← 31B real rollouts · 480 (exact stack, 32 lanes, 13-Nov)

ROLLOUT 4 · astanin/python-tabulate

astanin__python-tabulate-268

Solved

342 stotal time

17tool calls

19model calls

5,450tokens generated

156,501prompt tokens read

No error recorded

⚠ The request log has 18 model calls but the trace has 20 agent calls, so tokens are listed separately instead of beside each turn.

Turn by turn

Token shading: p ≥ 0.9 0.5 – 0.9 0.2 – 0.5 below 0.2. Hover a token for its id and probability.

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 11 s173 tokens out2,665 prompt tokens

The user is reporting an `AttributeError` when using `tabulate` with a list of dataclasses and `SEPARATING_LINE`.
The error occurs at line 1451 in `tabulate/__init__.py`.
The error message says `'str' object has no attribute '<the first attribute of dataclass>'`.
This suggests that when `rows` contains `SEPARATING_LINE` (which is likely a string), the code tries to treat it as a dataclass and access its attributes using `getattr`, which fails.

Let's start by examining the code around line 1451 in `tabulate/__init__.py`.

TOOL CALL · run_command

{
  "command": "grep -n \"SEPARATING_LINE\" tabulate/__init__.py"
}

Result of run_command 312 characters

{
  "status": "ok",
  "stdout": "51:SEPARATING_LINE = \"\\001\"\n108:        (len(row) >= 1 and row[0] == SEPARATING_LINE)\n109:        or (len(row) >= 2 and row[1] == SEPARATING_LINE)\n1290:            rows.insert(index, SEPARATING_LINE)\n2453:            # the SEPARATING_LINE flag\n",
  "stderr": "",
  "exit_code": 0
}

Turn 2at 16 s89 tokens out2,981 prompt tokens

Okay, `SEPARATING_LINE` is defined as `"\001"`.
The error report mentioned line 1451. Let's read the code around that line in `tabulate/__init__.py`.

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
}

Turn 3at 39 s347 tokens out3,587 prompt tokens

The problematic part seems to be:
```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]
```
The code checks if the *first* row is a dataclass. If it is, it assumes *all* rows are dataclasses and tries to call `getattr(row, f)` on every row.
If one of the rows is `SEPARATING_LINE` (which is a string), `getattr(row, f)` will fail.

I should create a reproduction script to confirm this.

TOOL CALL · run_command

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

Result of run_command 796 characters

{
  "status": "ok",
  "stdout": "'str' object has no attribute 'name'\n",
  "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
}

Turn 4at 102 s958 tokens out4,219 prompt tokens

The reproduction script confirms the issue.
Now I need to fix it.
The fix should ensure that if a row is `SEPARATING_LINE`, it is preserved as is, instead of trying to access attributes of the dataclass.

Wait, looking at the code:
```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]
```
The `rows` list is being transformed into a list of lists.
If `row` is `SEPARATING_LINE`, it should probably remain `SEPARATING_LINE` (or maybe be wrapped in a list, but `tabulate` usually handles `SEPARATING_LINE` throughout its internal processing).

Let's see how other parts of the code handle `SEPARATING_LINE`.
Looking at line 108:
```python
107:    def _is_separating_line(row):
108:        (len(row) >= 1 and row[0] == SEPARATING_LINE)
109:        or (len(row) >= 2 and row[1] == SEPARATING_LINE)
```
Wait, `_is_separating_line` checks if the row is a list/sequence and its first or second element is `SEPARATING_LINE`.
However, the `SEPARATING_LINE` constant is just a string.

If `rows` is a list of dataclasses and a string, we need to make sure the string is handled correctly.

If I change:
`rows = [[getattr(row, f) for f in field_names] for row in rows]`
to something like:
`rows = [[getattr(row, f) for f in field_names] if not (isinstance(row, str) and row == SEPARATING_LINE) else row for row in rows]`

But wait, if `row` is `SEPARATING_LINE`, it remains as a string. Later in `tabulate`, it might be expected to be a list-like object if it's in the `rows` list?
Actually, let's see how `tabulate` handles `rows` after `_normalize_tabular_data`.

If I make `row` just `[SEPARATING_LINE]` if it's `SEPARATING_LINE`, it might satisfy `_is_separating_line`.

Wait, looking at the `_is_separating_line` implementation:
```python
    def _is_separating_line(row):
        (len(row) >= 1 and row[0] == SEPARATING_LINE)
        or (len(row) >= 2 and row[1] == SEPARATING_LINE)
```
If `row` is the string `"\001"`, then `len(row)` is 1 and `row[0]` is `"\001"`, so `_is_separating_line("\001")` would actually be true if it doesn't crash on `len(row)`.
Actually, for a string, `row[0]` is the first character.

Let's test what `_is_separating_line(SEPARATING_LINE)` returns.

TOOL CALL · run_command

{
  "command": "python3 - <<'PY'\nfrom tabulate import SEPARATING_LINE\ndef _is_separating_line(row):\n    try:\n        return (len(row) >= 1 and row[0] == SEPARATING_LINE) \\\n               or (len(row) >= 2 and row[1] == SEPARATING_LINE)\n    except Exception as e:\n        return str(e)\n\nprint(f\"SEPARATING_LINE: {repr(SEPARATING_LINE)}\")\nprint(f\"Is separating line: {_is_separating_line(SEPARATING_LINE)}\")\nPY\n"
}

Result of run_command 112 characters

{
  "status": "ok",
  "stdout": "SEPARATING_LINE: '\\x01'\nIs separating line: True\n",
  "stderr": "",
  "exit_code": 0
}

Turn 5at 126 s375 tokens out5,234 prompt tokens

So `_is_separating_line(SEPARATING_LINE)` is `True`.

The problem is that the list comprehension `[[getattr(row, f) for f in field_names] for row in rows]` tries to iterate over `field_names` and call `getattr` on `row`. If `row` is `SEPARATING_LINE`, it fails.

The correct logic should be: if `row` is `SEPARATING_LINE`, just keep it as it is.

Let's verify if keeping it as `SEPARATING_LINE` works in the rest of the `tabulate` process.

TOOL CALL · run_command

{
  "command": "python3 - <<'PY'\nfrom dataclasses import dataclass\nfrom tabulate import tabulate, SEPARATING_LINE\n\n@dataclass\nclass User:\n    name: str\n    age: int\n\ndata = [\n    User(\"Alice\", 30),\n    SEPARATING_LINE,\n    User(\"Bob\", 25),\n]\n\n# I will mock the change by manually converting data before calling tabulate\n# instead of editing the source code yet.\nfield_names = [\"name\", \"age\"]\nrows = [[getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row for row in data]\n\n# Now we need to call the internal function or just see if tabulate can handle it\n# But tabulate calls _normalize_tabular_data which will fail again.\n# Let's see what happens if we pass rows already as a list of lists (and one string).\nprint(tabulate(rows, headers=[\"Name\", \"Age\"]))\nPY\n"
}

Result of run_command 135 characters

{
  "status": "ok",
  "stdout": "Name      Age\n------  -----\nAlice      30\n------  -----\nBob        25\n",
  "stderr": "",
  "exit_code": 0
}

Turn 6at 146 s352 tokens out5,674 prompt tokens

It works! The output shows the separating line was correctly handled.

So the fix is to check if `row` is `SEPARATING_LINE` before trying to access its attributes.

The current code is:
```python
            rows = [[getattr(row, f) for f in field_names] for row in rows]
```

I should change it to:
```python
            rows = [[getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row for row in rows]
```

Let's double check if there are any other places where this pattern occurs.
The code also handles dictionaries:
```python
            rows = [[row.get(k) for k in keys] for row in rows]
```
Does this one handle `SEPARATING_LINE`?
If `row` is `SEPARATING_LINE` (a string), `row.get(k)` will fail because strings don't have a `get` method.

Let's test the dictionary case.

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    print(e)\n    import traceback\n    traceback.print_exc()\nPY\n"
}

Result of run_command 475 characters

{
  "status": "ok",
  "stdout": "'str' object has no attribute 'keys'\n",
  "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
}

Turn 7at 152 s91 tokens out6,200 prompt tokens

Ah, it fails even earlier for dictionaries!
At line 1422 (approx):
```python
    for k in row.keys():
```

Let's look at that part of `tabulate/__init__.py`.

TOOL CALL · read_file

{
  "end_line": 1435,
  "filepath": "tabulate/__init__.py",
  "start_line": 1400
}

Result of read_file 1,688 characters

{
  "status": "ok",
  "filepath": "tabulate/__init__.py",
  "content": "            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]",
  "start_line": 1400,
  "end_line": 1435,
  "total_lines": 2787,
  "is_truncated": false
}

Turn 8at 177 s363 tokens out6,762 prompt tokens

Yes, here:
```python
1421:            for row in rows:
1422:                for k in row.keys():
1423:                    # Save unique items in input order
1424:                    if k not in uniq_keys:
1425:                        keys.append(k)
1426:                        uniq_keys.add(k)
```
This loop is calculating the `keys` from all the dicts in `rows`. If one of the `rows` is `SEPARATING_LINE`, `row.keys()` fails.

So I need to filter out `SEPARATING_LINE` in all these normalization loops.

Let's identify all places where `rows` is iterated and attributes or methods are accessed based on the assumption that all elements in `rows` are of the same type as `rows[0]`.

In `_normalize_tabular_data`:
1. The dict-like object section (lines 1421-1426).
2. The dict-like object data extraction (line 1438): `rows = [[row.get(k) for k in keys] for row in rows]`.
3. The dataclass section (line 1464): `rows = [[getattr(row, f) for f in field_names] for row in rows]`.

Wait, there's also the `namedtuple` section. Let's check it.

TOOL CALL · read_file

{
  "end_line": 1415,
  "filepath": "tabulate/__init__.py",
  "start_line": 1405
}

Result of read_file 621 characters

{
  "status": "ok",
  "filepath": "tabulate/__init__.py",
  "content": "            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",
  "start_line": 1405,
  "end_line": 1415,
  "total_lines": 2787,
  "is_truncated": false
}

Turn 9at 208 s514 tokens out7,332 prompt tokens

For `namedtuple`, it only accesses `rows[0]._fields` to get headers. It doesn't seem to iterate and access attributes in the same way as dataclasses or dicts in that specific block. Wait, let's see if there's a `rows` transformation for `namedtuple`.

Actually, if it's a `namedtuple`, `rows` remains a list of tuples, and `tabulate` can handle lists of tuples.

So let's focus on:
1. Dict keys collection.
2. Dict data extraction.
3. Dataclass data extraction.

Let's re-examine the dict keys collection:
```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)
```
I should 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)
```

And the dict data extraction:
```python
            rows = [[row.get(k) for k in keys] for row in rows]
```
To:
```python
            rows = [[row.get(k) for k in keys] if row != SEPARATING_LINE else row for row in rows]
```

And the dataclass data extraction:
```python
            rows = [[getattr(row, f) for f in field_names] for row in rows]
```
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 `SEPARATING_LINE` is used anywhere else in `_normalize_tabular_data`.
I'll search for `_normalize_tabular_data` and see how it's implemented overall.

TOOL CALL · read_file

{
  "end_line": 1470,
  "filepath": "tabulate/__init__.py",
  "start_line": 1380
}

Result of read_file 3,926 characters

{
  "status": "ok",
  "filepath": "tabulate/__init__.py",
  "content": "                    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": 1380,
  "end_line": 1470,
  "total_lines": 2787,
  "is_truncated": false
}

Turn 10at 229 s501 tokens out8,902 prompt tokens

Yes, I've identified the three places.

Now let's apply the fixes.

Fix 1: Dict keys collection (lines 1421-1426)
```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)
```
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)
```

Fix 2: Dict data extraction (line 1438)
```python
            rows = [[row.get(k) for k in keys] for row in rows]
```
to
```python
            rows = [[row.get(k) for k in keys] if row != SEPARATING_LINE else row for row in rows]
```

Fix 3: Dataclass data extraction (line 1464)
```python
            rows = [[getattr(row, f) for f in field_names] for row in rows]
```
to
```python
            rows = [[getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row for row in rows]
```

Let's start with the first fix.

TOOL CALL · edit_file

{
  "filepath": "tabulate/__init__.py",
  "new_string": "            for row in rows:\n                if row == SEPARATING_LINE:\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 528 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 row == SEPARATING_LINE:\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
}

Turn 11at 234 s91 tokens out9,574 prompt tokens

Now the second fix.

TOOL CALL · edit_file

{
  "filepath": "tabulate/__init__.py",
  "new_string": "            rows = [[row.get(k) for k in keys] if row != SEPARATING_LINE else row for row in rows]",
  "old_string": "            rows = [[row.get(k) for k in keys] for row in rows]"
}

Result of edit_file 563 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 row != SEPARATING_LINE else row for row in rows]\n \n         elif (\n             headers == \"keys\"\n",
  "is_truncated": false
}

Turn 12at 237 s95 tokens out9,855 prompt tokens

And the third fix.

TOOL CALL · edit_file

{
  "filepath": "tabulate/__init__.py",
  "new_string": "            rows = [[getattr(row, f) for f in field_names] if row != SEPARATING_LINE 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 653 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 row != SEPARATING_LINE else row for row in rows]\n \n         elif headers == \"keys\" and len(rows) > 0:\n             # keys are column indices\n",
  "is_truncated": false
}

Turn 13at 249 s279 tokens out10,172 prompt tokens

Now let's verify everything with the reproduction script and also a dict version.

TOOL CALL · run_command

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

Result of run_command 250 characters

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

Turn 14at 294 s726 tokens out10,555 prompt tokens

Both cases now work.

Now I should check if there are other types of rows in `_normalize_tabular_data` that might be affected.
For example, what if `rows` is a list of mixed types?
The code currently checks `rows[0]` to decide how to handle the entire list.

```python
        elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):
            # dict-like object
            ...
        elif (
            dataclasses is not None
            and len(rows) > 0
            and dataclasses.is_dataclass(rows[0])
        ):
            # Python 3.7+'s dataclass
            ...
```

If `rows[0]` is `SEPARATING_LINE`, then `hasattr(rows[0], "keys")` is False and `dataclasses.is_dataclass(rows[0])` is False.
So it will go to:
```python
        elif headers == "keys" and len(rows) > 0:
            # keys are column indices
            headers = list(map(str, range(len(rows[0]))))
```
In this case `rows[0]` is `"\001"`, so `len(rows[0])` is 1. `headers` becomes `["0"]`.
Then `rows` remains as it is.

Wait, what if `rows` starts with `SEPARATING_LINE`?
Example: `[SEPARATING_LINE, User("Alice", 30)]`.
In this case, `rows[0]` is `SEPARATING_LINE`.
The current logic:
1. `hasattr(rows[0], "keys")` -> False.
2. `dataclasses.is_dataclass(rows[0])` -> False.
3. `headers == "keys" and len(rows) > 0` -> True.
   `headers = ["0"]`.
   `rows` remains `[SEPARATING_LINE, User("Alice", 30)]`.

Later, when tabulating:
`User("Alice", 30)` will be treated as a sequence (if it's iterable) or stringified.
Dataclasses are not iterable by default.
So `tabulate` will probably call `str(User("Alice", 30))`.

This is probably acceptable because `tabulate` makes a decision based on the first row. If the first row is a `SEPARATING_LINE`, it doesn't know that subsequent rows are dataclasses.

But the bug reports were specifically about `SEPARATING_LINE` being *one of* the rows, usually not the first one.

Let's check if there's any other place in the whole file that should be fixed.
The `_normalize_tabular_data` seems to be the main place where type-specific extraction happens.

Let's double check if there are any other similar patterns.
Searching for `getattr(row, f)` or `row.get(k)`.

TOOL CALL · run_command

{
  "command": "grep -rn --include=\"*.py\" -E \"getattr\\(row, .*\\)|\\.get\\(\" tabulate/"
}

Result of run_command 1,505 characters

{
  "status": "ok",
  "stdout": "tabulate/__init__.py:147:        \" \" + alignment.get(a, \"\") + c + \" \" for c, a in zip(cell_values, colaligns)\ntabulate/__init__.py:156:    values = (alignment.get(a, \"\") + v for a, v in zip(colaligns, cell_values))\ntabulate/__init__.py:174:            \"<{0}{1}>{2}</{0}>\".format(celltag, alignment.get(a, \"\"), c)\ntabulate/__init__.py:179:            \"<{0}{1}>{2}</{0}>\".format(celltag, alignment.get(a, \"\"), htmlescape(c))\ntabulate/__init__.py:196:        \"{}{} {} \".format(celltag, alignment.get(a, \"\"), header + c + header)\ntabulate/__init__.py:204:    tabular_columns_fmt = \"\".join([alignment.get(a, \"l\") for a in colaligns])\ntabulate/__init__.py:286:        return escrules.get(c, c)\ntabulate/__init__.py:1184:    moregeneric = max(types.get(type1, 5), types.get(type2, 5))\ntabulate/__init__.py:1433:                headers = [headers.get(k, k) for k in keys]\ntabulate/__init__.py:1437:                    headers = [firstdict.get(k, k) for k in keys]\ntabulate/__init__.py:1445:            rows = [[row.get(k) for k in keys] if row != SEPARATING_LINE else row for row in rows]\ntabulate/__init__.py:1466:            rows = [[getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row for row in rows]\ntabulate/__init__.py:2166:        tablefmt = multiline_formats.get(tablefmt, tablefmt)\ntabulate/__init__.py:2265:        tablefmt = _table_formats.get(tablefmt, _table_formats[\"simple\"])\n",
  "stderr": "",
  "exit_code": 0
}

Turn 15at 307 s141 tokens out11,846 prompt tokens

The other `.get()` calls are on configuration dictionaries (`alignment`, `escrules`, `multiline_formats`, etc.), not on rows of data.

One more thing: the issue description mentioned line 1451. In my version of the file, it was 1464. This is because of version differences or my local line numbering. My `read_file` and `edit_file` were based on the actual content.

Let's run the full test suite to make sure no regressions.

TOOL CALL · run_command

{
  "command": "pytest test/test_regression.py -q --tb=short --no-header"
}

Result of run_command 172 characters

{
  "status": "ok",
  "stdout": "................s..ss..............                                      [100%]\n32 passed, 3 skipped in 0.33s\n",
  "stderr": "",
  "exit_code": 0
}

Turn 16at 311 s36 tokens out12,057 prompt tokens

Let's run all tests.

TOOL CALL · run_command

{
  "command": "pytest test/ -q --tb=short --no-header"
}

Result of run_command 1,296 characters

{
  "status": "ok",
  "stdout": "........................................................s..s............ [ 24%]\n.s..................s......s......s......s......s......s......s.......s. [ 49%]\n.s..s..s..s..s..s....................................................... [ 74%]\n.............................................s..ss.................sss.. [ 98%]\n...                                                                      [100%]\n=============================== warnings summary ===============================\ntest/test_input.py::test_numpy_record_array\n  /workspace/test/test_input.py:172: DeprecationWarning: Data type alias 'a' was deprecated in NumPy 2.0. Use the 'S' alias instead.\n    na = numpy.asarray(\n\ntest/test_input.py::test_numpy_record_array_keys\n  /workspace/test/test_input.py:198: DeprecationWarning: Data type alias 'a' was deprecated in NumPy 2.0. Use the 'S' alias instead.\n    na = numpy.asarray(\n\ntest/test_input.py::test_numpy_record_array_headers\n  /workspace/test/test_input.py:224: DeprecationWarning: Data type alias 'a' was deprecated in NumPy 2.0. Use the 'S' alias instead.\n    na = numpy.asarray(\n\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n268 passed, 23 skipped, 3 warnings in 1.22s\n",
  "stderr": "",
  "exit_code": 0
}

Turn 17at 320 s107 tokens out12,498 prompt tokens

All tests passed.

I have:
1. Reproduced the bug with dataclasses.
2. Identified a similar bug with dictionaries.
3. Fixed both by checking for `SEPARATING_LINE` before accessing attributes/keys.
4. Verified the fix with reproduction scripts.
5. Verified no regressions with the full test suite.

I'll do one final check of the diff.

TOOL CALL · run_command

{
  "command": "git status --short && git diff"
}

Result of run_command 1,653 characters

{
  "status": "ok",
  "stdout": " M tabulate/__init__.py\ndiff --git a/tabulate/__init__.py b/tabulate/__init__.py\nindex 11bb865..db9dc9e 100644\n--- a/tabulate/__init__.py\n+++ b/tabulate/__init__.py\n@@ -1419,6 +1419,8 @@ def _normalize_tabular_data(tabular_data, headers, showindex=\"default\"):\n                 uniq_keys.update(keys)\n                 rows = rows[1:]\n             for row in rows:\n+                if row == SEPARATING_LINE:\n+                    continue\n                 for k in row.keys():\n                     # Save unique items in input order\n                     if k not in uniq_keys:\n@@ -1440,7 +1442,7 @@ def _normalize_tabular_data(tabular_data, headers, showindex=\"default\"):\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 row != SEPARATING_LINE else row for row in rows]\n \n         elif (\n             headers == \"keys\"\n@@ -1461,7 +1463,7 @@ def _normalize_tabular_data(tabular_data, headers, showindex=\"default\"):\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 row != SEPARATING_LINE else row for row in rows]\n \n         elif headers == \"keys\" and len(rows) > 0:\n             # keys are column indices\n",
  "stderr": "",
  "exit_code": 0
}

Turn 18at 330 s110 tokens out13,119 prompt tokens

The diff looks correct and minimal.
No untracked files in `/workspace`.

One last thing, the prompt mentioned "All parallel codepaths (sync/async, multiple classes/dialects, type hints)".
In this case, the "parallel codepaths" within `_normalize_tabular_data` are the different ways of handling input data (dicts, dataclasses, etc.). I've covered both dicts and dataclasses.

I'm confident.

TOOL CALL · submit_patch

Result of submit_patch 56 characters

{
  "status": "ok",
  "patch_size": 1523,
  "files_changed": 1
}

Turn 19at 338 s102 tokens out13,269 prompt tokens

The task is complete.

Turn 20at 338 s

The issue was that `tabulate` failed when `SEPARATING_LINE` was present in a list of dataclasses or dictionaries, as it tried to access attributes or keys from the `SEPARATING_LINE` string. I updated `_normalize_tabular_data` in `tabulate/__init__.py` to explicitly skip `SEPARATING_LINE` when collecting dictionary keys and to preserve it when extracting values from both dataclasses and dictionaries.

Model calls (not matched to turns)

Generated tokens 173 tokens · stopped: STOP

The user is reporting an `AttributeError` when using `tabulate` with a list of dataclasses and `SEPARATING_LINE`.
The error occurs at line 1451 in `tabulate/__init__.py`.
The error message says `'str' object has no attribute '<the first attribute of dataclass>'`.
This suggests that when `rows` contains `SEPARATING_LINE` (which is likely a string), the code tries to treat it as a dataclass and access its attributes using `getattr`, which fails.

Let's start by examining the code around line 1451 in `tabulate/__init__.py`.

<|channel>thought The user is reporting an `AttributeError` when using `tabulate` with a list of dataclasses and `SEPARATING_LINE`. The error occurs at line 1451 in `tabulate/__init__.py`. The error message says `'str' object has no attribute '<the first attribute of dataclass>'`. This suggests that when `rows` contains `SEPARATING_LINE` (which is likely a string), the code tries to treat it as a dataclass and access its attributes using `getattr`, which fails. Let's start by examining the code around line 1451 in `tabulate/__init__.py`.<channel|><|tool_call>call:run_command{command:<|"|>grep -n "SEPARATING_LINE" tabulate/__init__.py<|"|>}<tool_call|><|tool_response>

Generated tokens 89 tokens · stopped: STOP

Okay, `SEPARATING_LINE` is defined as `"\001"`.
The error report mentioned line 1451. Let's read the code around that line in `tabulate/__init__.py`.

Okay, `SEPARATING_LINE` is defined as `"\001"`. The error report mentioned line 1451. Let's read the code around that line in `tabulate/__init__.py`. <channel|><|tool_call>call:read_file{filepath:<|"|>tabulate/__init__.py<|"|>,start_line:1430,end_line:1470}<tool_call|><|tool_response>

Generated tokens 347 tokens · stopped: STOP

The problematic part seems to be:
```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]
```
The code checks if the *first* row is a dataclass. If it is, it assumes *all* rows are dataclasses and tries to call `getattr(row, f)` on every row.
If one of the rows is `SEPARATING_LINE` (which is a string), `getattr(row, f)` will fail.

I should create a reproduction script to confirm this.

The problematic part seems to be: ```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] ``` The code checks if the *first* row is a dataclass. If it is, it assumes *all* rows are dataclasses and tries to call `getattr(row, f)` on every row. If one of the rows is `SEPARATING_LINE` (which is a string), `getattr(row, f)` will fail. I should create a reproduction script to confirm this. <channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY' from dataclasses import dataclass from tabulate import tabulate, SEPARATING_LINE @dataclass class User: name: str age: int data = [ User("Alice", 30), SEPARATING_LINE, User("Bob", 25), ] try: print(tabulate(data, headers="keys")) except Exception as e: print(e) import traceback traceback.print_exc() PY <|"|>}<tool_call|><|tool_response>

Generated tokens 958 tokens · stopped: STOP

The reproduction script confirms the issue.
Now I need to fix it.
The fix should ensure that if a row is `SEPARATING_LINE`, it is preserved as is, instead of trying to access attributes of the dataclass.

Wait, looking at the code:
```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]
```
The `rows` list is being transformed into a list of lists.
If `row` is `SEPARATING_LINE`, it should probably remain `SEPARATING_LINE` (or maybe be wrapped in a list, but `tabulate` usually handles `SEPARATING_LINE` throughout its internal processing).

Let's see how other parts of the code handle `SEPARATING_LINE`.
Looking at line 108:
```python
107:    def _is_separating_line(row):
108:        (len(row) >= 1 and row[0] == SEPARATING_LINE)
109:        or (len(row) >= 2 and row[1] == SEPARATING_LINE)
```
Wait, `_is_separating_line` checks if the row is a list/sequence and its first or second element is `SEPARATING_LINE`.
However, the `SEPARATING_LINE` constant is just a string.

If `rows` is a list of dataclasses and a string, we need to make sure the string is handled correctly.

If I change:
`rows = [[getattr(row, f) for f in field_names] for row in rows]`
to something like:
`rows = [[getattr(row, f) for f in field_names] if not (isinstance(row, str) and row == SEPARATING_LINE) else row for row in rows]`

But wait, if `row` is `SEPARATING_LINE`, it remains as a string. Later in `tabulate`, it might be expected to be a list-like object if it's in the `rows` list?
Actually, let's see how `tabulate` handles `rows` after `_normalize_tabular_data`.

If I make `row` just `[SEPARATING_LINE]` if it's `SEPARATING_LINE`, it might satisfy `_is_separating_line`.

Wait, looking at the `_is_separating_line` implementation:
```python
    def _is_separating_line(row):
        (len(row) >= 1 and row[0] == SEPARATING_LINE)
        or (len(row) >= 2 and row[1] == SEPARATING_LINE)
```
If `row` is the string `"\001"`, then `len(row)` is 1 and `row[0]` is `"\001"`, so `_is_separating_line("\001")` would actually be true if it doesn't crash on `len(row)`.
Actually, for a string, `row[0]` is the first character.

Let's test what `_is_separating_line(SEPARATING_LINE)` returns.

The reproduction script confirms the issue. Now I need to fix it. The fix should ensure that if a row is `SEPARATING_LINE`, it is preserved as is, instead of trying to access attributes of the dataclass. Wait, looking at the code: ```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] ``` The `rows` list is being transformed into a list of lists. If `row` is `SEPARATING_LINE`, it should probably remain `SEPARATING_LINE` (or maybe be wrapped in a list, but `tabulate` usually handles `SEPARATING_LINE` throughout its internal processing). Let's see how other parts of the code handle `SEPARATING_LINE`. Looking at line 108: ```python 107: def _is_separating_line(row): 108: (len(row) >= 1 and row[0] == SEPARATING_LINE) 109: or (len(row) >= 2 and row[1] == SEPARATING_LINE) ``` Wait, `_is_separating_line` checks if the row is a list/sequence and its first or second element is `SEPARATING_LINE`. However, the `SEPARATING_LINE` constant is just a string. If `rows` is a list of dataclasses and a string, we need to make sure the string is handled correctly. If I change: `rows = [[getattr(row, f) for f in field_names] for row in rows]` to something like: `rows = [[getattr(row, f) for f in field_names] if not (isinstance(row, str) and row == SEPARATING_LINE) else row for row in rows]` But wait, if `row` is `SEPARATING_LINE`, it remains as a string. Later in `tabulate`, it might be expected to be a list-like object if it's in the `rows` list? Actually, let's see how `tabulate` handles `rows` after `_normalize_tabular_data`. If I make `row` just `[SEPARATING_LINE]` if it's `SEPARATING_LINE`, it might satisfy `_is_separating_line`. Wait, looking at the `_is_separating_line` implementation: ```python def _is_separating_line(row): (len(row) >= 1 and row[0] == SEPARATING_LINE) or (len(row) >= 2 and row[1] == SEPARATING_LINE) ``` If `row` is the string `"\001"`, then `len(row)` is 1 and `row[0]` is `"\001"`, so `_is_separating_line("\001")` would actually be true if it doesn't crash on `len(row)`. Actually, for a string, `row[0]` is the first character. Let's test what `_is_separating_line(SEPARATING_LINE)` returns. <channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY' from tabulate import SEPARATING_LINE def _is_separating_line(row): try: return (len(row) >= 1 and row[0] == SEPARATING_LINE) \ or (len(row) >= 2 and row[1] == SEPARATING_LINE) except Exception as e: return str(e) print(f"SEPARATING_LINE: {repr(SEPARATING_LINE)}") print(f"Is separating line: {_is_separating_line(SEPARATING_LINE)}") PY <|"|>}<tool_call|><|tool_response>

Generated tokens 375 tokens · stopped: STOP

So `_is_separating_line(SEPARATING_LINE)` is `True`.

The problem is that the list comprehension `[[getattr(row, f) for f in field_names] for row in rows]` tries to iterate over `field_names` and call `getattr` on `row`. If `row` is `SEPARATING_LINE`, it fails.

The correct logic should be: if `row` is `SEPARATING_LINE`, just keep it as it is.

Let's verify if keeping it as `SEPARATING_LINE` works in the rest of the `tabulate` process.

So `_is_separating_line(SEPARATING_LINE)` is `True`. The problem is that the list comprehension `[[getattr(row, f) for f in field_names] for row in rows]` tries to iterate over `field_names` and call `getattr` on `row`. If `row` is `SEPARATING_LINE`, it fails. The correct logic should be: if `row` is `SEPARATING_LINE`, just keep it as it is. Let's verify if keeping it as `SEPARATING_LINE` works in the rest of the `tabulate` process. <channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY' from dataclasses import dataclass from tabulate import tabulate, SEPARATING_LINE @dataclass class User: name: str age: int data = [ User("Alice", 30), SEPARATING_LINE, User("Bob", 25), ] # I will mock the change by manually converting data before calling tabulate # instead of editing the source code yet. field_names = ["name", "age"] rows = [[getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row for row in data] # Now we need to call the internal function or just see if tabulate can handle it # But tabulate calls _normalize_tabular_data which will fail again. # Let's see what happens if we pass rows already as a list of lists (and one string). print(tabulate(rows, headers=["Name", "Age"])) PY <|"|>}<tool_call|><|tool_response>

Generated tokens 352 tokens · stopped: STOP

It works! The output shows the separating line was correctly handled.

So the fix is to check if `row` is `SEPARATING_LINE` before trying to access its attributes.

The current code is:
```python
            rows = [[getattr(row, f) for f in field_names] for row in rows]
```

I should change it to:
```python
            rows = [[getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row for row in rows]
```

Let's double check if there are any other places where this pattern occurs.
The code also handles dictionaries:
```python
            rows = [[row.get(k) for k in keys] for row in rows]
```
Does this one handle `SEPARATING_LINE`?
If `row` is `SEPARATING_LINE` (a string), `row.get(k)` will fail because strings don't have a `get` method.

Let's test the dictionary case.

It works! The output shows the separating line was correctly handled. So the fix is to check if `row` is `SEPARATING_LINE` before trying to access its attributes. The current code is: ```python rows = [[getattr(row, f) for f in field_names] for row in rows] ``` I should change it to: ```python rows = [[getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row for row in rows] ``` Let's double check if there are any other places where this pattern occurs. The code also handles dictionaries: ```python rows = [[row.get(k) for k in keys] for row in rows] ``` Does this one handle `SEPARATING_LINE`? If `row` is `SEPARATING_LINE` (a string), `row.get(k)` will fail because strings don't have a `get` method. Let's test the dictionary case. <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: print(e) import traceback traceback.print_exc() PY <|"|>}<tool_call|><|tool_response>

Generated tokens 91 tokens · stopped: STOP

Ah, it fails even earlier for dictionaries!
At line 1422 (approx):
```python
    for k in row.keys():
```

Let's look at that part of `tabulate/__init__.py`.

Ah, it fails even earlier for dictionaries! At line 1422 (approx): ```python for k in row.keys(): ``` Let's look at that part of `tabulate/__init__.py`. <channel|><|tool_call>call:read_file{end_line:1435,filepath:<|"|>tabulate/__init__.py<|"|>,start_line:1400}<tool_call|><|tool_response>

Generated tokens 363 tokens · stopped: STOP

Yes, here:
```python
1421:            for row in rows:
1422:                for k in row.keys():
1423:                    # Save unique items in input order
1424:                    if k not in uniq_keys:
1425:                        keys.append(k)
1426:                        uniq_keys.add(k)
```
This loop is calculating the `keys` from all the dicts in `rows`. If one of the `rows` is `SEPARATING_LINE`, `row.keys()` fails.

So I need to filter out `SEPARATING_LINE` in all these normalization loops.

Let's identify all places where `rows` is iterated and attributes or methods are accessed based on the assumption that all elements in `rows` are of the same type as `rows[0]`.

In `_normalize_tabular_data`:
1. The dict-like object section (lines 1421-1426).
2. The dict-like object data extraction (line 1438): `rows = [[row.get(k) for k in keys] for row in rows]`.
3. The dataclass section (line 1464): `rows = [[getattr(row, f) for f in field_names] for row in rows]`.

Wait, there's also the `namedtuple` section. Let's check it.

Yes, here: ```python 1421: for row in rows: 1422: for k in row.keys(): 1423: # Save unique items in input order 1424: if k not in uniq_keys: 1425: keys.append(k) 1426: uniq_keys.add(k) ``` This loop is calculating the `keys` from all the dicts in `rows`. If one of the `rows` is `SEPARATING_LINE`, `row.keys()` fails. So I need to filter out `SEPARATING_LINE` in all these normalization loops. Let's identify all places where `rows` is iterated and attributes or methods are accessed based on the assumption that all elements in `rows` are of the same type as `rows[0]`. In `_normalize_tabular_data`: 1. The dict-like object section (lines 1421-1426). 2. The dict-like object data extraction (line 1438): `rows = [[row.get(k) for k in keys] for row in rows]`. 3. The dataclass section (line 1464): `rows = [[getattr(row, f) for f in field_names] for row in rows]`. Wait, there's also the `namedtuple` section. Let's check it. <channel|><|tool_call>call:read_file{end_line:1415,filepath:<|"|>tabulate/__init__.py<|"|>,start_line:1405}<tool_call|><|tool_response>

Generated tokens 514 tokens · stopped: STOP

For `namedtuple`, it only accesses `rows[0]._fields` to get headers. It doesn't seem to iterate and access attributes in the same way as dataclasses or dicts in that specific block. Wait, let's see if there's a `rows` transformation for `namedtuple`.

Actually, if it's a `namedtuple`, `rows` remains a list of tuples, and `tabulate` can handle lists of tuples.

So let's focus on:
1. Dict keys collection.
2. Dict data extraction.
3. Dataclass data extraction.

Let's re-examine the dict keys collection:
```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)
```
I should 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)
```

And the dict data extraction:
```python
            rows = [[row.get(k) for k in keys] for row in rows]
```
To:
```python
            rows = [[row.get(k) for k in keys] if row != SEPARATING_LINE else row for row in rows]
```

And the dataclass data extraction:
```python
            rows = [[getattr(row, f) for f in field_names] for row in rows]
```
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 `SEPARATING_LINE` is used anywhere else in `_normalize_tabular_data`.
I'll search for `_normalize_tabular_data` and see how it's implemented overall.

For `namedtuple`, it only accesses `rows[0]._fields` to get headers. It doesn't seem to iterate and access attributes in the same way as dataclasses or dicts in that specific block. Wait, let's see if there's a `rows` transformation for `namedtuple`. Actually, if it's a `namedtuple`, `rows` remains a list of tuples, and `tabulate` can handle lists of tuples. So let's focus on: 1. Dict keys collection. 2. Dict data extraction. 3. Dataclass data extraction. Let's re-examine the dict keys collection: ```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) ``` I should 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) ``` And the dict data extraction: ```python rows = [[row.get(k) for k in keys] for row in rows] ``` To: ```python rows = [[row.get(k) for k in keys] if row != SEPARATING_LINE else row for row in rows] ``` And the dataclass data extraction: ```python rows = [[getattr(row, f) for f in field_names] for row in rows] ``` 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 `SEPARATING_LINE` is used anywhere else in `_normalize_tabular_data`. I'll search for `_normalize_tabular_data` and see how it's implemented overall.<channel|><|tool_call>call:read_file{end_line:1470,filepath:<|"|>tabulate/__init__.py<|"|>,start_line:1380}<tool_call|><|tool_response>

Generated tokens 501 tokens · stopped: STOP

Yes, I've identified the three places.

Now let's apply the fixes.

Fix 1: Dict keys collection (lines 1421-1426)
```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)
```
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)
```

Fix 2: Dict data extraction (line 1438)
```python
            rows = [[row.get(k) for k in keys] for row in rows]
```
to
```python
            rows = [[row.get(k) for k in keys] if row != SEPARATING_LINE else row for row in rows]
```

Fix 3: Dataclass data extraction (line 1464)
```python
            rows = [[getattr(row, f) for f in field_names] for row in rows]
```
to
```python
            rows = [[getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row for row in rows]
```

Let's start with the first fix.

Yes, I've identified the three places. Now let's apply the fixes. Fix 1: Dict keys collection (lines 1421-1426) ```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) ``` 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) ``` Fix 2: Dict data extraction (line 1438) ```python rows = [[row.get(k) for k in keys] for row in rows] ``` to ```python rows = [[row.get(k) for k in keys] if row != SEPARATING_LINE else row for row in rows] ``` Fix 3: Dataclass data extraction (line 1464) ```python rows = [[getattr(row, f) for f in field_names] for row in rows] ``` to ```python rows = [[getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row for row in rows] ``` Let's start with the first fix.<channel|><|tool_call>call:edit_file{filepath:<|"|>tabulate/__init__.py<|"|>,new_string:<|"|> 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)<|"|>,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>

Generated tokens 91 tokens · stopped: STOP

Now the second fix.

Now the second fix. <channel|><|tool_call>call:edit_file{filepath:<|"|>tabulate/__init__.py<|"|>,new_string:<|"|> rows = [[row.get(k) for k in keys] if row != SEPARATING_LINE else row for row in rows]<|"|>,old_string:<|"|> rows = [[row.get(k) for k in keys] for row in rows]<|"|>}<tool_call|><|tool_response>

Generated tokens 95 tokens · stopped: STOP

And the third fix.

And the third fix. <channel|><|tool_call>call:edit_file{filepath:<|"|>tabulate/__init__.py<|"|>,new_string:<|"|> rows = [[getattr(row, f) for f in field_names] if row != SEPARATING_LINE 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>

Generated tokens 279 tokens · stopped: STOP

Now let's verify everything with the reproduction script and also a dict version.

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

Generated tokens 726 tokens · stopped: STOP

Both cases now work.

Now I should check if there are other types of rows in `_normalize_tabular_data` that might be affected.
For example, what if `rows` is a list of mixed types?
The code currently checks `rows[0]` to decide how to handle the entire list.

```python
        elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):
            # dict-like object
            ...
        elif (
            dataclasses is not None
            and len(rows) > 0
            and dataclasses.is_dataclass(rows[0])
        ):
            # Python 3.7+'s dataclass
            ...
```

If `rows[0]` is `SEPARATING_LINE`, then `hasattr(rows[0], "keys")` is False and `dataclasses.is_dataclass(rows[0])` is False.
So it will go to:
```python
        elif headers == "keys" and len(rows) > 0:
            # keys are column indices
            headers = list(map(str, range(len(rows[0]))))
```
In this case `rows[0]` is `"\001"`, so `len(rows[0])` is 1. `headers` becomes `["0"]`.
Then `rows` remains as it is.

Wait, what if `rows` starts with `SEPARATING_LINE`?
Example: `[SEPARATING_LINE, User("Alice", 30)]`.
In this case, `rows[0]` is `SEPARATING_LINE`.
The current logic:
1. `hasattr(rows[0], "keys")` -> False.
2. `dataclasses.is_dataclass(rows[0])` -> False.
3. `headers == "keys" and len(rows) > 0` -> True.
   `headers = ["0"]`.
   `rows` remains `[SEPARATING_LINE, User("Alice", 30)]`.

Later, when tabulating:
`User("Alice", 30)` will be treated as a sequence (if it's iterable) or stringified.
Dataclasses are not iterable by default.
So `tabulate` will probably call `str(User("Alice", 30))`.

This is probably acceptable because `tabulate` makes a decision based on the first row. If the first row is a `SEPARATING_LINE`, it doesn't know that subsequent rows are dataclasses.

But the bug reports were specifically about `SEPARATING_LINE` being *one of* the rows, usually not the first one.

Let's check if there's any other place in the whole file that should be fixed.
The `_normalize_tabular_data` seems to be the main place where type-specific extraction happens.

Let's double check if there are any other similar patterns.
Searching for `getattr(row, f)` or `row.get(k)`.

Both cases now work. Now I should check if there are other types of rows in `_normalize_tabular_data` that might be affected. For example, what if `rows` is a list of mixed types? The code currently checks `rows[0]` to decide how to handle the entire list. ```python elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"): # dict-like object ... elif ( dataclasses is not None and len(rows) > 0 and dataclasses.is_dataclass(rows[0]) ): # Python 3.7+'s dataclass ... ``` If `rows[0]` is `SEPARATING_LINE`, then `hasattr(rows[0], "keys")` is False and `dataclasses.is_dataclass(rows[0])` is False. So it will go to: ```python elif headers == "keys" and len(rows) > 0: # keys are column indices headers = list(map(str, range(len(rows[0])))) ``` In this case `rows[0]` is `"\001"`, so `len(rows[0])` is 1. `headers` becomes `["0"]`. Then `rows` remains as it is. Wait, what if `rows` starts with `SEPARATING_LINE`? Example: `[SEPARATING_LINE, User("Alice", 30)]`. In this case, `rows[0]` is `SEPARATING_LINE`. The current logic: 1. `hasattr(rows[0], "keys")` -> False. 2. `dataclasses.is_dataclass(rows[0])` -> False. 3. `headers == "keys" and len(rows) > 0` -> True. `headers = ["0"]`. `rows` remains `[SEPARATING_LINE, User("Alice", 30)]`. Later, when tabulating: `User("Alice", 30)` will be treated as a sequence (if it's iterable) or stringified. Dataclasses are not iterable by default. So `tabulate` will probably call `str(User("Alice", 30))`. This is probably acceptable because `tabulate` makes a decision based on the first row. If the first row is a `SEPARATING_LINE`, it doesn't know that subsequent rows are dataclasses. But the bug reports were specifically about `SEPARATING_LINE` being *one of* the rows, usually not the first one. Let's check if there's any other place in the whole file that should be fixed. The `_normalize_tabular_data` seems to be the main place where type-specific extraction happens. Let's double check if there are any other similar patterns. Searching for `getattr(row, f)` or `row.get(k)`. <channel|><|tool_call>call:run_command{command:<|"|>grep -rn --include="*.py" -E "getattr\(row, .*\)|\.get\(" tabulate/<|"|>}<tool_call|><|tool_response>

Generated tokens 141 tokens · stopped: STOP

The other `.get()` calls are on configuration dictionaries (`alignment`, `escrules`, `multiline_formats`, etc.), not on rows of data.

One more thing: the issue description mentioned line 1451. In my version of the file, it was 1464. This is because of version differences or my local line numbering. My `read_file` and `edit_file` were based on the actual content.

Let's run the full test suite to make sure no regressions.

The other `.get()` calls are on configuration dictionaries (`alignment`, `escrules`, `multiline_formats`, etc.), not on rows of data. One more thing: the issue description mentioned line 1451. In my version of the file, it was 1464. This is because of version differences or my local line numbering. My `read_file` and `edit_file` were based on the actual content. Let's run the full test suite to make sure no regressions.<channel|><|tool_call>call:run_command{command:<|"|>pytest test/test_regression.py -q --tb=short --no-header<|"|>}<tool_call|><|tool_response>

Generated tokens 36 tokens · stopped: STOP

Let's run all tests.

Let's run all tests. <channel|><|tool_call>call:run_command{command:<|"|>pytest test/ -q --tb=short --no-header<|"|>}<tool_call|><|tool_response>

Generated tokens 107 tokens · stopped: STOP

All tests passed.

I have:
1. Reproduced the bug with dataclasses.
2. Identified a similar bug with dictionaries.
3. Fixed both by checking for `SEPARATING_LINE` before accessing attributes/keys.
4. Verified the fix with reproduction scripts.
5. Verified no regressions with the full test suite.

I'll do one final check of the diff.

All tests passed. I have: 1. Reproduced the bug with dataclasses. 2. Identified a similar bug with dictionaries. 3. Fixed both by checking for `SEPARATING_LINE` before accessing attributes/keys. 4. Verified the fix with reproduction scripts. 5. Verified no regressions with the full test suite. I'll do one final check of the diff. <channel|><|tool_call>call:run_command{command:<|"|>git status --short && git diff<|"|>}<tool_call|><|tool_response>

Generated tokens 110 tokens · stopped: STOP

The diff looks correct and minimal.
No untracked files in `/workspace`.

One last thing, the prompt mentioned "All parallel codepaths (sync/async, multiple classes/dialects, type hints)".
In this case, the "parallel codepaths" within `_normalize_tabular_data` are the different ways of handling input data (dicts, dataclasses, etc.). I've covered both dicts and dataclasses.

I'm confident.

The diff looks correct and minimal. No untracked files in `/workspace`. One last thing, the prompt mentioned "All parallel codepaths (sync/async, multiple classes/dialects, type hints)". In this case, the "parallel codepaths" within `_normalize_tabular_data` are the different ways of handling input data (dicts, dataclasses, etc.). I've covered both dicts and dataclasses. I'm confident. <channel|><|tool_call>call:submit_patch{}<tool_call|><|tool_response>

Patch, tests and logs

Final code change Included

diff --git a/tabulate/__init__.py b/tabulate/__init__.py
index 11bb865..db9dc9e 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 row == SEPARATING_LINE:
+                    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 row != SEPARATING_LINE 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 row != SEPARATING_LINE 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-89d00969d3424ef3b519818431dfb151.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 0.69s ==============================

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="0.692" timestamp="2026-11-14T03:55:00.199355+00:00" hostname="4d30e3bf37d9"><testcase classname="test.test_input" name="test_iterable_of_iterables" time="0.000" /><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="0.119" /><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="0.529" /><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="0.001" /><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.
  [  10.6s] 💭 (526 chars) "The user is reporting an `AttributeError` when using `tabulate` with a list of dataclasses 
and `SEPARATING_LINE`.
The er..."
  [  10.6s] 🔧 run_command grep -n "SEPARATING_LINE" tabulate/__init__.py
  [  10.7s]   → 51:SEPARATING_LINE = "\001"
  [  15.5s] 💭 (149 chars) "Okay, `SEPARATING_LINE` is defined as `"\001"`.
The error report mentioned line 1451. Let's read the code around that li..."
  [  15.5s] 📖 read_file (tabulate/__init__.py)
  [  15.5s]   ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'content': '                # a dict of headers for
a list of dicts…
  [  39.1s] 💭 (750 chars) "The problematic part seems to be:
```python
        elif (
            dataclasses is not None
            and len(rows)..."
  [  39.1s] 🔧 run_command (22 lines) python3 - <<'PY'
  [  39.2s]   → 'str' object has no attribute 'name'
  [ 101.8s] 💭 (2539 chars) "The reproduction script confirms the issue.
Now I need to fix it.
The fix should ensure that if a row is `SEPARATING_LIN..."
  [ 101.8s] 🔧 run_command (12 lines) python3 - <<'PY'
  [ 101.9s]   → SEPARATING_LINE: '\x01'
  [ 125.3s] 💭 (439 chars) "So `_is_separating_line(SEPARATING_LINE)` is `True`.

The problem is that the list comprehension `[[getattr(row, f) for ..."
  [ 125.3s] 🔧 run_command (25 lines) python3 - <<'PY'
  [ 125.4s]   → Name      Age
  [ 146.0s] 💭 (796 chars) "It works! The output shows the separating line was correctly handled.

So the fix is to check if `row` is `SEPARATING_LI..."
  [ 146.0s] 🔧 run_command (16 lines) python3 - <<'PY'
  [ 146.1s]   → 'str' object has no attribute 'keys'
  [ 151.8s] 💭 (157 chars) "Ah, it fails even earlier for dictionaries!
At line 1422 (approx):
```python
    for k in row.keys():
```

Let's look at..."
  [ 151.8s] 📖 read_file (tabulate/__init__.py)
  [ 151.9s]   ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'content': '            and 
getattr(tabular_data.dtype, "names")\n …
  [ 176.4s] 💭 (1039 chars) "Yes, here:
```python
1421:            for row in rows:
1422:                for k in row.keys():
1423:                  ..."
  [ 176.4s] 📖 read_file (tabulate/__init__.py)
  [ 176.5s]   ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'content': '            headers == "keys"\n        
and len(rows…
  [ 207.7s] 💭 (1772 chars) "For `namedtuple`, it only accesses `rows[0]._fields` to get headers. It doesn't seem to 
iterate and access attributes in..."
  [ 207.7s] 📖 read_file (tabulate/__init__.py)
  [ 207.8s]   ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'content': '                    keys[:0] = 
[tabular_data.index.name…
  [ 229.0s] 💭 (1236 chars) "Yes, I've identified the three places.

Now let's apply the fixes.

Fix 1: Dict keys collection (lines 1421-1426)
```pyt..."
  [ 229.0s] ✏️ edit_file (tabulate/__init__.py)
  [ 229.1s]   ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'occurrences': 1, 'strategy': 'exact', 'diff': '---
a/tabulate/__in…
  [ 233.5s] 💭 (19 chars) "Now the second fix."
  [ 233.5s] ✏️ edit_file (tabulate/__init__.py)
  [ 233.6s]   ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'occurrences': 1, 'strategy': 'exact', 'diff': '---
a/tabulate/__in…
  [ 236.7s] 💭 (18 chars) "And the third fix."
  [ 236.7s] ✏️ edit_file (tabulate/__init__.py)
  [ 236.9s]   ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'occurrences': 1, 'strategy': 'exact', 'diff': '---
a/tabulate/__in…
  [ 248.6s] 💭 (81 chars) "Now let's verify everything with the reproduction script and also a dict version."
  [ 248.6s] 🔧 run_command (37 lines) python3 - <<'PY'
  [ 248.7s]   → Testing dataclasses:
  [ 293.5s] 💭 (2207 chars) "Both cases now work.

Now I should check if there are other types of rows in `_normalize_tabular_data` that might be aff..."
  [ 293.5s] 🔧 run_command grep -rn --include="*.py" -E "getattr\(row, .*\)|\.get\(" tabulate/
  [ 293.6s]   → tabulate/__init__.py:147:        " " + alignment.get(a, "") + c + " " for c, a in zip(cell_values, 
colaligns)
  [ 306.6s] 💭 (425 chars) "The other `.get()` calls are on configuration dictionaries (`alignment`, `escrules`, 
`multiline_formats`, etc.), not on ..."
  [ 306.6s] 🔧 run_command pytest test/test_regression.py -q --tb=short --no-header
  [ 307.4s]   → ................s..ss..............                                      [100%]
  [ 310.4s] 💭 (20 chars) "Let's run all tests."
  [ 310.4s] 🔧 run_command pytest test/ -q --tb=short --no-header
  [ 311.8s]   → ........................................................s..s............ [ 24%]
  [ 319.8s] 💭 (333 chars) "All tests passed.

I have:
1. Reproduced the bug with dataclasses.
2. Identified a similar bug with dictionaries.
3. Fix..."
  [ 319.8s] 🔧 run_command git status --short && git diff
  [ 319.8s]   →  M tabulate/__init__.py
  [ 329.4s] 💭 (390 chars) "The diff looks correct and minimal.
No untracked files in `/workspace`.

One last thing, the prompt mentioned "All paral..."
  [ 329.4s] 🤖 submit_patch
  [ 329.5s]   → submit_patch: {"status": "ok", "patch_size": 1523, "files_changed": 1}
  [ 337.8s] 💭 (21 chars) "The task is complete."
  [ 337.8s] ✅ Agent finished: The issue was that `tabulate` failed when `SEPARATING_LINE` was present in a list of 
dataclasses or dictionaries, as it tried to access attributes or keys from the `SEPARATING_LINE` string. I updated ...

Native result row

{
  "agent_error": null,
  "duration_seconds": 342.11,
  "error": null,
  "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": 1523,
  "phase1_seconds": 340.39,
  "repo": "astanin/python-tabulate",
  "request_log_path": "/kaggle-root/working/r31/rollouts_X/requests/astanin__python-tabulate-268__p0__r4.jsonl",
  "resolved": true,
  "rollout_idx": 4,
  "run_key": "astanin__python-tabulate-268__p0__r4",
  "strict_clean_patch": true,
  "tool_calls": 17,
  "total_llm_calls": 19,
  "trace_path": "/kaggle-root/working/r31/rollouts_X/traces/trace_astanin__python-tabulate-268__p0__r4.json"
}