HandleNumerical

Use this directly if you want to process only numeric columns, or need granular control (e.g. excluding an ID column from processing). See Concepts → How it decides: numerical columns for the full decision logic.

from lochan_eda import HandleNumerical

num_handler = HandleNumerical(X_train)   # auto-selects numeric columns from X_train

Methods

Method	Parameters	Returns	Description
num_imputer(is_train=True, exclude=None)	exclude: column name or list of names to skip	pandas.DataFrame	Fills missing values using mean, median, or mode depending on column shape.
outlier_manager(is_train=True, exclude=None)	same	pandas.DataFrame	Clips, winsorizes, or transforms outliers.
scaler(is_train=True, exclude=None)	same	pandas.DataFrame	Fits/applies the chosen sklearn scaler per column.
full_handler(is_train=True)	—	pandas.DataFrame	Runs num_imputer → outlier_manager → scaler in sequence.

exclude is not available on full_handler

full_handler() does not currently forward an exclude argument — it always processes every numeric column. If you need to protect a column (e.g. customer_id) from processing, call the three granular methods yourself, as shown below.

Granular usage (train → test)

from lochan_eda import HandleNumerical

# Instantiate once, on the raw training dataframe
num_handler = HandleNumerical(X_train)

# TRAIN: learn + apply rules, skipping an ID-like column
X_train_num = num_handler.num_imputer(is_train=True, exclude="customer_id")
X_train_num = num_handler.outlier_manager(is_train=True, exclude="customer_id")
X_train_num = num_handler.scaler(is_train=True, exclude="customer_id")

# TEST: swap in the test data, then re-apply the exact same learned rules
num_handler.num_df = X_test.select_dtypes(include=["number"]).copy()
X_test_num = num_handler.num_imputer(is_train=False, exclude="customer_id")
X_test_num = num_handler.outlier_manager(is_train=False, exclude="customer_id")
X_test_num = num_handler.scaler(is_train=False, exclude="customer_id")

Simple usage (no exclusions needed)

X_train_num = num_handler.full_handler(is_train=True)

num_handler.num_df = X_test.select_dtypes(include=["number"]).copy()
X_test_num = num_handler.full_handler(is_train=False)

Learned attributes

Attribute	Description
drop_cols_	Numeric columns dropped for exceeding the 40% missing-value threshold.
impute_values_	{column: fill_value} used for imputation.
outlier_rules_	{column: rule_dict} describing the outlier treatment applied.
scalers_	{column: fitted scaler object}.
transforms_	{column: "log1p"} for columns that received a log transform before scaling.

See Inspecting Learned Rules for full usage examples.