gators.data_cleaning.DropHighCardinality¶

class gators.data_cleaning.DropHighCardinality(max_categories: int)[source]¶

Drop the categorical columns having a large cardinality.

Parameters

max_categoriesint: Maximum number of categories allowed.

Examples

Imports and initialization:

>>> from gators.data_cleaning import DropHighCardinality
>>> obj = DropHighCardinality(max_categories=2)

The fit, transform, and fit_transform methods accept:

dask dataframes:

>>> import dask.dataframe as dd
>>> import pandas as pd
>>> X = dd.from_pandas(pd.DataFrame({'A': ['a', 'b', 'c'], 'B': ['d', 'd', 'e']}), npartitions=1)

koalas dataframes:

>>> import databricks.koalas as ks
>>> X = ks.DataFrame({'A': ['a', 'b', 'c'], 'B': ['d', 'd', 'e']})

and pandas dataframes:

>>> import pandas as pd
>>> X = pd.DataFrame({'A': ['a', 'b', 'c'], 'B': ['d', 'd', 'e']})

The result is a transformed dataframe belonging to the same dataframe library.

>>> obj.fit_transform(X)
   B
0  d
1  d
2  e

>>> X = pd.DataFrame({'A': ['a', 'b', 'c'], 'B': ['d', 'd', 'e']})
>>> _ = obj.fit(X)
>>> obj.transform_numpy(X.to_numpy())
array([['d'],
       ['d'],
       ['e']], dtype=object)

fit(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series] = None) → gators.data_cleaning.drop_high_cardinality.DropHighCardinality[source]¶

Fit the transformer on the dataframe X.

Get the list of column names to remove and the array of: indices to be kept.

Parameters

XDataFrame: Input dataframe.
ySeries, default None.: Target values.

Returns

selfDropHighCardinality: Instance of itself.

static get_columns_to_drop(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], max_categories: int) → List[str][source]¶

Get the names of the columns to drop.

Parameters

Xpd.DataFrame: Dataframe.
max_categoriesint: Maximum number of categories allowed.

Returns

List[str]: List of the column names to drop.

static check_array(X: numpy.ndarray)¶

Validate array.

Parameters

Xnp.ndarray: Array.

check_array_is_numerics(X: numpy.ndarray)¶

Check if array is only numerics.

Parameters

Xnp.ndarray: Array.

static check_binary_target(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series])¶

Raise an error if the target is not binary.

Parameters

ySeries: Target values.

static check_dataframe(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶

Validate dataframe.

Parameters

XDataFrame: Dataframe.

static check_dataframe_contains_numerics(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶

Check if dataframe is only numerics.

Parameters

XDataFrame: Dataframe.

static check_dataframe_is_numerics(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶

Check if dataframe is only numerics.

Parameters

XDataFrame: Dataframe.

check_dataframe_with_objects(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶

Check if dataframe contains object columns.

Parameters

XDataFrame: Dataframe.

check_datatype(dtype, accepted_dtypes)¶

Check if dataframe is only numerics.

Parameters

XDataFrame: Dataframe.

static check_multiclass_target(y: Union[pd.Series, ks.Series, dd.Series])¶

Raise an error if the target is not discrete.

Parameters

ySeries: Target values.

check_nans(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], columns: List[str])¶

Raise an error if X contains NaN values.

Parameters

XDataFrame: Dataframe.
theta_vecList[float]: List of columns.

static check_regression_target(y: Union[pd.Series, ks.Series, dd.Series])¶

Raise an error if the target is not discrete.

Parameters

ySeries: Target values.

static check_target(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series])¶

Validate target.

Parameters

XDataFrame: Dataframe.
ySeries: Target values.

fit_transform(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series] = None) → Union[pd.DataFrame, ks.DataFrame, dd.DataFrame]¶

Fit and Transform the dataframe X.

Parameters

XDataFrame.: Input dataframe.
ySeries, default None.: Input target.

Returns

XDataFrame: Transformed dataframe.

static get_column_names(inplace: bool, columns: List[str], suffix: str)¶

Return the names of the modified columns.

Parameters

inplacebool: If True return columns. If False return columns__suffix.
columnsList[str]: List of columns.
suffixstr: Suffix used if inplace is False.

Returns

List[str]: List of column names.

static get_idx_columns_to_keep(columns: List[str], columns_to_drop: List[str]) → numpy.array¶

Get the column indices to keep.

Parameters

theta_vecList[float]: List of columns of a dataset.
columns_to_dropList[str]: List of columns to drop.

Returns

np.array:: Column indices to keep.

get_params(deep=True)¶

Get parameters for this estimator.

Parameters

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsdict: Parameter names mapped to their values.

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**paramsdict: Estimator parameters.

Returns

selfestimator instance: Estimator instance.

transform(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame]) → Union[pd.DataFrame, ks.DataFrame, dd.DataFrame]¶

Transform the dataframe X.

Parameters

XDataFrame: Input dataset.

Returns

XDataFrame: Transformed dataset.

transform_numpy(X: numpy.ndarray) → numpy.ndarray¶

Transform the array X.

Parameters

Xnp.ndarray: Input array.

Returns

Xnp.ndarray: Transformed array.

gators.data_cleaning._BaseDataCleaning

gators.data_cleaning.DropHighNaNRatio