ROLLOUT 3 · astanin/python-tabulate
astanin__python-tabulate-268
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15model calls
6,797tokens generated
136,254prompt tokens read
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⚠ The request log has 14 model calls but the trace has 16 agent calls, so tokens are listed separately instead of beside each turn.
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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 12 s188 tokens out2,665 prompt tokens
The user is reporting an `AttributeError` when using `tabulate` with a list of dataclasses where one or more rows is `SEPARATING_LINE`. The traceback points to `tabulate/__init__.py` line 1451: `rows = [[getattr(row, f) for f in field_names] for row in rows ]` This suggests that when `tabulate` expects a dataclass object for each row, it encounters a string (presumably `SEPARATING_LINE`) and tries to call `getattr` on it. `SEPARATING_LINE` is likely a special marker string used in `tabulate`. Let's start by exploring the code and reproducing the bug.
TOOL CALL · run_command
{
"command": "grep -rn --include=\"*.py\" \"SEPARATING_LINE\" ."
}Result of run_command 1,375 characters
{
"status": "ok",
"stdout": "./tabulate/__init__.py:51:SEPARATING_LINE = \"\\001\"\n./tabulate/__init__.py:108: (len(row) >= 1 and row[0] == SEPARATING_LINE)\n./tabulate/__init__.py:109: or (len(row) >= 2 and row[1] == SEPARATING_LINE)\n./tabulate/__init__.py:1290: rows.insert(index, SEPARATING_LINE)\n./tabulate/__init__.py:2453: # the SEPARATING_LINE flag\n./test/test_output.py:5:from tabulate import tabulate, simple_separated_format, SEPARATING_LINE\n./test/test_output.py:12:_test_table_with_sep_line = [[\"spam\", 41.9999], SEPARATING_LINE, [\"eggs\", \"451.0\"]]\n./test/test_output.py:118: SEPARATING_LINE,\n./test/test_output.py:261: table = [[\"Earth\", 6371], [\"Mars\", 3390], SEPARATING_LINE, [\"Moon\", 1737]]\n./test/test_output.py:307: SEPARATING_LINE,\n./test/test_output.py:2676: [[\"one\", \"two\"], SEPARATING_LINE, [\"three\", \"four\"]],\n./test/test_output.py:2921: dd = [(0, 101), SEPARATING_LINE, (1, 102), (2, 103)]\n./test/test_internal.py:318: T.SEPARATING_LINE,\n./test/test_internal.py:320: T.SEPARATING_LINE,\n./test/test_internal.py:322: T.SEPARATING_LINE,\n./test/test_internal.py:335: T.SEPARATING_LINE,\n./test/test_internal.py:337: T.SEPARATING_LINE,\n./test/test_internal.py:339: T.SEPARATING_LINE,\n",
"stderr": "",
"exit_code": 0
}Turn 2at 19 s140 tokens out3,444 prompt tokens
Okay, `SEPARATING_LINE` is defined as `"\001"` in `tabulate/__init__.py`. The issue is that when `rows` contains dataclasses and `SEPARATING_LINE`, the code tries to access attributes of the `SEPARATING_LINE` (which is a string) as if it were a dataclass. Let's look at the code around line 1451 in `tabulate/__init__.py`.
TOOL CALL · read_file
{
"end_line": 1470,
"filepath": "tabulate/__init__.py",
"start_line": 1430
}Result of read_file 1,841 characters
{
"status": "ok",
"filepath": "tabulate/__init__.py",
"content": " # a dict of headers for a list of dicts\n headers = [headers.get(k, k) for k in keys]\n headers = list(map(str, headers))\n elif headers == \"firstrow\":\n if len(rows) > 0:\n headers = [firstdict.get(k, k) for k in keys]\n headers = list(map(str, headers))\n else:\n headers = []\n elif headers:\n raise ValueError(\n \"headers for a list of dicts is not a dict or a keyword\"\n )\n rows = [[row.get(k) for k in keys] for row in rows]\n\n elif (\n headers == \"keys\"\n and hasattr(tabular_data, \"description\")\n and hasattr(tabular_data, \"fetchone\")\n and hasattr(tabular_data, \"rowcount\")\n ):\n # Python Database API cursor object (PEP 0249)\n # print tabulate(cursor, headers='keys')\n headers = [column[0] for column in tabular_data.description]\n\n elif (\n dataclasses is not None\n and len(rows) > 0\n and dataclasses.is_dataclass(rows[0])\n ):\n # Python 3.7+'s dataclass\n field_names = [field.name for field in dataclasses.fields(rows[0])]\n if headers == \"keys\":\n headers = field_names\n rows = [[getattr(row, f) for f in field_names] for row in rows]\n\n elif headers == \"keys\" and len(rows) > 0:\n # keys are column indices\n headers = list(map(str, range(len(rows[0]))))\n\n # take headers from the first row if necessary",
"start_line": 1430,
"end_line": 1470,
"total_lines": 2787,
"is_truncated": false
}Turn 3at 41 s328 tokens out4,101 prompt tokens
The problematic lines are:
```python
elif (
dataclasses is not None
and len(rows) > 0
and dataclasses.is_dataclass(rows[0])
):
# Python 3.7+'s dataclass
field_names = [field.name for field in dataclasses.fields(rows[0])\n if headers == "keys":
headers = field_names
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
The issue is that it checks if `rows[0]` is a dataclass, and if so, it assumes all `row` in `rows` are dataclasses and tries to call `getattr(row, f)` on them. But `row` could be `SEPARATING_LINE`.
Let's verify this with a reproduction script.
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nfrom dataclasses import dataclass\nfrom tabulate import tabulate, SEPARATING_LINE\n\n@dataclass\nclass User:\n name: str\n age: int\n\ndata = [\n User(\"Alice\", 30),\n SEPARATING_LINE,\n User(\"Bob\", 25)\n]\n\ntry:\n print(tabulate(data, headers=\"keys\"))\nexcept Exception as e:\n import traceback\n traceback.print_exc()\nPY\n"
}Result of run_command 758 characters
{
"status": "ok",
"stdout": "",
"stderr": "Traceback (most recent call last):\n File \"<stdin>\", line 16, in <module>\n File \"/workspace/tabulate/__init__.py\", line 2088, in tabulate\n list_of_lists, headers, headers_pad = _normalize_tabular_data(\n File \"/workspace/tabulate/__init__.py\", line 1464, in _normalize_tabular_data\n rows = [[getattr(row, f) for f in field_names] for row in rows]\n File \"/workspace/tabulate/__init__.py\", line 1464, in <listcomp>\n rows = [[getattr(row, f) for f in field_names] for row in rows]\n File \"/workspace/tabulate/__init__.py\", line 1464, in <listcomp>\n rows = [[getattr(row, f) for f in field_names] for row in rows]\nAttributeError: 'str' object has no attribute 'name'\n",
"exit_code": 0
}Turn 4at 77 s457 tokens out4,703 prompt tokens
The reproduction script confirmed the issue. `AttributeError: 'str' object has no attribute 'name'` occurs because `row` is `SEPARATING_LINE`. Now I need to fix it. I should check if the `row` is `SEPARATING_LINE` before trying to call `getattr`. However, if `row` is `SEPARATING_LINE`, it should probably be kept as `SEPARATING_LINE` instead of being converted to a list of its attributes (which doesn't make sense). Wait, `_normalize_tabular_data` is supposed to convert the input data into a list of lists. Let's look at how other types are handled. If it's a list of lists, it stays as is (mostly). If it's a list of dicts, it's converted to a list of lists of values. For dataclasses, it's also converting a list of dataclass instances to a list of lists of values. If a row is `SEPARATING_LINE`, it should probably be preserved as `SEPARATING_LINE`. Let's check how `SEPARATING_LINE` is handled later in the code. Actually, if I look at the beginning of `_normalize_tabular_data` or where `SEPARATING_LINE` is checked. I saw this earlier: `./tabulate/__init__.py:108: (len(row) >= 1 and row[0] == SEPARATING_LINE)` Wait, if `rows` is a list of dataclasses, then `row` is a dataclass. `len(row)` will fail if it's a dataclass unless it implements `__len__`. Actually, `_normalize_tabular_data` is called inside `tabulate`. Let's look at the rest of the function `_normalize_tabular_data` to see how it handles various types.
TOOL CALL · read_file
{
"end_line": 1490,
"filepath": "tabulate/__init__.py",
"start_line": 1350
}Result of read_file 5,919 characters
{
"status": "ok",
"filepath": "tabulate/__init__.py",
"content": " If showindex is an iterable, show its values as row indices.\n\n \"\"\"\n\n try:\n bool(headers)\n is_headers2bool_broken = False # noqa\n except ValueError: # numpy.ndarray, pandas.core.index.Index, ...\n is_headers2bool_broken = True # noqa\n headers = list(headers)\n\n index = None\n if hasattr(tabular_data, \"keys\") and hasattr(tabular_data, \"values\"):\n # dict-like and pandas.DataFrame?\n if hasattr(tabular_data.values, \"__call__\"):\n # likely a conventional dict\n keys = tabular_data.keys()\n rows = list(\n izip_longest(*tabular_data.values())\n ) # columns have to be transposed\n elif hasattr(tabular_data, \"index\"):\n # values is a property, has .index => it's likely a pandas.DataFrame (pandas 0.11.0)\n keys = list(tabular_data)\n if (\n showindex in [\"default\", \"always\", True]\n and tabular_data.index.name is not None\n ):\n if isinstance(tabular_data.index.name, list):\n keys[:0] = tabular_data.index.name\n else:\n keys[:0] = [tabular_data.index.name]\n vals = tabular_data.values # values matrix doesn't need to be transposed\n # for DataFrames add an index per default\n index = list(tabular_data.index)\n rows = [list(row) for row in vals]\n else:\n raise ValueError(\"tabular data doesn't appear to be a dict or a DataFrame\")\n\n if headers == \"keys\":\n headers = list(map(str, keys)) # headers should be strings\n\n else: # it's a usual iterable of iterables, or a NumPy array, or an iterable of dataclasses\n rows = list(tabular_data)\n\n if headers == \"keys\" and not rows:\n # an empty table (issue #81)\n headers = []\n elif (\n headers == \"keys\"\n and hasattr(tabular_data, \"dtype\")\n and getattr(tabular_data.dtype, \"names\")\n ):\n # numpy record array\n headers = tabular_data.dtype.names\n elif (\n headers == \"keys\"\n and len(rows) > 0\n and isinstance(rows[0], tuple)\n and hasattr(rows[0], \"_fields\")\n ):\n # namedtuple\n headers = list(map(str, rows[0]._fields))\n elif len(rows) > 0 and hasattr(rows[0], \"keys\") and hasattr(rows[0], \"values\"):\n # dict-like object\n uniq_keys = set() # implements hashed lookup\n keys = [] # storage for set\n if headers == \"firstrow\":\n firstdict = rows[0] if len(rows) > 0 else {}\n keys.extend(firstdict.keys())\n uniq_keys.update(keys)\n rows = rows[1:]\n for row in rows:\n for k in row.keys():\n # Save unique items in input order\n if k not in uniq_keys:\n keys.append(k)\n uniq_keys.add(k)\n if headers == \"keys\":\n headers = keys\n elif isinstance(headers, dict):\n # a dict of headers for a list of dicts\n headers = [headers.get(k, k) for k in keys]\n headers = list(map(str, headers))\n elif headers == \"firstrow\":\n if len(rows) > 0:\n headers = [firstdict.get(k, k) for k in keys]\n headers = list(map(str, headers))\n else:\n headers = []\n elif headers:\n raise ValueError(\n \"headers for a list of dicts is not a dict or a keyword\"\n )\n rows = [[row.get(k) for k in keys] for row in rows]\n\n elif (\n headers == \"keys\"\n and hasattr(tabular_data, \"description\")\n and hasattr(tabular_data, \"fetchone\")\n and hasattr(tabular_data, \"rowcount\")\n ):\n # Python Database API cursor object (PEP 0249)\n # print tabulate(cursor, headers='keys')\n headers = [column[0] for column in tabular_data.description]\n\n elif (\n dataclasses is not None\n and len(rows) > 0\n and dataclasses.is_dataclass(rows[0])\n ):\n # Python 3.7+'s dataclass\n field_names = [field.name for field in dataclasses.fields(rows[0])]\n if headers == \"keys\":\n headers = field_names\n rows = [[getattr(row, f) for f in field_names] for row in rows]\n\n elif headers == \"keys\" and len(rows) > 0:\n # keys are column indices\n headers = list(map(str, range(len(rows[0]))))\n\n # take headers from the first row if necessary\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:",
"start_line": 1350,
"end_line": 1490,
"total_lines": 2787,
"is_truncated": false
}Turn 5at 89 s190 tokens out6,791 prompt tokens
I see how `SEPARATING_LINE` is handled at the end of `_normalize_tabular_data`:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
But before that, when it's a list of dataclasses, it does:
```python
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
This is where it fails if `row` is `SEPARATING_LINE`.
I should modify this line to check if `row` is a separating line.
But wait, how is `_is_separating_line` defined?
TOOL CALL · run_command
{
"command": "grep -rn \"_is_separating_line\" tabulate/__init__.py"
}Result of run_command 274 characters
{
"status": "ok",
"stdout": "105:def _is_separating_line(row):\n1278: if _is_separating_line(row):\n1484: rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))\n2454: if _is_separating_line(row):\n",
"stderr": "",
"exit_code": 0
}Turn 6at 92 s54 tokens out7,106 prompt tokens
Let's see the definition of `_is_separating_line`.
TOOL CALL · read_file
{
"end_line": 115,
"filepath": "tabulate/__init__.py",
"start_line": 100
}Result of read_file 572 characters
{
"status": "ok",
"filepath": "tabulate/__init__.py",
"content": " \"with_header_hide\",\n ],\n)\n\n\ndef _is_separating_line(row):\n row_type = type(row)\n is_sl = (row_type == list or row_type == str) and (\n (len(row) >= 1 and row[0] == SEPARATING_LINE)\n or (len(row) >= 2 and row[1] == SEPARATING_LINE)\n )\n return is_sl\n\n\ndef _pipe_segment_with_colons(align, colwidth):\n \"\"\"Return a segment of a horizontal line with optional colons which",
"start_line": 100,
"end_line": 115,
"total_lines": 2787,
"is_truncated": false
}Turn 7at 119 s529 tokens out7,387 prompt tokens
Wait, the definition of `_is_separating_line` is:
```python
def _is_separating_line(row):
row_type = type(row)
is_sl = (row_type == list or row_type == str) and (
(len(row) >= 1 and row[0] == SEPARATING_LINE)
or (len(row) >= 2 and row[1] == SEPARATING_LINE)
)
return is_sl
```
If `row` is `SEPARATING_LINE` (which is `"\001"`), then `row_type == str` is True, `len(row)` is 1, and `row[0]` is `"\001"`, so `_is_separating_line(row)` returns True.
So I should use `_is_separating_line(row)` when iterating over the rows of dataclasses.
The current code:
```python
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
Should become something like:
```python
rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]
```
Wait, if it's a separating line, we should keep it as is.
Let's double check if there are other places where this might happen.
The code for dict-like objects is:
```python
rows = [[row.get(k) for k in keys] for row in rows]
```
If `row` is `SEPARATING_LINE` (a string), `row.get(k)` will fail because strings don't have a `.get()` method.
Let's test this!
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nfrom tabulate import tabulate, SEPARATING_LINE\n\ndata = [\n {\"name\": \"Alice\", \"age\": 30},\n SEPARATING_LINE,\n {\"name\": \"Bob\", \"age\": 25}\n]\n\ntry:\n print(tabulate(data, headers=\"keys\"))\nexcept Exception as e:\n import traceback\n traceback.print_exc()\nPY\n"
}Result of run_command 437 characters
{
"status": "ok",
"stdout": "",
"stderr": "Traceback (most recent call last):\n File \"<stdin>\", line 10, in <module>\n File \"/workspace/tabulate/__init__.py\", line 2088, in tabulate\n list_of_lists, headers, headers_pad = _normalize_tabular_data(\n File \"/workspace/tabulate/__init__.py\", line 1422, in _normalize_tabular_data\n for k in row.keys():\nAttributeError: 'str' object has no attribute 'keys'\n",
"exit_code": 0
}Turn 8at 337 s3,853 tokens out8,079 prompt tokens
Wow, so the bug also affects dict-like objects!
Wait, look at the code for dict-like objects again:
```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 == "keys":
headers = keys
elif isinstance(headers, dict):
# a dict of headers for a list of dicts
headers = [headers.get(k, k) for k in keys]
headers = list(map(str, headers))
elif headers == "firstrow":
if len(rows) > 0:
headers = [firstdict.get(k, k) for k in keys]
headers = list(map(str, headers))
else:
headers = []
elif headers:
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]
```
If `rows[0]` is a dict, but some subsequent `row` is `SEPARATING_LINE`, then `row.keys()` will fail.
So I need to fix both the dict-like objects case and the dataclass case.
For dict-like objects:
1. In the loop that collects all keys:
```python
for row in rows:
for k in row.keys():
```
It should be:
```python
for row in rows:
if _is_separating_line(row):
continue
for k in row.keys():
```
2. In the list comprehension that creates the rows:
```python
rows = [[row.get(k) for k in keys] for row in rows]
```
It should be:
```python
rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows]
```
And for dataclasses:
```python
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
It should be:
```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 use `_is_separating_line(row)`, it works for both strings and lists.
Let's check if there are any other cases.
Looking at `_normalize_tabular_data` again:
```python
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]
```
In this case, `tabular_data` is a cursor. `rows = list(tabular_data)` at the beginning.
A cursor's rows are typically tuples.
If one of the rows returned by the cursor was `SEPARATING_LINE` (which is unlikely but possible if the database returned it), would it fail?
Actually, `rows = list(tabular_data)` is called. Then it goes to the `elif` for DB API cursor.
But `rows` is not modified there. It just sets `headers`.
Then it goes to the end of the function:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
If `r` is a tuple, `list(r)` works. If `r` is `SEPARATING_LINE`, `_is_separating_line(r)` is True, and it stays as `SEPARATING_LINE`.
So the DB API cursor case is fine.
What about:
```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`?
`len(SEPARATING_LINE)` is 1. So `range(1)` would be `[0]`. `headers` would be `["0"]`.
Then at the end:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
If `rows[0]` is `SEPARATING_LINE`, it stays.
If `rows[1]` is a list, it stays as a list.
If `rows[1]` is a tuple, it becomes a list.
This seems fine, unless the user wanted the headers to be based on a non-separating line. But if the first row is a separating line, that's what they get.
Wait, what if `rows[0]` is `SEPARATING_LINE` in the dict-like case?
```python
elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):
```
If `rows[0]` is `SEPARATING_LINE`, `hasattr(rows[0], "keys")` is False. So it skips this block.
What if `rows[0]` is a dict, but `rows[1]` is `SEPARATING_LINE`? Then it enters the dict-like block and fails as I demonstrated.
So, let's implement the fixes.
First, the dict-like objects case.
Lines 1403-1406:
```python
for row in rows:
for k in row.keys():
# Save unique items in input order
if k not in uniq_keys:
```
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:
```
Lines 1421-1422:
```python
elif headers:
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]
```
Change to:
```python
elif headers:
raise ValueError(
"headers for a list of dicts is not a dict or a keyword"
)
rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows]
```
And the dataclass case.
Lines 1463-1464:
```python
if headers == "keys":
headers = field_names
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
Change to:
```python
if headers == "keys":
headers = field_names
rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]
```
Wait, let me check if I missed any other place where `rows` is iterated and attributes are accessed.
Namedtuples:
```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))
```
In this case, `rows` is not modified. It's just `rows = list(tabular_data)`.
The `rows` are namedtuples. At the end:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
This will convert namedtuples to lists, and keep `SEPARATING_LINE` as is.
So namedtuples are fine.
Numpy record arrays:
```python
elif (
headers == "keys"
and hasattr(tabular_data, "dtype")
and getattr(tabular_data.dtype, "names")
):
# numpy record array
headers = tabular_data.dtype.names
```
Same here, `rows` is not modified until the end.
So it seems only dict-like and dataclasses need the fix.
Wait, in the dict-like case, I should also consider the `firstrow` case.
```python
if headers == "firstrow":
firstdict = rows[0] if len(rows) > 0 else {}
keys.extend(firstdict.keys())
uniq_keys.update(keys)
rows = rows[1:]
```
If `rows[0]` is `SEPARATING_LINE`, then `firstdict` is `SEPARATING_LINE`, and `firstdict.keys()` will fail.
So I should also handle the case where `rows[0]` is a separating line when `headers == "firstrow"`.
But if the user says `headers="firstrow"`, they expect the first row to contain the headers. If the first row is a separating line, that's an error in their data or they meant something else. However, we should probably be robust.
Wait, if `rows[0]` is `SEPARATING_LINE`, should we skip it and look for the first non-separating line to be the headers? That might be too complex.
If `rows[0]` is `SEPARATING_LINE` and `headers="firstrow"`, it's probably okay to let it fail or handle it.
Given the current implementation:
```python
firstdict = rows[0] if len(rows) > 0 else {}
keys.extend(firstdict.keys())
```
If `rows[0]` is `"\001"`, this fails.
Should I change it to:
```python
if headers == "firstrow":
# Find the first row that is not a separating line
firstdict = {}
first_row_idx = -1
for i, row in enumerate(rows):
if not _is_separating_line(row):
firstdict = row
first_row_idx = i
break
if first_row_idx != -1:
keys.extend(firstdict.keys())
uniq_keys.update(keys)
# We still want to keep the separating lines before the header row?
# No, usually the header row is the very first row.
rows = rows[first_row_idx + 1:]
else:
# All rows are separating lines
rows = []
```
Actually, the current `tabulate` logic seems to assume that if `headers="firstrow"`, the first row *is* the header.
If I look at the `dataclass` case, it does:
```python
elif (
dataclasses is not None
and len(rows) > 0
and dataclasses.is_dataclass(rows[0])
):
```
If `rows[0]` is `SEPARATING_LINE`, this block is skipped entirely.
So if `rows[0]` is `SEPARATING_LINE`, `tabulate` will not treat it as a dataclass or dict-like object. It will fall through to the `elif headers == "keys" and len(rows) > 0:` block or just continue to the end.
So the `header="firstrow"` for dicts also has this "first row must be a dict" requirement.
If `rows[0]` is `SEPARATING_LINE`, the `elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):` block is skipped.
So the only concern is when `rows[0]` is a dict (or dataclass), but some *subsequent* row is `SEPARATING_LINE`.
Wait, what if `rows[0]` is a dict, but we are not using `headers="firstrow"`?
```python
elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):
# dict-like object
uniq_keys = set()
keys = []
if headers == "firstrow":
# ...
for row in rows:
for k in row.keys():
# ...
```
If `rows[0]` is a dict, we enter this block. Then we iterate over `rows`. If any `row` is `SEPARATING_LINE`, `row.keys()` fails. This is exactly what I found with my reproduction script.
So the fixes I proposed:
1. In the `keys` collection loop, skip separating lines.
2. In the `rows` value collection list comprehension, handle separating lines.
Same for dataclasses.
Let's refine the dict-like loop fix.
Current:
```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)
```
New:
```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 the list comprehension:
Current:
```python
rows = [[row.get(k) for k in keys] for row in rows]
```
New:
```python
rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows]
```
And for dataclasses:
Current:
```python
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
New:
```python
rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]
```
Wait, let me check if `_is_separating_line` is available in the scope of `_normalize_tabular_data`.
Yes, it is defined at the top of the module.
Wait, I just noticed something. `_is_separating_line` is used at the end of `_normalize_tabular_data`:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
This converts all non-separating rows to lists.
If I change the dataclass case to:
```python
rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]
```
Then for dataclasses, `rows` becomes a list of lists (and separating lines).
Then the final line:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
will call `list(r)` on the lists, which is fine.
Let's apply the fixes.
First, the dict-like objects key collection 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:",
"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:"
}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
}Turn 9at 340 s100 tokens out12,105 prompt tokens
Next, the dict-like objects row conversion.
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
}Turn 10at 344 s101 tokens out12,399 prompt tokens
Now the dataclass row conversion.
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
}Turn 11at 352 s247 tokens out12,726 prompt tokens
Now let's verify the fix with the reproduction scripts.
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nfrom dataclasses import dataclass\nfrom tabulate import tabulate, SEPARATING_LINE\n\n@dataclass\nclass User:\n name: str\n age: int\n\ndata_dc = [\n User(\"Alice\", 30),\n SEPARATING_LINE,\n User(\"Bob\", 25)\n]\n\ndata_dict = [\n {\"name\": \"Alice\", \"age\": 30},\n SEPARATING_LINE,\n {\"name\": \"Bob\", \"age\": 25}\n]\n\nprint(\"Testing dataclasses:\")\ntry:\n print(tabulate(data_dc, headers=\"keys\"))\nexcept Exception as e:\n import traceback\n traceback.print_exc()\n\nprint(\"\\nTesting dicts:\")\ntry:\n print(tabulate(data_dict, headers=\"keys\"))\nexcept Exception as e:\n import traceback\n traceback.print_exc()\nPY\n"
}Result of run_command 250 characters
{
"status": "ok",
"stdout": "Testing dataclasses:\nname age\n------ -----\nAlice 30\n------ -----\nBob 25\n\nTesting dicts:\nname age\n------ -----\nAlice 30\n------ -----\nBob 25\n",
"stderr": "",
"exit_code": 0
}Turn 12at 357 s93 tokens out13,077 prompt tokens
The tests passed. Now I should check if I broke anything else. Let me run the existing tests in `test/test_output.py` and `test/test_internal.py` since they seem to use `SEPARATING_LINE`.
TOOL CALL · run_command
{
"command": "pytest test/test_output.py test/test_internal.py -q --tb=short --no-header"
}Result of run_command 336 characters
{
"status": "ok",
"stdout": ".........s..................s......s......s......s......s......s......s. [ 35%]\n......s..s..s..s..s..s..s............................................... [ 70%]\n...................................................s..s.... [100%]\n186 passed, 17 skipped in 0.91s\n",
"stderr": "",
"exit_code": 0
}Turn 13at 376 s336 tokens out13,291 prompt tokens
Everything seems to be working fine. I'll do one more check: what if the first row is `SEPARATING_LINE`?
If `rows[0]` is `SEPARATING_LINE`, `_is_separating_line(rows[0])` is True.
`dataclasses.is_dataclass(rows[0])` will be False.
`hasattr(rows[0], "keys")` will be False.
So it will fall through to:
```python
elif headers == "keys" and len(rows) > 0:
# keys are column indices
headers = list(map(str, range(len(rows[0]))))
```
Since `rows[0]` is `"\001"`, `len(rows[0])` is 1. `headers` becomes `["0"]`.
Then:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
`rows[0]` remains `"\001"`. Other rows (if they are lists/tuples) will be converted to lists.
This doesn't seem like it would crash.
One more edge case: what if `rows` is empty?
`len(rows) > 0` checks are present in all those blocks, so it should be fine.
Final check on the diff.
TOOL CALL · run_command
{
"command": "git status --short && git diff"
}Result of run_command 1,667 characters
{
"status": "ok",
"stdout": " M tabulate/__init__.py\ndiff --git a/tabulate/__init__.py b/tabulate/__init__.py\nindex 11bb865..11b6457 100644\n--- a/tabulate/__init__.py\n+++ b/tabulate/__init__.py\n@@ -1419,6 +1419,8 @@ def _normalize_tabular_data(tabular_data, headers, showindex=\"default\"):\n uniq_keys.update(keys)\n rows = rows[1:]\n for row in rows:\n+ if _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@@ -1440,7 +1442,7 @@ def _normalize_tabular_data(tabular_data, headers, showindex=\"default\"):\n raise ValueError(\n \"headers for a list of dicts is not a dict or a keyword\"\n )\n- rows = [[row.get(k) for k in keys] for row in rows]\n+ rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows]\n \n elif (\n headers == \"keys\"\n@@ -1461,7 +1463,7 @@ def _normalize_tabular_data(tabular_data, headers, showindex=\"default\"):\n field_names = [field.name for field in dataclasses.fields(rows[0])]\n if headers == \"keys\":\n headers = field_names\n- rows = [[getattr(row, f) for f in field_names] for row in rows]\n+ rows = [[getattr(row, f) for f in field_names] if 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",
"stderr": "",
"exit_code": 0
}Turn 14at 380 s32 tokens out14,154 prompt tokens
The diff looks minimal and correct. I've fixed both the dict-like and dataclass cases.
TOOL CALL · submit_patch
Result of submit_patch 56 characters
{
"status": "ok",
"patch_size": 1537,
"files_changed": 1
}Turn 15at 396 s149 tokens out14,226 prompt tokens
I have successfully reproduced the bug, implemented a fix that handles both dataclasses and dict-like objects when containing `SEPARATING_LINE` markers, and verified the fix with both a targeted reproduction script and the project's test suite.
Turn 16at 396 s
The issue was that `tabulate` assumed all rows in a list of dataclasses (or dicts) were of the same type when extracting their values, causing an `AttributeError` when encountering a `SEPARATING_LINE` marker. I updated `_normalize_tabular_data` in `tabulate/__init__.py` to check for and preserve `SEPARATING_LINE` markers during both key collection and value extraction for dict-like and dataclass inputs.
Model calls (not matched to turns)
Generated tokens 188 tokens · stopped: STOP
The user is reporting an `AttributeError` when using `tabulate` with a list of dataclasses where one or more rows is `SEPARATING_LINE`. The traceback points to `tabulate/__init__.py` line 1451: `rows = [[getattr(row, f) for f in field_names] for row in rows ]` This suggests that when `tabulate` expects a dataclass object for each row, it encounters a string (presumably `SEPARATING_LINE`) and tries to call `getattr` on it. `SEPARATING_LINE` is likely a special marker string used in `tabulate`. Let's start by exploring the code and reproducing the bug.
<|channel>thought
The user is reporting an `AttributeError` when using `tabulate` with a list of dataclasses where one or more rows is `SEPARATING_LINE`.
The traceback points to `tabulate/__init__.py` line 1451:
`rows = [[getattr(row, f) for f in field_names] for row in rows ]`
This suggests that when `tabulate` expects a dataclass object for each row, it encounters a string (presumably `SEPARATING_LINE`) and tries to call `getattr` on it.
`SEPARATING_LINE` is likely a special marker string used in `tabulate`.
Let's start by exploring the code and reproducing the bug.<channel|><|tool_call>call:run_command{command:<|"|>grep -rn --include="*.py" "SEPARATING_LINE" .<|"|>}<tool_call|><|tool_response>
Generated tokens 140 tokens · stopped: STOP
Okay, `SEPARATING_LINE` is defined as `"\001"` in `tabulate/__init__.py`. The issue is that when `rows` contains dataclasses and `SEPARATING_LINE`, the code tries to access attributes of the `SEPARATING_LINE` (which is a string) as if it were a dataclass. Let's look at the code around line 1451 in `tabulate/__init__.py`.
Okay, `SEPARATING_LINE` is defined as `"\001"` in `tabulate/__init__.py`.
The issue is that when `rows` contains dataclasses and `SEPARATING_LINE`, the code tries to access attributes of the `SEPARATING_LINE` (which is a string) as if it were a dataclass.
Let's look at the code around line 1451 in `tabulate/__init__.py`.
<channel|><|tool_call>call:read_file{filepath:<|"|>tabulate/__init__.py<|"|>,end_line:1470,start_line:1430}<tool_call|><|tool_response>
Generated tokens 328 tokens · stopped: STOP
The problematic lines are:
```python
elif (
dataclasses is not None
and len(rows) > 0
and dataclasses.is_dataclass(rows[0])
):
# Python 3.7+'s dataclass
field_names = [field.name for field in dataclasses.fields(rows[0])\n if headers == "keys":
headers = field_names
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
The issue is that it checks if `rows[0]` is a dataclass, and if so, it assumes all `row` in `rows` are dataclasses and tries to call `getattr(row, f)` on them. But `row` could be `SEPARATING_LINE`.
Let's verify this with a reproduction script.
The problematic lines are:
```python
elif (
dataclasses is not None
and len(rows) > 0
and dataclasses.is_dataclass(rows[0])
):
# Python 3.7+'s dataclass
field_names = [field.name for field in dataclasses.fields(rows[0])\n if headers == "keys":
headers = field_names
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
The issue is that it checks if `rows[0]` is a dataclass, and if so, it assumes all `row` in `rows` are dataclasses and tries to call `getattr(row, f)` on them. But `row` could be `SEPARATING_LINE`.
Let's verify this with a reproduction script.
<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
from dataclasses import dataclass
from tabulate import tabulate, SEPARATING_LINE
@dataclass
class User:
name: str
age: int
data = [
User("Alice", 30),
SEPARATING_LINE,
User("Bob", 25)
]
try:
print(tabulate(data, headers="keys"))
except Exception as e:
import traceback
traceback.print_exc()
PY
<|"|>}<tool_call|><|tool_response>
Generated tokens 457 tokens · stopped: STOP
The reproduction script confirmed the issue. `AttributeError: 'str' object has no attribute 'name'` occurs because `row` is `SEPARATING_LINE`. Now I need to fix it. I should check if the `row` is `SEPARATING_LINE` before trying to call `getattr`. However, if `row` is `SEPARATING_LINE`, it should probably be kept as `SEPARATING_LINE` instead of being converted to a list of its attributes (which doesn't make sense). Wait, `_normalize_tabular_data` is supposed to convert the input data into a list of lists. Let's look at how other types are handled. If it's a list of lists, it stays as is (mostly). If it's a list of dicts, it's converted to a list of lists of values. For dataclasses, it's also converting a list of dataclass instances to a list of lists of values. If a row is `SEPARATING_LINE`, it should probably be preserved as `SEPARATING_LINE`. Let's check how `SEPARATING_LINE` is handled later in the code. Actually, if I look at the beginning of `_normalize_tabular_data` or where `SEPARATING_LINE` is checked. I saw this earlier: `./tabulate/__init__.py:108: (len(row) >= 1 and row[0] == SEPARATING_LINE)` Wait, if `rows` is a list of dataclasses, then `row` is a dataclass. `len(row)` will fail if it's a dataclass unless it implements `__len__`. Actually, `_normalize_tabular_data` is called inside `tabulate`. Let's look at the rest of the function `_normalize_tabular_data` to see how it handles various types.
The reproduction script confirmed the issue. `AttributeError: 'str' object has no attribute 'name'` occurs because `row` is `SEPARATING_LINE`.
Now I need to fix it. I should check if the `row` is `SEPARATING_LINE` before trying to call `getattr`.
However, if `row` is `SEPARATING_LINE`, it should probably be kept as `SEPARATING_LINE` instead of being converted to a list of its attributes (which doesn't make sense).
Wait, `_normalize_tabular_data` is supposed to convert the input data into a list of lists.
Let's look at how other types are handled.
If it's a list of lists, it stays as is (mostly).
If it's a list of dicts, it's converted to a list of lists of values.
For dataclasses, it's also converting a list of dataclass instances to a list of lists of values.
If a row is `SEPARATING_LINE`, it should probably be preserved as `SEPARATING_LINE`.
Let's check how `SEPARATING_LINE` is handled later in the code.
Actually, if I look at the beginning of `_normalize_tabular_data` or where `SEPARATING_LINE` is checked.
I saw this earlier:
`./tabulate/__init__.py:108: (len(row) >= 1 and row[0] == SEPARATING_LINE)`
Wait, if `rows` is a list of dataclasses, then `row` is a dataclass. `len(row)` will fail if it's a dataclass unless it implements `__len__`.
Actually, `_normalize_tabular_data` is called inside `tabulate`.
Let's look at the rest of the function `_normalize_tabular_data` to see how it handles various types.
<channel|><|tool_call>call:read_file{end_line:1490,filepath:<|"|>tabulate/__init__.py<|"|>,start_line:1350}<tool_call|><|tool_response>
Generated tokens 190 tokens · stopped: STOP
I see how `SEPARATING_LINE` is handled at the end of `_normalize_tabular_data`:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
But before that, when it's a list of dataclasses, it does:
```python
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
This is where it fails if `row` is `SEPARATING_LINE`.
I should modify this line to check if `row` is a separating line.
But wait, how is `_is_separating_line` defined?
I see how `SEPARATING_LINE` is handled at the end of `_normalize_tabular_data`:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
But before that, when it's a list of dataclasses, it does:
```python
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
This is where it fails if `row` is `SEPARATING_LINE`.
I should modify this line to check if `row` is a separating line.
But wait, how is `_is_separating_line` defined?
<channel|><|tool_call>call:run_command{command:<|"|>grep -rn "_is_separating_line" tabulate/__init__.py<|"|>}<tool_call|><|tool_response>
Generated tokens 54 tokens · stopped: STOP
Let's see the definition of `_is_separating_line`.
Let's see the definition of `_is_separating_line`.
<channel|><|tool_call>call:read_file{end_line:115,filepath:<|"|>tabulate/__init__.py<|"|>,start_line:100}<tool_call|><|tool_response>
Generated tokens 529 tokens · stopped: STOP
Wait, the definition of `_is_separating_line` is:
```python
def _is_separating_line(row):
row_type = type(row)
is_sl = (row_type == list or row_type == str) and (
(len(row) >= 1 and row[0] == SEPARATING_LINE)
or (len(row) >= 2 and row[1] == SEPARATING_LINE)
)
return is_sl
```
If `row` is `SEPARATING_LINE` (which is `"\001"`), then `row_type == str` is True, `len(row)` is 1, and `row[0]` is `"\001"`, so `_is_separating_line(row)` returns True.
So I should use `_is_separating_line(row)` when iterating over the rows of dataclasses.
The current code:
```python
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
Should become something like:
```python
rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]
```
Wait, if it's a separating line, we should keep it as is.
Let's double check if there are other places where this might happen.
The code for dict-like objects is:
```python
rows = [[row.get(k) for k in keys] for row in rows]
```
If `row` is `SEPARATING_LINE` (a string), `row.get(k)` will fail because strings don't have a `.get()` method.
Let's test this!
Wait, the definition of `_is_separating_line` is:
```python
def _is_separating_line(row):
row_type = type(row)
is_sl = (row_type == list or row_type == str) and (
(len(row) >= 1 and row[0] == SEPARATING_LINE)
or (len(row) >= 2 and row[1] == SEPARATING_LINE)
)
return is_sl
```
If `row` is `SEPARATING_LINE` (which is `"\001"`), then `row_type == str` is True, `len(row)` is 1, and `row[0]` is `"\001"`, so `_is_separating_line(row)` returns True.
So I should use `_is_separating_line(row)` when iterating over the rows of dataclasses.
The current code:
```python
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
Should become something like:
```python
rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]
```
Wait, if it's a separating line, we should keep it as is.
Let's double check if there are other places where this might happen.
The code for dict-like objects is:
```python
rows = [[row.get(k) for k in keys] for row in rows]
```
If `row` is `SEPARATING_LINE` (a string), `row.get(k)` will fail because strings don't have a `.get()` method.
Let's test this!
<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
from tabulate import tabulate, SEPARATING_LINE
data = [
{"name": "Alice", "age": 30},
SEPARATING_LINE,
{"name": "Bob", "age": 25}
]
try:
print(tabulate(data, headers="keys"))
except Exception as e:
import traceback
traceback.print_exc()
PY
<|"|>}<tool_call|><|tool_response>
Generated tokens 3,853 tokens · stopped: STOP
Wow, so the bug also affects dict-like objects!
Wait, look at the code for dict-like objects again:
```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 == "keys":
headers = keys
elif isinstance(headers, dict):
# a dict of headers for a list of dicts
headers = [headers.get(k, k) for k in keys]
headers = list(map(str, headers))
elif headers == "firstrow":
if len(rows) > 0:
headers = [firstdict.get(k, k) for k in keys]
headers = list(map(str, headers))
else:
headers = []
elif headers:
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]
```
If `rows[0]` is a dict, but some subsequent `row` is `SEPARATING_LINE`, then `row.keys()` will fail.
So I need to fix both the dict-like objects case and the dataclass case.
For dict-like objects:
1. In the loop that collects all keys:
```python
for row in rows:
for k in row.keys():
```
It should be:
```python
for row in rows:
if _is_separating_line(row):
continue
for k in row.keys():
```
2. In the list comprehension that creates the rows:
```python
rows = [[row.get(k) for k in keys] for row in rows]
```
It should be:
```python
rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows]
```
And for dataclasses:
```python
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
It should be:
```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 use `_is_separating_line(row)`, it works for both strings and lists.
Let's check if there are any other cases.
Looking at `_normalize_tabular_data` again:
```python
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]
```
In this case, `tabular_data` is a cursor. `rows = list(tabular_data)` at the beginning.
A cursor's rows are typically tuples.
If one of the rows returned by the cursor was `SEPARATING_LINE` (which is unlikely but possible if the database returned it), would it fail?
Actually, `rows = list(tabular_data)` is called. Then it goes to the `elif` for DB API cursor.
But `rows` is not modified there. It just sets `headers`.
Then it goes to the end of the function:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
If `r` is a tuple, `list(r)` works. If `r` is `SEPARATING_LINE`, `_is_separating_line(r)` is True, and it stays as `SEPARATING_LINE`.
So the DB API cursor case is fine.
What about:
```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`?
`len(SEPARATING_LINE)` is 1. So `range(1)` would be `[0]`. `headers` would be `["0"]`.
Then at the end:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
If `rows[0]` is `SEPARATING_LINE`, it stays.
If `rows[1]` is a list, it stays as a list.
If `rows[1]` is a tuple, it becomes a list.
This seems fine, unless the user wanted the headers to be based on a non-separating line. But if the first row is a separating line, that's what they get.
Wait, what if `rows[0]` is `SEPARATING_LINE` in the dict-like case?
```python
elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):
```
If `rows[0]` is `SEPARATING_LINE`, `hasattr(rows[0], "keys")` is False. So it skips this block.
What if `rows[0]` is a dict, but `rows[1]` is `SEPARATING_LINE`? Then it enters the dict-like block and fails as I demonstrated.
So, let's implement the fixes.
First, the dict-like objects case.
Lines 1403-1406:
```python
for row in rows:
for k in row.keys():
# Save unique items in input order
if k not in uniq_keys:
```
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:
```
Lines 1421-1422:
```python
elif headers:
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]
```
Change to:
```python
elif headers:
raise ValueError(
"headers for a list of dicts is not a dict or a keyword"
)
rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows]
```
And the dataclass case.
Lines 1463-1464:
```python
if headers == "keys":
headers = field_names
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
Change to:
```python
if headers == "keys":
headers = field_names
rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]
```
Wait, let me check if I missed any other place where `rows` is iterated and attributes are accessed.
Namedtuples:
```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))
```
In this case, `rows` is not modified. It's just `rows = list(tabular_data)`.
The `rows` are namedtuples. At the end:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
This will convert namedtuples to lists, and keep `SEPARATING_LINE` as is.
So namedtuples are fine.
Numpy record arrays:
```python
elif (
headers == "keys"
and hasattr(tabular_data, "dtype")
and getattr(tabular_data.dtype, "names")
):
# numpy record array
headers = tabular_data.dtype.names
```
Same here, `rows` is not modified until the end.
So it seems only dict-like and dataclasses need the fix.
Wait, in the dict-like case, I should also consider the `firstrow` case.
```python
if headers == "firstrow":
firstdict = rows[0] if len(rows) > 0 else {}
keys.extend(firstdict.keys())
uniq_keys.update(keys)
rows = rows[1:]
```
If `rows[0]` is `SEPARATING_LINE`, then `firstdict` is `SEPARATING_LINE`, and `firstdict.keys()` will fail.
So I should also handle the case where `rows[0]` is a separating line when `headers == "firstrow"`.
But if the user says `headers="firstrow"`, they expect the first row to contain the headers. If the first row is a separating line, that's an error in their data or they meant something else. However, we should probably be robust.
Wait, if `rows[0]` is `SEPARATING_LINE`, should we skip it and look for the first non-separating line to be the headers? That might be too complex.
If `rows[0]` is `SEPARATING_LINE` and `headers="firstrow"`, it's probably okay to let it fail or handle it.
Given the current implementation:
```python
firstdict = rows[0] if len(rows) > 0 else {}
keys.extend(firstdict.keys())
```
If `rows[0]` is `"\001"`, this fails.
Should I change it to:
```python
if headers == "firstrow":
# Find the first row that is not a separating line
firstdict = {}
first_row_idx = -1
for i, row in enumerate(rows):
if not _is_separating_line(row):
firstdict = row
first_row_idx = i
break
if first_row_idx != -1:
keys.extend(firstdict.keys())
uniq_keys.update(keys)
# We still want to keep the separating lines before the header row?
# No, usually the header row is the very first row.
rows = rows[first_row_idx + 1:]
else:
# All rows are separating lines
rows = []
```
Actually, the current `tabulate` logic seems to assume that if `headers="firstrow"`, the first row *is* the header.
If I look at the `dataclass` case, it does:
```python
elif (
dataclasses is not None
and len(rows) > 0
and dataclasses.is_dataclass(rows[0])
):
```
If `rows[0]` is `SEPARATING_LINE`, this block is skipped entirely.
So if `rows[0]` is `SEPARATING_LINE`, `tabulate` will not treat it as a dataclass or dict-like object. It will fall through to the `elif headers == "keys" and len(rows) > 0:` block or just continue to the end.
So the `header="firstrow"` for dicts also has this "first row must be a dict" requirement.
If `rows[0]` is `SEPARATING_LINE`, the `elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):` block is skipped.
So the only concern is when `rows[0]` is a dict (or dataclass), but some *subsequent* row is `SEPARATING_LINE`.
Wait, what if `rows[0]` is a dict, but we are not using `headers="firstrow"`?
```python
elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):
# dict-like object
uniq_keys = set()
keys = []
if headers == "firstrow":
# ...
for row in rows:
for k in row.keys():
# ...
```
If `rows[0]` is a dict, we enter this block. Then we iterate over `rows`. If any `row` is `SEPARATING_LINE`, `row.keys()` fails. This is exactly what I found with my reproduction script.
So the fixes I proposed:
1. In the `keys` collection loop, skip separating lines.
2. In the `rows` value collection list comprehension, handle separating lines.
Same for dataclasses.
Let's refine the dict-like loop fix.
Current:
```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)
```
New:
```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 the list comprehension:
Current:
```python
rows = [[row.get(k) for k in keys] for row in rows]
```
New:
```python
rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows]
```
And for dataclasses:
Current:
```python
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
New:
```python
rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]
```
Wait, let me check if `_is_separating_line` is available in the scope of `_normalize_tabular_data`.
Yes, it is defined at the top of the module.
Wait, I just noticed something. `_is_separating_line` is used at the end of `_normalize_tabular_data`:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
This converts all non-separating rows to lists.
If I change the dataclass case to:
```python
rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]
```
Then for dataclasses, `rows` becomes a list of lists (and separating lines).
Then the final line:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
will call `list(r)` on the lists, which is fine.
Let's apply the fixes.
First, the dict-like objects key collection loop.Wow, so the bug also affects dict-like objects!
Wait, look at the code for dict-like objects again:
```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 == "keys":
headers = keys
elif isinstance(headers, dict):
# a dict of headers for a list of dicts
headers = [headers.get(k, k) for k in keys]
headers = list(map(str, headers))
elif headers == "firstrow":
if len(rows) > 0:
headers = [firstdict.get(k, k) for k in keys]
headers = list(map(str, headers))
else:
headers = []
elif headers:
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]
```
If `rows[0]` is a dict, but some subsequent `row` is `SEPARATING_LINE`, then `row.keys()` will fail.
So I need to fix both the dict-like objects case and the dataclass case.
For dict-like objects:
1. In the loop that collects all keys:
```python
for row in rows:
for k in row.keys():
```
It should be:
```python
for row in rows:
if _is_separating_line(row):
continue
for k in row.keys():
```
2. In the list comprehension that creates the rows:
```python
rows = [[row.get(k) for k in keys] for row in rows]
```
It should be:
```python
rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows]
```
And for dataclasses:
```python
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
It should be:
```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 use `_is_separating_line(row)`, it works for both strings and lists.
Let's check if there are any other cases.
Looking at `_normalize_tabular_data` again:
```python
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]
```
In this case, `tabular_data` is a cursor. `rows = list(tabular_data)` at the beginning.
A cursor's rows are typically tuples.
If one of the rows returned by the cursor was `SEPARATING_LINE` (which is unlikely but possible if the database returned it), would it fail?
Actually, `rows = list(tabular_data)` is called. Then it goes to the `elif` for DB API cursor.
But `rows` is not modified there. It just sets `headers`.
Then it goes to the end of the function:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
If `r` is a tuple, `list(r)` works. If `r` is `SEPARATING_LINE`, `_is_separating_line(r)` is True, and it stays as `SEPARATING_LINE`.
So the DB API cursor case is fine.
What about:
```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`?
`len(SEPARATING_LINE)` is 1. So `range(1)` would be `[0]`. `headers` would be `["0"]`.
Then at the end:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
If `rows[0]` is `SEPARATING_LINE`, it stays.
If `rows[1]` is a list, it stays as a list.
If `rows[1]` is a tuple, it becomes a list.
This seems fine, unless the user wanted the headers to be based on a non-separating line. But if the first row is a separating line, that's what they get.
Wait, what if `rows[0]` is `SEPARATING_LINE` in the dict-like case?
```python
elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):
```
If `rows[0]` is `SEPARATING_LINE`, `hasattr(rows[0], "keys")` is False. So it skips this block.
What if `rows[0]` is a dict, but `rows[1]` is `SEPARATING_LINE`? Then it enters the dict-like block and fails as I demonstrated.
So, let's implement the fixes.
First, the dict-like objects case.
Lines 1403-1406:
```python
for row in rows:
for k in row.keys():
# Save unique items in input order
if k not in uniq_keys:
```
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:
```
Lines 1421-1422:
```python
elif headers:
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]
```
Change to:
```python
elif headers:
raise ValueError(
"headers for a list of dicts is not a dict or a keyword"
)
rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows]
```
And the dataclass case.
Lines 1463-1464:
```python
if headers == "keys":
headers = field_names
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
Change to:
```python
if headers == "keys":
headers = field_names
rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]
```
Wait, let me check if I missed any other place where `rows` is iterated and attributes are accessed.
Namedtuples:
```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))
```
In this case, `rows` is not modified. It's just `rows = list(tabular_data)`.
The `rows` are namedtuples. At the end:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
This will convert namedtuples to lists, and keep `SEPARATING_LINE` as is.
So namedtuples are fine.
Numpy record arrays:
```python
elif (
headers == "keys"
and hasattr(tabular_data, "dtype")
and getattr(tabular_data.dtype, "names")
):
# numpy record array
headers = tabular_data.dtype.names
```
Same here, `rows` is not modified until the end.
So it seems only dict-like and dataclasses need the fix.
Wait, in the dict-like case, I should also consider the `firstrow` case.
```python
if headers == "firstrow":
firstdict = rows[0] if len(rows) > 0 else {}
keys.extend(firstdict.keys())
uniq_keys.update(keys)
rows = rows[1:]
```
If `rows[0]` is `SEPARATING_LINE`, then `firstdict` is `SEPARATING_LINE`, and `firstdict.keys()` will fail.
So I should also handle the case where `rows[0]` is a separating line when `headers == "firstrow"`.
But if the user says `headers="firstrow"`, they expect the first row to contain the headers. If the first row is a separating line, that's an error in their data or they meant something else. However, we should probably be robust.
Wait, if `rows[0]` is `SEPARATING_LINE`, should we skip it and look for the first non-separating line to be the headers? That might be too complex.
If `rows[0]` is `SEPARATING_LINE` and `headers="firstrow"`, it's probably okay to let it fail or handle it.
Given the current implementation:
```python
firstdict = rows[0] if len(rows) > 0 else {}
keys.extend(firstdict.keys())
```
If `rows[0]` is `"\001"`, this fails.
Should I change it to:
```python
if headers == "firstrow":
# Find the first row that is not a separating line
firstdict = {}
first_row_idx = -1
for i, row in enumerate(rows):
if not _is_separating_line(row):
firstdict = row
first_row_idx = i
break
if first_row_idx != -1:
keys.extend(firstdict.keys())
uniq_keys.update(keys)
# We still want to keep the separating lines before the header row?
# No, usually the header row is the very first row.
rows = rows[first_row_idx + 1:]
else:
# All rows are separating lines
rows = []
```
Actually, the current `tabulate` logic seems to assume that if `headers="firstrow"`, the first row *is* the header.
If I look at the `dataclass` case, it does:
```python
elif (
dataclasses is not None
and len(rows) > 0
and dataclasses.is_dataclass(rows[0])
):
```
If `rows[0]` is `SEPARATING_LINE`, this block is skipped entirely.
So if `rows[0]` is `SEPARATING_LINE`, `tabulate` will not treat it as a dataclass or dict-like object. It will fall through to the `elif headers == "keys" and len(rows) > 0:` block or just continue to the end.
So the `header="firstrow"` for dicts also has this "first row must be a dict" requirement.
If `rows[0]` is `SEPARATING_LINE`, the `elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):` block is skipped.
So the only concern is when `rows[0]` is a dict (or dataclass), but some *subsequent* row is `SEPARATING_LINE`.
Wait, what if `rows[0]` is a dict, but we are not using `headers="firstrow"`?
```python
elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):
# dict-like object
uniq_keys = set()
keys = []
if headers == "firstrow":
# ...
for row in rows:
for k in row.keys():
# ...
```
If `rows[0]` is a dict, we enter this block. Then we iterate over `rows`. If any `row` is `SEPARATING_LINE`, `row.keys()` fails. This is exactly what I found with my reproduction script.
So the fixes I proposed:
1. In the `keys` collection loop, skip separating lines.
2. In the `rows` value collection list comprehension, handle separating lines.
Same for dataclasses.
Let's refine the dict-like loop fix.
Current:
```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)
```
New:
```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 the list comprehension:
Current:
```python
rows = [[row.get(k) for k in keys] for row in rows]
```
New:
```python
rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows]
```
And for dataclasses:
Current:
```python
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
New:
```python
rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]
```
Wait, let me check if `_is_separating_line` is available in the scope of `_normalize_tabular_data`.
Yes, it is defined at the top of the module.
Wait, I just noticed something. `_is_separating_line` is used at the end of `_normalize_tabular_data`:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
This converts all non-separating rows to lists.
If I change the dataclass case to:
```python
rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]
```
Then for dataclasses, `rows` becomes a list of lists (and separating lines).
Then the final line:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
will call `list(r)` on the lists, which is fine.
Let's apply the fixes.
First, the dict-like objects key collection 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:<|"|>,old_string:<|"|> for row in rows:
for k in row.keys():
# Save unique items in input order
if k not in uniq_keys:<|"|>}<tool_call|><|tool_response>
Generated tokens 100 tokens · stopped: STOP
Next, the dict-like objects row conversion.
Next, the dict-like objects row conversion.
<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>
Generated tokens 101 tokens · stopped: STOP
Now the dataclass row conversion.
Now the dataclass row conversion.
<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>
Generated tokens 247 tokens · stopped: STOP
Now let's verify the fix with the reproduction scripts.
Now let's verify the fix with the reproduction scripts.
<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
from dataclasses import dataclass
from tabulate import tabulate, SEPARATING_LINE
@dataclass
class User:
name: str
age: int
data_dc = [
User("Alice", 30),
SEPARATING_LINE,
User("Bob", 25)
]
data_dict = [
{"name": "Alice", "age": 30},
SEPARATING_LINE,
{"name": "Bob", "age": 25}
]
print("Testing dataclasses:")
try:
print(tabulate(data_dc, headers="keys"))
except Exception as e:
import traceback
traceback.print_exc()
print("\nTesting dicts:")
try:
print(tabulate(data_dict, headers="keys"))
except Exception as e:
import traceback
traceback.print_exc()
PY
<|"|>}<tool_call|><|tool_response>
Generated tokens 93 tokens · stopped: STOP
The tests passed. Now I should check if I broke anything else. Let me run the existing tests in `test/test_output.py` and `test/test_internal.py` since they seem to use `SEPARATING_LINE`.
The tests passed. Now I should check if I broke anything else. Let me run the existing tests in `test/test_output.py` and `test/test_internal.py` since they seem to use `SEPARATING_LINE`.
<channel|><|tool_call>call:run_command{command:<|"|>pytest test/test_output.py test/test_internal.py -q --tb=short --no-header<|"|>}<tool_call|><|tool_response>
Generated tokens 336 tokens · stopped: STOP
Everything seems to be working fine. I'll do one more check: what if the first row is `SEPARATING_LINE`?
If `rows[0]` is `SEPARATING_LINE`, `_is_separating_line(rows[0])` is True.
`dataclasses.is_dataclass(rows[0])` will be False.
`hasattr(rows[0], "keys")` will be False.
So it will fall through to:
```python
elif headers == "keys" and len(rows) > 0:
# keys are column indices
headers = list(map(str, range(len(rows[0]))))
```
Since `rows[0]` is `"\001"`, `len(rows[0])` is 1. `headers` becomes `["0"]`.
Then:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
`rows[0]` remains `"\001"`. Other rows (if they are lists/tuples) will be converted to lists.
This doesn't seem like it would crash.
One more edge case: what if `rows` is empty?
`len(rows) > 0` checks are present in all those blocks, so it should be fine.
Final check on the diff.
Everything seems to be working fine. I'll do one more check: what if the first row is `SEPARATING_LINE`?
If `rows[0]` is `SEPARATING_LINE`, `_is_separating_line(rows[0])` is True.
`dataclasses.is_dataclass(rows[0])` will be False.
`hasattr(rows[0], "keys")` will be False.
So it will fall through to:
```python
elif headers == "keys" and len(rows) > 0:
# keys are column indices
headers = list(map(str, range(len(rows[0]))))
```
Since `rows[0]` is `"\001"`, `len(rows[0])` is 1. `headers` becomes `["0"]`.
Then:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
`rows[0]` remains `"\001"`. Other rows (if they are lists/tuples) will be converted to lists.
This doesn't seem like it would crash.
One more edge case: what if `rows` is empty?
`len(rows) > 0` checks are present in all those blocks, so it should be fine.
Final check on the diff.
<channel|><|tool_call>call:run_command{command:<|"|>git status --short && git diff<|"|>}<tool_call|><|tool_response>
Generated tokens 32 tokens · stopped: STOP
The diff looks minimal and correct. I've fixed both the dict-like and dataclass cases.
The diff looks minimal and correct. I've fixed both the dict-like and dataclass cases.
<channel|><|tool_call>call:submit_patch{}<tool_call|><|tool_response>
Patch, tests and logs
Final code change Included
diff --git a/tabulate/__init__.py b/tabulate/__init__.py
index 11bb865..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-ad7664f9cc9949a2896a6bad0b3c5b3d.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="0.686" timestamp="2026-11-14T03:55:56.800702+00:00" hostname="5f66337d4731"><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="0.117" /><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="0.524" /><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.
[ 11.1s] 💭 (558 chars) "The user is reporting an `AttributeError` when using `tabulate` with a list of dataclasses
where one or more rows is `SE..."
[ 11.1s] 🔧 run_command grep -rn --include="*.py" "SEPARATING_LINE" .
[ 11.1s] → ./tabulate/__init__.py:51:SEPARATING_LINE = "\001"
[ 18.8s] 💭 (324 chars) "Okay, `SEPARATING_LINE` is defined as `"\001"` in `tabulate/__init__.py`.
The issue is that when `rows` contains datacl..."
[ 18.8s] 📖 read_file (tabulate/__init__.py)
[ 18.8s] ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'content': ' # a dict of headers for
a list of dicts…
[ 40.5s] 💭 (693 chars) "The problematic lines are:
```python
elif (
dataclasses is not None
and len(rows) > 0
..."
[ 40.5s] 🔧 run_command (21 lines) python3 - <<'PY'
[ 40.7s] 🔧
[ 76.5s] 💭 (1448 chars) "The reproduction script confirmed the issue. `AttributeError: 'str' object has no attribute
'name'` occurs because `row`..."
[ 76.5s] 📖 read_file (tabulate/__init__.py)
[ 76.6s] ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'content': ' If showindex is an iterable, show
its values as row…
[ 88.6s] 💭 (491 chars) "I see how `SEPARATING_LINE` is handled at the end of `_normalize_tabular_data`:
```python
rows = list(map(lambda r: ..."
[ 88.6s] 🔧 run_command grep -rn "_is_separating_line" tabulate/__init__.py
[ 88.6s] → 105:def _is_separating_line(row):
[ 92.0s] 💭 (50 chars) "Let's see the definition of `_is_separating_line`."
[ 92.0s] 📖 read_file (tabulate/__init__.py)
[ 92.1s] ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'content': ' "with_header_hide",\n
],\n)\n\n\ndef _is_sep…
[ 118.5s] 💭 (1209 chars) "Wait, the definition of `_is_separating_line` is:
```python
def _is_separating_line(row):
row_type = type(row)
i..."
[ 118.5s] 🔧 run_command (15 lines) python3 - <<'PY'
[ 118.5s] 🔧
[ 336.3s] 💭 (13305 chars) "Wow, so the bug also affects dict-like objects!
Wait, look at the code for dict-like objects again:
```python
e..."
[ 336.3s] ✏️ edit_file (tabulate/__init__.py)
[ 336.4s] ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'occurrences': 1, 'strategy': 'exact', 'diff': '---
a/tabulate/__in…
[ 339.7s] 💭 (43 chars) "Next, the dict-like objects row conversion."
[ 339.7s] ✏️ edit_file (tabulate/__init__.py)
[ 339.9s] ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'occurrences': 1, 'strategy': 'exact', 'diff': '---
a/tabulate/__in…
[ 343.1s] 💭 (33 chars) "Now the dataclass row conversion."
[ 343.1s] ✏️ edit_file (tabulate/__init__.py)
[ 343.3s] ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'occurrences': 1, 'strategy': 'exact', 'diff': '---
a/tabulate/__in…
[ 351.7s] 💭 (55 chars) "Now let's verify the fix with the reproduction scripts."
[ 351.7s] 🔧 run_command (35 lines) python3 - <<'PY'
[ 351.8s] → Testing dataclasses:
[ 356.6s] 💭 (187 chars) "The tests passed. Now I should check if I broke anything else. Let me run the existing tests
in `test/test_output.py` an..."
[ 356.6s] 🔧 run_command pytest test/test_output.py test/test_internal.py -q --tb=short --no-header
[ 358.0s] → .........s..................s......s......s......s......s......s......s. [ 35%]
[ 375.4s] 💭 (921 chars) "Everything seems to be working fine. I'll do one more check: what if the first row is
`SEPARATING_LINE`?
If `rows[0]` i..."
[ 375.4s] 🔧 run_command git status --short && git diff
[ 375.4s] → M tabulate/__init__.py
[ 379.5s] 💭 (86 chars) "The diff looks minimal and correct. I've fixed both the dict-like and dataclass cases."
[ 379.5s] 🤖 submit_patch
[ 379.5s] → submit_patch: {"status": "ok", "patch_size": 1537, "files_changed": 1}
[ 395.2s] 💭 (244 chars) "I have successfully reproduced the bug, implemented a fix that handles both dataclasses and
dict-like objects when conta..."
[ 395.2s] ✅ Agent finished: The issue was that `tabulate` assumed all rows in a list of dataclasses (or dicts) were
of the same type when extracting their values, causing an `AttributeError` when encountering a `SEPARATING_LINE`...
Native result row
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"resolved": true,
"status_counts": {
"PASSED": 33
},
"strict_clean_patch": true,
"test_exit_code": 0
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"instance_id": "astanin__python-tabulate-268",
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"patch_chars": 1537,
"phase1_seconds": 397.94,
"repo": "astanin/python-tabulate",
"request_log_path": "/kaggle-root/working/r31/rollouts_X/requests/astanin__python-tabulate-268__p0__r3.jsonl",
"resolved": true,
"rollout_idx": 3,
"run_key": "astanin__python-tabulate-268__p0__r3",
"strict_clean_patch": true,
"tool_calls": 13,
"total_llm_calls": 15,
"trace_path": "/kaggle-root/working/r31/rollouts_X/traces/trace_astanin__python-tabulate-268__p0__r3.json"
}