data-cleaning

name	data-cleaning
description	Use when preparing raw data for modeling — handling missing values, duplicates, inconsistent types, outliers, and bad categorical values. Emphasizes fitting all imputation on train-only to avoid leakage.

Data Cleaning

Overview

Cleaning turns raw data into a consistent, model-ready table without leaking information from the future or the test set. The golden rule: every statistic used to clean (means, medians, modes, bounds, category maps) must be learned from the training split only, then applied to validation/test.

When to use

• Raw data has nulls, duplicates, mixed types, or junk categories.
• Before feature-engineering and modeling.
• After EDA flagged specific quality issues.

Workflow

1. Deduplicate — exact and key-based duplicates. Decide which to keep (latest timestamp, highest completeness).
2. Fix types — parse dates, cast numerics stored as strings, normalize booleans.
3. Standardize categoricals — trim whitespace, unify case, map synonyms ("US"/"USA"/"United States").
4. Handle missing values — choose per-column strategy (see below).
5. Treat outliers — cap/winsorize or flag; never blindly delete.
6. Validate — assert schema, ranges, and row counts after each step.

Missing-value strategy

Situation	Strategy
Numeric, MCAR, small %	Median impute (robust to skew)
Numeric, informative missingness	Impute + add was_missing indicator
Categorical	Impute with "Missing" as its own category
Time series	Forward/backward fill within group
>50% missing	Consider dropping the column

Reference snippet (leakage-safe)

from sklearn.model_selection import train_test_split
from sklearn.impute import SimpleImputer

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

# Fit imputers on TRAIN ONLY
num_imputer = SimpleImputer(strategy="median").fit(X_train[num_cols])
X_train[num_cols] = num_imputer.transform(X_train[num_cols])
X_test[num_cols]  = num_imputer.transform(X_test[num_cols])  # reuse train stats

Prefer doing this inside a Pipeline/ColumnTransformer (see the sklearn-pipelines skill) so leakage is impossible by construction.

Outlier handling

# Winsorize numeric columns to train-derived 1st/99th percentiles
lo = X_train[col].quantile(0.01)
hi = X_train[col].quantile(0.99)
X_train[col] = X_train[col].clip(lo, hi)
X_test[col]  = X_test[col].clip(lo, hi)

Pitfalls

• df.fillna(df.mean()) on the whole dataset — classic leakage. Compute stats on train only.
• dropna() on rows silently shrinks and biases the dataset; prefer imputation + indicators.
• Deleting outliers that are real signal (fraud, rare events) — investigate before removing.
• Cleaning before splitting — split first, then clean using train statistics.

Hand-off

Produce a clean, typed dataframe plus a documented list of decisions (what was imputed/capped/dropped and why) so feature-engineering and reproducible-ml can rely on it.