Class: SimpleImputer

Univariate imputer for completing missing values with simple strategies.

Replace missing values using a descriptive statistic (e.g. mean, median, or most frequent) along each column, or using a constant value.

Constructors

new SimpleImputer()

new SimpleImputer(opts?): SimpleImputer

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.add_indicator`?	`boolean`	If `true`, a `MissingIndicator` transform will stack onto output of the imputer’s transform. This allows a predictive estimator to account for missingness despite imputation. If a feature has no missing values at fit/train time, the feature won’t appear on the missing indicator even if there are missing values at transform/test time.
`opts.copy`?	`boolean`	If `true`, a copy of X will be created. If `false`, imputation will be done in-place whenever possible. Note that, in the following cases, a new copy will always be made, even if `copy=False`:
`opts.fill_value`?	`string`	When strategy == “constant”, `fill_value` is used to replace all occurrences of missing_values. For string or object data types, `fill_value` must be a string. If `undefined`, `fill_value` will be 0 when imputing numerical data and “missing_value” for strings or object data types.
`opts.keep_empty_features`?	`boolean`	If `true`, features that consist exclusively of missing values when `fit` is called are returned in results when `transform` is called. The imputed value is always `0` except when `strategy="constant"` in which case `fill_value` will be used instead.
`opts.missing_values`?	`string` \| `number`	The placeholder for the missing values. All occurrences of `missing_values` will be imputed. For pandas’ dataframes with nullable integer dtypes with missing values, `missing_values` can be set to either `np.nan` or `pd.NA`.
`opts.strategy`?	`string`	The imputation strategy.

Returns SimpleImputer

Defined in generated/impute/SimpleImputer.ts:25

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/impute/SimpleImputer.ts:23
`_isInitialized`	`boolean`	`false`	generated/impute/SimpleImputer.ts:22
`_py`	`PythonBridge`	`undefined`	generated/impute/SimpleImputer.ts:21
`id`	`string`	`undefined`	generated/impute/SimpleImputer.ts:18
`opts`	`any`	`undefined`	generated/impute/SimpleImputer.ts:19

Accessors

feature_names_in_

Get Signature

get feature_names_in_(): Promise<ArrayLike>

Names of features seen during fit. Defined only when X has feature names that are all strings.

Returns Promise<ArrayLike>

Defined in generated/impute/SimpleImputer.ts:467

indicator_

Get Signature

get indicator_(): Promise<any>

Indicator used to add binary indicators for missing values. undefined if add_indicator=False.

Returns Promise<any>

Defined in generated/impute/SimpleImputer.ts:417

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/impute/SimpleImputer.ts:442

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/impute/SimpleImputer.ts:68

statistics_

Get Signature

get statistics_(): Promise<any[]>

The imputation fill value for each feature. Computing statistics can result in np.nan values. During transform, features corresponding to np.nan statistics will be discarded.

Returns Promise<any[]>

Defined in generated/impute/SimpleImputer.ts:392

Methods

dispose()

dispose(): Promise<void>

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Returns Promise<void>

Defined in generated/impute/SimpleImputer.ts:120

fit()

fit(opts): Promise<any>

Fit the imputer on X.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`any`	Input data, where `n_samples` is the number of samples and `n_features` is the number of features.
`opts.y`?	`any`	Not used, present here for API consistency by convention.

Returns Promise<any>

Defined in generated/impute/SimpleImputer.ts:137

fit_transform()

fit_transform(opts): Promise<any[]>

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.fit_params`?	`any`	Additional fit parameters.
`opts.X`?	`ArrayLike`[]	Input samples.
`opts.y`?	`ArrayLike`	Target values (`undefined` for unsupervised transformations).

Returns Promise<any[]>

Defined in generated/impute/SimpleImputer.ts:176

get_feature_names_out()

get_feature_names_out(opts): Promise<any>

Get output feature names for transformation.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.input_features`?	`any`	Input features.

Returns Promise<any>

Defined in generated/impute/SimpleImputer.ts:218

get_metadata_routing()

get_metadata_routing(opts): Promise<any>

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.routing`?	`any`	A `MetadataRequest` encapsulating routing information.

Returns Promise<any>

Defined in generated/impute/SimpleImputer.ts:254

init()

init(py): Promise<void>

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Parameters

Parameter	Type
`py`	`PythonBridge`

Returns Promise<void>

Defined in generated/impute/SimpleImputer.ts:81

inverse_transform()

inverse_transform(opts): Promise<ArrayLike[]>

Convert the data back to the original representation.

Inverts the transform operation performed on an array. This operation can only be performed after SimpleImputer is instantiated with add_indicator=True.

Note that inverse_transform can only invert the transform in features that have binary indicators for missing values. If a feature has no missing values at fit time, the feature won’t have a binary indicator, and the imputation done at transform time won’t be inverted.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	The imputed data to be reverted to original data. It has to be an augmented array of imputed data and the missing indicator mask.

Returns Promise<ArrayLike[]>

Defined in generated/impute/SimpleImputer.ts:292