Revert "Add to_dataframe method to BinaryAPIReponse (#56)" (#72)

Author: Sean Smith

pyproject.toml

@@ -14,8 +14,6 @@ dependencies = [
     "anyio>=3.5.0, <5",
     "distro>=1.7.0, <2",
     "sniffio",
-    "pandas==2.2.3",
-    "numpy==2.0.2",
 ]
 requires-python = ">= 3.8"
 classifiers = [
@@ -57,8 +55,6 @@ dev-dependencies = [
     "importlib-metadata>=6.7.0",
     "rich>=13.7.1",
     "nest_asyncio==1.6.0",
-    "pandas==2.2.3",
-    "numpy==2.0.2",
 ]
 
 [tool.rye.scripts]

requirements-dev.lock

@@ -102,7 +102,3 @@ virtualenv==20.24.5
     # via nox
 zipp==3.17.0
     # via importlib-metadata
-pandas==2.2.3
-    # via contextual-client
-numpy==2.0.2
-    # via contextual-client

requirements.lock

@@ -43,7 +43,3 @@ typing-extensions==4.12.2
     # via contextual-client
     # via pydantic
     # via pydantic-core
-pandas==2.2.3
-    # via contextual-client
-numpy==2.0.2
-    # via contextual-client

src/contextual/_response.py

@@ -1,8 +1,6 @@
 from __future__ import annotations
 
 import os
-import ast
-import json
 import inspect
 import logging
 import datetime
@@ -25,7 +23,6 @@
 import anyio
 import httpx
 import pydantic
-from pandas import DataFrame  # type: ignore[import]
 
 from ._types import NoneType
 from ._utils import is_given, extract_type_arg, is_annotated_type, is_type_alias_type, extract_type_var_from_base
@@ -482,61 +479,6 @@ class BinaryAPIResponse(APIResponse[bytes]):
     the API request, e.g. `.with_streaming_response.get_binary_response()`
     """
 
-    def to_dataframe(self) -> DataFrame:
-        """Convert the response data to a pandas DataFrame.
-
-        Note: This method requires the `pandas` library to be installed.
-
-        Returns:
-            DataFrame: Processed evaluation data
-        """
-        # Read the binary content
-        content = self.read()
-
-        # Now decode the content
-        lines = content.decode("utf-8").strip().split("\n")
-
-        # Parse each line and flatten the results
-        data = []
-        for line in lines:
-            try:
-                entry = json.loads(line)
-                # Parse the results field directly from JSON
-                if 'results' in entry:
-                    if isinstance(entry['results'], str):
-                        # Try to handle string representations that are valid JSON
-                        try:
-                            results = json.loads(entry['results'])
-                        except Exception as e:
-                            # If not valid JSON, fall back to safer processing
-                            results = ast.literal_eval(entry['results'])
-                    else:
-                        # Already a dictionary
-                        results = entry['results']
-
-                    # Remove the original results field
-                    del entry['results']
-                    # Flatten the nested dictionary structure
-                    if isinstance(results, dict):
-                        for key, value in results.items():  # type: ignore
-                            if isinstance(value, dict):
-                                for subkey, subvalue in value.items():  # type: ignore
-                                    if isinstance(subvalue, dict):
-                                        # Handle one more level of nesting
-                                        for subsubkey, subsubvalue in subvalue.items():  # type: ignore
-                                            entry[f'{key}_{subkey}_{subsubkey}'] = subsubvalue
-                                    else:
-                                        entry[f'{key}_{subkey}'] = subvalue
-                            else:
-                                entry[key] = value
-
-                data.append(entry) # type: ignore
-            except Exception as e:
-                log.error(f"Error processing line: {e}")
-                log.error(f"Problematic line: {line[:200]}...")  # Print first 200 chars of the line
-                continue
-        return DataFrame(data)
-
     def write_to_file(
         self,
         file: str | os.PathLike[str],

tests/test_response.py

@@ -73,24 +73,6 @@ def test_response_parse_mismatched_basemodel(client: ContextualAI) -> None:
         response.parse(to=PydanticModel)
 
 
-def test_response_binary_response_to_dataframe(client: ContextualAI) -> None:
-    response = BinaryAPIResponse(
-        raw=httpx.Response(
-            200,
-            content=b'{"prompt": "What was Apple\'s total net sales for 2022?", "reference": "...", "response": "...", "guideline": "", "knowledge": "[]", "results": "{\'equivalence_score\': {\'score\': 0.0, \'metadata\': \\"The generated response does not provide any information about Apple\'s total net sales for 2022, whereas the reference response provides the specific figure.\\"}, \'factuality_v4.5_score\': {\'score\': 0.0, \'metadata\': {\'description\': \'There are claims but no knowledge so response is ungrounded.\'}}}", "status": "completed"}\r\n',
-        ),
-        client=client,
-        stream=False,
-        stream_cls=None,
-        cast_to=bytes,
-        options=FinalRequestOptions.construct(method="get", url="/foo"),
-    )
-    df = response.to_dataframe()
-    assert df.shape == (1, 10)
-    assert df["prompt"].astype(str).iloc[0] == "What was Apple's total net sales for 2022?"  # type: ignore
-    assert df["equivalence_score_score"].astype(float).iloc[0] == 0.0  # type: ignore
-
-
 @pytest.mark.asyncio
 async def test_async_response_parse_mismatched_basemodel(async_client: AsyncContextualAI) -> None:
     response = AsyncAPIResponse(