Feat(dspy): from_pandas support

dspy/datasets/dataloader.py

@@ -2,14 +2,17 @@
 from collections.abc import Mapping
 from typing import List, Tuple, Union
 
+import pandas as pd
 from datasets import load_dataset
 
 import dspy
 from dspy.datasets.dataset import Dataset
 
 
 class DataLoader(Dataset):
- def __init__(self,):
+ def __init__(
+ self,
+ ):
  pass
 
  def from_huggingface(
@@ -27,40 +30,64 @@ def from_huggingface(
  raise ValueError("Invalid input keys provided. Please provide a tuple of input keys.")
 
  dataset = load_dataset(dataset_name, *args, **kwargs)
- 
+
  if isinstance(dataset, list) and isinstance(kwargs["split"], list):
- dataset = {split_name:dataset[idx] for idx, split_name in enumerate(kwargs["split"])}
+ dataset = {split_name: dataset[idx] for idx, split_name in enumerate(kwargs["split"])}
 
  try:
  returned_split = {}
  for split_name in dataset.keys():
  if fields:
- returned_split[split_name] = [dspy.Example({field:row[field] for field in fields}).with_inputs(*input_keys) for row in dataset[split_name]]
+ returned_split[split_name] = [
+ dspy.Example({field: row[field] for field in fields}).with_inputs(*input_keys)
+ for row in dataset[split_name]
+ ]
  else:
- returned_split[split_name] = [dspy.Example({field:row[field] for field in row.keys()}).with_inputs(*input_keys) for row in dataset[split_name]]
+ returned_split[split_name] = [
+ dspy.Example({field: row[field] for field in row.keys()}).with_inputs(*input_keys)
+ for row in dataset[split_name]
+ ]
 
  return returned_split
  except AttributeError:
  if fields:
- return [dspy.Example({field:row[field] for field in fields}).with_inputs(*input_keys) for row in dataset]
+ return [
+ dspy.Example({field: row[field] for field in fields}).with_inputs(*input_keys) for row in dataset
+ ]
  else:
- return [dspy.Example({field:row[field] for field in row.keys()}).with_inputs(*input_keys) for row in dataset]
+ return [
+ dspy.Example({field: row[field] for field in row.keys()}).with_inputs(*input_keys)
+ for row in dataset
+ ]
 
- def from_csv(self, file_path:str, fields: List[str] = None, input_keys: Tuple[str] = ()) -> List[dspy.Example]:
+ def from_csv(self, file_path: str, fields: List[str] = None, input_keys: Tuple[str] = ()) -> List[dspy.Example]:
  dataset = load_dataset("csv", data_files=file_path)["train"]
- 
+
  if not fields:
  fields = list(dataset.features)
-
- return [dspy.Example({field:row[field] for field in fields}).with_inputs(*input_keys) for row in dataset]
 
- def from_json(self, file_path:str, fields: List[str] = None, input_keys: Tuple[str] = ()) -> List[dspy.Example]:
+ return [dspy.Example({field: row[field] for field in fields}).with_inputs(*input_keys) for row in dataset]
+
+ def from_pandas(
+ self,
+ df: pd.DataFrame,
+ fields: list[str] = None,
+ input_keys: tuple[str] = (),
+ ) -> list[dspy.Example]:
+ if fields is None:
+ fields = list(df.columns)
+
+ return [
+ dspy.Example({field: row[field] for field in fields}).with_inputs(*input_keys) for _, row in df.iterrows()
+ ]
+
+ def from_json(self, file_path: str, fields: List[str] = None, input_keys: Tuple[str] = ()) -> List[dspy.Example]:
  dataset = load_dataset("json", data_files=file_path)["train"]
- 
+
  if not fields:
  fields = list(dataset.features)
- 
- return [dspy.Example({field:row[field] for field in fields}).with_inputs(*input_keys) for row in dataset]
+
+ return [dspy.Example({field: row[field] for field in fields}).with_inputs(*input_keys) for row in dataset]
 
  def from_parquet(self, file_path: str, fields: List[str] = None, input_keys: Tuple[str] = ()) -> List[dspy.Example]:
  dataset = load_dataset("parquet", data_files=file_path)["train"]
@@ -79,7 +106,7 @@ def sample(
  ) -> List[dspy.Example]:
  if not isinstance(dataset, list):
  raise ValueError(f"Invalid dataset provided of type {type(dataset)}. Please provide a list of examples.")
- 
+
  return random.sample(dataset, n, *args, **kwargs)
 
  def train_test_split(
@@ -115,6 +142,6 @@ def train_test_split(
  test_end = len(dataset_shuffled) - train_end
 
  train_dataset = dataset_shuffled[:train_end]
- test_dataset = dataset_shuffled[train_end:train_end + test_end]
+ test_dataset = dataset_shuffled[train_end : train_end + test_end]
 
- return {'train': train_dataset, 'test': test_dataset}
+ return {"train": train_dataset, "test": test_dataset}