AutomatedEDA

The top-level orchestrator. Use this unless you specifically need to control the numerical or categorical pipeline in isolation.

from lochan_eda import AutomatedEDA

eda = AutomatedEDA()

run_pipeline(df, target=None, is_train=True)

Runs the full numerical + categorical pipeline end-to-end and returns a single, concatenated, fully numeric dataframe.

Parameters

Parameter	Type	Default	Description
df	pandas.DataFrame	required	The dataframe to process.
target	pandas.Series or None	None	Required if you want high-cardinality categorical columns to use TargetEncoder instead of frequency encoding. Only used when is_train=True.
is_train	bool	True	True to learn rules on this dataframe (training split). False to apply previously learned rules (test/inference split).

Returns

pandas.DataFrame — fully numeric, NaN-free. On is_train=False, columns are automatically reindexed to match the training output (missing columns filled with 0, extra columns dropped).

Example

X_train_clean = eda.run_pipeline(df=X_train, target=y_train, is_train=True)
X_test_clean  = eda.run_pipeline(df=X_test, is_train=False)

Attributes

Attribute	Description
num_handler	The internal HandleNumerical instance, created on the first is_train=True call.
cat_handler	The internal HandleCategorical instance, created on the first is_train=True call.
final_columns_	The full training-time output column order, used to align test-time output.

Calling test mode before training

eda.num_handler and eda.cat_handler are None until the first is_train=True call. Calling run_pipeline(..., is_train=False) before that raises AttributeError by design.