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ROLLOUT 5 · astanin/python-tabulate

astanin__python-tabulate-268

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454 stotal time

20tool calls

20model calls

7,266tokens generated

174,277prompt tokens read

Agent exceeded session timeout (7.5 min)

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System instructions 4,822 characters

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

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

## Execution Protocol

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

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

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

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

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

Task prompt 2,360 characters

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

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

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

Can be assigned to me, I have a fix.

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

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

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

Turn 1at 9.2 s150 tokens out2,665 prompt tokens

The user is reporting an `AttributeError: 'str' object has no attribute '<the first attribute of dataclass>'` when using `tabulate` with a list of dataclasses and `SEPARATING_LINE`.
The traceback points to `tabulate/__init__.py`, line 1451 (approximately).

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

First, I'll locate the problematic code in `tabulate/__init__.py`.

TOOL CALL · run_command

{
  "command": "grep -n \"rows = \\[\\[getattr(row, f) for f in field_names\\]\\]\" tabulate/__init__.py"
}

Result of run_command 127 characters

{
  "status": "error",
  "error_type": "CommandError",
  "error_message": "",
  "details": {
    "stdout": "",
    "stderr": "",
    "exit_code": 1
  }
}

Generated tokens 150 tokens · stopped: STOP

<|channel>thought The user is reporting an `AttributeError: 'str' object has no attribute '<the first attribute of dataclass>'` when using `tabulate` with a list of dataclasses and `SEPARATING_LINE`. The traceback points to `tabulate/__init__.py`, line 1451 (approximately). Let's start by exploring the code around that line and reproducing the issue. First, I'll locate the problematic code in `tabulate/__init__.py`.<channel|><|tool_call>call:run_command{command:<|"|>grep -n "rows = \[\[getattr(row, f) for f in field_names\]\]" tabulate/__init__.py<|"|>}<tool_call|><|tool_response>

Turn 2at 14 s107 tokens out2,871 prompt tokens

The grep failed. Maybe the line is slightly different or I should search for the `AttributeError` context.
The problem statement says:
`rows = [[getattr(row, f) for f in field_names] for row in rows ]`
Let's try searching for `getattr(row, f)` in `tabulate/__init__.py`.

TOOL CALL · run_command

{
  "command": "grep -n \"getattr(row, f)\" tabulate/__init__.py"
}

Result of run_command 142 characters

{
  "status": "ok",
  "stdout": "1464:            rows = [[getattr(row, f) for f in field_names] for row in rows]\n",
  "stderr": "",
  "exit_code": 0
}

Generated tokens 107 tokens · stopped: STOP

The grep failed. Maybe the line is slightly different or I should search for the `AttributeError` context. The problem statement says: `rows = [[getattr(row, f) for f in field_names] for row in rows ]` Let's try searching for `getattr(row, f)` in `tabulate/__init__.py`. <channel|><|tool_call>call:run_command{command:<|"|>grep -n "getattr(row, f)" tabulate/__init__.py<|"|>}<tool_call|><|tool_response>

Turn 3at 18 s61 tokens out3,044 prompt tokens

Okay, it's at line 1464. Now let's read the surrounding code.

TOOL CALL · read_file

{
  "end_line": 1500,
  "filepath": "tabulate/__init__.py",
  "start_line": 1400
}

Result of read_file 4,345 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]\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\n    if headers == \"firstrow\" and len(rows) > 0:\n        if index is not None:\n            headers = [index[0]] + list(rows[0])\n            index = index[1:]\n        else:\n            headers = rows[0]\n        headers = list(map(str, headers))  # headers should be strings\n        rows = rows[1:]\n    elif headers == \"firstrow\":\n        headers = []\n\n    headers = list(map(str, headers))\n    #    rows = list(map(list, rows))\n    rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))\n\n    # add or remove an index column\n    showindex_is_a_str = type(showindex) in [str, bytes]\n    if showindex == \"default\" and index is not None:\n        rows = _prepend_row_index(rows, index)\n    elif isinstance(showindex, Sized) and not showindex_is_a_str:\n        rows = _prepend_row_index(rows, list(showindex))\n    elif isinstance(showindex, Iterable) and not showindex_is_a_str:\n        rows = _prepend_row_index(rows, showindex)\n    elif showindex == \"always\" or (_bool(showindex) and not showindex_is_a_str):\n        if index is None:\n            index = list(range(len(rows)))\n        rows = _prepend_row_index(rows, index)\n    elif showindex == \"never\" or (not _bool(showindex) and not showindex_is_a_str):\n        pass\n",
  "start_line": 1400,
  "end_line": 1500,
  "total_lines": 2787,
  "is_truncated": false
}

Generated tokens 61 tokens · stopped: STOP

Okay, it's at line 1464. Now let's read the surrounding code.<channel|><|tool_call>call:read_file{end_line:1500,filepath:<|"|>tabulate/__init__.py<|"|>,start_line:1400}<tool_call|><|tool_response>

Turn 4at 40 s373 tokens out4,339 prompt tokens

The problematic block is:
```python
        elif (
            dataclasses is not None
            and len(rows) > 0
            and dataclasses.is_dataclass(rows[0])
        ):
            # Python 3.7+'s dataclass
            field_names = [field.name for field in dataclasses.fields(rows[0])],
            if headers == "keys":
                headers = field_names
            rows = [[getattr(row, f) for f in field_names] for row in rows]
```
(Wait, I see a comma at the end of `field_names = [field.name for field in dataclasses.fields(rows[0])],` in the `read_file` output? Let me re-check. No, it was my mental reading. Looking at the `read_file` output again: `field_names = [field.name for field in dataclasses.fields(rows[0])]` - no comma).

The issue is that `rows` can contain `SEPARATING_LINE`.
If `rows[0]` is a dataclass, the code assumes ALL rows are dataclasses and tries to call `getattr(row, f)` on them.
If one of the rows is `SEPARATING_LINE` (which is a string), `getattr(row, f)` will fail with `AttributeError: 'str' object has no attribute ...`.

Let's see what `SEPARATING_LINE` is. I'll search for it 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
}

Generated tokens 373 tokens · stopped: STOP

The problematic block is: ```python elif ( dataclasses is not None and len(rows) > 0 and dataclasses.is_dataclass(rows[0]) ): # Python 3.7+'s dataclass field_names = [field.name for field in dataclasses.fields(rows[0])], if headers == "keys": headers = field_names rows = [[getattr(row, f) for f in field_names] for row in rows] ``` (Wait, I see a comma at the end of `field_names = [field.name for field in dataclasses.fields(rows[0])],` in the `read_file` output? Let me re-check. No, it was my mental reading. Looking at the `read_file` output again: `field_names = [field.name for field in dataclasses.fields(rows[0])]` - no comma). The issue is that `rows` can contain `SEPARATING_LINE`. If `rows[0]` is a dataclass, the code assumes ALL rows are dataclasses and tries to call `getattr(row, f)` on them. If one of the rows is `SEPARATING_LINE` (which is a string), `getattr(row, f)` will fail with `AttributeError: 'str' object has no attribute ...`. Let's see what `SEPARATING_LINE` is. I'll search for it in `tabulate/__init__.py`. <channel|><|tool_call>call:run_command{command:<|"|>grep -n "SEPARATING_LINE" tabulate/__init__.py<|"|>}<tool_call|><|tool_response>

Turn 5at 43 s76 tokens out4,854 prompt tokens

`SEPARATING_LINE = "\001"`.
And `_is_separating_line` probably handles this. Let's check `_is_separating_line`.

TOOL CALL · run_command

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

Result of run_command 95 characters

{
  "status": "ok",
  "stdout": "105:def _is_separating_line(row):\n",
  "stderr": "",
  "exit_code": 0
}

Generated tokens 76 tokens · stopped: STOP

`SEPARATING_LINE = "\001"`. And `_is_separating_line` probably handles this. Let's check `_is_separating_line`. <channel|><|tool_call>call:run_command{command:<|"|>grep -n "def _is_separating_line" tabulate/__init__.py<|"|>}<tool_call|><|tool_response>

Turn 6at 46 s51 tokens out4,984 prompt tokens

Let's read `_is_separating_line`.

TOOL CALL · read_file

{
  "end_line": 110,
  "filepath": "tabulate/__init__.py",
  "start_line": 105
}

Result of read_file 379 characters

{
  "status": "ok",
  "filepath": "tabulate/__init__.py",
  "content": "def _is_separating_line(row):\n    row_type = type(row)\n    is_sl = (row_type == list or row_type == str) and (\n        (len(row) >= 1 and row[0] == SEPARATING_LINE)\n        or (len(row) >= 2 and row[1] == SEPARATING_LINE)\n    )",
  "start_line": 105,
  "end_line": 110,
  "total_lines": 2787,
  "is_truncated": false
}

Generated tokens 51 tokens · stopped: STOP

Let's read `_is_separating_line`. <channel|><|tool_call>call:read_file{end_line:110,filepath:<|"|>tabulate/__init__.py<|"|>,start_line:105}<tool_call|><|tool_response>

Turn 7at 72 s320 tokens out5,199 prompt tokens

Wait, the `_is_separating_line` implementation looks slightly strange:
```python
def _is_separating_line(row):
    row_type = type(row)
    is_sl = (row_type == list or row_type == str) and (
        (len(row) >= 1 and row[0] == SEPARATING_LINE)
        or (len(row) >= 2 and row[1] == SEPARATING_LINE)
    )
```
Wait, if `row` is `SEPARATING_LINE` (which is `"\001"`), then `row[0]` is `"\001"`, which is `SEPARATING_LINE`. So `_is_separating_line(SEPARATING_LINE)` is True.

Now let's reproduce the bug.

TOOL CALL · run_command

{
  "command": "python3 - <<'PY'\nfrom dataclasses import dataclass\nfrom tabulate import tabulate, SEPARATING_LINE\n\n@dataclass\nclass Row:\n    a: int\n    b: str\n\ndata = [\n    Row(1, \"first\"),\n    SEPARATING_LINE,\n    Row(2, \"second\"),\n]\n\ntry:\n    print(tabulate(data, headers='keys'))\nexcept Exception as e:\n    import traceback\n    traceback.print_exc()\nPY\n"
}

Result of run_command 755 characters

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

Generated tokens 320 tokens · stopped: STOP

Wait, the `_is_separating_line` implementation looks slightly strange: ```python def _is_separating_line(row): row_type = type(row) is_sl = (row_type == list or row_type == str) and ( (len(row) >= 1 and row[0] == SEPARATING_LINE) or (len(row) >= 2 and row[1] == SEPARATING_LINE) ) ``` Wait, if `row` is `SEPARATING_LINE` (which is `"\001"`), then `row[0]` is `"\001"`, which is `SEPARATING_LINE`. So `_is_separating_line(SEPARATING_LINE)` is True. Now let's reproduce the bug. <channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY' from dataclasses import dataclass from tabulate import tabulate, SEPARATING_LINE @dataclass class Row: a: int b: str data = [ Row(1, "first"), SEPARATING_LINE, Row(2, "second"), ] try: print(tabulate(data, headers='keys')) except Exception as e: import traceback traceback.print_exc() PY <|"|>}<tool_call|><|tool_response>

Turn 8at 99 s475 tokens out5,793 prompt tokens

The reproduction confirmed the issue.
The fix should be to avoid calling `getattr` on rows that are separating lines.

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 = [[getattr(row, f) for f in field_names] for row in rows]` line is where it fails.

I should change this to something that handles `SEPARATING_LINE`.
For example:
```python
            rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]
```
Wait, `_is_separating_line` returns a boolean. If it's a separating line, we should just keep it as is.

Let's check how other types are handled.
For dict-like objects:
```python
            rows = [[row.get(k) for k in keys] for row in rows]
```
Wait, it doesn't seem to check for `_is_separating_line` there either!
If I have a list of dicts and a `SEPARATING_LINE`, will it fail?

TOOL CALL · run_command

{
  "command": "python3 - <<'PY'\nfrom tabulate import tabulate, SEPARATING_LINE\n\ndata = [\n    {\"a\": 1, \"b\": \"first\"},\n    SEPARATING_LINE,\n    {\"a\": 2, \"b\": \"second\"},\n]\n\ntry:\n    print(tabulate(data, headers='keys'))\nexcept Exception as e:\n    import traceback\n    traceback.print_exc()\nPY\n"
}

Result of run_command 437 characters

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

Generated tokens 475 tokens · stopped: STOP

The reproduction confirmed the issue. The fix should be to avoid calling `getattr` on rows that are separating lines. 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 = [[getattr(row, f) for f in field_names] for row in rows]` line is where it fails. I should change this to something that handles `SEPARATING_LINE`. For example: ```python rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows] ``` Wait, `_is_separating_line` returns a boolean. If it's a separating line, we should just keep it as is. Let's check how other types are handled. For dict-like objects: ```python rows = [[row.get(k) for k in keys] for row in rows] ``` Wait, it doesn't seem to check for `_is_separating_line` there either! If I have a list of dicts and a `SEPARATING_LINE`, will it fail? <channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY' from tabulate import tabulate, SEPARATING_LINE data = [ {"a": 1, "b": "first"}, SEPARATING_LINE, {"a": 2, "b": "second"}, ] try: print(tabulate(data, headers='keys')) except Exception as e: import traceback traceback.print_exc() PY <|"|>}<tool_call|><|tool_response>

Turn 9at 292 s3,548 tokens out6,431 prompt tokens

Wow, it fails for dicts too! The issue description only mentioned dataclasses, but it seems it's a general problem for any row-type extraction that doesn't check for separating lines.

Let's look at where `_normalize_tabular_data` handles different types.
The core problem is that `_normalize_tabular_data` assumes all rows are of the same type as the first row (or follow the same structure).

Let's re-examine the dict-like object handling:
```python
        elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):
            # dict-like object
            uniq_keys = set()  # implements hashed lookup
            keys = []  # storage for set
            if headers == "firstrow":
                firstdict = rows[0] if len(rows) > 0 else {}
                keys.extend(firstdict.keys())
                uniq_keys.update(keys)
                rows = rows[1:]
            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)
            # ... headers processing ...
            rows = [[row.get(k) for k in keys] for row in rows]
```
In the `for row in rows:` loop, it calls `row.keys()`. This fails if `row` is `SEPARATING_LINE`.

So I need to fix this in both the dict-like object section AND the dataclass section (and potentially others).

Let's look at other sections of `_normalize_tabular_data`.

Section 1: Numpy record array
```python
        if (
            hasattr(tabular_data, "dtype")
            and getattr(tabular_data.dtype, "names")
        ):
            # numpy record array
            headers = tabular_data.dtype.names
```
Numpy arrays usually don't have `SEPARATING_LINE` embedded in them unless they are object arrays.

Section 2: namedtuple
```python
        elif (
            headers == "keys"
            and len(rows) > 0
            and isinstance(rows[0], tuple)
            and hasattr(rows[0], "_fields")
        ):
            # namedtuple
            headers = list(map(str, rows[0]._fields))
```
Namedtuples don't seem to have their data converted to lists here, they just set the headers. The conversion to list happens later? No, let's check the end of `_normalize_tabular_data`.

Actually, let's see what happens after the if/elif blocks.
The if/elif blocks seem to be focusing on determining `headers` and normalizing `rows` to a list of lists.

Wait, let's look at the end of the if/elif blocks:
```python
    # take headers from the first row if necessary
    if headers == "firstrow" and len(rows) > 0:
        # ...
    elif headers == "firstrow":
        headers = []

    headers = list(map(str, headers))
    #    rows = list(map(list, rows))
    rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
The `rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))` line converts each row to a list, unless it's a separating line.
But this happens *after* we've already tried to access attributes or keys on each row.

So, any code that iterates over `rows` and assumes they are of a certain type must check `_is_separating_line(row)`.

Let's identify all such places.

1. Dict-like objects:
   - `for row in rows:` loop that calls `row.keys()`
   - `rows = [[row.get(k) for k in keys] for row in rows]`

2. Dataclasses:
   - `rows = [[getattr(row, f) for f in field_names] for row in rows]`

Are there others?
Let's look at the `if/elif` structure again.

```python
        if (
            hasattr(tabular_data, "dtype")
            and getattr(tabular_data.dtype, "names")
        ):
            # numpy record array
            headers = tabular_data.dtype.names
        elif (
            headers == "keys"
            and len(rows) > 0
            and isinstance(rows[0], tuple)
            and hasattr(rows[0], "_fields")
        ):
            # namedtuple
            headers = list(map(str, rows[0]._fields))
        elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):
            # dict-like object
            # ... (the failing part)
        elif (
            headers == "keys"
            and hasattr(tabular_data, "description")
            and hasattr(tabular_data, "fetchone")
            and hasattr(tabular_data, "rowcount")
        ):
            # Python Database API cursor object (PEP 0249)
            # print tabulate(cursor, headers='keys')
            headers = [column[0] for column in tabular_data.description
        elif (
            dataclasses is not None
            and len(rows) > 0
            and dataclasses.is_dataclass(rows[0])
        ):
            # Python 3.7+'s dataclass
            # ... (the failing part)
        elif headers == "keys" and len(rows) > 0:
            # keys are column indices
            headers = list(map(str, range(len(rows[0]))))
```

Wait, the `namedtuple` case doesn't normalize `rows` to a list of lists inside the `elif`. It just sets `headers`.
The normalization to list of lists for namedtuple happens at the end: `rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))`. Since `namedtuple` is a tuple, `list(r)` works. And since `_is_separating_line` is checked first, it's safe.

So the main problem areas are:
1. Dict-like objects
2. Dataclasses

Let's fix the dict-like objects first.

In the dict-like object section:
```python
            for row in rows:
                for k in row.keys():
                    # Save unique items in input order
                    if k not in uniq_keys:
                        keys.append(k)
                        uniq_keys.add(k)
```
Change to:
```python
            for row in rows:
                if _is_separating_line(row):
                    continue
                for k in row.keys():
                    # Save unique items in input order
                    if k not in uniq_keys:
                        keys.append(k)
                        uniq_keys.add(k)
```

And:
```python
            rows = [[row.get(k) for k in keys] for row in rows]
```
Change to:
```python
            rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows]
```

Now for the dataclass section:
```python
            rows = [[getattr(row, f) for f in field_names] for row in rows]
```
Change to:
```python
            rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]
```

Let's check if there are other places.
What about the `headers == "firstrow"` part for dicts?
```python
            if headers == "firstrow":
                firstdict = rows[0] if len(rows) > 0 else {}
                keys.extend(firstdict.keys())
                uniq_keys.update(keys)
                rows = rows[1:]
```
If `rows[0]` is a `SEPARATING_LINE`, this will fail.
Wait, can `rows[0]` be a `SEPARATING_LINE`?
If the user provides `[SEPARATING_LINE, {"a":1}, {"a":2}]`, then `rows[0]` is `SEPARATING_LINE`.
Does `tabulate` support `SEPARATING_LINE` at the start?
Usually `SEPARATING_LINE` is used to separate data rows.

If `rows[0]` is a `SEPARATING_LINE`, `hasattr(rows[0], "keys")` (where `rows[0]` is a string) will be False.
Wait, does `str` have a `keys` attribute? No.
So if `rows[0]` is `SEPARATING_LINE`, it won't enter the `elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):` block.
Wait, then it might fall through to other blocks.

If `rows[0]` is `SEPARATING_LINE`, it'll skip the dict-like block and the dataclass block.
It might hit `elif headers == "keys" and len(rows) > 0:`.
```python
        elif headers == "keys" and len(rows) > 0:
            # keys are column indices
            headers = list(map(str, range(len(rows[0]))))
```
If `rows[0]` is `SEPARATING_LINE` (a string), `len(rows[0])` is 1. So `headers` would be `["0"]`. This seems wrong.

However, the issue report specifically says "print a list of dataclasses when one or more rows is SEPARATING_LINE". This suggests that at least one of the rows (probably the first one) *is* a dataclass.
The reproduction case:
```python
data = [
    Row(1, "first"),
    SEPARATING_LINE,
    Row(2, "second"),
]
```
Here `rows[0]` is a `Row` object, so it enters the dataclass block.

Let's verify the fix for dataclasses first, as that was the reported issue.
Wait, I should probably fix the dict-like case too because it's obviously broken in the same way.

Let's re-read the dict-like block carefully.

```python
        elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):
            # dict-like object
            uniq_keys = set()  # implements hashed lookup
            keys = []  # storage for set
            if headers == "firstrow":
                firstdict = rows[0] if len(rows) > 0 else {}
                keys.extend(firstdict.keys())
                uniq_keys.update(keys)
                rows = rows[1:]
            for row in rows:
                for k in row.keys():
                    # Save unique items in input order
                    if k not in uniq_keys:
                        keys.append(k)
                        uniq_keys.add(k)
```

If `headers == "firstrow"`, and `rows[0]` is a `SEPARATING_LINE`, `hasattr(rows[0], "keys")` is False, so it won't even enter this block.
If `rows[0]` is a dict, it enters this block. Then the `for row in rows:` loop iterates over the remaining rows. If any of those are `SEPARATING_LINE`, it fails at `row.keys()`.

So the fixes I planned:
1. For dict-like:
   - In `for row in rows:`, skip if `_is_separating_line(row)`.
   - In `rows = [[row.get(k) for k in keys] for row in rows]`, check `_is_separating_line(row)`.
2. For dataclass:
   - In `rows = [[getattr(row, f) for f in field_names] for row in rows]`, check `_is_separating_line(row)`.

Let's implement these.

Wait, there is one more place:
```python
    # take headers from the first row if necessary
    if headers == "firstrow" and len(rows) > 0:
        if index is not None:
            headers = [index[0]] + list(rows[0])
            index = index[1:]
        else:
            headers = rows[0]
        headers = list(map(str, headers))  # headers should be strings
        rows = rows[1:]
```
If `rows[0]` is a `SEPARATING_LINE` here, `list(rows[0])` will be `//001]`. This seems unlikely to be the intended use case for `headers="firstrow"`, but it's interesting. However, as we saw, `_normalize_tabular_data` usually handles this by checking `rows[0]` type at the beginning.

Let's focus on the dicts and dataclasses.

Detailed changes for `tabulate/__init__.py`:

**Dict-like section:**
Around line 1418:
```python
            for row in rows:
                for k in row.keys():
```
to
```python
            for row in rows:
                if _is_separating_line(row):
                    continue
                for k in row.keys():
```

Around 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 not _is_separating_line(row) else row for row in rows]
```

**Dataclass section:**
Around 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 not _is_separating_line(row) else row for row in rows]
```

Wait, if I change `rows = [[row.get(k) for k in keys] for row in rows]` to `rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows]`, then `rows` becomes a list where some elements are lists and some are strings (the `SEPARATING_LINE`).
Then later:
```python
    rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
If `r` is already a list, `list(r)` just makes a copy. If `r` is a `SEPARATING_LINE` (string), it stays as a string. This is correct.

Let's apply the changes.

First, I'll read the dict-like block again to make sure I have the exact context.

TOOL CALL · read_file

{
  "end_line": 1440,
  "filepath": "tabulate/__init__.py",
  "start_line": 1410
}

Result of read_file 1,529 characters

{
  "status": "ok",
  "filepath": "tabulate/__init__.py",
  "content": "            # 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(",
  "start_line": 1410,
  "end_line": 1440,
  "total_lines": 2787,
  "is_truncated": false
}

Generated tokens 3,548 tokens · stopped: STOP

Wow, it fails for dicts too! The issue description only mentioned dataclasses, but it seems it's a general problem for any row-type extraction that doesn't check for separating lines. Let's look at where `_normalize_tabular_data` handles different types. The core problem is that `_normalize_tabular_data` assumes all rows are of the same type as the first row (or follow the same structure). Let's re-examine the dict-like object handling: ```python elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"): # dict-like object uniq_keys = set() # implements hashed lookup keys = [] # storage for set if headers == "firstrow": firstdict = rows[0] if len(rows) > 0 else {} keys.extend(firstdict.keys()) uniq_keys.update(keys) rows = rows[1:] 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) # ... headers processing ... rows = [[row.get(k) for k in keys] for row in rows] ``` In the `for row in rows:` loop, it calls `row.keys()`. This fails if `row` is `SEPARATING_LINE`. So I need to fix this in both the dict-like object section AND the dataclass section (and potentially others). Let's look at other sections of `_normalize_tabular_data`. Section 1: Numpy record array ```python if ( hasattr(tabular_data, "dtype") and getattr(tabular_data.dtype, "names") ): # numpy record array headers = tabular_data.dtype.names ``` Numpy arrays usually don't have `SEPARATING_LINE` embedded in them unless they are object arrays. Section 2: namedtuple ```python elif ( headers == "keys" and len(rows) > 0 and isinstance(rows[0], tuple) and hasattr(rows[0], "_fields") ): # namedtuple headers = list(map(str, rows[0]._fields)) ``` Namedtuples don't seem to have their data converted to lists here, they just set the headers. The conversion to list happens later? No, let's check the end of `_normalize_tabular_data`. Actually, let's see what happens after the if/elif blocks. The if/elif blocks seem to be focusing on determining `headers` and normalizing `rows` to a list of lists. Wait, let's look at the end of the if/elif blocks: ```python # take headers from the first row if necessary if headers == "firstrow" and len(rows) > 0: # ... elif headers == "firstrow": headers = [] headers = list(map(str, headers)) # rows = list(map(list, rows)) rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows)) ``` The `rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))` line converts each row to a list, unless it's a separating line. But this happens *after* we've already tried to access attributes or keys on each row. So, any code that iterates over `rows` and assumes they are of a certain type must check `_is_separating_line(row)`. Let's identify all such places. 1. Dict-like objects: - `for row in rows:` loop that calls `row.keys()` - `rows = [[row.get(k) for k in keys] for row in rows]` 2. Dataclasses: - `rows = [[getattr(row, f) for f in field_names] for row in rows]` Are there others? Let's look at the `if/elif` structure again. ```python if ( hasattr(tabular_data, "dtype") and getattr(tabular_data.dtype, "names") ): # numpy record array headers = tabular_data.dtype.names elif ( headers == "keys" and len(rows) > 0 and isinstance(rows[0], tuple) and hasattr(rows[0], "_fields") ): # namedtuple headers = list(map(str, rows[0]._fields)) elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"): # dict-like object # ... (the failing part) elif ( headers == "keys" and hasattr(tabular_data, "description") and hasattr(tabular_data, "fetchone") and hasattr(tabular_data, "rowcount") ): # Python Database API cursor object (PEP 0249) # print tabulate(cursor, headers='keys') headers = [column[0] for column in tabular_data.description elif ( dataclasses is not None and len(rows) > 0 and dataclasses.is_dataclass(rows[0]) ): # Python 3.7+'s dataclass # ... (the failing part) elif headers == "keys" and len(rows) > 0: # keys are column indices headers = list(map(str, range(len(rows[0])))) ``` Wait, the `namedtuple` case doesn't normalize `rows` to a list of lists inside the `elif`. It just sets `headers`. The normalization to list of lists for namedtuple happens at the end: `rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))`. Since `namedtuple` is a tuple, `list(r)` works. And since `_is_separating_line` is checked first, it's safe. So the main problem areas are: 1. Dict-like objects 2. Dataclasses Let's fix the dict-like objects first. In the dict-like object section: ```python for row in rows: for k in row.keys(): # Save unique items in input order if k not in uniq_keys: keys.append(k) uniq_keys.add(k) ``` Change to: ```python for row in rows: if _is_separating_line(row): continue for k in row.keys(): # Save unique items in input order if k not in uniq_keys: keys.append(k) uniq_keys.add(k) ``` And: ```python rows = [[row.get(k) for k in keys] for row in rows] ``` Change to: ```python rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows] ``` Now for the dataclass section: ```python rows = [[getattr(row, f) for f in field_names] for row in rows] ``` Change to: ```python rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows] ``` Let's check if there are other places. What about the `headers == "firstrow"` part for dicts? ```python if headers == "firstrow": firstdict = rows[0] if len(rows) > 0 else {} keys.extend(firstdict.keys()) uniq_keys.update(keys) rows = rows[1:] ``` If `rows[0]` is a `SEPARATING_LINE`, this will fail. Wait, can `rows[0]` be a `SEPARATING_LINE`? If the user provides `[SEPARATING_LINE, {"a":1}, {"a":2}]`, then `rows[0]` is `SEPARATING_LINE`. Does `tabulate` support `SEPARATING_LINE` at the start? Usually `SEPARATING_LINE` is used to separate data rows. If `rows[0]` is a `SEPARATING_LINE`, `hasattr(rows[0], "keys")` (where `rows[0]` is a string) will be False. Wait, does `str` have a `keys` attribute? No. So if `rows[0]` is `SEPARATING_LINE`, it won't enter the `elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):` block. Wait, then it might fall through to other blocks. If `rows[0]` is `SEPARATING_LINE`, it'll skip the dict-like block and the dataclass block. It might hit `elif headers == "keys" and len(rows) > 0:`. ```python elif headers == "keys" and len(rows) > 0: # keys are column indices headers = list(map(str, range(len(rows[0])))) ``` If `rows[0]` is `SEPARATING_LINE` (a string), `len(rows[0])` is 1. So `headers` would be `["0"]`. This seems wrong. However, the issue report specifically says "print a list of dataclasses when one or more rows is SEPARATING_LINE". This suggests that at least one of the rows (probably the first one) *is* a dataclass. The reproduction case: ```python data = [ Row(1, "first"), SEPARATING_LINE, Row(2, "second"), ] ``` Here `rows[0]` is a `Row` object, so it enters the dataclass block. Let's verify the fix for dataclasses first, as that was the reported issue. Wait, I should probably fix the dict-like case too because it's obviously broken in the same way. Let's re-read the dict-like block carefully. ```python elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"): # dict-like object uniq_keys = set() # implements hashed lookup keys = [] # storage for set if headers == "firstrow": firstdict = rows[0] if len(rows) > 0 else {} keys.extend(firstdict.keys()) uniq_keys.update(keys) rows = rows[1:] for row in rows: for k in row.keys(): # Save unique items in input order if k not in uniq_keys: keys.append(k) uniq_keys.add(k) ``` If `headers == "firstrow"`, and `rows[0]` is a `SEPARATING_LINE`, `hasattr(rows[0], "keys")` is False, so it won't even enter this block. If `rows[0]` is a dict, it enters this block. Then the `for row in rows:` loop iterates over the remaining rows. If any of those are `SEPARATING_LINE`, it fails at `row.keys()`. So the fixes I planned: 1. For dict-like: - In `for row in rows:`, skip if `_is_separating_line(row)`. - In `rows = [[row.get(k) for k in keys] for row in rows]`, check `_is_separating_line(row)`. 2. For dataclass: - In `rows = [[getattr(row, f) for f in field_names] for row in rows]`, check `_is_separating_line(row)`. Let's implement these. Wait, there is one more place: ```python # take headers from the first row if necessary if headers == "firstrow" and len(rows) > 0: if index is not None: headers = [index[0]] + list(rows[0]) index = index[1:] else: headers = rows[0] headers = list(map(str, headers)) # headers should be strings rows = rows[1:] ``` If `rows[0]` is a `SEPARATING_LINE` here, `list(rows[0])` will be `//001]`. This seems unlikely to be the intended use case for `headers="firstrow"`, but it's interesting. However, as we saw, `_normalize_tabular_data` usually handles this by checking `rows[0]` type at the beginning. Let's focus on the dicts and dataclasses. Detailed changes for `tabulate/__init__.py`: **Dict-like section:** Around line 1418: ```python for row in rows: for k in row.keys(): ``` to ```python for row in rows: if _is_separating_line(row): continue for k in row.keys(): ``` Around 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 not _is_separating_line(row) else row for row in rows] ``` **Dataclass section:** Around 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 not _is_separating_line(row) else row for row in rows] ``` Wait, if I change `rows = [[row.get(k) for k in keys] for row in rows]` to `rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows]`, then `rows` becomes a list where some elements are lists and some are strings (the `SEPARATING_LINE`). Then later: ```python rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows)) ``` If `r` is already a list, `list(r)` just makes a copy. If `r` is a `SEPARATING_LINE` (string), it stays as a string. This is correct. Let's apply the changes. First, I'll read the dict-like block again to make sure I have the exact context. <channel|><|tool_call>call:read_file{end_line:1440,filepath:<|"|>tabulate/__init__.py<|"|>,start_line:1410}<tool_call|><|tool_response>

Turn 10at 300 s64 tokens out10,395 prompt tokens

I need the part after line 1440 to see the `rows = [...]` line for dicts.

TOOL CALL · read_file

{
  "end_line": 1450,
  "filepath": "tabulate/__init__.py",
  "start_line": 1440
}

Result of read_file 571 characters

{
  "status": "ok",
  "filepath": "tabulate/__init__.py",
  "content": "                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        ):",
  "start_line": 1440,
  "end_line": 1450,
  "total_lines": 2787,
  "is_truncated": false
}

Generated tokens 64 tokens · stopped: STOP

I need the part after line 1440 to see the `rows = [...]` line for dicts. <channel|><|tool_call>call:read_file{end_line:1450,filepath:<|"|>tabulate/__init__.py<|"|>,start_line:1440}<tool_call|><|tool_response>

Turn 11at 307 s182 tokens out10,638 prompt tokens

Okay, now I have everything.

Let's apply the edits.

Edit 1: Dict-like objects loop.

TOOL CALL · edit_file

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

Result of edit_file 530 characters

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

Generated tokens 182 tokens · stopped: STOP

Okay, now I have everything. Let's apply the edits. Edit 1: Dict-like objects loop. <channel|><|tool_call>call:edit_file{filepath:<|"|>tabulate/__init__.py<|"|>,new_string:<|"|> for row in rows: if _is_separating_line(row): continue for k in row.keys(): # Save unique items in input order if k not in uniq_keys: keys.append(k) uniq_keys.add(k)<|"|>,old_string:<|"|> for row in rows: for k in row.keys(): # Save unique items in input order if k not in uniq_keys: keys.append(k) uniq_keys.add(k)<|"|>}<tool_call|><|tool_response>

Turn 12at 312 s101 tokens out10,992 prompt tokens

Edit 2: Dict-like objects rows normalization.

TOOL CALL · edit_file

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

Result of edit_file 569 characters

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

Generated tokens 101 tokens · stopped: STOP

Edit 2: Dict-like objects rows normalization. <channel|><|tool_call>call:edit_file{filepath:<|"|>tabulate/__init__.py<|"|>,new_string:<|"|> rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows]<|"|>,old_string:<|"|> rows = [[row.get(k) for k in keys] for row in rows]<|"|>}<tool_call|><|tool_response>

Turn 13at 317 s104 tokens out11,287 prompt tokens

Edit 3: Dataclass rows normalization.

TOOL CALL · edit_file

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

Result of edit_file 659 characters

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

Generated tokens 104 tokens · stopped: STOP

Edit 3: Dataclass rows normalization. <channel|><|tool_call>call:edit_file{filepath:<|"|>tabulate/__init__.py<|"|>,new_string:<|"|> rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]<|"|>,old_string:<|"|> rows = [[getattr(row, f) for f in field_names] for row in rows]<|"|>}<tool_call|><|tool_response>

Turn 14at 329 s262 tokens out11,617 prompt tokens

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

TOOL CALL · run_command

{
  "command": "python3 - <<'PY'\nfrom dataclasses import dataclass\nfrom tabulate import tabulate, SEPARATING_LINE\n\n@dataclass\nclass Row:\n    a: int\n    b: str\n\ndata_dc = [\n    Row(1, \"first\"),\n    SEPARATING_LINE,\n    Row(2, \"second\"),\n]\n\ndata_dict = [\n    {\"a\": 1, \"b\": \"first\"},\n    SEPARATING_LINE,\n    {\"a\": 2, \"b\": \"second\"},\n]\n\nprint(\"Testing dataclasses:\")\ntry:\n    print(tabulate(data_dc, headers='keys'))\n    print(\"Success!\")\nexcept Exception as e:\n    import traceback\n    traceback.print_exc()\n\nprint(\"\\nTesting dicts:\")\ntry:\n    print(tabulate(data_dict, headers='keys'))\n    print(\"Success!\")\nexcept Exception as e:\n    import traceback\n    traceback.print_exc()\nPY\n"
}

Result of run_command 238 characters

{
  "status": "ok",
  "stdout": "Testing dataclasses:\n  a  b\n---  ------\n  1  first\n---  ------\n  2  second\nSuccess!\n\nTesting dicts:\n  a  b\n---  ------\n  1  first\n---  ------\n  2  second\nSuccess!\n",
  "stderr": "",
  "exit_code": 0
}

Generated tokens 262 tokens · stopped: STOP

Now let's verify the fix with the reproduction script for both dataclasses and dicts. <channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY' from dataclasses import dataclass from tabulate import tabulate, SEPARATING_LINE @dataclass class Row: a: int b: str data_dc = [ Row(1, "first"), SEPARATING_LINE, Row(2, "second"), ] data_dict = [ {"a": 1, "b": "first"}, SEPARATING_LINE, {"a": 2, "b": "second"}, ] print("Testing dataclasses:") try: print(tabulate(data_dc, headers='keys')) print("Success!") except Exception as e: import traceback traceback.print_exc() print("\nTesting dicts:") try: print(tabulate(data_dict, headers='keys')) print("Success!") except Exception as e: import traceback traceback.print_exc() PY <|"|>}<tool_call|><|tool_response>

Turn 15at 337 s168 tokens out11,991 prompt tokens

The reproduction cases now pass.
Let's check if there are any other edge cases.
What if `rows[0]` is a `SEPARATING_LINE`?

TOOL CALL · run_command

{
  "command": "python3 - <<'PY'\nfrom dataclasses import dataclass\nfrom tabulate import tabulate, SEPARATING_LINE\n\n@dataclass\nclass Row:\n    a: int\n    b: str\n\ndata = [\n    SEPARATING_LINE,\n    Row(1, \"first\"),\n    Row(2, \"second\"),\n]\n\ntry:\n    print(tabulate(data, headers='keys'))\nexcept Exception as e:\n    import traceback\n    traceback.print_exc()\nPY\n"
}

Result of run_command 626 characters

{
  "status": "ok",
  "stdout": "",
  "stderr": "Traceback (most recent call last):\n  File \"<stdin>\", line 16, in <module>\n  File \"/workspace/tabulate/__init__.py\", line 2090, in tabulate\n    list_of_lists, headers, headers_pad = _normalize_tabular_data(\n  File \"/workspace/tabulate/__init__.py\", line 1486, in _normalize_tabular_data\n    rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))\n  File \"/workspace/tabulate/__init__.py\", line 1486, in <lambda>\n    rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))\nTypeError: 'Row' object is not iterable\n",
  "exit_code": 0
}

Generated tokens 168 tokens · stopped: STOP

The reproduction cases now pass. Let's check if there are any other edge cases. What if `rows[0]` is a `SEPARATING_LINE`? <channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY' from dataclasses import dataclass from tabulate import tabulate, SEPARATING_LINE @dataclass class Row: a: int b: str data = [ SEPARATING_LINE, Row(1, "first"), Row(2, "second"), ] try: print(tabulate(data, headers='keys')) except Exception as e: import traceback traceback.print_exc() PY <|"|>}<tool_call|><|tool_response>

Turn 16at 365 s539 tokens out12,394 prompt tokens

Ah, if `rows[0]` is `SEPARATING_LINE`, it skips the dataclass and dict blocks.
Then it hits this:
```python
    rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
Since `Row` objects are not iterable, `list(r)` fails.
But is this a bug we should fix now? The issue report specifically focused on when `SEPARATING_LINE` is among the rows, and usually `tabulate` expects a consistent type of row. If the first row is a `SEPARATING_LINE`, `tabulate` has no way of knowing the structure of the subsequent rows if they are dataclasses (since it uses `rows[0]` to determine the fields).

If the user wants to use `SEPARATING_LINE` at the beginning, they should probably ensure the first row is of the actual data type so `tabulate` can infer the headers.

Wait, if the用户 provides a list of dataclasses AND `headers` are provided (not as `'keys'`), does it still fail?

```python
tabulate(data, headers=["A", "B"])
```
If `data = [SEPARATING_LINE, Row(1, "first")]`, then `rows[0]` is `SEPARATING_LINE`.
It skips the dataclass block.
And then it tries `list(r)` on `Row(1, "first")`, which fails.

However, if `rows[0]` is a `Row` object, it enters the dataclass block, and my fix handles `SEPARATING_LINE` in subsequent rows.

If `rows[0]` is a `SEPARATING_LINE`, the current implementation of `tabulate` fails anyway (even before my changes).

Let's check if `namedtuple` works when `rows[0]` is `SEPARATING_LINE`.

TOOL CALL · run_command

{
  "command": "python3 - <<'PY'\nfrom collections import namedtuple\nfrom tabulate import tabulate, SEPARATING_LINE\n\nRow = namedtuple('Row', ['a', 'b'])\ndata = [\n    SEPARATING_LINE,\n    Row(1, 'first'),\n]\n\ntry:\n    print(tabulate(data, headers='keys'))\nexcept Exception as e:\n    import traceback\n    traceback.print_exc()\nPY\n"
}

Result of run_command 80 characters

{
  "status": "ok",
  "stdout": "  0\n---\n---\n  1\n",
  "stderr": "",
  "exit_code": 0
}

Generated tokens 539 tokens · stopped: STOP

Ah, if `rows[0]` is `SEPARATING_LINE`, it skips the dataclass and dict blocks. Then it hits this: ```python rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows)) ``` Since `Row` objects are not iterable, `list(r)` fails. But is this a bug we should fix now? The issue report specifically focused on when `SEPARATING_LINE` is among the rows, and usually `tabulate` expects a consistent type of row. If the first row is a `SEPARATING_LINE`, `tabulate` has no way of knowing the structure of the subsequent rows if they are dataclasses (since it uses `rows[0]` to determine the fields). If the user wants to use `SEPARATING_LINE` at the beginning, they should probably ensure the first row is of the actual data type so `tabulate` can infer the headers. Wait, if the用户 provides a list of dataclasses AND `headers` are provided (not as `'keys'`), does it still fail? ```python tabulate(data, headers=["A", "B"]) ``` If `data = [SEPARATING_LINE, Row(1, "first")]`, then `rows[0]` is `SEPARATING_LINE`. It skips the dataclass block. And then it tries `list(r)` on `Row(1, "first")`, which fails. However, if `rows[0]` is a `Row` object, it enters the dataclass block, and my fix handles `SEPARATING_LINE` in subsequent rows. If `rows[0]` is a `SEPARATING_LINE`, the current implementation of `tabulate` fails anyway (even before my changes). Let's check if `namedtuple` works when `rows[0]` is `SEPARATING_LINE`. <channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY' from collections import namedtuple from tabulate import tabulate, SEPARATING_LINE Row = namedtuple('Row', ['a', 'b']) data = [ SEPARATING_LINE, Row(1, 'first'), ] try: print(tabulate(data, headers='keys')) except Exception as e: import traceback traceback.print_exc() PY <|"|>}<tool_call|><|tool_response>

Turn 17at 386 s327 tokens out12,985 prompt tokens

Wait, that's interesting. Why did it work for namedtuple?
Because `namedtuple` didn't have a normalization step that converts rows to lists *inside* the `elif` block. It only happens at the end:
```python
    rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
And since `Row(1, 'first')` is a `namedtuple`, it IS iterable. So `list(r)` works.
But for dataclasses, it's NOT iterable.

So the problem is indeed that dataclasses (and other non-iterable types) rely on the `_normalize_tabular_data` logic to be converted to lists. If `rows[0]` is not a dataclass, the `_normalize_tabular_data` function doesn't know it should treat the other rows as dataclasses.

This seems like a separate (and perhaps deeper) issue than what was reported. The reported issue was that `SEPARATING_LINE` causing a crash when rows are dataclasses. My fix addresses this by ensuring that when `rows[0]` is a dataclass, `SEPARATING_LINE` rows are handled correctly.

Let's check the existing tests to see if there are any other cases we should consider.
`tests/test_input.py` seems relevant.

TOOL CALL · run_command

{
  "command": "grep -n \"dataclass\" tests/test_input.py"
}

Result of run_command 235 characters

{
  "status": "error",
  "error_type": "CommandError",
  "error_message": "grep: tests/test_input.py: No such file or directory\n",
  "details": {
    "stdout": "",
    "stderr": "grep: tests/test_input.py: No such file or directory\n",
    "exit_code": 2
  }
}

Generated tokens 327 tokens · stopped: STOP

Wait, that's interesting. Why did it work for namedtuple? Because `namedtuple` didn't have a normalization step that converts rows to lists *inside* the `elif` block. It only happens at the end: ```python rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows)) ``` And since `Row(1, 'first')` is a `namedtuple`, it IS iterable. So `list(r)` works. But for dataclasses, it's NOT iterable. So the problem is indeed that dataclasses (and other non-iterable types) rely on the `_normalize_tabular_data` logic to be converted to lists. If `rows[0]` is not a dataclass, the `_normalize_tabular_data` function doesn't know it should treat the other rows as dataclasses. This seems like a separate (and perhaps deeper) issue than what was reported. The reported issue was that `SEPARATING_LINE` causing a crash when rows are dataclasses. My fix addresses this by ensuring that when `rows[0]` is a dataclass, `SEPARATING_LINE` rows are handled correctly. Let's check the existing tests to see if there are any other cases we should consider. `tests/test_input.py` seems relevant.<channel|><|tool_call>call:run_command{command:<|"|>grep -n "dataclass" tests/test_input.py<|"|>}<tool_call|><|tool_response>

Turn 18at 400 s169 tokens out13,404 prompt tokens

Wait, why did `grep` fail? Let me check the workspace layout.
The workspace layout said:
```
./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
```
It's in `test/test_input.py`, not `tests/test_input.py`. (Note the `test/` vs `tests/`). My bad.

TOOL CALL · run_command

{
  "command": "grep -n \"dataclass\" test/test_input.py"
}

Result of run_command 750 characters

{
  "status": "ok",
  "stdout": "481:def test_py37orlater_list_of_dataclasses_keys():\n482:    \"Input: a list of dataclasses with first item's fields as keys and headers\"\n484:        from dataclasses import make_dataclass\n486:        Person = make_dataclass(\"Person\", [\"name\", \"age\", \"height\"])\n499:        skip(\"test_py37orlater_list_of_dataclasses_keys is skipped\")\n502:def test_py37orlater_list_of_dataclasses_headers():\n503:    \"Input: a list of dataclasses with user-supplied headers\"\n505:        from dataclasses import make_dataclass\n507:        Person = make_dataclass(\"Person\", [\"name\", \"age\", \"height\"])\n520:        skip(\"test_py37orlater_list_of_dataclasses_headers is skipped\")\n",
  "stderr": "",
  "exit_code": 0
}

Generated tokens 169 tokens · stopped: STOP

Wait, why did `grep` fail? Let me check the workspace layout. The workspace layout said: ``` ./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 ``` It's in `test/test_input.py`, not `tests/test_input.py`. (Note the `test/` vs `tests/`). My bad. <channel|><|tool_call>call:run_command{command:<|"|>grep -n "dataclass" test/test_input.py<|"|>}<tool_call|><|tool_response>

Turn 19at 405 s90 tokens out13,846 prompt tokens

Wait, these tests are skipped?
```python
    skip("test_py37orlater_list_of_dataclasses_keys is skipped")
```
That's odd. Let's read the content of these tests.

TOOL CALL · read_file

{
  "end_line": 525,
  "filepath": "test/test_input.py",
  "start_line": 480
}

Result of read_file 1,880 characters

{
  "status": "ok",
  "filepath": "test/test_input.py",
  "content": "\ndef test_py37orlater_list_of_dataclasses_keys():\n    \"Input: a list of dataclasses with first item's fields as keys and headers\"\n    try:\n        from dataclasses import make_dataclass\n\n        Person = make_dataclass(\"Person\", [\"name\", \"age\", \"height\"])\n        ld = [Person(\"Alice\", 23, 169.5), Person(\"Bob\", 27, 175.0)]\n        result = tabulate(ld, headers=\"keys\")\n        expected = \"\\n\".join(\n            [\n                \"name      age    height\",\n                \"------  -----  --------\",\n                \"Alice      23     169.5\",\n                \"Bob        27     175\",\n            ]\n        )\n        assert_equal(expected, result)\n    except ImportError:\n        skip(\"test_py37orlater_list_of_dataclasses_keys is skipped\")\n\n\ndef test_py37orlater_list_of_dataclasses_headers():\n    \"Input: a list of dataclasses with user-supplied headers\"\n    try:\n        from dataclasses import make_dataclass\n\n        Person = make_dataclass(\"Person\", [\"name\", \"age\", \"height\"])\n        ld = [Person(\"Alice\", 23, 169.5), Person(\"Bob\", 27, 175.0)]\n        result = tabulate(ld, headers=[\"person\", \"years\", \"cm\"])\n        expected = \"\\n\".join(\n            [\n                \"person      years     cm\",\n                \"--------  -------  -----\",\n                \"Alice          23  169.5\",\n                \"Bob            27  175\",\n            ]\n        )\n        assert_equal(expected, result)\n    except ImportError:\n        skip(\"test_py37orlater_list_of_dataclasses_headers is skipped\")\n\n\ndef test_list_bytes():\n    \"Input: a list of bytes. (issue #192)\"\n    lb = [[\"你好\".encode(\"utf-8\")], [\"你好\"]]",
  "start_line": 480,
  "end_line": 525,
  "total_lines": 530,
  "is_truncated": false
}

Generated tokens 90 tokens · stopped: STOP

Wait, these tests are skipped? ```python skip("test_py37orlater_list_of_dataclasses_keys is skipped") ``` That's odd. Let's read the content of these tests. <channel|><|tool_call>call:read_file{end_line:525,filepath:<|"|>test/test_input.py<|"|>,start_line:480}<tool_call|><|tool_response>

Turn 20at 413 s99 tokens out14,548 prompt tokens

Okay, the `skip` was just my `grep` a bit confused by the indentation in the `ImportError` block. They are not skipped.

I should run these tests to make sure I haven't broken anything.

TOOL CALL · run_command

{
  "command": "pytest test/test_input.py -k \"test_py37orlater_list_of_dataclasses\" -q --tb=short --no-header"
}

Result of run_command 289 characters

{
  "status": "ok",
  "stdout": "..                                                                       [100%]\n2 passed, 30 deselected in 0.02s\n",
  "stderr": "",
  "exit_code": 0,
  "budget_warning": "Only 10 tool call(s) remaining (20/30 used). Finalize your edits and call submit_patch soon."
}

Generated tokens 99 tokens · stopped: STOP

Okay, the `skip` was just my `grep` a bit confused by the indentation in the `ImportError` block. They are not skipped. I should run these tests to make sure I haven't broken anything. <channel|><|tool_call>call:run_command{command:<|"|>pytest test/test_input.py -k "test_py37orlater_list_of_dataclasses" -q --tb=short --no-header<|"|>}<tool_call|><|tool_response>

Patch, tests and logs

Final code change Included

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

Test output Included

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Got:
-  -
1  2
3  4
-  -

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

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

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

Expected 2:
-  -
2  1
4  3
-  -

Got:
-  -
1  2
3  4
-  -

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

Expected 2:
-  -
2  1
4  3
-  -

Got:
-  -
1  2
3  4
-  -

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

- generated xml file: /tmp/rebench-grade-cb554a43ec5a497086a367e6b29f6a2e.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="0.689" timestamp="2026-11-14T03:56:52.568946+00:00" hostname="a3b7da16fa1a"><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="0.118" /><testcase classname="test.test_input" name="test_numpy_2d_firstrow" time="0.000" /><testcase classname="test.test_input" name="test_numpy_2d_keys" time="0.000" /><testcase classname="test.test_input" name="test_numpy_record_array" time="0.000" /><testcase classname="test.test_input" name="test_numpy_record_array_keys" time="0.000" /><testcase classname="test.test_input" name="test_numpy_record_array_headers" time="0.000" /><testcase classname="test.test_input" name="test_pandas" time="0.526" /><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="0.000" /><testcase classname="test.test_input" name="test_py37orlater_list_of_dataclasses_with_separating_line" time="0.000" /><testcase classname="test.test_input" name="test_list_bytes" time="0.000" /></testsuite></testsuites>

Agent log Included

  [   0.0s] 📋 System Prompt: You are a principal Python software engineer resolving a repository issue in `/workspace`.
  [   0.0s] 🎯 Task Prompt: You are evaluating a software engineering task for repository astanin/python-tabulate.
  [   8.8s] 💭 (403 chars) "The user is reporting an `AttributeError: 'str' object has no attribute '<the first 
attribute of dataclass>'` when using..."
  [   8.8s] 🔧 run_command grep -n "rows = [\[getattr(row, f) for f in field_names\]\]" tabulate/__init__.
  [   8.8s]   → error (exit 0): 
  [  13.9s] 💭 (270 chars) "The grep failed. Maybe the line is slightly different or I should search for the 
`AttributeError` context.
The problem s..."
  [  13.9s] 🔧 run_command grep -n "getattr(row, f)" tabulate/__init__.py
  [  14.0s]   → 1464:            rows = [[getattr(row, f) for f in field_names] for row in rows]
  [  17.7s] 💭 (61 chars) "Okay, it's at line 1464. Now let's read the surrounding code."
  [  17.7s] 📖 read_file (tabulate/__init__.py)
  [  17.8s]   ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'content': '            and 
getattr(tabular_data.dtype, "names")\n …
  [  39.1s] 💭 (1156 chars) "The problematic block is:
```python
        elif (
            dataclasses is not None
            and len(rows) > 0
   ..."
  [  39.1s] 🔧 run_command grep -n "SEPARATING_LINE" tabulate/__init__.py
  [  39.2s]   → 51:SEPARATING_LINE = "\001"
  [  42.9s] 💭 (111 chars) "`SEPARATING_LINE = "\001"`.
And `_is_separating_line` probably handles this. Let's check `_is_separating_line`."
  [  42.9s] 🔧 run_command grep -n "def _is_separating_line" tabulate/__init__.py
  [  43.0s]   → 105:def _is_separating_line(row):
  [  45.8s] 💭 (33 chars) "Let's read `_is_separating_line`."
  [  45.8s] 📖 read_file (tabulate/__init__.py)
  [  45.8s]   ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'content': 'def _is_separating_line(row):\n    
row_type = type(row)…
  [  71.7s] 💭 (505 chars) "Wait, the `_is_separating_line` implementation looks slightly strange:
```python
def _is_separating_line(row):
    row_t..."
  [  71.7s] 🔧 run_command (21 lines) python3 - <<'PY'
  [  71.8s]   🔧 
  [  98.2s] 💭 (1249 chars) "The reproduction confirmed the issue.
The fix should be to avoid calling `getattr` on rows that are separating lines.

L..."
  [  98.2s] 🔧 run_command (15 lines) python3 - <<'PY'
  [  98.2s]   🔧 
  [ 291.6s] 💭 (12058 chars) "Wow, it fails for dicts too! The issue description only mentioned dataclasses, but it 
seems it's a general problem for a..."
  [ 291.6s] 📖 read_file (tabulate/__init__.py)
  [ 291.7s]   ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'content': '            # namedtuple\n            
headers = list(ma…
  [ 299.1s] 💭 (73 chars) "I need the part after line 1440 to see the `rows = [...]` line for dicts."
  [ 299.1s] 📖 read_file (tabulate/__init__.py)
  [ 299.2s]   ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'content': '                raise ValueError(\n    
…
  [ 306.7s] 💭 (85 chars) "Okay, now I have everything.

Let's apply the edits.

Edit 1: Dict-like objects loop."
  [ 306.7s] ✏️ edit_file (tabulate/__init__.py)
  [ 306.8s]   ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'occurrences': 1, 'strategy': 'exact', 'diff': '---
a/tabulate/__in…
  [ 311.9s] 💭 (45 chars) "Edit 2: Dict-like objects rows normalization."
  [ 311.9s] ✏️ edit_file (tabulate/__init__.py)
  [ 312.1s]   ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'occurrences': 1, 'strategy': 'exact', 'diff': '---
a/tabulate/__in…
  [ 316.3s] 💭 (37 chars) "Edit 3: Dataclass rows normalization."
  [ 316.3s] ✏️ edit_file (tabulate/__init__.py)
  [ 316.4s]   ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'occurrences': 1, 'strategy': 'exact', 'diff': '---
a/tabulate/__in…
  [ 328.9s] 💭 (85 chars) "Now let's verify the fix with the reproduction script for both dataclasses and dicts."
  [ 328.9s] 🔧 run_command (37 lines) python3 - <<'PY'
  [ 329.0s]   → Testing dataclasses:
  [ 336.2s] 💭 (121 chars) "The reproduction cases now pass.
Let's check if there are any other edge cases.
What if `rows[0]` is a `SEPARATING_LINE`..."
  [ 336.2s] 🔧 run_command (21 lines) python3 - <<'PY'
  [ 336.3s]   🔧 
  [ 364.7s] 💭 (1437 chars) "Ah, if `rows[0]` is `SEPARATING_LINE`, it skips the dataclass and dict blocks.
Then it hits this:
```python
    rows = l..."
  [ 364.7s] 🔧 run_command (16 lines) python3 - <<'PY'
  [ 364.8s]   →   0
  [ 385.9s] 💭 (1096 chars) "Wait, that's interesting. Why did it work for namedtuple?
Because `namedtuple` didn't have a normalization step that con..."
  [ 385.9s] 🔧 run_command grep -n "dataclass" tests/test_input.py
  [ 385.9s]   → error (exit 0): 
  [ 399.4s] 💭 (375 chars) "Wait, why did `grep` fail? Let me check the workspace layout.
The workspace layout said:
```
./test
./test/common.py
./t..."
  [ 399.4s] 🔧 run_command grep -n "dataclass" test/test_input.py
  [ 399.5s]   → 481:def test_py37orlater_list_of_dataclasses_keys():
  [ 404.5s] 💭 (160 chars) "Wait, these tests are skipped?
```python
    skip("test_py37orlater_list_of_dataclasses_keys is skipped")
```
That's odd..."
  [ 404.5s] 📖 read_file (test/test_input.py)
  [ 404.5s]   ✓ {'status': 'ok', 'filepath': 'test/test_input.py', 'content': '\ndef 
test_py37orlater_list_of_dataclasses_keys():\n    "…
  [ 412.1s] 💭 (185 chars) "Okay, the `skip` was just my `grep` a bit confused by the indentation in the `ImportError` 
block. They are not skipped.
..."
  [ 412.1s] 🔧 run_command pytest test/test_input.py -k "test_py37orlater_list_of_dataclasses" -q --tb=shor
  [ 412.5s]   → ..                                                                       [100%]

Native result row

{
  "agent_error": "Agent exceeded session timeout (7.5 min)",
  "duration_seconds": 454.37,
  "error": "Agent exceeded session timeout (7.5 min)",
  "grade": {
    "collected_tests": 33,
    "competition_junit_ok": true,
    "error": null,
    "required_issues_count": 0,
    "resolved": true,
    "status_counts": {
      "PASSED": 33
    },
    "strict_clean_patch": true,
    "test_exit_code": 0
  },
  "instance_id": "astanin__python-tabulate-268",
  "pass_idx": 0,
  "patch_chars": 1537,
  "phase1_seconds": 452.74,
  "repo": "astanin/python-tabulate",
  "request_log_path": "/kaggle-root/working/r31/rollouts_X/requests/astanin__python-tabulate-268__p0__r5.jsonl",
  "resolved": true,
  "rollout_idx": 5,
  "run_key": "astanin__python-tabulate-268__p0__r5",
  "strict_clean_patch": true,
  "tool_calls": 20,
  "total_llm_calls": 20,
  "trace_path": "/kaggle-root/working/r31/rollouts_X/traces/trace_astanin__python-tabulate-268__p0__r5.json"
}