gators.data_cleaning._BaseDataCleaning¶
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class 
gators.data_cleaning._BaseDataCleaning[source]¶ Base data cleaning transformer.
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transform(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame]) → Union[pd.DataFrame, ks.DataFrame, dd.DataFrame][source]¶ Transform the dataframe X.
- Parameters
 - XDataFrame
 Input dataset.
- Returns
 - XDataFrame
 Transformed dataset.
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transform_numpy(X: numpy.ndarray) → numpy.ndarray[source]¶ Transform the array X.
- Parameters
 - Xnp.ndarray
 Input array.
- Returns
 - Xnp.ndarray
 Transformed array.
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static 
get_idx_columns_to_keep(columns: List[str], columns_to_drop: List[str]) → numpy.array[source]¶ 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.
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static 
check_array(X: numpy.ndarray)¶ Validate array.
- Parameters
 - Xnp.ndarray
 Array.
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check_array_is_numerics(X: numpy.ndarray)¶ Check if array is only numerics.
- Parameters
 - Xnp.ndarray
 Array.
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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.
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static 
check_dataframe(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶ Validate dataframe.
- Parameters
 - XDataFrame
 Dataframe.
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static 
check_dataframe_contains_numerics(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶ Check if dataframe is only numerics.
- Parameters
 - XDataFrame
 Dataframe.
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static 
check_dataframe_is_numerics(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶ Check if dataframe is only numerics.
- Parameters
 - XDataFrame
 Dataframe.
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check_dataframe_with_objects(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶ Check if dataframe contains object columns.
- Parameters
 - XDataFrame
 Dataframe.
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check_datatype(dtype, accepted_dtypes)¶ Check if dataframe is only numerics.
- Parameters
 - XDataFrame
 Dataframe.
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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.
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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.
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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.
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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.
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abstract 
fit(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series] = None) → gators.transformers.transformer.Transformer¶ Fit the transformer on the dataframe X.
- Parameters
 - XDataFrame.
 Input dataframe.
- ySeries, default None.
 Target values.
- Returns
 - selfTransformer
 Instance of itself.
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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.
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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.
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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.
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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.
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