gators.data_cleaning.DropHighNaNRatio¶
-
class
gators.data_cleaning.DropHighNaNRatio(max_ratio: float)[source]¶ Drop the columns having a large NaN values ratio.
- Parameters
- max_ratiofloat
Max nan ratio allowed.
Examples
Imports and initialization:
>>> from gators.data_cleaning import DropHighNaNRatio >>> obj = DropHighNaNRatio(max_ratio=0.5)
The fit, transform, and fit_transform methods accept:
dask dataframes:
>>> import dask.dataframe as dd >>> import pandas as pd >>> import numpy as np >>> X = dd.from_pandas(pd.DataFrame( ... {'A': [1, 2, 3], 'B': ['1', None, None], 'C': [1., np.nan, np.nan]}), npartitions=1)
koalas dataframes:
>>> import databricks.koalas as ks >>> X = ks.DataFrame( ... {'A': [1, 2, 3], 'B': ['1', None, None], 'C': [1., np.nan, np.nan]})
and pandas dataframes:
>>> import pandas as pd >>> X = pd.DataFrame( ... {'A': [1, 2, 3], 'B': ['1', None, None], 'C': [1., np.nan, np.nan]})
The result is a transformed dataframe belonging to the same dataframe library.
>>> obj.fit_transform(X) A 0 1 1 2 2 3
>>> X = pd.DataFrame( ... {'A': [1, 2, 3], 'B': ['1', None, None], 'C': [1., np.nan, np.nan]}) >>> _ = obj.fit(X) >>> obj.transform_numpy(X.to_numpy()) array([[1], [2], [3]], 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_nan_ratio.DropHighNaNRatio[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
- selfDropHighNaNRatio
Instance of itself.
-
static
get_columns_to_drop(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], max_ratio: float) → List[str][source]¶ Get the list of column names to drop.
- Parameters
- Xpd.DataFrame
Input dataset.
- max_ratiofloat
Max nan ratio allowed.
- Returns
- List[str]
List of 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.