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.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new SimpleImputer(opts?: object): SimpleImputer;Parameters
| Name | 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. Default Value false |
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: Default Value true |
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. Default Value false |
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. Default Value 'mean' |
opts.verbose? | number | Controls the verbosity of the imputer. Default Value 0 |
Returns
Defined in: generated/impute/SimpleImputer.ts:25 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/impute/SimpleImputer.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/impute/SimpleImputer.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/impute/SimpleImputer.ts:21 (opens in a new tab)
id
string
Defined in: generated/impute/SimpleImputer.ts:18 (opens in a new tab)
opts
any
Defined in: generated/impute/SimpleImputer.ts:19 (opens in a new tab)
Accessors
feature_names_in_
Names of features seen during fit. Defined only when X has feature names that are all strings.
Signature
feature_names_in_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/impute/SimpleImputer.ts:456 (opens in a new tab)
indicator_
Indicator used to add binary indicators for missing values. undefined if add\_indicator=False.
Signature
indicator_(): Promise<any>;Returns
Promise<any>
Defined in: generated/impute/SimpleImputer.ts:406 (opens in a new tab)
n_features_in_
Number of features seen during fit.
Signature
n_features_in_(): Promise<number>;Returns
Promise<number>
Defined in: generated/impute/SimpleImputer.ts:431 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/impute/SimpleImputer.ts:75 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/impute/SimpleImputer.ts:79 (opens in a new tab)
statistics_
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.
Signature
statistics_(): Promise<any[]>;Returns
Promise<any[]>
Defined in: generated/impute/SimpleImputer.ts:381 (opens in a new tab)
Methods
dispose()
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Signature
dispose(): Promise<void>;Returns
Promise<void>
Defined in: generated/impute/SimpleImputer.ts:134 (opens in a new tab)
fit()
Fit the imputer on X.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | 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:151 (opens in a new tab)
fit_transform()
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit\_params and returns a transformed version of X.
Signature
fit_transform(opts: object): Promise<any[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Input samples. |
opts.fit_params? | any | Additional fit parameters. |
opts.y? | ArrayLike | Target values (undefined for unsupervised transformations). |
Returns
Promise<any[]>
Defined in: generated/impute/SimpleImputer.ts:191 (opens in a new tab)
get_feature_names_out()
Get output feature names for transformation.
Signature
get_feature_names_out(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.input_features? | any | Input features. |
Returns
Promise<any>
Defined in: generated/impute/SimpleImputer.ts:238 (opens in a new tab)
init()
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Signature
init(py: PythonBridge): Promise<void>;Parameters
| Name | Type |
|---|---|
py | PythonBridge |
Returns
Promise<void>
Defined in: generated/impute/SimpleImputer.ts:88 (opens in a new tab)
inverse_transform()
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.
Signature
inverse_transform(opts: object): Promise<ArrayLike[]>;Parameters
| Name | 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:278 (opens in a new tab)
set_output()
Set output container.
See Introducing the set_output API for an example on how to use the API.
Signature
set_output(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.transform? | "default" | "pandas" | Configure output of transform and fit\_transform. |
Returns
Promise<any>
Defined in: generated/impute/SimpleImputer.ts:315 (opens in a new tab)
transform()
Impute all missing values in X.
Signature
transform(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | The input data to complete. |
Returns
Promise<ArrayLike>
Defined in: generated/impute/SimpleImputer.ts:348 (opens in a new tab)