DataForgeML
Automated data profiling and splitting pipeline for ML datasets.
DataForgeML inspects your dataset, detects each column's semantic type (numeric, categorical, boolean, text, datetime, or identifier), computes per-column statistics and missingness, and produces a structured result ready for downstream feature engineering — no manual schema wrangling required.
Installation
pip install dataforge-ml
Quick Start
from dataforge_ml import DataLoader, PipelineConfig, StructuralProfiler
df = DataLoader().load("titanic.csv")
config = PipelineConfig()
result = StructuralProfiler(config).profile(df)
print(result.columns["Age"].semantic_type) # SemanticType.Numeric
print(result.dataset.row_count) # total rows
DataLoader auto-detects encoding and delimiter. Supported formats: CSV, TSV, Parquet, JSON, NDJSON, JSONL, XLSX, XLS, Arrow, Feather.
Column Type Overrides
Override the auto-detected type for any column before profiling:
config = PipelineConfig()
config.set_column_type("PassengerId", "identifier") # skip stats entirely
config.set_column_type(["Survived", "Pclass"], "categorical")
result = StructuralProfiler(config).profile(df)
To drop a column from all processing entirely, use add_exclusion:
config = PipelineConfig()
config.add_exclusion(["PassengerId", "Name"])
Splitting
from dataforge_ml import DataLoader, DataSplitter
df = DataLoader().load("titanic.csv")
splitter = DataSplitter(df, target="Survived", random_seed=42)
# Random train/test split (stratified by default when target is set)
split = splitter.random_split(test_size=0.2)
print(split.train.shape, split.test.shape)
# Chronological split (no temporal leakage)
split = splitter.time_split(time_column="date", test_size=0.2)
# K-fold cross-validation
for fold in splitter.kfold(k=5):
print(f"Fold {fold.fold_index}: train={fold.train_size}, val={fold.val_size}")
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for dataforge-ml 2.0.5
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