AutomatedCleaning

AutomatedCleaning is a Python library for automated data cleaning, now powered by a Rust compute backend. It preprocesses and analyzes datasets — handling missing values, outliers, spelling corrections, text cleaning, PII masking and more — while the CPU-heavy work runs in compiled Rust for speed and true (GIL-free) parallelism.

Why Rust + Python?

The library is a hybrid: a friendly Python API on top, a fast Rust engine underneath.

• 🐍 Python frontend — the API you call, plus everything that leans on the Python ecosystem (Polars I/O, scikit-learn imputation, Plotly dashboards, Presidio PII, Claude AI).
• 🦀 Rust backend (automatedcleaning._rustcore) — the number-crunching and per-row string work: text preprocessing, symbol stripping, IQR outliers, skewness, correlation, multicollinearity and JSON detection.

You install one wheel and write normal Python — the Rust is invisible.

┌─────────────────────────── your code ───────────────────────────┐
│  import automatedcleaning as ac                                  │
│  df = ac.load_data("data.csv"); ac.clean_data(df)                │
└──────────────────────────────┬──────────────────────────────────┘
                               │ calls
┌──────────────────────────────▼──────────────────────────────────┐
│  Python frontend  (automatedcleaning/cleaning.py)                │
│    Polars • scikit-learn • Plotly • Presidio • LangChain         │
└──────────────────────────────┬──────────────────────────────────┘
                               │ delegates hot loops
┌──────────────────────────────▼──────────────────────────────────┐
│  🦀 Rust backend  (_rustcore, built with PyO3 + rayon)           │
│    text cleaning • skewness • IQR • correlation • JSON detect     │
└──────────────────────────────────────────────────────────────────┘

Benchmark — text preprocessing over 50,000 rows: ~3.8× faster than the pure-Python path, with the GIL released so it scales across cores.

Features

• Supports both large (100+ GB) and small datasets
• Detects and handles missing values and duplicate records
• Identifies and corrects spelling errors in categorical values
• Detects and removes outliers (IQR)
• Detects and fixes data imbalance
• Identifies and corrects skewness in numerical data
• Checks for correlation and detects multicollinearity
• Analyzes cardinality in categorical columns
• Identifies and cleans text columns
• Detects JSON-type columns
• Detects and masks PII columns
• Performs univariate, bivariate, and multivariate analysis (interactive dashboard)

Installation

From PyPI (recommended)

pip install AutomatedCleaning

Prebuilt abi3 wheels are published for Linux, macOS and Windows and work on CPython 3.11+ — no Rust toolchain needed to install. The import name is also automatedcleaning (import automatedcleaning as ac).

From source

Building from source requires the Rust toolchain (because the backend is compiled):

# 1. Install Rust (https://rustup.rs) and maturin
pip install maturin

# 2. Clone and build
git clone https://github.com/DataSpoof/dataspoof-data-cleaning.git
cd dataspoof-data-cleaning

# Build & install into the current environment
pip install .

# --- or, for development (needs an active virtualenv) ---
python -m venv .venv && source .venv/bin/activate   # Windows: .venv\Scripts\activate
maturin develop --release

Quick start

import automatedcleaning as ac

# Load any CSV / TSV / JSON / Parquet file (via Polars)
df = ac.load_data("dataset.csv")

# Run the full interactive cleaning pipeline
df_cleaned = ac.clean_data(df, background_image_path="assets/gradient.png")

clean_data() walks the dataset through the whole pipeline and asks a few interactive questions (column spelling fixes, categorical corrections, target column for imbalance). It writes cleaned_data.csv and an EDA dashboard to output/eda/dashboard.html.

Pipelines: fit once, apply anywhere (recommended for production)

clean_data() is interactive and re-learns everything from whatever data it is given. For scheduled jobs, train/test splits and production scoring, use a pipeline instead. It asks no questions, learns its cleaning rules on training data, and replays exactly those rules on new data, so a test set or a production batch never influences how it is cleaned.

import automatedcleaning as ac

pipe = ac.default_cleaning_pipeline()
train_clean = pipe.fit_transform(train_df)   # learn the rules on training data
test_clean = pipe.transform(test_df)         # same rules, nothing re-learned

print(pipe.report_)                          # what each step did (rows, columns, nulls, time)
pipe.save("cleaning_pipeline.json")          # everything it learned, as plain JSON (no pickle)

# later, in a scheduled job or an API
pipe = ac.Pipeline.load("cleaning_pipeline.json")
batch_clean = pipe.transform(batch_df)

The default pipeline: fix missing-value placeholders → normalise text → repair numbers and dates stored as text → drop duplicate rows (training only) → drop useless columns (mostly missing, constant, ID-like, duplicated) → impute, adding a <col>_was_missing flag for columns ≥5% missing → group rare categories → cap extreme outliers (3×IQR) → drop near-duplicate numeric columns. Change any step by name:

pipe = ac.default_cleaning_pipeline(clip_outliers=False, impute={"numeric": "mean"})

Build your own pipeline

Mix the stateful steps (which learn in fit) with any of the functions above:

pipe = ac.Pipeline([
    "replace_disguised_missing",                       # any library function, by name
    ac.ConvertNumericText(),
    ac.Impute(numeric="median", add_indicators=True),
    ac.EncodeCategorical(method="onehot"),
    ac.Scale(method="standard"),
], name="churn-features")

Stateful steps: drop_columns, drop_collinear, convert_numeric_text, parse_dates, impute, clip_outliers, remove_outliers, log_transform_skewed, scale, group_rare_categories, encode_categorical, drop_duplicates. Run ac.available_steps() to list every step name with a description.

Config files

Keep pipelines in version control as YAML or JSON:

# cleaning.yaml
name: churn-cleaning
on_schema_mismatch: raise        # warn (default) | raise | ignore
steps:
  - step: replace_disguised_missing
    params: {numeric_sentinels: [-999]}
  - step: drop_columns
    params: {max_missing: 0.5}
  - step: impute
    params: {numeric: median, add_indicators: true}
  - step: remove_outliers          # drops rows during fit only

pipe = ac.Pipeline.from_config("cleaning.yaml")

Built for production

• Schema checks: transform compares incoming columns and types with the training data and warns, or raises with on_schema_mismatch="raise".
• Training-only steps: row-removing steps (drop_duplicates, remove_outliers) run during fit and are skipped by transform, so scoring never silently drops records.
• Audit trail: every run produces a RunReport with a run ID, timings, per-step changes, data fingerprints, the config hash and library versions. Pass audit_log="runs.jsonl" to append each run to a file.
• Safe persistence: saved pipelines are plain JSON you can review, diff and load safely. Nothing is pickled.
• Extensible: subclass ac.Step and decorate it with @ac.register_step to add your own steps; they work in configs and in saved pipelines like the built-in ones.

Using individual steps

Every stage is also a standalone function you can call directly — useful for scripting or building your own non-interactive pipeline. These delegate to the Rust backend:

import polars as pl
import automatedcleaning as ac

df = ac.load_data("dataset.csv")

df = ac.detect_column_types_and_process_text(df)  # classify + clean text columns (Rust)
df = ac.handle_negative_values(df)                # negatives -> absolute values (Rust)
df = ac.replace_symbols(df)                       # strip $ ₹ , - •  (Rust)
df = ac.handle_missing_values(df)                 # KNN impute + mode fill (Python/sklearn)
df = ac.handle_duplicates(df)                     # drop duplicate rows (Polars)
df = ac.remove_outliers(df)                       # IQR outlier removal (Rust)
df = ac.fix_skewness(df)                          # log-transform skewed columns (Rust)
df = ac.check_multicollinearity(df, threshold=0.7)# drop correlated features (Rust)
df, cardinality = ac.check_cardinality(df)        # report + drop constant columns
df = ac.fix_json_columns(df)                      # expand JSON columns (Rust detection)
df = ac.detect_and_mask_pii_polars(df)            # detect + mask PII (Presidio)
ac.generate_dashboard(df)                         # Plotly EDA dashboard
ac.save_cleaned_data(df, "cleaned_data.csv")

Text preprocessing on its own

ac.preprocess_text("I can't wait!! Visit https://x.co @bob 😀 it's GR8")
# -> 'cannot wait visit'

Additional cleaning steps (automatedcleaning.steps)

Beyond the core pipeline, ~45 focused, composable steps are available directly on ac.*. String-heavy ones (whitespace, Unicode NFKC, mojibake, disguised-missing, booleans) run in the Rust backend.

df = ac.standardize_column_names(df)          # trim/lowercase/snake_case/de-dupe
df = ac.drop_empty_rows_and_columns(df)
df = ac.drop_high_missing_columns(df, 0.5)
df = ac.remove_duplicate_columns(df)
df = ac.drop_id_like_columns(df)
df = ac.replace_disguised_missing(df, numeric_sentinels=[999, -1])  # "NA","?"… -> null (Rust)
df = ac.add_missing_indicators(df)
df = ac.impute_missing(df, numeric_strategy="mice")   # mean|median|knn|mice|interpolate|ffill|bfill
df = ac.normalize_whitespace(df)              # Rust
df = ac.normalize_unicode(df)                 # NFKC + control-char strip (Rust)
df = ac.fix_mojibake(df)                       # "é" -> "é" (Rust)
df = ac.standardize_casing(df, {"name": "title"})
df = ac.parse_dates(df, ["signup"])           # -> native datetime
df = ac.extract_date_parts(df, ["signup"])    # year/month/day/weekday/quarter
df = ac.normalize_booleans(df, ["active"])    # yes/no/Y/N/1/0 -> bool (Rust)
df = ac.downcast_numeric(df)                  # int64->int32, etc.
df = ac.group_rare_categories(df, 0.01)
df = ac.fuzzy_cluster_categories(df)          # merge "New York"/"newyork" (rapidfuzz)
df = ac.encode_categorical(df, "onehot")      # onehot|label|frequency|ordinal|target
df = ac.remove_outliers_zscore(df)            # + _mad, _isolation_forest, _by_group
df = ac.winsorize(df)                          # cap instead of drop
df = ac.fuzzy_deduplicate(df, ["name","email"])
df = ac.scale_numeric(df, "standard")         # minmax|standard|robust
df = ac.bin_numeric(df, ["age"], bins=5)
df = ac.apply_range_rules(df, {"age": (0, 120)}, action="clip")
df = ac.validate_formats(df, {"email": "email", "phone": "phone"})
df = ac.normalize_phone_numbers(df, ["phone"], region="US")   # -> E.164
df = ac.normalize_emails(df, ["email"]); df = ac.canonicalize_urls(df, ["site"])

Steps that need optional libraries (detect_language, redact_profanity, correct_spelling_text) are installed via the extra:

pip install "AutomatedCleaning[text]"

Supported file formats

load_data() reads .csv, .tsv, .json, and .parquet (Excel must be converted first).

Requirements

• Python ≥ 3.11
• Key runtime deps (installed automatically): polars, pandas, numpy, pyarrow, nltk, scikit-learn, matplotlib, seaborn, missingno, plotly, pyfiglet, langchain-anthropic, presidio-analyzer, presidio-anonymizer
• The automatic categorical spell-correction step optionally uses the Anthropic Claude API (you'll be prompted for a key); you can skip it or correct values manually.

Building & contributing

The project uses maturin as its build backend (pyproject.toml). See BUILD.md for the full architecture and build/publish guide.

Cargo.toml            # Rust crate config
src/
  lib.rs              # PyO3 module — functions exposed to Python
  text.rs             # text preprocessing (contractions, regex, stopwords)
  stats.rs            # skewness, IQR, correlation, multicollinearity
  clean.rs            # whitespace, Unicode, mojibake, booleans, placeholders
automatedcleaning/
  core/               # shared Step/Pipeline core (no imports from the rest of the package)
  cleaners.py         # stateful cleaning steps + default_cleaning_pipeline()
  steps.py            # 43 standalone cleaning functions
  cleaning.py         # interactive clean_data() pipeline and EDA dashboard
tests/                # pytest suite

Run the tests (needs the Rust toolchain to build the extension):

python -m venv .venv && source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install maturin
maturin develop --release --extras test
pytest

Cross-platform wheels are built and published to PyPI automatically via GitHub Actions (.github/workflows/CI.yml) on every tagged release (vX.Y.Z).

License

MIT © Abhishek Kumar Singh / DataSpoof