ROLLOUT 1 · astanin/python-tabulate
astanin__python-tabulate-268
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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 17 s262 tokens out2,665 prompt tokens
The user is reporting a bug in `tabulate` where `SEPARATING_LINE` does not work when printing a list of dataclasses. The error is an `AttributeError: 'str' object has no attribute '<the first attribute of dataclass>'`. This happens in `tabulate/__init__.py`, line 1451 (approximately), inside a list comprehension: `rows = [[getattr(row, f) for f in field_names] for row in rows ]` This suggests that `tabulate` is trying to treat all rows as dataclasses (or objects with attributes), but `SEPARATING_LINE` is a string, and strings don't have the attributes of the dataclass. I need to: 1. Find the line in `tabulate/__init__.py` that handles dataclasses and `SEPARATING_LINE`. 2. Create a reproduction script. 3. Fix the code to handle `SEPARATING_LINE` correctly even when the other rows are dataclasses. Let's start by locating the code.
TOOL CALL · run_command
{
"command": "grep -n \"getattr(row, f)\" tabulate/__init__.py"
}Result of run_command 142 characters
{
"status": "ok",
"stdout": "1464: rows = [[getattr(row, f) for f in field_names] for row in rows]\n",
"stderr": "",
"exit_code": 0
}Generated tokens 262 tokens · stopped: STOP
<|channel>thought
The user is reporting a bug in `tabulate` where `SEPARATING_LINE` does not work when printing a list of dataclasses.
The error is an `AttributeError: 'str' object has no attribute '<the first attribute of dataclass>'`.
This happens in `tabulate/__init__.py`, line 1451 (approximately), inside a list comprehension:
`rows = [[getattr(row, f) for f in field_names] for row in rows ]`
This suggests that `tabulate` is trying to treat all rows as dataclasses (or objects with attributes), but `SEPARATING_LINE` is a string, and strings don't have the attributes of the dataclass.
I need to:
1. Find the line in `tabulate/__init__.py` that handles dataclasses and `SEPARATING_LINE`.
2. Create a reproduction script.
3. Fix the code to handle `SEPARATING_LINE` correctly even when the other rows are dataclasses.
Let's start by locating the code.<channel|><|tool_call>call:run_command{command:<|"|>grep -n "getattr(row, f)" tabulate/__init__.py<|"|>}<tool_call|><|tool_response>
Turn 2at 22 s80 tokens out2,994 prompt tokens
Okay, it's on line 1464 of `tabulate/__init__.py`. Let's read the context around that line to understand how it's being used.
TOOL CALL · read_file
{
"end_line": 1480,
"filepath": "tabulate/__init__.py",
"start_line": 1440
}Result of read_file 1,751 characters
{
"status": "ok",
"filepath": "tabulate/__init__.py",
"content": " raise ValueError(\n \"headers for a list of dicts is not a dict or a keyword\"\n )\n rows = [[row.get(k) for k in keys] for row in rows]\n\n elif (\n headers == \"keys\"\n and hasattr(tabular_data, \"description\")\n and hasattr(tabular_data, \"fetchone\")\n and hasattr(tabular_data, \"rowcount\")\n ):\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 = []",
"start_line": 1440,
"end_line": 1480,
"total_lines": 2787,
"is_truncated": false
}Generated tokens 80 tokens · stopped: STOP
Okay, it's on line 1464 of `tabulate/__init__.py`.
Let's read the context around that line to understand how it's being used.
<channel|><|tool_call>call:read_file{end_line:1480,filepath:<|"|>tabulate/__init__.py<|"|>,start_line:1440}<tool_call|><|tool_response>
Turn 3at 35 s258 tokens out3,588 prompt tokens
The problematic part is:
```python
elif (
dataclasses is not None
and len(rows) > 0
and dataclasses.is_dataclass(rows[0])
):
# Python 3.7+'s dataclass
field_names = [field.name for field in dataclasses.fields(rows[0])\n if headers == "keys":
headers = field_names
rows = [[getattr(row, f) for f in field_names] for row in rows]\n```
If `rows[0]` is a dataclass, it assumes all subsequent `row` in `rows` are also dataclasses (or at least have those attributes). But if one of the rows is `tabulate.SEPARATING_LINE`, it's a string, and `getattr(row, f)` will fail.
`SEPARATING_LINE` is probably a special marker that should be handled differently.
Let's find where `SEPARATING_LINE` is defined.
TOOL CALL · run_command
{
"command": "grep -rn \"SEPARATING_LINE\" tabulate/__init__.py"
}Result of run_command 312 characters
{
"status": "ok",
"stdout": "51:SEPARATING_LINE = \"\\001\"\n108: (len(row) >= 1 and row[0] == SEPARATING_LINE)\n109: or (len(row) >= 2 and row[1] == SEPARATING_LINE)\n1290: rows.insert(index, SEPARATING_LINE)\n2453: # the SEPARATING_LINE flag\n",
"stderr": "",
"exit_code": 0
}Generated tokens 258 tokens · stopped: STOP
The problematic part is:
```python
elif (
dataclasses is not None
and len(rows) > 0
and dataclasses.is_dataclass(rows[0])
):
# Python 3.7+'s dataclass
field_names = [field.name for field in dataclasses.fields(rows[0])\n if headers == "keys":
headers = field_names
rows = [[getattr(row, f) for f in field_names] for row in rows]\n```
If `rows[0]` is a dataclass, it assumes all subsequent `row` in `rows` are also dataclasses (or at least have those attributes). But if one of the rows is `tabulate.SEPARATING_LINE`, it's a string, and `getattr(row, f)` will fail.
`SEPARATING_LINE` is probably a special marker that should be handled differently.
Let's find where `SEPARATING_LINE` is defined.
<channel|><|tool_call>call:run_command{command:<|"|>grep -rn "SEPARATING_LINE" tabulate/__init__.py<|"|>}<tool_call|><|tool_response>
Turn 4at 48 s166 tokens out3,988 prompt tokens
`SEPARATING_LINE = "\001"`. Now let's create a reproduction script to confirm the bug.
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nfrom dataclasses import dataclass\nfrom tabulate import tabulate, SEPARATING_LINE\n\n@dataclass\nclass Data:\n name: str\n age: int\n\ndata = [\n Data(\"Alice\", 30),\n SEPARATING_LINE,\n Data(\"Bob\", 25),\n]\n\ntry:\n print(tabulate(data, headers=\"keys\"))\nexcept Exception as e:\n print(e)\n import traceback\n traceback.print_exc()\nPY\n"
}Result of run_command 796 characters
{
"status": "ok",
"stdout": "'str' object has no attribute 'name'\n",
"stderr": "Traceback (most recent call last):\n File \"<stdin>\", line 16, in <module>\n File \"/workspace/tabulate/__init__.py\", line 2088, in tabulate\n list_of_lists, headers, headers_pad = _normalize_tabular_data(\n File \"/workspace/tabulate/__init__.py\", line 1464, in _normalize_tabular_data\n rows = [[getattr(row, f) for f in field_names] for row in rows]\n File \"/workspace/tabulate/__init__.py\", line 1464, in <listcomp>\n rows = [[getattr(row, f) for f in field_names] for row in rows]\n File \"/workspace/tabulate/__init__.py\", line 1464, in <listcomp>\n rows = [[getattr(row, f) for f in field_names] for row in rows]\nAttributeError: 'str' object has no attribute 'name'\n",
"exit_code": 0
}Generated tokens 166 tokens · stopped: STOP
`SEPARATING_LINE = "\001"`.
Now let's create a reproduction script to confirm the bug.
<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
from dataclasses import dataclass
from tabulate import tabulate, SEPARATING_LINE
@dataclass
class Data:
name: str
age: int
data = [
Data("Alice", 30),
SEPARATING_LINE,
Data("Bob", 25),
]
try:
print(tabulate(data, headers="keys"))
except Exception as e:
print(e)
import traceback
traceback.print_exc()
PY
<|"|>}<tool_call|><|tool_response>
Turn 5at 174 s1,952 tokens out4,439 prompt tokens
Confirmed. The error is exactly what was reported.
Now I need to fix it. The code should skip calling `getattr` if the row is `SEPARATING_LINE`.
Looking at the code again:
```python
elif (
dataclasses is not None
and len(rows) > 0
and dataclasses.is_dataclass(rows[0])
):
# Python 3.7+'s dataclass
field_names = [field.name for field in dataclasses.fields(rows[0])]
if headers == "keys":
headers = field_names
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
I should change the list comprehension for `rows` to handle `SEPARATING_LINE`.
Wait, `SEPARATING_LINE` is usually handled later in the pipeline, but here we are normalizing the data.
If `row` is `SEPARATING_LINE`, it should probably remain `SEPARATING_LINE` or be converted to a list containing `SEPARATING_LINE`.
Wait, if `row` is `SEPARATING_LINE`, `getattr(row, f)` fails.
If I check `if row == SEPARATING_LINE`, I can decide what to do.
Let's see how other parts of the code handle `SEPARATING_LINE`.
At line 108:
```python
(len(row) >= 1 and row[0] == SEPARATING_LINE)
or (len(row) >= 2 and row[1] == SEPARATING_LINE)
```
This suggests that a row is considered a separating line if it's a list/tuple and its first or second element is `SEPARATING_LINE`.
Wait, `SEPARATING_LINE` is defined as `"\001"`.
When `tabulate` is called with a list of dataclasses, `rows` is a list of dataclasses.
When we insert `SEPARATING_LINE`, it's a string.
So `row` can be either a dataclass object or the string `"\001"`.
The list comprehension should be:
```python
rows = [
[getattr(row, f) for f in field_names] if not (
isinstance(row, str) and row == SEPARATING_LINE
) else row
for row in rows
]
```
But wait, if I leave it as `row` (a string), will the rest of the code handle it?
The code later expects `rows` to be a list of lists (or similar).
Let's check `_normalize_tabular_data` more broadly.
Actually, it might be simpler to just check if the row is the separating line and if so, keep it as is.
Let's see where `rows` is used after this.
The list comprehension: `rows = [[getattr(row, f) for f in field_names] for row in rows]` converts a list of objects into a list of lists.
If one of the items is `SEPARATING_LINE`, and we keep it as a string, `rows` will be something like `[[...], "\001", [...]]`.
Will that work?
Looking at the `tabulate` function, it calls `_normalize_tabular_data`.
Let's see what happens if I change it to:
```python
rows = [
[getattr(row, f) for f in field_names] if not (
isinstance(row, str) and row == SEPARATING_LINE
) else row
for row in rows
]
```
Let's test this hypothesis.
Wait, looking at the lines around 108:
```python
(len(row) >= 1 and row[0] == SEPARATING_LINE)
or (len(row) >= 2 and row[1] == SEPARATING_LINE)
```
This is probably in a function that iterates over `rows`. If `row` is a string, `len(row)` is the length of the string. `row[0]` will be the first character. If `row` is `"\001"`, then `row[0]` is `"\001"`, which equals `SEPARATING_LINE`. So `len(row) >= 1 and row[0] == SEPARATING_LINE` will be true.
So keeping it as a string `"\001"` should be fine.
Let's check if there are any other places where we need similar handling for dataclasses.
Wait, `rows = [[getattr(row, f) for f in field_names] for row in rows]` is the only place it's doing this for dataclasses.
Is there a sync/async variant? No, `tabulate` seems to be a single-threaded utility.
Let's try to implement this fix and verify.
Wait, what if the row is a list but containing `SEPARATING_LINE` whereas others are dataclasses?
The current logic only checks `is_dataclass(rows[0])`. If the first row is `SEPARATING_LINE`, it won't enter the dataclass branch.
Actually, the issue says "printing a list of dataclasses when one or more rows is SEPARATING_LINE".
This implies the first row is likely a dataclass.
If `rows[0]` is `SEPARATING_LINE`, then `dataclasses.is_dataclass(rows[0])` will be false. It will move to the next `elif`.
If `headers == "keys"` and `rows[0]` is `SEPARATING_LINE`, it might fail later if it expects `rows[0]` to be the source of keys.
But the specific error reported is at line 1464.
Let's check the logic around:
```python
elif (
dataclasses is not None
and len(rows) > 0
and dataclasses.is_dataclass(rows[0])
):
```
If `rows[0]` is `SEPARATING_LINE`, this block is skipped.
If `rows[0]` is a dataclass, but `rows[1]` is `SEPARATING_LINE`, then:
`field_names` is extracted from `rows[0]`.
Then the list comprehension runs over all `rows`.
`getattr(rows[1], f)` where `rows[1]` is `"\001"` fails.
So the fix is indeed to handle `SEPARATING_LINE` in the comprehension.
```python
rows = [
[getattr(row, f) for f in field_names] if not (
isinstance(row, str) and row == SEPARATING_LINE
) else row
for row in rows
]
```
Let's double check if `SEPARATING_LINE` can be anything else.
It is defined as `SEPARATING_LINE = "\001"`.
Is there any other way `row` can be a string here? In the context of a list of dataclasses, it's unlikely unless it's `SEPARATING_LINE`.
Actually, I should probably use `row == SEPARATING_LINE` as the condition.
Let's refine the fix.
```python
rows = [
[getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row
for row in rows
]
```
Wait, what if `row` is an object that `==` with `SEPARATING_LINE`? `SEPARATING_LINE` is a string.
Comparing an object with a string is generally safe in Python 3.
Is it possible that `row` is `None`? `None != "\001"` is True, so it will try `getattr(None, f)`, which will fail.
But the problem is specifically about `SEPARATING_LINE`.
Actually, the current code is:
```python
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
If I change it to:
```python
rows = [
[getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row
for row in rows
]
```
It should solve the reported issue.
Let's see if there are any other places where we handle dataclasses.
I'll grep for `is_dataclass`.
TOOL CALL · run_command
{
"command": "grep -rn \"is_dataclass\" tabulate/__init__.py"
}Result of run_command 116 characters
{
"status": "ok",
"stdout": "1458: and dataclasses.is_dataclass(rows[0])\n",
"stderr": "",
"exit_code": 0
}Generated tokens 1,952 tokens · stopped: STOP
Confirmed. The error is exactly what was reported.
Now I need to fix it. The code should skip calling `getattr` if the row is `SEPARATING_LINE`.
Looking at the code again:
```python
elif (
dataclasses is not None
and len(rows) > 0
and dataclasses.is_dataclass(rows[0])
):
# Python 3.7+'s dataclass
field_names = [field.name for field in dataclasses.fields(rows[0])]
if headers == "keys":
headers = field_names
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
I should change the list comprehension for `rows` to handle `SEPARATING_LINE`.
Wait, `SEPARATING_LINE` is usually handled later in the pipeline, but here we are normalizing the data.
If `row` is `SEPARATING_LINE`, it should probably remain `SEPARATING_LINE` or be converted to a list containing `SEPARATING_LINE`.
Wait, if `row` is `SEPARATING_LINE`, `getattr(row, f)` fails.
If I check `if row == SEPARATING_LINE`, I can decide what to do.
Let's see how other parts of the code handle `SEPARATING_LINE`.
At line 108:
```python
(len(row) >= 1 and row[0] == SEPARATING_LINE)
or (len(row) >= 2 and row[1] == SEPARATING_LINE)
```
This suggests that a row is considered a separating line if it's a list/tuple and its first or second element is `SEPARATING_LINE`.
Wait, `SEPARATING_LINE` is defined as `"\001"`.
When `tabulate` is called with a list of dataclasses, `rows` is a list of dataclasses.
When we insert `SEPARATING_LINE`, it's a string.
So `row` can be either a dataclass object or the string `"\001"`.
The list comprehension should be:
```python
rows = [
[getattr(row, f) for f in field_names] if not (
isinstance(row, str) and row == SEPARATING_LINE
) else row
for row in rows
]
```
But wait, if I leave it as `row` (a string), will the rest of the code handle it?
The code later expects `rows` to be a list of lists (or similar).
Let's check `_normalize_tabular_data` more broadly.
Actually, it might be simpler to just check if the row is the separating line and if so, keep it as is.
Let's see where `rows` is used after this.
The list comprehension: `rows = [[getattr(row, f) for f in field_names] for row in rows]` converts a list of objects into a list of lists.
If one of the items is `SEPARATING_LINE`, and we keep it as a string, `rows` will be something like `[[...], "\001", [...]]`.
Will that work?
Looking at the `tabulate` function, it calls `_normalize_tabular_data`.
Let's see what happens if I change it to:
```python
rows = [
[getattr(row, f) for f in field_names] if not (
isinstance(row, str) and row == SEPARATING_LINE
) else row
for row in rows
]
```
Let's test this hypothesis.
Wait, looking at the lines around 108:
```python
(len(row) >= 1 and row[0] == SEPARATING_LINE)
or (len(row) >= 2 and row[1] == SEPARATING_LINE)
```
This is probably in a function that iterates over `rows`. If `row` is a string, `len(row)` is the length of the string. `row[0]` will be the first character. If `row` is `"\001"`, then `row[0]` is `"\001"`, which equals `SEPARATING_LINE`. So `len(row) >= 1 and row[0] == SEPARATING_LINE` will be true.
So keeping it as a string `"\001"` should be fine.
Let's check if there are any other places where we need similar handling for dataclasses.
Wait, `rows = [[getattr(row, f) for f in field_names] for row in rows]` is the only place it's doing this for dataclasses.
Is there a sync/async variant? No, `tabulate` seems to be a single-threaded utility.
Let's try to implement this fix and verify.
Wait, what if the row is a list but containing `SEPARATING_LINE` whereas others are dataclasses?
The current logic only checks `is_dataclass(rows[0])`. If the first row is `SEPARATING_LINE`, it won't enter the dataclass branch.
Actually, the issue says "printing a list of dataclasses when one or more rows is SEPARATING_LINE".
This implies the first row is likely a dataclass.
If `rows[0]` is `SEPARATING_LINE`, then `dataclasses.is_dataclass(rows[0])` will be false. It will move to the next `elif`.
If `headers == "keys"` and `rows[0]` is `SEPARATING_LINE`, it might fail later if it expects `rows[0]` to be the source of keys.
But the specific error reported is at line 1464.
Let's check the logic around:
```python
elif (
dataclasses is not None
and len(rows) > 0
and dataclasses.is_dataclass(rows[0])
):
```
If `rows[0]` is `SEPARATING_LINE`, this block is skipped.
If `rows[0]` is a dataclass, but `rows[1]` is `SEPARATING_LINE`, then:
`field_names` is extracted from `rows[0]`.
Then the list comprehension runs over all `rows`.
`getattr(rows[1], f)` where `rows[1]` is `"\001"` fails.
So the fix is indeed to handle `SEPARATING_LINE` in the comprehension.
```python
rows = [
[getattr(row, f) for f in field_names] if not (
isinstance(row, str) and row == SEPARATING_LINE
) else row
for row in rows
]
```
Let's double check if `SEPARATING_LINE` can be anything else.
It is defined as `SEPARATING_LINE = "\001"`.
Is there any other way `row` can be a string here? In the context of a list of dataclasses, it's unlikely unless it's `SEPARATING_LINE`.
Actually, I should probably use `row == SEPARATING_LINE` as the condition.
Let's refine the fix.
```python
rows = [
[getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row
for row in rows
]
```
Wait, what if `row` is an object that `==` with `SEPARATING_LINE`? `SEPARATING_LINE` is a string.
Comparing an object with a string is generally safe in Python 3.
Is it possible that `row` is `None`? `None != "\001"` is True, so it will try `getattr(None, f)`, which will fail.
But the problem is specifically about `SEPARATING_LINE`.
Actually, the current code is:
```python
rows = [[getattr(row, f) for f in field_names] for row in rows]
```
If I change it to:
```python
rows = [
[getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row
for row in rows
]
```
It should solve the reported issue.
Let's see if there are any other places where we handle dataclasses.
I'll grep for `is_dataclass`.
<channel|><|tool_call>call:run_command{command:<|"|>grep -rn "is_dataclass" tabulate/__init__.py<|"|>}<tool_call|><|tool_response>
Turn 6at 187 s245 tokens out6,450 prompt tokens
Only one occurrence. Now let's test if the fix works and doesn't break anything else. I should also test what happens if `rows[0]` is `SEPARATING_LINE`. Repro script:
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nfrom dataclasses import dataclass\nfrom tabulate import tabulate, SEPARATING_LINE\n\n@dataclass\nclass Data:\n name: str\n age: int\n\n# Case 1: Dataclass first\nprint(\"Case 1:\")\ndata1 = [\n Data(\"Alice\", 30),\n SEPARATING_LINE,\n Data(\"Bob\", 25),\n]\nprint(tabulate(data1, headers=\"keys\"))\n\n# Case 2: SEPARATING_LINE first\nprint(\"\\nCase 2:\")\ndata2 = [\n SEPARATING_LINE,\n Data(\"Alice\", 30),\n Data(\"Bob\", 25),\n]\nprint(tabulate(data2, headers=\"keys\"))\nPY\n"
}Result of run_command 1,532 characters
{
"status": "error",
"error_type": "CommandError",
"error_message": "Traceback (most recent call last):\n File \"<stdin>\", line 16, in <module>\n File \"/workspace/tabulate/__init__.py\", line 2088, in tabulate\n list_of_lists, headers, headers_pad = _normalize_tabular_data(\n File \"/workspace/tabulate/__init__.py\", line 1464, in _normalize_tabular_data\n rows = [[getattr(row, f) for f in field_names] for row in rows]\n File \"/workspace/tabulate/__init__.py\", line 1464, in <listcomp>\n rows = [[getattr(row, f) for f in field_names] for row in rows]\n File \"/workspace/tabulate/__init__.py\", line 1464, in <listcomp>\n rows = [[getattr(row, f) for f in field_names] for row in rows]\nAttributeError: 'str' object has no attribute 'name'\n",
"details": {
"stdout": "Case 1:\n",
"stderr": "Traceback (most recent call last):\n File \"<stdin>\", line 16, in <module>\n File \"/workspace/tabulate/__init__.py\", line 2088, in tabulate\n list_of_lists, headers, headers_pad = _normalize_tabular_data(\n File \"/workspace/tabulate/__init__.py\", line 1464, in _normalize_tabular_data\n rows = [[getattr(row, f) for f in field_names] for row in rows]\n File \"/workspace/tabulate/__init__.py\", line 1464, in <listcomp>\n rows = [[getattr(row, f) for f in field_names] for row in rows]\n File \"/workspace/tabulate/__init__.py\", line 1464, in <listcomp>\n rows = [[getattr(row, f) for f in field_names] for row in rows]\nAttributeError: 'str' object has no attribute 'name'\n",
"exit_code": 1
}
}Generated tokens 245 tokens · stopped: STOP
Only one occurrence.
Now let's test if the fix works and doesn't break anything else.
I should also test what happens if `rows[0]` is `SEPARATING_LINE`.
Repro script:
<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
from dataclasses import dataclass
from tabulate import tabulate, SEPARATING_LINE
@dataclass
class Data:
name: str
age: int
# Case 1: Dataclass first
print("Case 1:")
data1 = [
Data("Alice", 30),
SEPARATING_LINE,
Data("Bob", 25),
]
print(tabulate(data1, headers="keys"))
# Case 2: SEPARATING_LINE first
print("\nCase 2:")
data2 = [
SEPARATING_LINE,
Data("Alice", 30),
Data("Bob", 25),
]
print(tabulate(data2, headers="keys"))
PY
<|"|>}<tool_call|><|tool_response>
Turn 7at 228 s611 tokens out7,230 prompt tokens
As expected, Case 1 fails. Case 2 was not even reached.
If I apply the fix:
```python
rows = [
[getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row
for row in rows
]
```
Now let's see Case 2.
If `rows[0]` is `SEPARATING_LINE`, it won't enter the `dataclasses.is_dataclass(rows[0])` block.
It'll go to:
```python
elif headers == "keys" and len(rows) > 0:
# keys are column indices
headers = list(map(str, range(len(rows[0]))))
```
But if `rows[0]` is `"\001"`, then `len(rows[0])` is 1.
So `headers` would be `["0"]`.
Then `rows` would remain `[SEPARATING_LINE, Data("Alice", 30), Data("Bob", 25)]`.
Later, `tabulate` will try to iterate over `rows` and treat each `row` as a list/tuple.
`Data("Alice", 30)` is not a list/tuple, so it might fail there.
Wait, the problem statement says "printing a list of dataclasses when one or more rows is SEPARATING_LINE". It doesn't say `SEPARATING_LINE` is the first row.
If `SEPARATING_LINE` is the first row, then `tabulate` doesn't know it's a list of dataclasses.
But let's focus on fixing the reported bug first.
One thing to consider: what if we want to support `SEPARATING_LINE` at the beginning?
We would need to find the first row that is *not* `SEPARATING_LINE` to determine if it's a list of dataclasses.
However, the current implementation of `_normalize_tabular_data` seems to rely on the first row.
Let's check if this is the desired behavior or if we should improve it.
The issue is specifically about the `AttributeError`.
If I fix the `AttributeError`, I've solved the reported problem.
Let's apply the fix.
TOOL CALL · edit_file
{
"filepath": "tabulate/__init__.py",
"new_string": " rows = [\n [getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row\n for row in rows\n ]",
"old_string": " rows = [[getattr(row, f) for f in field_names] for row in rows]"
}Result of edit_file 706 characters
{
"status": "ok",
"filepath": "tabulate/__init__.py",
"occurrences": 1,
"strategy": "exact",
"diff": "--- a/tabulate/__init__.py\n+++ b/tabulate/__init__.py\n@@ -1461,7 +1461,10 @@\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 = [\n+ [getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row\n+ for row in rows\n+ ]\n \n elif headers == \"keys\" and len(rows) > 0:\n # keys are column indices\n",
"is_truncated": false
}Generated tokens 611 tokens · stopped: STOP
As expected, Case 1 fails. Case 2 was not even reached.
If I apply the fix:
```python
rows = [
[getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row
for row in rows
]
```
Now let's see Case 2.
If `rows[0]` is `SEPARATING_LINE`, it won't enter the `dataclasses.is_dataclass(rows[0])` block.
It'll go to:
```python
elif headers == "keys" and len(rows) > 0:
# keys are column indices
headers = list(map(str, range(len(rows[0]))))
```
But if `rows[0]` is `"\001"`, then `len(rows[0])` is 1.
So `headers` would be `["0"]`.
Then `rows` would remain `[SEPARATING_LINE, Data("Alice", 30), Data("Bob", 25)]`.
Later, `tabulate` will try to iterate over `rows` and treat each `row` as a list/tuple.
`Data("Alice", 30)` is not a list/tuple, so it might fail there.
Wait, the problem statement says "printing a list of dataclasses when one or more rows is SEPARATING_LINE". It doesn't say `SEPARATING_LINE` is the first row.
If `SEPARATING_LINE` is the first row, then `tabulate` doesn't know it's a list of dataclasses.
But let's focus on fixing the reported bug first.
One thing to consider: what if we want to support `SEPARATING_LINE` at the beginning?
We would need to find the first row that is *not* `SEPARATING_LINE` to determine if it's a list of dataclasses.
However, the current implementation of `_normalize_tabular_data` seems to rely on the first row.
Let's check if this is the desired behavior or if we should improve it.
The issue is specifically about the `AttributeError`.
If I fix the `AttributeError`, I've solved the reported problem.
Let's apply the fix.
<channel|><|tool_call>call:edit_file{filepath:<|"|>tabulate/__init__.py<|"|>,new_string:<|"|> rows = [
[getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row
for row in rows
]<|"|>,old_string:<|"|> rows = [[getattr(row, f) for f in field_names] for row in rows]<|"|>}<tool_call|><|tool_response>
Turn 8at 235 s231 tokens out8,076 prompt tokens
Now let's run 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 Data:\n name: str\n age: int\n\n# Case 1: Dataclass first\nprint(\"Case 1:\")\ndata1 = [\n Data(\"Alice\", 30),\n SEPARATING_LINE,\n Data(\"Bob\", 25),\n]\nprint(tabulate(data1, headers=\"keys\"))\n\n# Case 2: SEPARATING_LINE first\nprint(\"\\nCase 2:\")\ndata2 = [\n SEPARATING_LINE,\n Data(\"Alice\", 30),\n Data(\"Bob\", 25),\n]\ntry:\n print(tabulate(data2, headers=\"keys\"))\nexcept Exception as e:\n print(f\"Case 2 failed: {e}\")\nPY\n"
}Result of run_command 201 characters
{
"status": "ok",
"stdout": "Case 1:\nname age\n------ -----\nAlice 30\n------ -----\nBob 25\n\nCase 2:\nCase 2 failed: 'Data' object is not iterable\n",
"stderr": "",
"exit_code": 0
}Generated tokens 231 tokens · stopped: STOP
Now let's run 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 Data:
name: str
age: int
# Case 1: Dataclass first
print("Case 1:")
data1 = [
Data("Alice", 30),
SEPARATING_LINE,
Data("Bob", 25),
]
print(tabulate(data1, headers="keys"))
# Case 2: SEPARATING_LINE first
print("\nCase 2:")
data2 = [
SEPARATING_LINE,
Data("Alice", 30),
Data("Bob", 25),
]
try:
print(tabulate(data2, headers="keys"))
except Exception as e:
print(f"Case 2 failed: {e}")
PY
<|"|>}<tool_call|><|tool_response>
Turn 9at 294 s952 tokens out8,398 prompt tokens
Case 1 is now fixed!
Case 2 still fails, but as I suspected, it's because `rows[0]` is `SEPARATING_LINE`, so it doesn't treat the list as a list of dataclasses.
Is this acceptable?
The issue report says "Using tabulate to print a list of dataclasses when one or more rows is SEPARATING_LINE".
In Case 2, it is still a list of dataclasses, just with a `SEPARATING_LINE` at the start.
If we want to support this, we should find the first row that is not `SEPARATING_LINE`.
Let's see how other parts of `_normalize_tabular_data` handle this.
Wait, let's look at the `elif` chain:
```python
if (
isinstance(tabular_data, (list, tuple))
and len(tabular_data) > 0
and isinstance(tabular_data[0], (list, tuple))
):
# ...
elif (
len(rows) > 0
and isinstance(rows[0], dict)
):
# ...
elif (
headers == "keys"
and hasattr(tabular_data, "description")
# ...
):
# ...
elif (
dataclasses is not None
and len(rows) > 0
and dataclasses.is_dataclass(rows[0])
):
# ...
```
It seems `tabulate` heavily relies on `rows[0]` to determine the type of the data.
If the user puts `SEPARATING_LINE` (which is a string) at the beginning of the list, they are basically telling `tabulate` that it's a list of strings (or at least not a list of dataclasses/dicts).
Wait, if `rows[0]` is a string, neither the `isinstance(rows[0], (list, tuple))` nor `isinstance(rows[0], dict)` nor `is_dataclass(rows[0])` will be true.
So it will likely fall through to the end, where `rows` remains a list of objects.
Then `tabulate` will try to iterate over each `row` in `rows`.
`SEPARATING_LINE` (a string) is iterable.
`Data("Alice", 30)` is NOT iterable.
That's why we get `'Data' object is not iterable`.
If we want to support `SEPARATING_LINE` at the beginning, we should find the first non-`SEPARATING_LINE` row for type detection.
However, let's check if this is really needed or if we should just fix the reported bug.
The reported bug was the `AttributeError`.
Looking at the issue description:
"Using tabulate to print a list of dataclasses when one or more rows is SEPARATING_LINE, produces: AttributeError: 'str' object has no attribute '<the first attribute of dataclass>'"
The fix I implemented solves exactly this.
Whether we should also handle `SEPARATING_LINE` at the top is a separate (though related) issue.
Given the budget and the goal of a "minimal, root-cause source patch", I think solving the `AttributeError` is enough.
But let's see if I can easily make it more robust.
To make it more robust, I would need to find the first row that is not `SEPARATING_LINE` in several places.
```python
# Instead of rows[0], use:
first_row = next((row for row in rows if row != SEPARATING_LINE), None)
```
And then use `first_row` instead of `rows[0]` in all the `elif` checks.
But that might be too much change and potentially introduce regressions if `SEPARATING_LINE` was intentionally used to break the type detection.
Actually, `SEPARATING_LINE` is meant to be a row separator. It probably should not interfere with type detection for the rest of the table.
Let's see if there are other tests that use `SEPARATING_LINE`.
TOOL CALL · run_command
{
"command": "grep -rn \"SEPARATING_LINE\" test/"
}Result of run_command 982 characters
{
"status": "ok",
"stdout": "test/test_output.py:5:from tabulate import tabulate, simple_separated_format, SEPARATING_LINE\ntest/test_output.py:12:_test_table_with_sep_line = [[\"spam\", 41.9999], SEPARATING_LINE, [\"eggs\", \"451.0\"]]\ntest/test_output.py:118: SEPARATING_LINE,\ntest/test_output.py:261: table = [[\"Earth\", 6371], [\"Mars\", 3390], SEPARATING_LINE, [\"Moon\", 1737]]\ntest/test_output.py:307: SEPARATING_LINE,\ntest/test_output.py:2676: [[\"one\", \"two\"], SEPARATING_LINE, [\"three\", \"four\"]],\ntest/test_output.py:2921: dd = [(0, 101), SEPARATING_LINE, (1, 102), (2, 103)]\ntest/test_internal.py:318: T.SEPARATING_LINE,\ntest/test_internal.py:320: T.SEPARATING_LINE,\ntest/test_internal.py:322: T.SEPARATING_LINE,\ntest/test_internal.py:335: T.SEPARATING_LINE,\ntest/test_internal.py:337: T.SEPARATING_LINE,\ntest/test_internal.py:339: T.SEPARATING_LINE,\n",
"stderr": "",
"exit_code": 0
}Generated tokens 952 tokens · stopped: STOP
Case 1 is now fixed!
Case 2 still fails, but as I suspected, it's because `rows[0]` is `SEPARATING_LINE`, so it doesn't treat the list as a list of dataclasses.
Is this acceptable?
The issue report says "Using tabulate to print a list of dataclasses when one or more rows is SEPARATING_LINE".
In Case 2, it is still a list of dataclasses, just with a `SEPARATING_LINE` at the start.
If we want to support this, we should find the first row that is not `SEPARATING_LINE`.
Let's see how other parts of `_normalize_tabular_data` handle this.
Wait, let's look at the `elif` chain:
```python
if (
isinstance(tabular_data, (list, tuple))
and len(tabular_data) > 0
and isinstance(tabular_data[0], (list, tuple))
):
# ...
elif (
len(rows) > 0
and isinstance(rows[0], dict)
):
# ...
elif (
headers == "keys"
and hasattr(tabular_data, "description")
# ...
):
# ...
elif (
dataclasses is not None
and len(rows) > 0
and dataclasses.is_dataclass(rows[0])
):
# ...
```
It seems `tabulate` heavily relies on `rows[0]` to determine the type of the data.
If the user puts `SEPARATING_LINE` (which is a string) at the beginning of the list, they are basically telling `tabulate` that it's a list of strings (or at least not a list of dataclasses/dicts).
Wait, if `rows[0]` is a string, neither the `isinstance(rows[0], (list, tuple))` nor `isinstance(rows[0], dict)` nor `is_dataclass(rows[0])` will be true.
So it will likely fall through to the end, where `rows` remains a list of objects.
Then `tabulate` will try to iterate over each `row` in `rows`.
`SEPARATING_LINE` (a string) is iterable.
`Data("Alice", 30)` is NOT iterable.
That's why we get `'Data' object is not iterable`.
If we want to support `SEPARATING_LINE` at the beginning, we should find the first non-`SEPARATING_LINE` row for type detection.
However, let's check if this is really needed or if we should just fix the reported bug.
The reported bug was the `AttributeError`.
Looking at the issue description:
"Using tabulate to print a list of dataclasses when one or more rows is SEPARATING_LINE, produces: AttributeError: 'str' object has no attribute '<the first attribute of dataclass>'"
The fix I implemented solves exactly this.
Whether we should also handle `SEPARATING_LINE` at the top is a separate (though related) issue.
Given the budget and the goal of a "minimal, root-cause source patch", I think solving the `AttributeError` is enough.
But let's see if I can easily make it more robust.
To make it more robust, I would need to find the first row that is not `SEPARATING_LINE` in several places.
```python
# Instead of rows[0], use:
first_row = next((row for row in rows if row != SEPARATING_LINE), None)
```
And then use `first_row` instead of `rows[0]` in all the `elif` checks.
But that might be too much change and potentially introduce regressions if `SEPARATING_LINE` was intentionally used to break the type detection.
Actually, `SEPARATING_LINE` is meant to be a row separator. It probably should not interfere with type detection for the rest of the table.
Let's see if there are other tests that use `SEPARATING_LINE`.
<channel|><|tool_call>call:run_command{command:<|"|>grep -rn "SEPARATING_LINE" test/<|"|>}<tool_call|><|tool_response>
Turn 10at 316 s325 tokens out9,784 prompt tokens
Most of these are lists of lists or tuples. `test/test_output.py:12: _test_table_with_sep_line = [["spam", 41.9999], SEPARATING_LINE, ["eggs", "451.0"]]` Here `rows[0]` is `["spam", 41.9999]`, which is a list. So it enters the first `if` block. If the user had `[SEPARATING_LINE, ["spam", 41.9999], ["eggs", "451.0"]]`, then `rows[0]` would be `SEPARATING_LINE` (a string). It would then fail later because `rows[1]` (the list) would be fine, but `rows[0]` (the string) would be iterated over in `tabulate` (each char of `"\001"` would be a column). Wait, `SEPARATING_LINE` is `"\001"`. So `rows[0]` is a string of length 1. It would be treated as a row with 1 column. If the rest of the rows are lists of 2 elements, `tabulate` usually handles rows of different lengths. Let's check if my fix affects anything else.
TOOL CALL · run_command
{
"command": "pytest test/test_output.py -q --tb=short --no-header"
}Result of run_command 336 characters
{
"status": "ok",
"stdout": ".........s..................s......s......s......s......s......s......s. [ 39%]\n......s..s..s..s..s..s..s............................................... [ 79%]\n..................................... [100%]\n166 passed, 15 skipped in 0.96s\n",
"stderr": "",
"exit_code": 0
}Generated tokens 325 tokens · stopped: STOP
Most of these are lists of lists or tuples.
`test/test_output.py:12: _test_table_with_sep_line = [["spam", 41.9999], SEPARATING_LINE, ["eggs", "451.0"]]`
Here `rows[0]` is `["spam", 41.9999]`, which is a list. So it enters the first `if` block.
If the user had `[SEPARATING_LINE, ["spam", 41.9999], ["eggs", "451.0"]]`, then `rows[0]` would be `SEPARATING_LINE` (a string).
It would then fail later because `rows[1]` (the list) would be fine, but `rows[0]` (the string) would be iterated over in `tabulate` (each char of `"\001"` would be a column).
Wait, `SEPARATING_LINE` is `"\001"`. So `rows[0]` is a string of length 1.
It would be treated as a row with 1 column.
If the rest of the rows are lists of 2 elements, `tabulate` usually handles rows of different lengths.
Let's check if my fix affects anything else.<channel|><|tool_call>call:run_command{command:<|"|>pytest test/test_output.py -q --tb=short --no-header<|"|>}<tool_call|><|tool_response>
Turn 11at 345 s409 tokens out10,227 prompt tokens
The tests pass.
I'm confident that my fix for the `AttributeError` is correct and doesn't break existing tests.
As for the "SEPARATING_LINE at the beginning" case, it seems like a limitation of the current architecture of `_normalize_tabular_data` which relies on the first row for type detection. I don't think I should change that unless it's part of the requested fix. The reported issue was specifically about the `AttributeError`.
Let's double check for any other parallel paths.
I already checked and `is_dataclass` only appearing once.
`getattr(row, f)` appears once in the dataclass block.
Wait, what about dicts?
```python
elif (
len(rows) > 0
and isinstance(rows[0], dict)
):
# ...
rows = [[row.get(k) for k in keys] for row in rows]
```
If `rows[0]` is a dict, and `rows[1]` is `SEPARATING_LINE` (a string), then `row.get(k)` will fail because `str` has no `get` method.
Wait! This is the same bug, just for dicts instead of dataclasses!
Let's verify this.
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nfrom tabulate import tabulate, SEPARATING_LINE\n\ndata = [\n {\"name\": \"Alice\", \"age\": 30},\n SEPARATING_LINE,\n {\"name\": \"Bob\", \"age\": 25},\n]\n\ntry:\n print(tabulate(data, headers=\"keys\"))\nexcept Exception as e:\n print(e)\n import traceback\n traceback.print_exc()\nPY\n"
}Result of run_command 475 characters
{
"status": "ok",
"stdout": "'str' object has no attribute 'keys'\n",
"stderr": "Traceback (most recent call last):\n File \"<stdin>\", line 10, in <module>\n File \"/workspace/tabulate/__init__.py\", line 2091, in tabulate\n list_of_lists, headers, headers_pad = _normalize_tabular_data(\n File \"/workspace/tabulate/__init__.py\", line 1422, in _normalize_tabular_data\n for k in row.keys():\nAttributeError: 'str' object has no attribute 'keys'\n",
"exit_code": 0
}Generated tokens 409 tokens · stopped: STOP
The tests pass.
I'm confident that my fix for the `AttributeError` is correct and doesn't break existing tests.
As for the "SEPARATING_LINE at the beginning" case, it seems like a limitation of the current architecture of `_normalize_tabular_data` which relies on the first row for type detection. I don't think I should change that unless it's part of the requested fix. The reported issue was specifically about the `AttributeError`.
Let's double check for any other parallel paths.
I already checked and `is_dataclass` only appearing once.
`getattr(row, f)` appears once in the dataclass block.
Wait, what about dicts?
```python
elif (
len(rows) > 0
and isinstance(rows[0], dict)
):
# ...
rows = [[row.get(k) for k in keys] for row in rows]
```
If `rows[0]` is a dict, and `rows[1]` is `SEPARATING_LINE` (a string), then `row.get(k)` will fail because `str` has no `get` method.
Wait! This is the same bug, just for dicts instead of dataclasses!
Let's verify this.
<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
from tabulate import tabulate, SEPARATING_LINE
data = [
{"name": "Alice", "age": 30},
SEPARATING_LINE,
{"name": "Bob", "age": 25},
]
try:
print(tabulate(data, headers="keys"))
except Exception as e:
print(e)
import traceback
traceback.print_exc()
PY
<|"|>}<tool_call|><|tool_response>
Turn 12at 356 s218 tokens out10,810 prompt tokens
Wow, yes! The same bug exists for dicts.
Let's look at the dict handling code:
```python
elif (
len(rows) > 0
and isinstance(rows[0], dict)
):
if headers == "keys":
if isinstance(rows[0], dict):
keys = rows[0].keys()
else:
# this is a weird case where rows[0] is not a dict
# but it passed the isinstance(rows[0], dict) check?
# No, that's not possible.
pass
# ...
# Actually, let's read the file.
```
Let's read the dict part of `_normalize_tabular_data` from `tabulate/__init__.py`.
TOOL CALL · read_file
{
"end_line": 1440,
"filepath": "tabulate/__init__.py",
"start_line": 1400
}Result of read_file 1,862 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(",
"start_line": 1400,
"end_line": 1440,
"total_lines": 2790,
"is_truncated": false
}Generated tokens 218 tokens · stopped: STOP
Wow, yes! The same bug exists for dicts.
Let's look at the dict handling code:
```python
elif (
len(rows) > 0
and isinstance(rows[0], dict)
):
if headers == "keys":
if isinstance(rows[0], dict):
keys = rows[0].keys()
else:
# this is a weird case where rows[0] is not a dict
# but it passed the isinstance(rows[0], dict) check?
# No, that's not possible.
pass
# ...
# Actually, let's read the file.
```
Let's read the dict part of `_normalize_tabular_data` from `tabulate/__init__.py`.
<channel|><|tool_call>call:read_file{end_line:1440,filepath:<|"|>tabulate/__init__.py<|"|>,start_line:1400}<tool_call|><|tool_response>
Turn 13at 358 s43 tokens out11,531 prompt tokens
And continuing:
TOOL CALL · read_file
{
"end_line": 1445,
"filepath": "tabulate/__init__.py",
"start_line": 1440
}Result of read_file 364 characters
{
"status": "ok",
"filepath": "tabulate/__init__.py",
"content": " raise ValueError(\n \"headers for a list of dicts is not a dict or a keyword\"\n )\n rows = [[row.get(k) for k in keys] for row in rows]\n\n elif (",
"start_line": 1440,
"end_line": 1445,
"total_lines": 2790,
"is_truncated": false
}Generated tokens 43 tokens · stopped: STOP
And continuing:
<channel|><|tool_call>call:read_file{end_line:1445,filepath:<|"|>tabulate/__init__.py<|"|>,start_line:1440}<tool_call|><|tool_response>
Turn 14at 384 s584 tokens out11,703 prompt tokens
The problematic parts for dict-like objects are:
1. `for k in row.keys():` (around line 1422)
2. `rows = [[row.get(k) for k in keys] for row in rows]` (around line 1443)
I need to wrap these in checks for `SEPARATING_LINE`.
Wait, the first one is inside a loop:
```python
for row in rows:
for k in row.keys():
# Save unique items in input order
if k not in uniq_keys:
keys.append(k)
uniq_keys.add(k)
```
If `row` is `SEPARATING_LINE`, it should be skipped.
```python
for row in rows:
if row == SEPARATING_LINE:
continue
for k in row.keys():
# Save unique items in input order
if k not in uniq_keys:
keys.append(k)
uniq_keys.add(k)
```
And the second one:
```python
rows = [[row.get(k) for k in keys] for row in rows]
```
Should be:
```python
rows = [
[row.get(k) for k in keys] if row != SEPARATING_LINE else row
for row in rows
]
```
Let's apply these changes.
Wait, I should first check if there are any other "dict-like" paths.
The check is `len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values")`.
Yes, this handles dicts and other dict-like objects.
Let's implement the fixes for dicts too, as it's clearly the same root cause.
First fix: and skipping `SEPARATING_LINE` when collecting keys.
TOOL CALL · edit_file
{
"filepath": "tabulate/__init__.py",
"new_string": " for row in rows:\n if row == SEPARATING_LINE:\n continue\n for k in row.keys():\n # Save unique items in input order\n if k not in uniq_keys:\n keys.append(k)\n uniq_keys.add(k)",
"old_string": " for row in rows:\n for k in row.keys():\n # Save unique items in input order\n if k not in uniq_keys:\n keys.append(k)\n uniq_keys.add(k)"
}Result of edit_file 528 characters
{
"status": "ok",
"filepath": "tabulate/__init__.py",
"occurrences": 1,
"strategy": "exact",
"diff": "--- a/tabulate/__init__.py\n+++ b/tabulate/__init__.py\n@@ -1419,6 +1419,8 @@\n uniq_keys.update(keys)\n rows = rows[1:]\n for row in rows:\n+ if row == SEPARATING_LINE:\n+ continue\n for k in row.keys():\n # Save unique items in input order\n if k not in uniq_keys:\n",
"is_truncated": false
}Generated tokens 584 tokens · stopped: STOP
The problematic parts for dict-like objects are:
1. `for k in row.keys():` (around line 1422)
2. `rows = [[row.get(k) for k in keys] for row in rows]` (around line 1443)
I need to wrap these in checks for `SEPARATING_LINE`.
Wait, the first one is inside a loop:
```python
for row in rows:
for k in row.keys():
# Save unique items in input order
if k not in uniq_keys:
keys.append(k)
uniq_keys.add(k)
```
If `row` is `SEPARATING_LINE`, it should be skipped.
```python
for row in rows:
if row == SEPARATING_LINE:
continue
for k in row.keys():
# Save unique items in input order
if k not in uniq_keys:
keys.append(k)
uniq_keys.add(k)
```
And the second one:
```python
rows = [[row.get(k) for k in keys] for row in rows]
```
Should be:
```python
rows = [
[row.get(k) for k in keys] if row != SEPARATING_LINE else row
for row in rows
]
```
Let's apply these changes.
Wait, I should first check if there are any other "dict-like" paths.
The check is `len(rows) > 0 and hasattr(rows[0], "keys") and hasattr(rows[0], "values")`.
Yes, this handles dicts and other dict-like objects.
Let's implement the fixes for dicts too, as it's clearly the same root cause.
First fix: and skipping `SEPARATING_LINE` when collecting keys.
<channel|><|tool_call>call:edit_file{filepath:<|"|>tabulate/__init__.py<|"|>,new_string:<|"|> for row in rows:
if row == SEPARATING_LINE:
continue
for k in row.keys():
# Save unique items in input order
if k not in uniq_keys:
keys.append(k)
uniq_keys.add(k)<|"|>,old_string:<|"|> for row in rows:
for k in row.keys():
# Save unique items in input order
if k not in uniq_keys:
keys.append(k)
uniq_keys.add(k)<|"|>}<tool_call|><|tool_response>
Turn 15at 387 s101 tokens out12,457 prompt tokens
Now the second fix for dicts:
TOOL CALL · edit_file
{
"filepath": "tabulate/__init__.py",
"new_string": " rows = [\n [row.get(k) for k in keys] if row != SEPARATING_LINE else row\n for row in rows\n ]",
"old_string": " rows = [[row.get(k) for k in keys] for row in rows]"
}Result of edit_file 616 characters
{
"status": "ok",
"filepath": "tabulate/__init__.py",
"occurrences": 1,
"strategy": "exact",
"diff": "--- a/tabulate/__init__.py\n+++ b/tabulate/__init__.py\n@@ -1442,7 +1442,10 @@\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 = [\n+ [row.get(k) for k in keys] if row != SEPARATING_LINE else row\n+ for row in rows\n+ ]\n \n elif (\n headers == \"keys\"\n",
"is_truncated": false
}Generated tokens 101 tokens · stopped: STOP
Now the second fix for dicts:
<channel|><|tool_call>call:edit_file{filepath:<|"|>tabulate/__init__.py<|"|>,new_string:<|"|> rows = [
[row.get(k) for k in keys] if row != SEPARATING_LINE else row
for row in rows
]<|"|>,old_string:<|"|> rows = [[row.get(k) for k in keys] for row in rows]<|"|>}<tool_call|><|tool_response>
Turn 16at 392 s133 tokens out12,761 prompt tokens
Now let's test with a dict.
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nfrom tabulate import tabulate, SEPARATING_LINE\n\ndata = [\n {\"name\": \"Alice\", \"age\": 30},\n SEPARATING_LINE,\n {\"name\": \"Bob\", \"age\": 25},\n]\n\ntry:\n print(tabulate(data, headers=\"keys\"))\nexcept Exception as e:\n print(e)\n import traceback\n traceback.print_exc()\nPY\n"
}Result of run_command 135 characters
{
"status": "ok",
"stdout": "name age\n------ -----\nAlice 30\n------ -----\nBob 25\n",
"stderr": "",
"exit_code": 0
}Generated tokens 133 tokens · stopped: STOP
Now let's test with a dict.
<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
from tabulate import tabulate, SEPARATING_LINE
data = [
{"name": "Alice", "age": 30},
SEPARATING_LINE,
{"name": "Bob", "age": 25},
]
try:
print(tabulate(data, headers="keys"))
except Exception as e:
print(e)
import traceback
traceback.print_exc()
PY
<|"|>}<tool_call|><|tool_response>
Turn 17at 400 s171 tokens out12,959 prompt tokens
Great! Now both dataclasses and dicts work with `SEPARATING_LINE`. Let's verify that we didn't break dataclasses.
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nfrom dataclasses import dataclass\nfrom tabulate import tabulate, SEPARATING_LINE\n\n@dataclass\nclass Data:\n name: str\n age: int\n\ndata = [\n Data(\"Alice\", 30),\n SEPARATING_LINE,\n Data(\"Bob\", 25),\n]\n\ntry:\n print(tabulate(data, headers=\"keys\"))\nexcept Exception as e:\n print(e)\n import traceback\n traceback.print_exc()\nPY\n"
}Result of run_command 135 characters
{
"status": "ok",
"stdout": "name age\n------ -----\nAlice 30\n------ -----\nBob 25\n",
"stderr": "",
"exit_code": 0
}Generated tokens 171 tokens · stopped: STOP
Great! Now both dataclasses and dicts work with `SEPARATING_LINE`.
Let's verify that we didn't break dataclasses.
<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
from dataclasses import dataclass
from tabulate import tabulate, SEPARATING_LINE
@dataclass
class Data:
name: str
age: int
data = [
Data("Alice", 30),
SEPARATING_LINE,
Data("Bob", 25),
]
try:
print(tabulate(data, headers="keys"))
except Exception as e:
print(e)
import traceback
traceback.print_exc()
PY
<|"|>}<tool_call|><|tool_response>
Turn 18at 406 s50 tokens out13,195 prompt tokens
All set. Now let's run the final check with the test suite.
TOOL CALL · run_command
{
"command": "pytest test/test_output.py -q --tb=short --no-header"
}Result of run_command 336 characters
{
"status": "ok",
"stdout": ".........s..................s......s......s......s......s......s......s. [ 39%]\n......s..s..s..s..s..s..s............................................... [ 79%]\n..................................... [100%]\n166 passed, 15 skipped in 0.38s\n",
"stderr": "",
"exit_code": 0
}Generated tokens 50 tokens · stopped: STOP
All set. Now let's run the final check with the test suite.
<channel|><|tool_call>call:run_command{command:<|"|>pytest test/test_output.py -q --tb=short --no-header<|"|>}<tool_call|><|tool_response>
Turn 19at 413 s111 tokens out13,362 prompt tokens
Everything looks good. I have: 1. Fixed the `AttributeError` for dataclasses when `SEPARATING_LINE` is present in the list. 2. Identified and fixed the same bug for dict-like objects. One final check: did I modify any protected files? No, only `tabulate/__init__.py`. Are there any untracked files? No. Let's double check the diff.
TOOL CALL · run_command
{
"command": "git diff"
}Result of run_command 1,734 characters
{
"status": "ok",
"stdout": "diff --git a/tabulate/__init__.py b/tabulate/__init__.py\nindex 11bb865..596e98a 100644\n--- a/tabulate/__init__.py\n+++ b/tabulate/__init__.py\n@@ -1419,6 +1419,8 @@ def _normalize_tabular_data(tabular_data, headers, showindex=\"default\"):\n uniq_keys.update(keys)\n rows = rows[1:]\n for row in rows:\n+ if row == SEPARATING_LINE:\n+ continue\n for k in row.keys():\n # Save unique items in input order\n if k not in uniq_keys:\n@@ -1440,7 +1442,10 @@ 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 = [\n+ [row.get(k) for k in keys] if row != SEPARATING_LINE else row\n+ for row in rows\n+ ]\n \n elif (\n headers == \"keys\"\n@@ -1461,7 +1466,10 @@ 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 = [\n+ [getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row\n+ for row in rows\n+ ]\n \n elif headers == \"keys\" and len(rows) > 0:\n # keys are column indices\n",
"stderr": "",
"exit_code": 0
}Generated tokens 111 tokens · stopped: STOP
Everything looks good.
I have:
1. Fixed the `AttributeError` for dataclasses when `SEPARATING_LINE` is present in the list.
2. Identified and fixed the same bug for dict-like objects.
One final check: did I modify any protected files?
No, only `tabulate/__init__.py`.
Are there any untracked files? No.
Let's double check the diff.
<channel|><|tool_call>call:run_command{command:<|"|>git diff<|"|>}<tool_call|><|tool_response>
Patch, tests and logs
Final code change Included
diff --git a/tabulate/__init__.py b/tabulate/__init__.py
index 11bb865..596e98a 100644
--- a/tabulate/__init__.py
+++ b/tabulate/__init__.py
@@ -1419,6 +1419,8 @@ def _normalize_tabular_data(tabular_data, headers, showindex="default"):
uniq_keys.update(keys)
rows = rows[1:]
for row in rows:
+ if row == SEPARATING_LINE:
+ continue
for k in row.keys():
# Save unique items in input order
if k not in uniq_keys:
@@ -1440,7 +1442,10 @@ def _normalize_tabular_data(tabular_data, headers, showindex="default"):
raise ValueError(
"headers for a list of dicts is not a dict or a keyword"
)
- rows = [[row.get(k) for k in keys] for row in rows]
+ rows = [
+ [row.get(k) for k in keys] if row != SEPARATING_LINE else row
+ for row in rows
+ ]
elif (
headers == "keys"
@@ -1461,7 +1466,10 @@ def _normalize_tabular_data(tabular_data, headers, showindex="default"):
field_names = [field.name for field in dataclasses.fields(rows[0])]
if headers == "keys":
headers = field_names
- rows = [[getattr(row, f) for f in field_names] for row in rows]
+ rows = [
+ [getattr(row, f) for f in field_names] if row != SEPARATING_LINE else row
+ for row in rows
+ ]
elif headers == "keys" and len(rows) > 0:
# keys are column indices
Test output Included
============================= test session starts ==============================
collected 33 items
test/test_input.py ................................. [100%]
==================================== PASSES ====================================
__________________________ test_iterable_of_iterables __________________________
----------------------------- Captured stdout call -----------------------------
Expected:
- - - - -
0 1 2 3 4
5 4 3 2 1
- - - - -
Got:
- - - - -
0 1 2 3 4
5 4 3 2 1
- - - - -
______________________ test_iterable_of_iterables_headers ______________________
----------------------------- Captured stdout call -----------------------------
Expected:
a b c d e
--- --- --- --- ---
0 1 2 3 4
5 4 3 2 1
Got:
a b c d e
--- --- --- --- ---
0 1 2 3 4
5 4 3 2 1
_____________________ test_iterable_of_iterables_firstrow ______________________
----------------------------- Captured stdout call -----------------------------
Expected:
a b c d e
--- --- --- --- ---
0 1 2 3 4
5 4 3 2 1
Got:
a b c d e
--- --- --- --- ---
0 1 2 3 4
5 4 3 2 1
______________________________ test_list_of_lists ______________________________
----------------------------- Captured stdout call -----------------------------
Expected:
string number
-- -------- --------
a one 1
b two
Got:
string number
-- -------- --------
a one 1
b two
_________________________ test_list_of_lists_firstrow __________________________
----------------------------- Captured stdout call -----------------------------
Expected:
string number
-- -------- --------
a one 1
b two
Got:
string number
-- -------- --------
a one 1
b two
___________________________ test_list_of_lists_keys ____________________________
----------------------------- Captured stdout call -----------------------------
Expected:
0 1 2
--- --- ---
a one 1
b two
Got:
0 1 2
--- --- ---
a one 1
b two
________________________________ test_dict_like ________________________________
----------------------------- Captured stdout call -----------------------------
Keys' order: dict_keys(['a', 'b'])
Expected 1:
a b
--- ---
0 101
1 102
2 103
104
Expected 2:
b a
--- ---
101 0
102 1
103 2
104
Got:
a b
--- ---
0 101
1 102
2 103
104
________________________________ test_numpy_2d _________________________________
----------------------------- Captured stdout call -----------------------------
Expected:
a b c
----- ----- -----
0.5 4 13.5
32 62.5 108
171.5 256 364.5
Got:
a b c
----- ----- -----
0.5 4 13.5
32 62.5 108
171.5 256 364.5
____________________________ test_numpy_2d_firstrow ____________________________
----------------------------- Captured stdout call -----------------------------
Expected:
1 8 27
--- --- ----
64 125 216
343 512 729
Got:
1 8 27
--- --- ----
64 125 216
343 512 729
______________________________ test_numpy_2d_keys ______________________________
----------------------------- Captured stdout call -----------------------------
Expected:
0 1 2
----- ----- -----
0.5 4 13.5
32 62.5 108
171.5 256 364.5
Got:
0 1 2
----- ----- -----
0.5 4 13.5
32 62.5 108
171.5 256 364.5
___________________________ test_numpy_record_array ____________________________
----------------------------- Captured stdout call -----------------------------
Expected:
----- -- -----
Alice 23 169.5
Bob 27 175
----- -- -----
Got:
----- -- -----
Alice 23 169.5
Bob 27 175
----- -- -----
_________________________ test_numpy_record_array_keys _________________________
----------------------------- Captured stdout call -----------------------------
Expected:
name age height
------ ----- --------
Alice 23 169.5
Bob 27 175
Got:
name age height
------ ----- --------
Alice 23 169.5
Bob 27 175
_______________________ test_numpy_record_array_headers ________________________
----------------------------- Captured stdout call -----------------------------
Expected:
person years cm
-------- ------- -----
Alice 23 169.5
Bob 27 175
Got:
person years cm
-------- ------- -----
Alice 23 169.5
Bob 27 175
_________________________________ test_pandas __________________________________
----------------------------- Captured stdout call -----------------------------
Expected:
string number
-- -------- --------
a one 1
b two nan
Got:
string number
-- -------- --------
a one 1
b two nan
_____________________________ test_pandas_firstrow _____________________________
----------------------------- Captured stdout call -----------------------------
Expected:
a one 1.0
--- ----- -----
b two nan
Got:
a one 1.0
--- ----- -----
b two nan
_______________________________ test_pandas_keys _______________________________
----------------------------- Captured stdout call -----------------------------
Expected:
string number
-- -------- --------
a one 1
b two nan
Got:
string number
-- -------- --------
a one 1
b two nan
_________________________________ test_sqlite3 _________________________________
----------------------------- Captured stdout call -----------------------------
Expected:
whom how old how tall
------ --------- ----------
Alice 23 169.5
Bob 27 175
Got:
whom how old how tall
------ --------- ----------
Alice 23 169.5
Bob 27 175
______________________________ test_sqlite3_keys _______________________________
----------------------------- Captured stdout call -----------------------------
Expected:
whom how old how tall
------ --------- ----------
Alice 23 169.5
Bob 27 175
Got:
whom how old how tall
------ --------- ----------
Alice 23 169.5
Bob 27 175
___________________________ test_list_of_namedtuples ___________________________
----------------------------- Captured stdout call -----------------------------
Expected:
- -
1 2
3 4
- -
Got:
- -
1 2
3 4
- -
________________________ test_list_of_namedtuples_keys _________________________
----------------------------- Captured stdout call -----------------------------
Expected:
foo bar
----- -----
1 2
3 4
Got:
foo bar
----- -----
1 2
3 4
______________________________ test_list_of_dicts ______________________________
----------------------------- Captured stdout call -----------------------------
Expected 1:
- -
1 2
3 4
- -
Expected 2:
- -
2 1
4 3
- -
Got:
- -
1 2
3 4
- -
____________________________ test_list_of_userdicts ____________________________
----------------------------- Captured stdout call -----------------------------
Expected 1:
- -
1 2
3 4
- -
Expected 2:
- -
2 1
4 3
- -
Got:
- -
1 2
3 4
- -
___________________________ test_list_of_dicts_keys ____________________________
----------------------------- Captured stdout call -----------------------------
Expected 1:
foo bar
----- -----
1 2
3 4
Expected 2:
bar foo
----- -----
2 1
4 3
Got:
foo bar
----- -----
1 2
3 4
_________________________ test_list_of_userdicts_keys __________________________
----------------------------- Captured stdout call -----------------------------
Expected 1:
foo bar
----- -----
1 2
3 4
Expected 2:
bar foo
----- -----
2 1
4 3
Got:
foo bar
----- -----
1 2
3 4
_____________________ test_list_of_dicts_with_missing_keys _____________________
----------------------------- Captured stdout call -----------------------------
Expected:
foo bar baz
----- ----- -----
1
2
4 3
Got:
foo bar baz
----- ----- -----
1
2
4 3
_________________________ test_list_of_dicts_firstrow __________________________
----------------------------- Captured stdout call -----------------------------
Expected 1:
FOO BAR baz
----- ----- -----
3 4 5
Expected 2:
BAR FOO baz
----- ----- -----
4 3 5
Got:
FOO BAR baz
----- ----- -----
3 4 5
___________________ test_list_of_dicts_with_dict_of_headers ____________________
----------------------------- Captured stdout call -----------------------------
Expected 1:
DIGITS LETTERS
-------- ---------
12345 ABCDE
Expected 2:
LETTERS DIGITS
--------- --------
ABCDE 12345
Got:
LETTERS DIGITS
--------- --------
ABCDE 12345
__________________________ test_list_of_ordereddicts ___________________________
----------------------------- Captured stdout call -----------------------------
Expected:
b a
--- ---
1 2
1 2
Got:
b a
--- ---
1 2
1 2
__________________ test_py37orlater_list_of_dataclasses_keys ___________________
----------------------------- Captured stdout call -----------------------------
Expected:
name age height
------ ----- --------
Alice 23 169.5
Bob 27 175
Got:
name age height
------ ----- --------
Alice 23 169.5
Bob 27 175
_________________ test_py37orlater_list_of_dataclasses_headers _________________
----------------------------- Captured stdout call -----------------------------
Expected:
person years cm
-------- ------- -----
Alice 23 169.5
Bob 27 175
Got:
person years cm
-------- ------- -----
Alice 23 169.5
Bob 27 175
__________ test_py37orlater_list_of_dataclasses_with_separating_line ___________
----------------------------- Captured stdout call -----------------------------
Expected:
name age height
------ ----- --------
Alice 23 169.5
------ ----- --------
Bob 27 175
Got:
name age height
------ ----- --------
Alice 23 169.5
------ ----- --------
Bob 27 175
_______________________________ test_list_bytes ________________________________
----------------------------- Captured stdout call -----------------------------
Expected:
bytes
---------------------------
b'\xe4\xbd\xa0\xe5\xa5\xbd'
你好
Got:
bytes
---------------------------
b'\xe4\xbd\xa0\xe5\xa5\xbd'
你好
- generated xml file: /tmp/rebench-grade-007dafb7125d48c9a9f80a916b1c2eec.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="0.683" timestamp="2026-11-14T03:56:41.990248+00:00" hostname="2f20d17bbe80"><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="0.115" /><testcase classname="test.test_input" name="test_numpy_2d_firstrow" time="0.000" /><testcase classname="test.test_input" name="test_numpy_2d_keys" time="0.000" /><testcase classname="test.test_input" name="test_numpy_record_array" time="0.000" /><testcase classname="test.test_input" name="test_numpy_record_array_keys" time="0.000" /><testcase classname="test.test_input" name="test_numpy_record_array_headers" time="0.000" /><testcase classname="test.test_input" name="test_pandas" time="0.524" /><testcase classname="test.test_input" name="test_pandas_firstrow" time="0.001" /><testcase classname="test.test_input" name="test_pandas_keys" time="0.000" /><testcase classname="test.test_input" name="test_sqlite3" time="0.002" /><testcase classname="test.test_input" name="test_sqlite3_keys" time="0.000" /><testcase classname="test.test_input" name="test_list_of_namedtuples" time="0.000" /><testcase classname="test.test_input" name="test_list_of_namedtuples_keys" time="0.000" /><testcase classname="test.test_input" name="test_list_of_dicts" time="0.000" /><testcase classname="test.test_input" name="test_list_of_userdicts" time="0.000" /><testcase classname="test.test_input" name="test_list_of_dicts_keys" time="0.000" /><testcase classname="test.test_input" name="test_list_of_userdicts_keys" time="0.000" /><testcase classname="test.test_input" name="test_list_of_dicts_with_missing_keys" time="0.000" /><testcase classname="test.test_input" name="test_list_of_dicts_firstrow" time="0.000" /><testcase classname="test.test_input" name="test_list_of_dicts_with_dict_of_headers" time="0.000" /><testcase classname="test.test_input" name="test_list_of_dicts_with_list_of_headers" time="0.000" /><testcase classname="test.test_input" name="test_list_of_ordereddicts" time="0.000" /><testcase classname="test.test_input" name="test_py37orlater_list_of_dataclasses_keys" time="0.001" /><testcase classname="test.test_input" name="test_py37orlater_list_of_dataclasses_headers" time="0.001" /><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.
[ 16.6s] 💭 (843 chars) "The user is reporting a bug in `tabulate` where `SEPARATING_LINE` does not work when
printing a list of dataclasses.
The..."
[ 16.6s] 🔧 run_command grep -n "getattr(row, f)" tabulate/__init__.py
[ 16.6s] → 1464: rows = [[getattr(row, f) for f in field_names] for row in rows]
[ 21.8s] 💭 (125 chars) "Okay, it's on line 1464 of `tabulate/__init__.py`.
Let's read the context around that line to understand how it's being ..."
[ 21.8s] 📖 read_file (tabulate/__init__.py)
[ 21.8s] ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'content': ' raise ValueError(\n
…
[ 34.3s] 💭 (811 chars) "The problematic part is:
```python
elif (
dataclasses is not None
and len(rows) > 0
..."
[ 34.3s] 🔧 run_command grep -rn "SEPARATING_LINE" tabulate/__init__.py
[ 34.4s] → 51:SEPARATING_LINE = "\001"
[ 47.6s] 💭 (87 chars) "`SEPARATING_LINE = "\001"`.
Now let's create a reproduction script to confirm the bug."
[ 47.6s] 🔧 run_command (22 lines) python3 - <<'PY'
[ 47.7s] → 'str' object has no attribute 'name'
[ 173.5s] 💭 (6550 chars) "Confirmed. The error is exactly what was reported.
Now I need to fix it. The code should skip calling `getattr` if the ..."
[ 173.5s] 🔧 run_command grep -rn "is_dataclass" tabulate/__init__.py
[ 173.6s] → 1458: and dataclasses.is_dataclass(rows[0])
[ 186.8s] 💭 (168 chars) "Only one occurrence.
Now let's test if the fix works and doesn't break anything else.
I should also test what happens i..."
[ 186.8s] 🔧 run_command (27 lines) python3 - <<'PY'
[ 186.8s] → error (exit 0):
[ 227.3s] 💭 (1682 chars) "As expected, Case 1 fails. Case 2 was not even reached.
If I apply the fix:
```python
rows = [
..."
[ 227.3s] ✏️ edit_file (tabulate/__init__.py)
[ 227.5s] ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'occurrences': 1, 'strategy': 'exact', 'diff': '---
a/tabulate/__in…
[ 235.0s] 💭 (44 chars) "Now let's run the reproduction script again."
[ 235.0s] 🔧 run_command (30 lines) python3 - <<'PY'
[ 235.1s] → Case 1:
[ 293.5s] 💭 (3375 chars) "Case 1 is now fixed!
Case 2 still fails, but as I suspected, it's because `rows[0]` is `SEPARATING_LINE`, so it doesn't ..."
[ 293.5s] 🔧 run_command grep -rn "SEPARATING_LINE" test/
[ 293.5s] → test/test_output.py:5:from tabulate import tabulate, simple_separated_format, SEPARATING_LINE
[ 315.9s] 💭 (819 chars) "Most of these are lists of lists or tuples.
`test/test_output.py:12: _test_table_with_sep_line = [["spam", 41.9999], SEP..."
[ 315.9s] 🔧 run_command pytest test/test_output.py -q --tb=short --no-header
[ 317.3s] → .........s..................s......s......s......s......s......s......s. [ 39%]
[ 344.3s] 💭 (1037 chars) "The tests pass.
I'm confident that my fix for the `AttributeError` is correct and doesn't break existing tests.
As for..."
[ 344.3s] 🔧 run_command (16 lines) python3 - <<'PY'
[ 344.3s] → 'str' object has no attribute 'keys'
[ 355.8s] 💭 (698 chars) "Wow, yes! The same bug exists for dicts.
Let's look at the dict handling code:
```python
elif (
len..."
[ 355.8s] 📖 read_file (tabulate/__init__.py)
[ 355.9s] ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'content': ' and
getattr(tabular_data.dtype, "names")\n …
[ 357.5s] 💭 (15 chars) "And continuing:"
[ 357.5s] 📖 read_file (tabulate/__init__.py)
[ 357.6s] ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'content': ' raise ValueError(\n
…
[ 383.5s] 💭 (1561 chars) "The problematic parts for dict-like objects are:
1. `for k in row.keys():` (around line 1422)
2. `rows = [[row.get(k) fo..."
[ 383.5s] ✏️ edit_file (tabulate/__init__.py)
[ 383.7s] ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'occurrences': 1, 'strategy': 'exact', 'diff': '---
a/tabulate/__in…
[ 386.9s] 💭 (29 chars) "Now the second fix for dicts:"
[ 386.9s] ✏️ edit_file (tabulate/__init__.py)
[ 387.1s] ✓ {'status': 'ok', 'filepath': 'tabulate/__init__.py', 'occurrences': 1, 'strategy': 'exact', 'diff': '---
a/tabulate/__in…
[ 391.8s] 💭 (27 chars) "Now let's test with a dict."
[ 391.8s] 🔧 run_command (16 lines) python3 - <<'PY'
[ 391.9s] → name age
[ 399.2s] 💭 (114 chars) "Great! Now both dataclasses and dicts work with `SEPARATING_LINE`.
Let's verify that we didn't break dataclasses."
[ 399.2s] 🔧 run_command (22 lines) python3 - <<'PY'
[ 399.3s] → name age
[ 405.7s] 💭 (59 chars) "All set. Now let's run the final check with the test suite."
[ 405.7s] 🔧 run_command pytest test/test_output.py -q --tb=short --no-header
[ 406.3s] → .........s..................s......s......s......s......s......s......s. [ 39%]
[ 413.0s] 💭 (334 chars) "Everything looks good.
I have:
1. Fixed the `AttributeError` for dataclasses when `SEPARATING_LINE` is present in the li..."
[ 413.0s] 🔧 run_command git diff
[ 413.1s] → diff --git a/tabulate/__init__.py b/tabulate/__init__.py
Native result row
{
"agent_error": "Agent exceeded session timeout (7.5 min)",
"duration_seconds": 454.37,
"error": "Agent exceeded session timeout (7.5 min)",
"grade": {
"collected_tests": 33,
"competition_junit_ok": true,
"error": null,
"required_issues_count": 0,
"resolved": true,
"status_counts": {
"PASSED": 33
},
"strict_clean_patch": true,
"test_exit_code": 0
},
"instance_id": "astanin__python-tabulate-268",
"pass_idx": 0,
"patch_chars": 1623,
"phase1_seconds": 452.69,
"repo": "astanin/python-tabulate",
"request_log_path": "/kaggle-root/working/r31/rollouts_X/requests/astanin__python-tabulate-268__p0__r1.jsonl",
"resolved": true,
"rollout_idx": 1,
"run_key": "astanin__python-tabulate-268__p0__r1",
"strict_clean_patch": true,
"tool_calls": 19,
"total_llm_calls": 19,
"trace_path": "/kaggle-root/working/r31/rollouts_X/traces/trace_astanin__python-tabulate-268__p0__r1.json"
}