tubular.imputers.MedianImputer¶
- class tubular.imputers.MedianImputer(columns=None, weight=None, **kwargs)[source]¶
Bases:
tubular.imputers.BaseImputer
Transformer to impute missing values with the median of the supplied columns.
- Parameters
columns (None or str or list, default = None) – Columns to impute, if the default of None is supplied all columns in X are used when the transform method is called.
weight (None or str, default=None) – Column containing weights
**kwargs – Arbitrary keyword arguments passed onto BaseTransformer.init method.
- impute_values_¶
Created during fit method. Dictionary of float / int (median) values of columns in the columns attribute. Keys of impute_values_ give the column names.
- Type
dict
- __init__(columns=None, weight=None, **kwargs) → None[source]¶
Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
([columns, weight])Initialize self.
check_is_fitted
(attribute)Check if particular attributes are on the object.
check_weights_column
(X, weights_column)Helper method for validating weights column in dataframe.
Method that returns the name of the current class when called.
Method to check that the columns attribute is set and all values are present in X.
Function to check or set columns attribute.
fit
(X[, y])Calculate median values to impute with from X.
fit_transform
(X[, y])Fit to data, then transform it.
get_params
([deep])Get parameters for this estimator.
set_params
(**params)Set the parameters of this estimator.
transform
(X)Impute missing values with median values calculated from fit method.
- check_is_fitted(attribute)¶
Check if particular attributes are on the object. This is useful to do before running transform to avoid trying to transform data without first running the fit method.
Wrapper for utils.validation.check_is_fitted function.
- Parameters
attributes (List) – List of str values giving names of attribute to check exist on self.
- static check_weights_column(X, weights_column)¶
Helper method for validating weights column in dataframe.
X (pd.DataFrame): df containing weight column weights_column (str): name of weight column
- classname()¶
Method that returns the name of the current class when called.
- columns_check(X)¶
Method to check that the columns attribute is set and all values are present in X.
- Parameters
X (pd.DataFrame) – Data to check columns are in.
- columns_set_or_check(X)¶
Function to check or set columns attribute.
If the columns attribute is None then set it to all columns in X. Otherwise run the columns_check method.
- Parameters
X (pd.DataFrame) – Data to check columns are in.
- fit(X, y=None)[source]¶
Calculate median values to impute with from X.
- Parameters
X (pd.DataFrame) – Data to “learn” the median values from.
y (None or pd.DataFrame or pd.Series, default = None) – Not required.
- fit_transform(X, y=None, **fit_params)¶
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
X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.
- Returns
X_new – Transformed array.
- Return type
ndarray array of shape (n_samples, n_features_new)
- get_params(deep=True)¶
Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
dict
- 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
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance
- transform(X)¶
Impute missing values with median values calculated from fit method.
- Parameters
X (pd.DataFrame) – Data to impute.
- Returns
X – Transformed input X with nulls imputed with the median value for the specified columns.
- Return type
pd.DataFrame