ROLLOUT 2 · astanin/python-tabulate
astanin__python-tabulate-268
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19model calls
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You are a principal Python software engineer resolving a repository issue in `/workspace`. Your sole objective is to produce a minimal, root-cause source patch that passes every hidden fail-to-pass and pass-to-pass regression test on the first grading run. ## Critical Environment & Grader Rules 1. **Air-Gapped Sandbox & Available CLI Tools**: No network access. Never run `pip`, `uv`, `conda`, or `git fetch`. Note: `rg` (`ripgrep`) is NOT installed in the container — always use `grep -rn --include="*.py"` for fast exact searches so you never waste a tool call on `command not found`. 2. **Protected Grading Paths**: Before running official verification, the grading harness **strips and reverts all changes** to `tests/`, `test/`, `testing/`, `test_*.py`, `*_test.py`, `conftest.py`, `pyproject.toml`, `setup.cfg`, `pytest.ini`, and `tox.ini`. Every part of your fix MUST reside in non-test production source files. 3. **Untracked File Trap (`git add -N .`)**: `submit_patch` stages ALL untracked files inside `/workspace`! Never leave reproduction or scratch files inside `/workspace` — run reproductions inline via `python3 - <<'PY'` or inside `/tmp/repro.py` in a single `run_command`. 4. **Stdout Truncation Rule**: `run_command` truncates stdout after the first 5,000 characters from the TOP. Always run pytest quietly with short tracebacks: `pytest path/to/test_file.py -k pattern -q --tb=short --no-header` If a test file is large, redirect output to `/tmp/pytest.log` and inspect `tail -n 80 /tmp/pytest.log` so failure tracebacks at the bottom are never cut off. 5. **Strict Pacing Discipline**: `get_status` and `submit_patch` are free and do NOT count toward `max_tool_calls`. Target **16-22 counted tool calls total**, and **begin your first `edit_file` change no later than tool call 15**. Reserve at least 4 calls for verification, `git status --short && git diff`, and submission. ## Execution Protocol ### Phase 1 — Pinpoint Code & Tests Immediately (1-2 tool calls) - The user message already contains `Workspace Layout` — do NOT waste a turn running `ls` on top-level directories. - Run a single compound `run_command` with `grep -rn --include="*.py"` to locate: (a) the exact symbols, error strings, or functions from the problem description, (b) **all parallel or sibling implementations** (for example sync + async variants, `Client` + `AsyncClient`, serializer + deserializer, multiple backends/dialects, base + subclass methods, `__init__.py` / `__all__` exports), and (c) the closest existing unit test files under `tests/`. ### Phase 2 — Deep Read & Spec Anchor (2-5 tool calls) - Use `read_file` with generous `start_line` and `end_line` windows (180-300 lines per call so you read whole modules in fewer calls) on the target function/class, callers, type annotations, and neighboring tests. - Before editing, verify every explicit and implicit requirement from the issue: exact parameter names, defaults, boundary/domain edge cases, exception types/messages, `None` vs empty collection vs falsy (`0`, `False`, `""`) semantics, mutability/copying, and return types. ### Phase 3 — Reproduce Bug in One Call (1 tool call) - Run ONE concise command (`python3 - <<'PY' ... PY` or targeted `pytest ... -q --tb=short --no-header`) via `run_command` to observe the exact pre-fix failure. Do NOT spend multiple tool calls writing separate reproduction scripts. ### Phase 4 — Surgical Implementation Across All Parallel Paths (1-4 tool calls) - Begin editing by tool call 15! Use `edit_file` with 4-6 lines of exact surrounding context copied from `read_file`. - Update ALL parallel codepaths (sync/async, multiple classes/dialects, type hints) consistently so partial fixes do not fail sibling tests. - **2-Strike Edit Rule**: If `edit_file` fails twice on the same block due to whitespace or escaping, do not retry `edit_file` a third time — apply an atomic Python transformation via `run_command` and immediately verify `python3 -m py_compile path/to/file.py`. ### Phase 5 — Edge-Case Verification, Diff Hygiene & Clean Exit (2-4 tool calls) - Run a single compound `run_command` testing both your reproduction + boundary edge cases (`None`, empty input, leading/trailing delimiters, subdomain/prefix boundaries, subclass/async parity) AND the relevant existing `pytest` module(s) with `-q --tb=short --no-header`. - Run `git status --short && git diff` via `run_command`. Confirm: - ZERO untracked (`??`) files in `/workspace` (`rm -f` any untracked workspace files immediately), - ZERO modifications to `tests/` or protected config files (`git checkout -- tests/` if touched), - Minimal, clean diff with valid imports and indentation. - Call `submit_patch` once verification passes, then respond with a brief 2-sentence plain-text summary and make NO further tool calls.
Task prompt 2,360 characters
You are evaluating a software engineering task for repository astanin/python-tabulate.
Problem Statement:
SEPARATING_LINE does not work with dataclasses
Using tabulate to print a list of dataclasses when one or more rows is SEPARATING_LINE, produces:
```
File "....\.venv\lib\site-packages\tabulate\__init__.py", line 1451, in <listcomp>
rows = [[getattr(row, f) for f in field_names] for row in rows ]
AttributeError: 'str' object has no attribute '<the first attribute of dataclass>'
```
Can be assigned to me, I have a fix.
## Task Budget (Session terminates when any budget is exhausted)
- Time allowance: 7.5 minutes
- Tool calls allowance: 30 calls
- Max loop iterations: 40 turns
## Execution Environment Rules
- Single command timeout: 90 seconds (commands exceeding this fail without ending the session)
- Command output limit: 5000 characters
- File view limit: 150 lines per read_file call
- File character limit: 10000 characters per read_file call
- Environment is offline (no network/PyPI access). All repository and test dependencies are ALREADY pre-installed. Do NOT attempt to run pip install or download packages.
## Instructions:
0. All source code is under `/workspace`. Do NOT search outside `/workspace` (e.g. `/usr/`, `/wheels/`, system site-packages). If imports fail, the issue is in the source code under `/workspace`, not in missing system packages.
1. Analyze the problem statement and any provided hints carefully to identify all requested script paths, CLI subcommands, or Python modules.
2. Inspect existing codebase conventions and test files before making edits.
3. Verify your implementation using targeted tests or inline assertions before submitting.
4. Call submit_patch only after your implementation is complete and verified.
5. As your final action, you must return a text-only response reporting your completion to terminate the session.
## Workspace Layout
The repository is located at `/workspace`. Here is the directory tree (up to 3 levels):
```
./CHANGELOG
./HOWTOPUBLISH
./LICENSE
./MANIFEST.in
./README
./README.md
./appveyor.yml
./benchmark.py
./pyproject.toml
./tabulate
./tabulate/__init__.py
./test
./test/common.py
./test/test_api.py
./test/test_cli.py
./test/test_input.py
./test/test_internal.py
./test/test_output.py
./test/test_regression.py
./test/test_textwrapper.py
./tox.ini
```
Turn 1at 11 s192 tokens out2,665 prompt tokens
The user is reporting a bug in `tabulate` where using `SEPARATING_LINE` with a list of dataclasses causes an `AttributeError`. The error message is: `AttributeError: 'str' object has no attribute '<the first attribute of dataclass>'` This happens because `tabulate` expects each row in `rows` to be an object with the specified attributes, but `SEPARATING_LINE` is a string. Let's investigate `tabulate/__init__.py` around line 1451 as mentioned in the error message. First, I'll search for `SEPARATING_LINE` in `tabulate/__init__.py` and look at the code around line 1451.
TOOL CALL · run_command
{
"command": "grep -n \"SEPARATING_LINE\" tabulate/__init__.py"
}Result of run_command 312 characters
{
"status": "ok",
"stdout": "51:SEPARATING_LINE = \"\\001\"\n108: (len(row) >= 1 and row[0] == SEPARATING_LINE)\n109: or (len(row) >= 2 and row[1] == SEPARATING_LINE)\n1290: rows.insert(index, SEPARATING_LINE)\n2453: # the SEPARATING_LINE flag\n",
"stderr": "",
"exit_code": 0
}Turn 2at 17 s84 tokens out3,000 prompt tokens
Okay, `SEPARATING_LINE` is defined at line 51. The error was reported at line 1451. Let me read that section of `tabulate/__init__.py`.
TOOL CALL · read_file
{
"end_line": 1500,
"filepath": "tabulate/__init__.py",
"start_line": 1400
}Result of read_file 4,345 characters
{
"status": "ok",
"filepath": "tabulate/__init__.py",
"content": " and getattr(tabular_data.dtype, \"names\")\n ):\n # numpy record array\n headers = tabular_data.dtype.names\n elif (\n headers == \"keys\"\n and len(rows) > 0\n and isinstance(rows[0], tuple)\n and hasattr(rows[0], \"_fields\")\n ):\n # namedtuple\n headers = list(map(str, rows[0]._fields))\n elif len(rows) > 0 and hasattr(rows[0], \"keys\") and hasattr(rows[0], \"values\"):\n # dict-like object\n uniq_keys = set() # implements hashed lookup\n keys = [] # storage for set\n if headers == \"firstrow\":\n firstdict = rows[0] if len(rows) > 0 else {}\n keys.extend(firstdict.keys())\n uniq_keys.update(keys)\n rows = rows[1:]\n for row in rows:\n for k in row.keys():\n # Save unique items in input order\n if k not in uniq_keys:\n keys.append(k)\n uniq_keys.add(k)\n if headers == \"keys\":\n headers = keys\n elif isinstance(headers, dict):\n # a dict of headers for a list of dicts\n headers = [headers.get(k, k) for k in keys]\n headers = list(map(str, headers))\n elif headers == \"firstrow\":\n if len(rows) > 0:\n headers = [firstdict.get(k, k) for k in keys]\n headers = list(map(str, headers))\n else:\n headers = []\n elif headers:\n raise ValueError(\n \"headers for a list of dicts is not a dict or a keyword\"\n )\n rows = [[row.get(k) for k in keys] for row in rows]\n\n elif (\n headers == \"keys\"\n and hasattr(tabular_data, \"description\")\n and hasattr(tabular_data, \"fetchone\")\n and hasattr(tabular_data, \"rowcount\")\n ):\n # Python Database API cursor object (PEP 0249)\n # print tabulate(cursor, headers='keys')\n headers = [column[0] for column in tabular_data.description]\n\n elif (\n dataclasses is not None\n and len(rows) > 0\n and dataclasses.is_dataclass(rows[0])\n ):\n # Python 3.7+'s dataclass\n field_names = [field.name for field in dataclasses.fields(rows[0])]\n if headers == \"keys\":\n headers = field_names\n rows = [[getattr(row, f) for f in field_names] for row in rows]\n\n elif headers == \"keys\" and len(rows) > 0:\n # keys are column indices\n headers = list(map(str, range(len(rows[0]))))\n\n # take headers from the first row if necessary\n if headers == \"firstrow\" and len(rows) > 0:\n if index is not None:\n headers = [index[0]] + list(rows[0])\n index = index[1:]\n else:\n headers = rows[0]\n headers = list(map(str, headers)) # headers should be strings\n rows = rows[1:]\n elif headers == \"firstrow\":\n headers = []\n\n headers = list(map(str, headers))\n # rows = list(map(list, rows))\n rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))\n\n # add or remove an index column\n showindex_is_a_str = type(showindex) in [str, bytes]\n if showindex == \"default\" and index is not None:\n rows = _prepend_row_index(rows, index)\n elif isinstance(showindex, Sized) and not showindex_is_a_str:\n rows = _prepend_row_index(rows, list(showindex))\n elif isinstance(showindex, Iterable) and not showindex_is_a_str:\n rows = _prepend_row_index(rows, showindex)\n elif showindex == \"always\" or (_bool(showindex) and not showindex_is_a_str):\n if index is None:\n index = list(range(len(rows)))\n rows = _prepend_row_index(rows, index)\n elif showindex == \"never\" or (not _bool(showindex) and not showindex_is_a_str):\n pass\n",
"start_line": 1400,
"end_line": 1500,
"total_lines": 2787,
"is_truncated": false
}Turn 3at 47 s458 tokens out4,317 prompt tokens
The problematic section is here (lines 1443-1451):
```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]
```
If `rows` contains a mixture of dataclass instances and `SEPARATING_LINE` (which is a string), then `getattr(row, f)` will fail when `row` is `SEPARATING_LINE`.
Wait, the code checks `dataclasses.is_dataclass(rows[0])` to decide if it should treat the rows as dataclasses. If the first row is a dataclass, it assumes all rows are dataclasses and tries to access their fields. If `SEPARATING_LINE` appears later in the list, it crashes.
I need to handle `SEPARATING_LINE` in that list comprehension.
Let me first create a reproduction script to confirm this.
TOOL CALL · write_file
{
"content": "\nfrom dataclasses import dataclass\nfrom tabulate import tabulate, SEPARATING_LINE\n\n@dataclass\nclass User:\n name: str\n age: int\n\nusers = [\n User(\"Alice\", 30),\n SEPARATING_LINE,\n User(\"Bob\", 25),\n]\n\ntry:\n print(tabulate(users, headers=\"keys\"))\nexcept AttributeError as e:\n print(f\"Caught expected AttributeError: {e}\")\nexcept Exception as e:\n print(f\"Caught unexpected exception: {type(e).__name__}: {e}\")\nelse:\n print(\"No exception raised\")\n",
"filepath": "/tmp/repro.py"
}Result of write_file 136 characters
{
"status": "error",
"error_type": "FileWriteError",
"error_message": "Path traversal detected: '/tmp/repro.py' escapes workspace root."
}Turn 4at 55 s203 tokens out4,829 prompt tokens
Ah, I cannot write to `/tmp/repro.py`. I should use `run_command` with `python3 - <<'PY'`.
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\nusers = [\n User(\"Alice\", 30),\n SEPARATING_LINE,\n User(\"Bob\", 25),\n]\n\ntry:\n print(tabulate(users, headers=\"keys\"))\nexcept AttributeError as e:\n print(f\"Caught expected AttributeError: {e}\")\nexcept Exception as e:\n print(f\"Caught unexpected exception: {type(e).__name__}: {e}\")\nelse:\n print(\"No exception raised\")\nPY\n"
}Result of run_command 130 characters
{
"status": "ok",
"stdout": "Caught expected AttributeError: 'str' object has no attribute 'name'\n",
"stderr": "",
"exit_code": 0
}Turn 5at 62 s105 tokens out5,085 prompt tokens
Confirmed. Now I need to fix it. The fix should be to check if `row` is the `SEPARATING_LINE` before calling `getattr`. But wait, is `SEPARATING_LINE` handled similarly in other parts of the code? Let's look at `_is_separating_line`.
TOOL CALL · run_command
{
"command": "grep -n \"def _is_separating_line\" tabulate/__init__.py"
}Result of run_command 95 characters
{
"status": "ok",
"stdout": "105:def _is_separating_line(row):\n",
"stderr": "",
"exit_code": 0
}Turn 6at 64 s51 tokens out5,244 prompt tokens
Let's examine `_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 122 s875 tokens out5,522 prompt tokens
Wait, `_is_separating_line` expects `row` to be a list or a string, and checks if the first or second element is `SEPARATING_LINE`.
But `SEPARATING_LINE` itself is a string `"\001"`.
If `row` is `"\001"`, then `row[0]` is `"\001"`, so `_is_separating_line(SEPARATING_LINE)` should return `True`.
Let's verify:
`row = "\001"`
`row_type == str` is True.
`len(row) >= 1` is True.
`row[0] == SEPARATING_LINE` is `"\001" == "\001"`, which is True.
So `_is_separating_line(SEPARATING_LINE)` is True.
Now back to the dataclass loop:
```python
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
I should change it to 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 `row` is `SEPARATING_LINE`, then `row` will remain `SEPARATING_LINE`.
Later in the code (line 1468):
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
This line converts every row to a list unless it's a separating line.
If I change the dataclass comprehension to preserve `SEPARATING_LINE`, then `rows` will be a list of lists and `SEPARATING_LINE` objects.
Example: `[[ "Alice", 30 ], SEPARATING_LINE, [ "Bob", 25 ]]`.
Then `list(map(lambda r: r if _is_separating_line(r) else list(r), rows))` will see:
1. `["Alice", 30]` -> `_is_separating_line` is False (since it's a list but `row[0]` is `"Alice"`) -> `list(["Alice", 30])` -> `["Alice", 30]`.
2. `SEPARATING_LINE` -> `_is_separating_line` is True -> `SEPARATING_LINE`.
3. `["Bob", 25]` -> `_is_separating_line` is False -> `list(["Bob", 25])` -> `["Bob", 25]`.
This seems correct.
Let's check if other row processing loops handle `SEPARATING_LINE` similarly.
Dict-like objects:
```python
rows = [[row.get(k) for k in keys] for row in rows]
```
If `row` is `SEPARATING_LINE` (a string), it doesn't have a `.get()` method. So it would fail here too.
Let's check the reproduction script with a dict instead of a dataclass.
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 AttributeError as e:\n print(f\"Caught expected AttributeError: {e}\")\nexcept Exception as e:\n print(f\"Caught unexpected exception: {type(e).__name__}: {e}\")\nelse:\n print(\"No exception raised\")\nPY\n"
}Result of run_command 130 characters
{
"status": "ok",
"stdout": "Caught expected AttributeError: 'str' object has no attribute 'keys'\n",
"stderr": "",
"exit_code": 0
}Turn 8at 131 s121 tokens out6,450 prompt tokens
Yes, it fails there too. The problem is that the logic assumes all rows in `rows` are of the same type as `rows[0]`. Let's see where `rows` is first determined. The code I read earlier was inside a big `if/elif` block. Let's read more of `tabulate/__init__.py` around that area.
TOOL CALL · read_file
{
"end_line": 1400,
"filepath": "tabulate/__init__.py",
"start_line": 1300
}Result of read_file 4,037 characters
{
"status": "ok",
"filepath": "tabulate/__init__.py",
"content": " + \"len(index)={} len(rows)={}\".format(len(index), len(rows))\n )\n sans_rows, separating_lines = _remove_separating_lines(rows)\n new_rows = []\n index_iter = iter(index)\n for row in sans_rows:\n index_v = next(index_iter)\n new_rows.append([index_v] + list(row))\n rows = new_rows\n _reinsert_separating_lines(rows, separating_lines)\n return rows\n\n\ndef _bool(val):\n \"A wrapper around standard bool() which doesn't throw on NumPy arrays\"\n try:\n return bool(val)\n except ValueError: # val is likely to be a numpy array with many elements\n return False\n\n\ndef _normalize_tabular_data(tabular_data, headers, showindex=\"default\"):\n \"\"\"Transform a supported data type to a list of lists, and a list of headers, with headers padding.\n\n Supported tabular data types:\n\n * list-of-lists or another iterable of iterables\n\n * list of named tuples (usually used with headers=\"keys\")\n\n * list of dicts (usually used with headers=\"keys\")\n\n * list of OrderedDicts (usually used with headers=\"keys\")\n\n * list of dataclasses (Python 3.7+ only, usually used with headers=\"keys\")\n\n * 2D NumPy arrays\n\n * NumPy record arrays (usually used with headers=\"keys\")\n\n * dict of iterables (usually used with headers=\"keys\")\n\n * pandas.DataFrame (usually used with headers=\"keys\")\n\n The first row can be used as headers if headers=\"firstrow\",\n column indices can be used as headers if headers=\"keys\".\n\n If showindex=\"default\", show row indices of the pandas.DataFrame.\n If showindex=\"always\", show row indices for all types of data.\n If showindex=\"never\", don't show row indices for all types of data.\n 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\")",
"start_line": 1300,
"end_line": 1400,
"total_lines": 2787,
"is_truncated": false
}Turn 9at 235 s2,020 tokens out7,718 prompt tokens
The function `_normalize_tabular_data` starts here.
If it's not a dict-like object, it does `rows = list(tabular_data)`.
Then it goes into the `if/elif` block I read before.
The problem is that if `rows` contains `SEPARATING_LINE`, the type checking only looks at `rows[0]`.
```python
elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):
# dict-like object
...
rows = [[row.get(k) for k in keys] for row in rows]
```
and
```python
elif (
dataclasses is not None
and len(rows) > 0
and dataclasses.is_dataclass(rows[0])
):
# Python 3.7+'s dataclass
...
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
Both of these should handle `SEPARATING_LINE`.
Let's see if there are other places that need similar fixes.
There's also this part:
```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]
```
Wait, in this case `rows` is not yet defined? No, wait, the preceding `else` (line 1364) has `rows = list(tabular_data)`.
Actually, looking at the code:
```python
else: # it's a usual iterable of iterables, or a NumPy array, or an iterable of dataclasses
rows = list(tabular_data)
if headers == "keys" and not rows:
# an empty table (issue #81)
headers = []
elif (
headers == "keys"
and hasattr(tabular_data, "dtype")
and getattr(tabular_data.dtype, "names")
):
# numpy record array
headers = tabular_data.dtype.names
elif (
headers == "keys"
and len(rows) > 0
and isinstance(rows[0], tuple)
and hasattr(rows[0], "_fields")
):
# namedtuple
headers = list(map(str, rows[0]._fields))
elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):
# dict-like object
...
rows = [[row.get(k) for k in keys] for row in rows]
elif (
headers == "keys"
and hasattr(tabular_data, "description")
and hasattr(tabular_data, "fetchone")
and hasattr(tabular_data, "rowcount")
):
# Python Database API cursor object (PEP 0249)
# print tabulate(cursor, headers='keys')
headers = [column[0] for column in tabular_data.description]
elif (
dataclasses is not None
and len(rows) > 0
and dataclasses.is_dataclass(rows[0])
):
# Python 3.7+'s dataclass
...
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
If `rows` contains `SEPARATING_LINE`, we should skip it during these transformations.
For the dict-like objects:
```python
rows = [[row.get(k) for k in keys] for row in rows]
```
should become:
```python
rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows]
```
For the dataclasses:
```python
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
should become:
```python
rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]
```
Let's check if `namedtuple` (lines 1416-1421) also has this problem.
```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))
```
Wait, for `namedtuple`, it only sets the headers. It doesn't seem to transform the `rows` here.
Wait, let's read further down.
```python
# take headers from the first row if necessary
if headers == "firstrow" and len(rows) > 0:
...
rows = rows[1:]
elif headers == "firstrow":
headers = []
headers = list(map(str, headers))
# rows = list(map(list, rows))
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
If they were namedtuples, `list(r)` will work correctly for both namedtuples and `SEPARATING_LINE` (since `SEPARATING_LINE` will be caught by `_is_separating_line(r)`).
What about the cursor object (line 1434)?
```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]
```
Here `rows = list(tabular_data)` was called. If `tabular_data` is a cursor that yields `SEPARATING_LINE`, then `rows` is a list of tuples (and `SEPARATING_LINE`). `list(r)` will work for tuples.
So it seems only the dict-like and dataclass cases transform the rows using `getattr` or `.get()`.
Wait, let's check the dict-like object case again.
```python
rows = [[row.get(k) for k in keys] for row in rows]
```
If `row` is `SEPARATING_LINE`, this will fail.
Let's apply the fixes.
First, for the dict-like objects:
Line 1431: `rows = [[row.get(k) for k in keys] for row in rows]`
Second, for the dataclasses:
Line 1451: `rows = [[getattr(row, f) for f in field_names] for row in rows]`
Wait, what if `rows[0]` is `SEPARATING_LINE`?
The code does:
```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")` will be false (since `SEPARATING_LINE` is a string). So it won't enter the dict-like block.
Similarly for dataclasses:
```python
elif (
dataclasses is not None
and len(rows) > 0
and dataclasses.is_dataclass(rows[0])
):
```
If `rows[0]` is `SEPARATING_LINE`, it won't enter the dataclass block.
This implies that `SEPARATING_LINE` cannot be the first row if we want `headers="keys"` to work for these types. But that's probably acceptable, as `tabulate` needs some way to infer the keys/fields.
Let's implement the fixes.
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@@ -1440,7 +1440,7 @@\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 240 s100 tokens out9,932 prompt tokens
Now for the dataclasses.
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@@ -1461,7 +1461,7 @@\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 248 s262 tokens out10,258 prompt tokens
Now let's verify both fixes with 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\nusers = [\n User(\"Alice\", 30),\n SEPARATING_LINE,\n User(\"Bob\", 25),\n]\n\nprint(\"Testing dataclasses:\")\ntry:\n print(tabulate(users, headers=\"keys\"))\n print(\"Success!\")\nexcept Exception as e:\n print(f\"Failed with {type(e).__name__}: {e}\")\n\nprint(\"\\nTesting dicts:\")\ndata = [\n {\"name\": \"Alice\", \"age\": 30},\n SEPARATING_LINE,\n {\"name\": \"Bob\", \"age\": 25},\n]\ntry:\n print(tabulate(data, headers=\"keys\"))\n print(\"Success!\")\nexcept Exception as e:\n print(f\"Failed with {type(e).__name__}: {e}\")\nPY\n"
}Result of run_command 251 characters
{
"status": "ok",
"stdout": "Testing dataclasses:\nname age\n------ -----\nAlice 30\n------ -----\nBob 25\nSuccess!\n\nTesting dicts:\nFailed with AttributeError: 'str' object has no attribute 'keys'\n",
"stderr": "",
"exit_code": 0
}Turn 12at 277 s628 tokens out10,615 prompt tokens
Wait, the dict test failed!
`Failed with AttributeError: 'str' object has no attribute 'keys'`
Let's 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] if not _is_separating_line(row) else row for row in rows]
```
The error is happening here:
```python
for row in rows:
for k in row.keys():
```
`row` is `SEPARATING_LINE` (a string), and it has no `.keys()` method.
I need to wrap this loop as well.
TOOL CALL · edit_file
{
"filepath": "tabulate/__init__.py",
"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\"",
"new_string": " for row in rows:\\n if _is_separating_line(row):\\n continue\\n for k in row.keys():\\n # Save unique items in input order\\n if k not in uniq_keys:\\n keys.append(k)\\n uniq_keys.add(k)\\n\",old_string:"
}Result of edit_file 220 characters
{
"error": "Invoking `edit_file()` failed as the following mandatory input parameters are not present:\nold_string\nYou could retry calling this tool, but it is IMPORTANT for you to provide all the mandatory parameters."
}Turn 13at 284 s169 tokens out11,310 prompt tokens
I made a mistake in the `edit_file` call.
TOOL CALL · edit_file
{
"filepath": "tabulate/__init__.py",
"new_string": " for row in rows:\n if _is_separating_line(row):\n continue\n for k in row.keys():\n # Save unique items in input order\n if k not in uniq_keys:\n keys.append(k)\n uniq_keys.add(k)",
"old_string": " for row in rows:\n for k in row.keys():\n # Save unique items in input order\n if k not in uniq_keys:\n keys.append(k)\n uniq_keys.add(k)"
}Result of edit_file 530 characters
{
"status": "ok",
"filepath": "tabulate/__init__.py",
"occurrences": 1,
"strategy": "exact",
"diff": "--- a/tabulate/__init__.py\n+++ b/tabulate/__init__.py\n@@ -1419,6 +1419,8 @@\n uniq_keys.update(keys)\n rows = rows[1:]\n for row in rows:\n+ if _is_separating_line(row):\n+ continue\n for k in row.keys():\n # Save unique items in input order\n if k not in uniq_keys:\n",
"is_truncated": false
}Turn 14at 292 s261 tokens out11,651 prompt tokens
Now let's try the reproduction script again.
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\nusers = [\n User(\"Alice\", 30),\n SEPARATING_LINE,\n User(\"Bob\", 25),\n]\n\nprint(\"Testing dataclasses:\")\ntry:\n print(tabulate(users, headers=\"keys\"))\n print(\"Success!\")\nexcept Exception as e:\n print(f\"Failed with {type(e).__name__}: {e}\")\n\nprint(\"\\nTesting dicts:\")\ndata = [\n {\"name\": \"Alice\", \"age\": 30},\n SEPARATING_LINE,\n {\"name\": \"Bob\", \"age\": 25},\n]\ntry:\n print(tabulate(data, headers=\"keys\"))\n print(\"Success!\")\nexcept Exception as e:\n print(f\"Failed with {type(e).__name__}: {e}\")\nPY\n"
}Result of run_command 270 characters
{
"status": "ok",
"stdout": "Testing dataclasses:\nname age\n------ -----\nAlice 30\n------ -----\nBob 25\nSuccess!\n\nTesting dicts:\nname age\n------ -----\nAlice 30\n------ -----\nBob 25\nSuccess!\n",
"stderr": "",
"exit_code": 0
}Turn 15at 369 s1,337 tokens out12,022 prompt tokens
Great! Both are working now.
Wait, what about `headers="firstrow"` for dicts?
```python
if headers == "firstrow":
firstdict = rows[0] if len(rows) > 0 else {}
keys.extend(firstdict.keys())
uniq_keys.update(keys)
rows = rows[1:]
```
If `rows[0]` is `SEPARATING_LINE`, `firstdict.keys()` will fail.
But as I noted before, if `rows[0]` is `SEPARATING_LINE`, `hasattr(rows[0], "keys")` will be false, so we won't even enter the dict-like block.
Wait, let's check that.
If `rows[0]` is `SEPARATING_LINE`, `hasattr(rows[0], "keys")` is `False` (strings don't have `.keys()`).
So it will skip:
```python
elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):
```
and move to:
```python
elif (
headers == "keys"
and hasattr(tabular_data, "description")
...
```
If it's none of those, it goes to the final `elif`:
```python
elif headers == "keys" and len(rows) > 0:
# keys are column indices
headers = list(map(str, range(len(rows[0]))))
```
If `rows[0]` is `SEPARATING_LINE` (a string), `range(len(rows[0]))` will be `range(1)`, so `headers` will be `["0"]`.
Then `rows` will be `list(map(lambda r: r if _is_separating_line(r) else list(r), rows))`.
If the subsequent rows are dicts, `list(dict)` returns a list of keys.
This might be weird, but it's existing behavior for when the first row doesn't match the expected type.
The bug report specifically mentioned that when some rows are `SEPARATING_LINE`, it fails. The case where a user puts `SEPARATING_LINE` as the first row of a list of dataclasses/dicts seems less likely to be the intended use case of `headers="keys"` or `headers="firstrow"`.
However, the user said: "Using tabulate to print a list of dataclasses when one or more rows is SEPARATING_LINE". This usually implies the data rows are dataclasses, and some are delimiters.
One more check: what if `tabular_data` is a dict of iterables?
```python
if hasattr(tabular_data, "keys") and hasattr(tabular_data, "values"):
# dict-like and pandas.DataFrame?
if hasattr(tabular_data.values, "__call__"):
# likely a conventional dict
keys = tabular_data.keys()
rows = list(
izip_longest(*tabular_data.values())
) # columns have to be transposed
```
In this case, `rows` will be a list of tuples. `SEPARATING_LINE` wouldn't be in here unless it was part of the values of the dict. If it is, then `rows` is a list of tuples, and the `list(r)` conversion at the end will handle it (if `_is_separating_line(r)` is True).
Let's check `_is_separating_line` again.
```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 a tuple, `row_type == list or row_type == str` is False. So `_is_separating_line` will be False for tuples.
This means if `rows` contains tuples, they will be converted via `list(r)`.
If the user provided `SEPARATING_LINE` in a list of dataclasses, `rows` was `[User, SEPARATING_LINE, User]`.
The transformations I added:
For dicts:
1. Skip `SEPARATING_LINE` when collecting keys.
2. Skip `SEPARATING_LINE` when extracting values.
For dataclasses:
1. Skip `SEPARATING_LINE` when extracting values.
This covers the reported issue.
I should also check if anything else needs fixing.
What if `rows[0]` is a dataclass, but `rows[1]` is `SEPARATING_LINE`?
Then `dataclasses.is_dataclass(rows[0])` is True.
`field_names` are extracted from `rows[0]`.
Then `rows` is transformed.
My fix:
```python
rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]
```
This means the `SEPARATING_LINE` is preserved.
Then:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
`SEPARATING_LINE` is preserved again.
Everything seems correct.
Let's run the tests in `test/test_input.py` if they exist, just to be sure we didn't break anything.
TOOL CALL · run_command
{
"command": "pytest test/test_input.py -q --tb=short --no-header"
}Result of run_command 959 characters
{
"status": "ok",
"stdout": "................................ [100%]\n=============================== warnings summary ===============================\ntest/test_input.py::test_numpy_record_array\n /workspace/test/test_input.py:172: DeprecationWarning: Data type alias 'a' was deprecated in NumPy 2.0. Use the 'S' alias instead.\n na = numpy.asarray(\n\ntest/test_input.py::test_numpy_record_array_keys\n /workspace/test/test_input.py:198: DeprecationWarning: Data type alias 'a' was deprecated in NumPy 2.0. Use the 'S' alias instead.\n na = numpy.asarray(\n\ntest/test_input.py::test_numpy_record_array_headers\n /workspace/test/test_input.py:224: DeprecationWarning: Data type alias 'a' was deprecated in NumPy 2.0. Use the 'S' alias instead.\n na = numpy.asarray(\n\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n32 passed, 3 warnings in 0.82s\n",
"stderr": "",
"exit_code": 0
}Turn 16at 372 s39 tokens out13,677 prompt tokens
Now let's run all tests to be absolutely sure.
TOOL CALL · run_command
{
"command": "pytest -q --tb=short --no-header"
}Result of run_command 1,296 characters
{
"status": "ok",
"stdout": "........................................................s..s............ [ 24%]\n.s..................s......s......s......s......s......s......s.......s. [ 49%]\n.s..s..s..s..s..s....................................................... [ 74%]\n.............................................s..ss.................sss.. [ 98%]\n... [100%]\n=============================== warnings summary ===============================\ntest/test_input.py::test_numpy_record_array\n /workspace/test/test_input.py:172: DeprecationWarning: Data type alias 'a' was deprecated in NumPy 2.0. Use the 'S' alias instead.\n na = numpy.asarray(\n\ntest/test_input.py::test_numpy_record_array_keys\n /workspace/test/test_input.py:198: DeprecationWarning: Data type alias 'a' was deprecated in NumPy 2.0. Use the 'S' alias instead.\n na = numpy.asarray(\n\ntest/test_input.py::test_numpy_record_array_headers\n /workspace/test/test_input.py:224: DeprecationWarning: Data type alias 'a' was deprecated in NumPy 2.0. Use the 'S' alias instead.\n na = numpy.asarray(\n\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n268 passed, 23 skipped, 3 warnings in 0.87s\n",
"stderr": "",
"exit_code": 0
}Turn 17at 376 s29 tokens out14,121 prompt tokens
All tests passed. One final check on the changes.
TOOL CALL · run_command
{
"command": "git diff"
}Result of run_command 1,642 characters
{
"status": "ok",
"stdout": "diff --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 18at 383 s119 tokens out14,669 prompt tokens
The diff looks correct. I have handled `SEPARATING_LINE` in both dict-like and dataclass path for both key extraction and value extraction. I should double check if `_is_separating_line` is available in the scope of `_normalize_tabular_data`. `_is_separating_line` is defined at line 105. `_normalize_tabular_data` is defined at line 118. Yes, it's in the same module.
TOOL CALL · submit_patch
Result of submit_patch 56 characters
{
"status": "ok",
"patch_size": 1537,
"files_changed": 1
}Turn 19at 439 s168 tokens out3,515 prompt tokens
The issue was that when using `SEPARATING_LINE` in a list of dataclasses or dict-like objects, `tabulate` crashed with an `AttributeError` because it tried to access keys or attributes on the separating line string. I fixed this by adding checks using `_is_separating_line()` in `_normalize_tabular_data` to skip these lines during key discovery and preserve them during row transformation.
Turn 20at 439 s
The issue was resolved by updating `_normalize_tabular_data` in `tabulate/__init__.py` to correctly handle `SEPARATING_LINE` when processing lists of dict-like objects or dataclasses. Now, separating lines are skipped during key discovery and preserved as-is during the transformation of rows into values, preventing `AttributeError` or `TypeError`.
Model calls (not matched to turns)
Generated tokens 192 tokens · stopped: STOP
The user is reporting a bug in `tabulate` where using `SEPARATING_LINE` with a list of dataclasses causes an `AttributeError`. The error message is: `AttributeError: 'str' object has no attribute '<the first attribute of dataclass>'` This happens because `tabulate` expects each row in `rows` to be an object with the specified attributes, but `SEPARATING_LINE` is a string. Let's investigate `tabulate/__init__.py` around line 1451 as mentioned in the error message. First, I'll search for `SEPARATING_LINE` in `tabulate/__init__.py` and look at the code around line 1451.
<|channel>thought
The user is reporting a bug in `tabulate` where using `SEPARATING_LINE` with a list of dataclasses causes an `AttributeError`.
The error message is: `AttributeError: 'str' object has no attribute '<the first attribute of dataclass>'`
This happens because `tabulate` expects each row in `rows` to be an object with the specified attributes, but `SEPARATING_LINE` is a string.
Let's investigate `tabulate/__init__.py` around line 1451 as mentioned in the error message.
First, I'll search for `SEPARATING_LINE` in `tabulate/__init__.py` and look at the code around line 1451.<channel|><|tool_call>call:run_command{command:<|"|>grep -n "SEPARATING_LINE" tabulate/__init__.py<|"|>}<tool_call|><|tool_response>
Generated tokens 84 tokens · stopped: STOP
Okay, `SEPARATING_LINE` is defined at line 51. The error was reported at line 1451. Let me read that section of `tabulate/__init__.py`.
Okay, `SEPARATING_LINE` is defined at line 51.
The error was reported at line 1451. Let me read that section of `tabulate/__init__.py`.
<channel|><|tool_call>call:read_file{filepath:<|"|>tabulate/__init__.py<|"|>,start_line:1400,end_line:1500}<tool_call|><|tool_response>
Generated tokens 458 tokens · stopped: STOP
The problematic section is here (lines 1443-1451):
```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]
```
If `rows` contains a mixture of dataclass instances and `SEPARATING_LINE` (which is a string), then `getattr(row, f)` will fail when `row` is `SEPARATING_LINE`.
Wait, the code checks `dataclasses.is_dataclass(rows[0])` to decide if it should treat the rows as dataclasses. If the first row is a dataclass, it assumes all rows are dataclasses and tries to access their fields. If `SEPARATING_LINE` appears later in the list, it crashes.
I need to handle `SEPARATING_LINE` in that list comprehension.
Let me first create a reproduction script to confirm this.
The problematic section is here (lines 1443-1451):
```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]
```
If `rows` contains a mixture of dataclass instances and `SEPARATING_LINE` (which is a string), then `getattr(row, f)` will fail when `row` is `SEPARATING_LINE`.
Wait, the code checks `dataclasses.is_dataclass(rows[0])` to decide if it should treat the rows as dataclasses. If the first row is a dataclass, it assumes all rows are dataclasses and tries to access their fields. If `SEPARATING_LINE` appears later in the list, it crashes.
I need to handle `SEPARATING_LINE` in that list comprehension.
Let me first create a reproduction script to confirm this.
<channel|><|tool_call>call:write_file{content:<|"|>
from dataclasses import dataclass
from tabulate import tabulate, SEPARATING_LINE
@dataclass
class User:
name: str
age: int
users = [
User("Alice", 30),
SEPARATING_LINE,
User("Bob", 25),
]
try:
print(tabulate(users, headers="keys"))
except AttributeError as e:
print(f"Caught expected AttributeError: {e}")
except Exception as e:
print(f"Caught unexpected exception: {type(e).__name__}: {e}")
else:
print("No exception raised")
<|"|>,filepath:<|"|>/tmp/repro.py<|"|>}<tool_call|><|tool_response>
Generated tokens 203 tokens · stopped: STOP
Ah, I cannot write to `/tmp/repro.py`. I should use `run_command` with `python3 - <<'PY'`.
Ah, I cannot write to `/tmp/repro.py`. I should use `run_command` with `python3 - <<'PY'`.
<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
users = [
User("Alice", 30),
SEPARATING_LINE,
User("Bob", 25),
]
try:
print(tabulate(users, headers="keys"))
except AttributeError as e:
print(f"Caught expected AttributeError: {e}")
except Exception as e:
print(f"Caught unexpected exception: {type(e).__name__}: {e}")
else:
print("No exception raised")
PY
<|"|>}<tool_call|><|tool_response>
Generated tokens 105 tokens · stopped: STOP
Confirmed. Now I need to fix it. The fix should be to check if `row` is the `SEPARATING_LINE` before calling `getattr`. But wait, is `SEPARATING_LINE` handled similarly in other parts of the code? Let's look at `_is_separating_line`.
Confirmed. Now I need to fix it.
The fix should be to check if `row` is the `SEPARATING_LINE` before calling `getattr`.
But wait, is `SEPARATING_LINE` handled similarly in other parts of the code?
Let's look at `_is_separating_line`.
<channel|><|tool_call>call:run_command{command:<|"|>grep -n "def _is_separating_line" tabulate/__init__.py<|"|>}<tool_call|><|tool_response>
Generated tokens 51 tokens · stopped: STOP
Let's examine `_is_separating_line`.
Let's examine `_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 875 tokens · stopped: STOP
Wait, `_is_separating_line` expects `row` to be a list or a string, and checks if the first or second element is `SEPARATING_LINE`.
But `SEPARATING_LINE` itself is a string `"\001"`.
If `row` is `"\001"`, then `row[0]` is `"\001"`, so `_is_separating_line(SEPARATING_LINE)` should return `True`.
Let's verify:
`row = "\001"`
`row_type == str` is True.
`len(row) >= 1` is True.
`row[0] == SEPARATING_LINE` is `"\001" == "\001"`, which is True.
So `_is_separating_line(SEPARATING_LINE)` is True.
Now back to the dataclass loop:
```python
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
I should change it to 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 `row` is `SEPARATING_LINE`, then `row` will remain `SEPARATING_LINE`.
Later in the code (line 1468):
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
This line converts every row to a list unless it's a separating line.
If I change the dataclass comprehension to preserve `SEPARATING_LINE`, then `rows` will be a list of lists and `SEPARATING_LINE` objects.
Example: `[[ "Alice", 30 ], SEPARATING_LINE, [ "Bob", 25 ]]`.
Then `list(map(lambda r: r if _is_separating_line(r) else list(r), rows))` will see:
1. `["Alice", 30]` -> `_is_separating_line` is False (since it's a list but `row[0]` is `"Alice"`) -> `list(["Alice", 30])` -> `["Alice", 30]`.
2. `SEPARATING_LINE` -> `_is_separating_line` is True -> `SEPARATING_LINE`.
3. `["Bob", 25]` -> `_is_separating_line` is False -> `list(["Bob", 25])` -> `["Bob", 25]`.
This seems correct.
Let's check if other row processing loops handle `SEPARATING_LINE` similarly.
Dict-like objects:
```python
rows = [[row.get(k) for k in keys] for row in rows]
```
If `row` is `SEPARATING_LINE` (a string), it doesn't have a `.get()` method. So it would fail here too.
Let's check the reproduction script with a dict instead of a dataclass.
Wait, `_is_separating_line` expects `row` to be a list or a string, and checks if the first or second element is `SEPARATING_LINE`.
But `SEPARATING_LINE` itself is a string `"\001"`.
If `row` is `"\001"`, then `row[0]` is `"\001"`, so `_is_separating_line(SEPARATING_LINE)` should return `True`.
Let's verify:
`row = "\001"`
`row_type == str` is True.
`len(row) >= 1` is True.
`row[0] == SEPARATING_LINE` is `"\001" == "\001"`, which is True.
So `_is_separating_line(SEPARATING_LINE)` is True.
Now back to the dataclass loop:
```python
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
I should change it to 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 `row` is `SEPARATING_LINE`, then `row` will remain `SEPARATING_LINE`.
Later in the code (line 1468):
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
This line converts every row to a list unless it's a separating line.
If I change the dataclass comprehension to preserve `SEPARATING_LINE`, then `rows` will be a list of lists and `SEPARATING_LINE` objects.
Example: `[[ "Alice", 30 ], SEPARATING_LINE, [ "Bob", 25 ]]`.
Then `list(map(lambda r: r if _is_separating_line(r) else list(r), rows))` will see:
1. `["Alice", 30]` -> `_is_separating_line` is False (since it's a list but `row[0]` is `"Alice"`) -> `list(["Alice", 30])` -> `["Alice", 30]`.
2. `SEPARATING_LINE` -> `_is_separating_line` is True -> `SEPARATING_LINE`.
3. `["Bob", 25]` -> `_is_separating_line` is False -> `list(["Bob", 25])` -> `["Bob", 25]`.
This seems correct.
Let's check if other row processing loops handle `SEPARATING_LINE` similarly.
Dict-like objects:
```python
rows = [[row.get(k) for k in keys] for row in rows]
```
If `row` is `SEPARATING_LINE` (a string), it doesn't have a `.get()` method. So it would fail here too.
Let's check the reproduction script with a dict instead of a dataclass.
<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 AttributeError as e:
print(f"Caught expected AttributeError: {e}")
except Exception as e:
print(f"Caught unexpected exception: {type(e).__name__}: {e}")
else:
print("No exception raised")
PY
<|"|>}<tool_call|><|tool_response>
Generated tokens 121 tokens · stopped: STOP
Yes, it fails there too. The problem is that the logic assumes all rows in `rows` are of the same type as `rows[0]`. Let's see where `rows` is first determined. The code I read earlier was inside a big `if/elif` block. Let's read more of `tabulate/__init__.py` around that area.
Yes, it fails there too. The problem is that the logic assumes all rows in `rows` are of the same type as `rows[0]`.
Let's see where `rows` is first determined.
The code I read earlier was inside a big `if/elif` block.
Let's read more of `tabulate/__init__.py` around that area.
<channel|><|tool_call>call:read_file{end_line:1400,filepath:<|"|>tabulate/__init__.py<|"|>,start_line:1300}<tool_call|><|tool_response>
Generated tokens 2,020 tokens · stopped: STOP
The function `_normalize_tabular_data` starts here.
If it's not a dict-like object, it does `rows = list(tabular_data)`.
Then it goes into the `if/elif` block I read before.
The problem is that if `rows` contains `SEPARATING_LINE`, the type checking only looks at `rows[0]`.
```python
elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):
# dict-like object
...
rows = [[row.get(k) for k in keys] for row in rows]
```
and
```python
elif (
dataclasses is not None
and len(rows) > 0
and dataclasses.is_dataclass(rows[0])
):
# Python 3.7+'s dataclass
...
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
Both of these should handle `SEPARATING_LINE`.
Let's see if there are other places that need similar fixes.
There's also this part:
```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]
```
Wait, in this case `rows` is not yet defined? No, wait, the preceding `else` (line 1364) has `rows = list(tabular_data)`.
Actually, looking at the code:
```python
else: # it's a usual iterable of iterables, or a NumPy array, or an iterable of dataclasses
rows = list(tabular_data)
if headers == "keys" and not rows:
# an empty table (issue #81)
headers = []
elif (
headers == "keys"
and hasattr(tabular_data, "dtype")
and getattr(tabular_data.dtype, "names")
):
# numpy record array
headers = tabular_data.dtype.names
elif (
headers == "keys"
and len(rows) > 0
and isinstance(rows[0], tuple)
and hasattr(rows[0], "_fields")
):
# namedtuple
headers = list(map(str, rows[0]._fields))
elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):
# dict-like object
...
rows = [[row.get(k) for k in keys] for row in rows]
elif (
headers == "keys"
and hasattr(tabular_data, "description")
and hasattr(tabular_data, "fetchone")
and hasattr(tabular_data, "rowcount")
):
# Python Database API cursor object (PEP 0249)
# print tabulate(cursor, headers='keys')
headers = [column[0] for column in tabular_data.description]
elif (
dataclasses is not None
and len(rows) > 0
and dataclasses.is_dataclass(rows[0])
):
# Python 3.7+'s dataclass
...
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
If `rows` contains `SEPARATING_LINE`, we should skip it during these transformations.
For the dict-like objects:
```python
rows = [[row.get(k) for k in keys] for row in rows]
```
should become:
```python
rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows]
```
For the dataclasses:
```python
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
should become:
```python
rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]
```
Let's check if `namedtuple` (lines 1416-1421) also has this problem.
```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))
```
Wait, for `namedtuple`, it only sets the headers. It doesn't seem to transform the `rows` here.
Wait, let's read further down.
```python
# take headers from the first row if necessary
if headers == "firstrow" and len(rows) > 0:
...
rows = rows[1:]
elif headers == "firstrow":
headers = []
headers = list(map(str, headers))
# rows = list(map(list, rows))
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
If they were namedtuples, `list(r)` will work correctly for both namedtuples and `SEPARATING_LINE` (since `SEPARATING_LINE` will be caught by `_is_separating_line(r)`).
What about the cursor object (line 1434)?
```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]
```
Here `rows = list(tabular_data)` was called. If `tabular_data` is a cursor that yields `SEPARATING_LINE`, then `rows` is a list of tuples (and `SEPARATING_LINE`). `list(r)` will work for tuples.
So it seems only the dict-like and dataclass cases transform the rows using `getattr` or `.get()`.
Wait, let's check the dict-like object case again.
```python
rows = [[row.get(k) for k in keys] for row in rows]
```
If `row` is `SEPARATING_LINE`, this will fail.
Let's apply the fixes.
First, for the dict-like objects:
Line 1431: `rows = [[row.get(k) for k in keys] for row in rows]`
Second, for the dataclasses:
Line 1451: `rows = [[getattr(row, f) for f in field_names] for row in rows]`
Wait, what if `rows[0]` is `SEPARATING_LINE`?
The code does:
```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")` will be false (since `SEPARATING_LINE` is a string). So it won't enter the dict-like block.
Similarly for dataclasses:
```python
elif (
dataclasses is not None
and len(rows) > 0
and dataclasses.is_dataclass(rows[0])
):
```
If `rows[0]` is `SEPARATING_LINE`, it won't enter the dataclass block.
This implies that `SEPARATING_LINE` cannot be the first row if we want `headers="keys"` to work for these types. But that's probably acceptable, as `tabulate` needs some way to infer the keys/fields.
Let's implement the fixes.
The function `_normalize_tabular_data` starts here.
If it's not a dict-like object, it does `rows = list(tabular_data)`.
Then it goes into the `if/elif` block I read before.
The problem is that if `rows` contains `SEPARATING_LINE`, the type checking only looks at `rows[0]`.
```python
elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):
# dict-like object
...
rows = [[row.get(k) for k in keys] for row in rows]
```
and
```python
elif (
dataclasses is not None
and len(rows) > 0
and dataclasses.is_dataclass(rows[0])
):
# Python 3.7+'s dataclass
...
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
Both of these should handle `SEPARATING_LINE`.
Let's see if there are other places that need similar fixes.
There's also this part:
```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]
```
Wait, in this case `rows` is not yet defined? No, wait, the preceding `else` (line 1364) has `rows = list(tabular_data)`.
Actually, looking at the code:
```python
else: # it's a usual iterable of iterables, or a NumPy array, or an iterable of dataclasses
rows = list(tabular_data)
if headers == "keys" and not rows:
# an empty table (issue #81)
headers = []
elif (
headers == "keys"
and hasattr(tabular_data, "dtype")
and getattr(tabular_data.dtype, "names")
):
# numpy record array
headers = tabular_data.dtype.names
elif (
headers == "keys"
and len(rows) > 0
and isinstance(rows[0], tuple)
and hasattr(rows[0], "_fields")
):
# namedtuple
headers = list(map(str, rows[0]._fields))
elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):
# dict-like object
...
rows = [[row.get(k) for k in keys] for row in rows]
elif (
headers == "keys"
and hasattr(tabular_data, "description")
and hasattr(tabular_data, "fetchone")
and hasattr(tabular_data, "rowcount")
):
# Python Database API cursor object (PEP 0249)
# print tabulate(cursor, headers='keys')
headers = [column[0] for column in tabular_data.description]
elif (
dataclasses is not None
and len(rows) > 0
and dataclasses.is_dataclass(rows[0])
):
# Python 3.7+'s dataclass
...
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
If `rows` contains `SEPARATING_LINE`, we should skip it during these transformations.
For the dict-like objects:
```python
rows = [[row.get(k) for k in keys] for row in rows]
```
should become:
```python
rows = [[row.get(k) for k in keys] if not _is_separating_line(row) else row for row in rows]
```
For the dataclasses:
```python
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
should become:
```python
rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]
```
Let's check if `namedtuple` (lines 1416-1421) also has this problem.
```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))
```
Wait, for `namedtuple`, it only sets the headers. It doesn't seem to transform the `rows` here.
Wait, let's read further down.
```python
# take headers from the first row if necessary
if headers == "firstrow" and len(rows) > 0:
...
rows = rows[1:]
elif headers == "firstrow":
headers = []
headers = list(map(str, headers))
# rows = list(map(list, rows))
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
If they were namedtuples, `list(r)` will work correctly for both namedtuples and `SEPARATING_LINE` (since `SEPARATING_LINE` will be caught by `_is_separating_line(r)`).
What about the cursor object (line 1434)?
```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]
```
Here `rows = list(tabular_data)` was called. If `tabular_data` is a cursor that yields `SEPARATING_LINE`, then `rows` is a list of tuples (and `SEPARATING_LINE`). `list(r)` will work for tuples.
So it seems only the dict-like and dataclass cases transform the rows using `getattr` or `.get()`.
Wait, let's check the dict-like object case again.
```python
rows = [[row.get(k) for k in keys] for row in rows]
```
If `row` is `SEPARATING_LINE`, this will fail.
Let's apply the fixes.
First, for the dict-like objects:
Line 1431: `rows = [[row.get(k) for k in keys] for row in rows]`
Second, for the dataclasses:
Line 1451: `rows = [[getattr(row, f) for f in field_names] for row in rows]`
Wait, what if `rows[0]` is `SEPARATING_LINE`?
The code does:
```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")` will be false (since `SEPARATING_LINE` is a string). So it won't enter the dict-like block.
Similarly for dataclasses:
```python
elif (
dataclasses is not None
and len(rows) > 0
and dataclasses.is_dataclass(rows[0])
):
```
If `rows[0]` is `SEPARATING_LINE`, it won't enter the dataclass block.
This implies that `SEPARATING_LINE` cannot be the first row if we want `headers="keys"` to work for these types. But that's probably acceptable, as `tabulate` needs some way to infer the keys/fields.
Let's implement the fixes.
<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 100 tokens · stopped: STOP
Now for the dataclasses.
Now for the dataclasses.
<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 262 tokens · stopped: STOP
Now let's verify both fixes with reproduction scripts.
Now let's verify both fixes with 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
users = [
User("Alice", 30),
SEPARATING_LINE,
User("Bob", 25),
]
print("Testing dataclasses:")
try:
print(tabulate(users, headers="keys"))
print("Success!")
except Exception as e:
print(f"Failed with {type(e).__name__}: {e}")
print("\nTesting dicts:")
data = [
{"name": "Alice", "age": 30},
SEPARATING_LINE,
{"name": "Bob", "age": 25},
]
try:
print(tabulate(data, headers="keys"))
print("Success!")
except Exception as e:
print(f"Failed with {type(e).__name__}: {e}")
PY
<|"|>}<tool_call|><|tool_response>
Generated tokens 628 tokens · stopped: STOP
Wait, the dict test failed!
`Failed with AttributeError: 'str' object has no attribute 'keys'`
Let's 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] if not _is_separating_line(row) else row for row in rows]
```
The error is happening here:
```python
for row in rows:
for k in row.keys():
```
`row` is `SEPARATING_LINE` (a string), and it has no `.keys()` method.
I need to wrap this loop as well.
Wait, the dict test failed!
`Failed with AttributeError: 'str' object has no attribute 'keys'`
Let's 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] if not _is_separating_line(row) else row for row in rows]
```
The error is happening here:
```python
for row in rows:
for k in row.keys():
```
`row` is `SEPARATING_LINE` (a string), and it has no `.keys()` method.
I need to wrap this loop as well.
<channel|><|tool_call>call:edit_file{filepath:<|"|>tabulate/__init__.py<|"|>,new_string:<|"|> for row in rows:\n if _is_separating_line(row):\n continue\n for k in row.keys():\n # Save unique items in input order\n if k not in uniq_keys:\n keys.append(k)\n uniq_keys.add(k)\n",old_string:<|"|> for row in rows:\n for k in row.keys():\n # Save unique items in input order\n if k not in uniq_keys:\n keys.append(k)\n uniq_keys.add(k)\n"}<tool_call|><|tool_response>
Generated tokens 169 tokens · stopped: STOP
I made a mistake in the `edit_file` call.
I made a mistake in the `edit_file` call.
<channel|><|tool_call>call:edit_file{filepath:<|"|>tabulate/__init__.py<|"|>,new_string:<|"|> for row in rows:
if _is_separating_line(row):
continue
for k in row.keys():
# Save unique items in input order
if k not in uniq_keys:
keys.append(k)
uniq_keys.add(k)<|"|>,old_string:<|"|> for row in rows:
for k in row.keys():
# Save unique items in input order
if k not in uniq_keys:
keys.append(k)
uniq_keys.add(k)<|"|>}<tool_call|><|tool_response>
Generated tokens 261 tokens · stopped: STOP
Now let's try the reproduction script again.
Now let's try the reproduction script again.
<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
users = [
User("Alice", 30),
SEPARATING_LINE,
User("Bob", 25),
]
print("Testing dataclasses:")
try:
print(tabulate(users, headers="keys"))
print("Success!")
except Exception as e:
print(f"Failed with {type(e).__name__}: {e}")
print("\nTesting dicts:")
data = [
{"name": "Alice", "age": 30},
SEPARATING_LINE,
{"name": "Bob", "age": 25},
]
try:
print(tabulate(data, headers="keys"))
print("Success!")
except Exception as e:
print(f"Failed with {type(e).__name__}: {e}")
PY
<|"|>}<tool_call|><|tool_response>
Generated tokens 1,337 tokens · stopped: STOP
Great! Both are working now.
Wait, what about `headers="firstrow"` for dicts?
```python
if headers == "firstrow":
firstdict = rows[0] if len(rows) > 0 else {}
keys.extend(firstdict.keys())
uniq_keys.update(keys)
rows = rows[1:]
```
If `rows[0]` is `SEPARATING_LINE`, `firstdict.keys()` will fail.
But as I noted before, if `rows[0]` is `SEPARATING_LINE`, `hasattr(rows[0], "keys")` will be false, so we won't even enter the dict-like block.
Wait, let's check that.
If `rows[0]` is `SEPARATING_LINE`, `hasattr(rows[0], "keys")` is `False` (strings don't have `.keys()`).
So it will skip:
```python
elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):
```
and move to:
```python
elif (
headers == "keys"
and hasattr(tabular_data, "description")
...
```
If it's none of those, it goes to the final `elif`:
```python
elif headers == "keys" and len(rows) > 0:
# keys are column indices
headers = list(map(str, range(len(rows[0]))))
```
If `rows[0]` is `SEPARATING_LINE` (a string), `range(len(rows[0]))` will be `range(1)`, so `headers` will be `["0"]`.
Then `rows` will be `list(map(lambda r: r if _is_separating_line(r) else list(r), rows))`.
If the subsequent rows are dicts, `list(dict)` returns a list of keys.
This might be weird, but it's existing behavior for when the first row doesn't match the expected type.
The bug report specifically mentioned that when some rows are `SEPARATING_LINE`, it fails. The case where a user puts `SEPARATING_LINE` as the first row of a list of dataclasses/dicts seems less likely to be the intended use case of `headers="keys"` or `headers="firstrow"`.
However, the user said: "Using tabulate to print a list of dataclasses when one or more rows is SEPARATING_LINE". This usually implies the data rows are dataclasses, and some are delimiters.
One more check: what if `tabular_data` is a dict of iterables?
```python
if hasattr(tabular_data, "keys") and hasattr(tabular_data, "values"):
# dict-like and pandas.DataFrame?
if hasattr(tabular_data.values, "__call__"):
# likely a conventional dict
keys = tabular_data.keys()
rows = list(
izip_longest(*tabular_data.values())
) # columns have to be transposed
```
In this case, `rows` will be a list of tuples. `SEPARATING_LINE` wouldn't be in here unless it was part of the values of the dict. If it is, then `rows` is a list of tuples, and the `list(r)` conversion at the end will handle it (if `_is_separating_line(r)` is True).
Let's check `_is_separating_line` again.
```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 a tuple, `row_type == list or row_type == str` is False. So `_is_separating_line` will be False for tuples.
This means if `rows` contains tuples, they will be converted via `list(r)`.
If the user provided `SEPARATING_LINE` in a list of dataclasses, `rows` was `[User, SEPARATING_LINE, User]`.
The transformations I added:
For dicts:
1. Skip `SEPARATING_LINE` when collecting keys.
2. Skip `SEPARATING_LINE` when extracting values.
For dataclasses:
1. Skip `SEPARATING_LINE` when extracting values.
This covers the reported issue.
I should also check if anything else needs fixing.
What if `rows[0]` is a dataclass, but `rows[1]` is `SEPARATING_LINE`?
Then `dataclasses.is_dataclass(rows[0])` is True.
`field_names` are extracted from `rows[0]`.
Then `rows` is transformed.
My fix:
```python
rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]
```
This means the `SEPARATING_LINE` is preserved.
Then:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
`SEPARATING_LINE` is preserved again.
Everything seems correct.
Let's run the tests in `test/test_input.py` if they exist, just to be sure we didn't break anything.Great! Both are working now.
Wait, what about `headers="firstrow"` for dicts?
```python
if headers == "firstrow":
firstdict = rows[0] if len(rows) > 0 else {}
keys.extend(firstdict.keys())
uniq_keys.update(keys)
rows = rows[1:]
```
If `rows[0]` is `SEPARATING_LINE`, `firstdict.keys()` will fail.
But as I noted before, if `rows[0]` is `SEPARATING_LINE`, `hasattr(rows[0], "keys")` will be false, so we won't even enter the dict-like block.
Wait, let's check that.
If `rows[0]` is `SEPARATING_LINE`, `hasattr(rows[0], "keys")` is `False` (strings don't have `.keys()`).
So it will skip:
```python
elif len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values"):
```
and move to:
```python
elif (
headers == "keys"
and hasattr(tabular_data, "description")
...
```
If it's none of those, it goes to the final `elif`:
```python
elif headers == "keys" and len(rows) > 0:
# keys are column indices
headers = list(map(str, range(len(rows[0]))))
```
If `rows[0]` is `SEPARATING_LINE` (a string), `range(len(rows[0]))` will be `range(1)`, so `headers` will be `["0"]`.
Then `rows` will be `list(map(lambda r: r if _is_separating_line(r) else list(r), rows))`.
If the subsequent rows are dicts, `list(dict)` returns a list of keys.
This might be weird, but it's existing behavior for when the first row doesn't match the expected type.
The bug report specifically mentioned that when some rows are `SEPARATING_LINE`, it fails. The case where a user puts `SEPARATING_LINE` as the first row of a list of dataclasses/dicts seems less likely to be the intended use case of `headers="keys"` or `headers="firstrow"`.
However, the user said: "Using tabulate to print a list of dataclasses when one or more rows is SEPARATING_LINE". This usually implies the data rows are dataclasses, and some are delimiters.
One more check: what if `tabular_data` is a dict of iterables?
```python
if hasattr(tabular_data, "keys") and hasattr(tabular_data, "values"):
# dict-like and pandas.DataFrame?
if hasattr(tabular_data.values, "__call__"):
# likely a conventional dict
keys = tabular_data.keys()
rows = list(
izip_longest(*tabular_data.values())
) # columns have to be transposed
```
In this case, `rows` will be a list of tuples. `SEPARATING_LINE` wouldn't be in here unless it was part of the values of the dict. If it is, then `rows` is a list of tuples, and the `list(r)` conversion at the end will handle it (if `_is_separating_line(r)` is True).
Let's check `_is_separating_line` again.
```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 a tuple, `row_type == list or row_type == str` is False. So `_is_separating_line` will be False for tuples.
This means if `rows` contains tuples, they will be converted via `list(r)`.
If the user provided `SEPARATING_LINE` in a list of dataclasses, `rows` was `[User, SEPARATING_LINE, User]`.
The transformations I added:
For dicts:
1. Skip `SEPARATING_LINE` when collecting keys.
2. Skip `SEPARATING_LINE` when extracting values.
For dataclasses:
1. Skip `SEPARATING_LINE` when extracting values.
This covers the reported issue.
I should also check if anything else needs fixing.
What if `rows[0]` is a dataclass, but `rows[1]` is `SEPARATING_LINE`?
Then `dataclasses.is_dataclass(rows[0])` is True.
`field_names` are extracted from `rows[0]`.
Then `rows` is transformed.
My fix:
```python
rows = [[getattr(row, f) for f in field_names] if not _is_separating_line(row) else row for row in rows]
```
This means the `SEPARATING_LINE` is preserved.
Then:
```python
rows = list(map(lambda r: r if _is_separating_line(r) else list(r), rows))
```
`SEPARATING_LINE` is preserved again.
Everything seems correct.
Let's run the tests in `test/test_input.py` if they exist, just to be sure we didn't break anything.<channel|><|tool_call>call:run_command{command:<|"|>pytest test/test_input.py -q --tb=short --no-header<|"|>}<tool_call|><|tool_response>
Generated tokens 39 tokens · stopped: STOP
Now let's run all tests to be absolutely sure.
Now let's run all tests to be absolutely sure.
<channel|><|tool_call>call:run_command{command:<|"|>pytest -q --tb=short --no-header<|"|>}<tool_call|><|tool_response>
Generated tokens 29 tokens · stopped: STOP
All tests passed. One final check on the changes.
All tests passed.
One final check on the changes.<channel|><|tool_call>call:run_command{command:<|"|>git diff<|"|>}<tool_call|><|tool_response>
Generated tokens 119 tokens · stopped: STOP
The diff looks correct. I have handled `SEPARATING_LINE` in both dict-like and dataclass path for both key extraction and value extraction. I should double check if `_is_separating_line` is available in the scope of `_normalize_tabular_data`. `_is_separating_line` is defined at line 105. `_normalize_tabular_data` is defined at line 118. Yes, it's in the same module.
The diff looks correct. I have handled `SEPARATING_LINE` in both dict-like and dataclass path for both key extraction and value extraction.
I should double check if `_is_separating_line` is available in the scope of `_normalize_tabular_data`.
`_is_separating_line` is defined at line 105.
`_normalize_tabular_data` is defined at line 118.
Yes, it's in the same module.<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-4d4721aa60114360a8e9939878942a99.xml --
=========================== short test summary info ============================
PASSED test/test_input.py::test_iterable_of_iterables
PASSED test/test_input.py::test_iterable_of_iterables_headers
PASSED test/test_input.py::test_iterable_of_iterables_firstrow
PASSED test/test_input.py::test_list_of_lists
PASSED test/test_input.py::test_list_of_lists_firstrow
PASSED test/test_input.py::test_list_of_lists_keys
PASSED test/test_input.py::test_dict_like
PASSED test/test_input.py::test_numpy_2d
PASSED test/test_input.py::test_numpy_2d_firstrow
PASSED test/test_input.py::test_numpy_2d_keys
PASSED test/test_input.py::test_numpy_record_array
PASSED test/test_input.py::test_numpy_record_array_keys
PASSED test/test_input.py::test_numpy_record_array_headers
PASSED test/test_input.py::test_pandas
PASSED test/test_input.py::test_pandas_firstrow
PASSED test/test_input.py::test_pandas_keys
PASSED test/test_input.py::test_sqlite3
PASSED test/test_input.py::test_sqlite3_keys
PASSED test/test_input.py::test_list_of_namedtuples
PASSED test/test_input.py::test_list_of_namedtuples_keys
PASSED test/test_input.py::test_list_of_dicts
PASSED test/test_input.py::test_list_of_userdicts
PASSED test/test_input.py::test_list_of_dicts_keys
PASSED test/test_input.py::test_list_of_userdicts_keys
PASSED test/test_input.py::test_list_of_dicts_with_missing_keys
PASSED test/test_input.py::test_list_of_dicts_firstrow
PASSED test/test_input.py::test_list_of_dicts_with_dict_of_headers
PASSED test/test_input.py::test_list_of_dicts_with_list_of_headers
PASSED test/test_input.py::test_list_of_ordereddicts
PASSED test/test_input.py::test_py37orlater_list_of_dataclasses_keys
PASSED test/test_input.py::test_py37orlater_list_of_dataclasses_headers
PASSED test/test_input.py::test_py37orlater_list_of_dataclasses_with_separating_line
PASSED test/test_input.py::test_list_bytes
============================== 33 passed in 0.68s ==============================
Test errors Included
Test report (JUnit) Included
<?xml version="1.0" encoding="utf-8"?><testsuites name="pytest tests"><testsuite name="pytest" errors="0" failures="0" skipped="0" tests="33" time="0.684" timestamp="2026-11-14T03:56:35.126740+00:00" hostname="f2ca75b195bf"><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="0.116" /><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="0.523" /><testcase classname="test.test_input" name="test_pandas_firstrow" time="0.001" /><testcase classname="test.test_input" name="test_pandas_keys" time="0.000" /><testcase classname="test.test_input" name="test_sqlite3" time="0.002" /><testcase classname="test.test_input" name="test_sqlite3_keys" time="0.000" /><testcase classname="test.test_input" name="test_list_of_namedtuples" time="0.000" /><testcase classname="test.test_input" name="test_list_of_namedtuples_keys" time="0.000" /><testcase classname="test.test_input" name="test_list_of_dicts" time="0.000" /><testcase classname="test.test_input" name="test_list_of_userdicts" time="0.000" /><testcase classname="test.test_input" name="test_list_of_dicts_keys" time="0.000" /><testcase classname="test.test_input" name="test_list_of_userdicts_keys" time="0.000" /><testcase classname="test.test_input" name="test_list_of_dicts_with_missing_keys" time="0.000" /><testcase classname="test.test_input" name="test_list_of_dicts_firstrow" time="0.000" /><testcase classname="test.test_input" name="test_list_of_dicts_with_dict_of_headers" time="0.000" /><testcase classname="test.test_input" name="test_list_of_dicts_with_list_of_headers" time="0.000" /><testcase classname="test.test_input" name="test_list_of_ordereddicts" time="0.000" /><testcase classname="test.test_input" name="test_py37orlater_list_of_dataclasses_keys" time="0.001" /><testcase classname="test.test_input" name="test_py37orlater_list_of_dataclasses_headers" time="0.001" /><testcase classname="test.test_input" name="test_py37orlater_list_of_dataclasses_with_separating_line" time="0.001" /><testcase classname="test.test_input" name="test_list_bytes" time="0.000" /></testsuite></testsuites>
Agent log Included
[ 0.0s] 📋 System Prompt: You are a principal Python software engineer resolving a repository issue in `/workspace`.
[ 0.0s] 🎯 Task Prompt: You are evaluating a software engineering task for repository astanin/python-tabulate.
[ 10.8s] 💭 (575 chars) "The user is reporting a bug in `tabulate` where using `SEPARATING_LINE` with a list of
dataclasses causes an `AttributeE..."
[ 10.8s] 🔧 run_command grep -n "SEPARATING_LINE" tabulate/__init__.py
[ 10.9s] → 51:SEPARATING_LINE = "\001"
[ 16.3s] 💭 (135 chars) "Okay, `SEPARATING_LINE` is defined at line 51.
The error was reported at line 1451. Let me read that section of `tabulat..."
[ 16.3s] 📖 read_file (tabulate/__init__.py)
[ 16.4s] ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'content': ' and
getattr(tabular_data.dtype, "names")\n …
[ 46.9s] 💭 (1036 chars) "The problematic section is here (lines 1443-1451):
```python
elif (
dataclasses is not None
..."
[ 46.9s] 📝 write_file (/tmp/repro.py, 23 lines)
[ 46.9s] ❌ {'status': 'error', 'error_type': 'FileWriteError', 'error_message': "Path traversal detected:
'/tmp/repro.py' escapes w…
[ 54.1s] 💭 (90 chars) "Ah, I cannot write to `/tmp/repro.py`. I should use `run_command` with `python3 - <<'PY'`."
[ 54.1s] 🔧 run_command (24 lines) python3 - <<'PY'
[ 54.2s] → Caught expected AttributeError: 'str' object has no attribute 'name'
[ 61.8s] 💭 (234 chars) "Confirmed. Now I need to fix it.
The fix should be to check if `row` is the `SEPARATING_LINE` before calling `getattr`.
..."
[ 61.8s] 🔧 run_command grep -n "def _is_separating_line" tabulate/__init__.py
[ 61.8s] → 105:def _is_separating_line(row):
[ 63.9s] 💭 (36 chars) "Let's examine `_is_separating_line`."
[ 63.9s] 📖 read_file (tabulate/__init__.py)
[ 64.0s] ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'content': ' "with_header_hide",\n
],\n)\n\n\ndef _is_sep…
[ 121.3s] 💭 (2033 chars) "Wait, `_is_separating_line` expects `row` to be a list or a string, and checks if the first
or second element is `SEPARA..."
[ 121.3s] 🔧 run_command (18 lines) python3 - <<'PY'
[ 121.3s] → Caught expected AttributeError: 'str' object has no attribute 'keys'
[ 130.3s] 💭 (279 chars) "Yes, it fails there too. The problem is that the logic assumes all rows in `rows` are of the
same type as `rows[0]`.
Le..."
[ 130.3s] 📖 read_file (tabulate/__init__.py)
[ 130.3s] ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'content': ' + "len(index)={}
len(rows)={}".format(len(i…
[ 234.7s] 💭 (6616 chars) "The function `_normalize_tabular_data` starts here.
If it's not a dict-like object, it does `rows = list(tabular_data)`...."
[ 234.7s] ✏️ edit_file (tabulate/__init__.py)
[ 234.9s] ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'occurrences': 1, 'strategy': 'exact', 'diff': '---
a/tabulate/__in…
[ 239.1s] 💭 (24 chars) "Now for the dataclasses."
[ 239.1s] ✏️ edit_file (tabulate/__init__.py)
[ 239.3s] ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'occurrences': 1, 'strategy': 'exact', 'diff': '---
a/tabulate/__in…
[ 247.3s] 💭 (54 chars) "Now let's verify both fixes with reproduction scripts."
[ 247.3s] 🔧 run_command (34 lines) python3 - <<'PY'
[ 247.3s] → Testing dataclasses:
[ 277.0s] 💭 (1840 chars) "Wait, the dict test failed!
`Failed with AttributeError: 'str' object has no attribute 'keys'`
Let's look at the code f..."
[ 277.0s] ✏️ edit_file (tabulate/__init__.py)
[ 277.0s] ❌ {'error': 'Invoking `edit_file()` failed as the following mandatory input parameters are not
present:\nold_string\nYou c…
[ 283.7s] 💭 (41 chars) "I made a mistake in the `edit_file` call."
[ 283.7s] ✏️ edit_file (tabulate/__init__.py)
[ 283.8s] ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'occurrences': 1, 'strategy': 'exact', 'diff': '---
a/tabulate/__in…
[ 291.4s] 💭 (44 chars) "Now let's try the reproduction script again."
[ 291.4s] 🔧 run_command (34 lines) python3 - <<'PY'
[ 291.5s] → Testing dataclasses:
[ 368.6s] 💭 (4235 chars) "Great! Both are working now.
Wait, what about `headers="firstrow"` for dicts?
```python
if headers == "firs..."
[ 368.6s] 🔧 run_command pytest test/test_input.py -q --tb=short --no-header
[ 369.8s] → ................................ [100%]
[ 371.5s] 💭 (46 chars) "Now let's run all tests to be absolutely sure."
[ 371.5s] 🔧 run_command pytest -q --tb=short --no-header
[ 372.6s] → ........................................................s..s............ [ 24%]
[ 375.4s] 💭 (50 chars) "All tests passed.
One final check on the changes."
[ 375.4s] 🔧 run_command git diff
[ 375.4s] → diff --git a/tabulate/__init__.py b/tabulate/__init__.py
[ 382.6s] 💭 (369 chars) "The diff looks correct. I have handled `SEPARATING_LINE` in both dict-like and dataclass
path for both key extraction an..."
[ 382.6s] 🤖 submit_patch
[ 382.7s] → submit_patch: {"status": "ok", "patch_size": 1537, "files_changed": 1}
[ 438.4s] 💭 (390 chars) "The issue was that when using `SEPARATING_LINE` in a list of dataclasses or dict-like
objects, `tabulate` crashed with a..."
[ 438.4s] ✅ Agent finished: The issue was resolved by updating `_normalize_tabular_data` in `tabulate/__init__.py` to
correctly handle `SEPARATING_LINE` when processing lists of dict-like objects or dataclasses. Now, separating ...
Native result row
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"status_counts": {
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"rollout_idx": 2,
"run_key": "astanin__python-tabulate-268__p0__r2",
"strict_clean_patch": true,
"tool_calls": 16,
"total_llm_calls": 19,
"trace_path": "/kaggle-root/working/r31/rollouts_X/traces/trace_astanin__python-tabulate-268__p0__r2.json"
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