 1, 'strategy': 'exact', 'diff': '---
a/tabulate/__in…
  [ 336.9s] 💭 (51 chars) "Now the dict-like transformation.
Around line 1442."
  [ 336.9s] ✏️ edit_file (tabulate/__init__.py)
  [ 337.1s]   ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'occurrences': 1, 'strategy': 'exact', 'diff': '---
a/tabulate/__in…
  [ 340.5s] 💭 (51 chars) "Now the dataclass transformation.
Around line 1464."
  [ 340.5s] ✏️ edit_file (tabulate/__init__.py)
  [ 340.6s]   ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'occurrences': 1, 'strategy': 'exact', 'diff': '---
a/tabulate/__in…
  [ 349.5s] 💭 (57 chars) "Now let's verify the fix with both dataclasses and dicts."
  [ 349.5s] 🔧 run_command (37 lines) python3 - <<'PY'
  [ 349.6s]   → Testing dataclasses:
  [ 362.0s] 💭 (403 chars) "The fix works for both dataclasses and dicts.

Wait, let me check the output of the dataclass test.
`name      age`
`---..."
  [ 362.0s] 🔧 run_command (22 lines) python3 - <<'PY'
  [ 362.0s]   → name      age
  [ 369.9s] 💭 (197 chars) "It also works!

One more thing: what if the data is a list of dicts and we have `SEPARATING_LINE` as the first element? ..."
  [ 369.9s] 🔧 run_command (16 lines) python3 - <<'PY'
  [ 370.0s]   → 0
  [ 430.5s] 💭 (2798 chars) "Wait, it "succeeded" but the output is weird.

```
0
----
----
name
name
```

This is because `rows[0]` is `SEPARATING_L..."
  [ 430.5s] 🔧 run_command (16 lines) python3 - <<'PY'
  [ 430.6s]   →

Native result row

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  "agent_error": "Agent exceeded session timeout (7.5 min)",
  "duration_seconds": 454.44,
  "error": "Agent exceeded session timeout (7.5 min)",
  "grade": {
    "collected_tests": 33,
    "competition_junit_ok": true,
    "error": null,
    "required_issues_count": 0,
    "resolved": true,
    "status_counts": {
      "PASSED": 33
    },
    "strict_clean_patch": true,
    "test_exit_code": 0
  },
  "instance_id": "astanin__python-tabulate-268",
  "pass_idx": 0,
  "patch_chars": 1537,
  "phase1_seconds": 452.76,
  "repo": "astanin/python-tabulate",
  "request_log_path": "/kaggle-root/working/r31/rollouts_X/requests/astanin__python-tabulate-268__p0__r7.jsonl",
  "resolved": true,
  "rollout_idx": 7,
  "run_key": "astanin__python-tabulate-268__p0__r7",
  "strict_clean_patch": true,
  "tool_calls": 16,
  "total_llm_calls": 16,
  "trace_path": "/kaggle-root/working/r31/rollouts_X/traces/trace_astanin__python-tabulate-268__p0__r7.json"
}