tubular.numeric.TwoColumnOperatorTransformer¶

class tubular.numeric.TwoColumnOperatorTransformer(pd_method_name, columns, new_column_name, pd_method_kwargs={'axis': 0}, **kwargs)[source]¶

Bases: tubular.base.DataFrameMethodTransformer

This transformer applies a pandas.DataFrame method to two columns (add, sub, mul, div, mod, pow).

Transformer assigns the output of the method to a new column. The method will be applied in the form (column 1)operator(column 2), so order matters (if the method does not commute). It is possible to supply other key word arguments to the transform method, which will be passed to the pandas.DataFrame method being called.

Parameters

pd_method_name (str) – The name of the pandas.DataFrame method to be called.
column1_name (str) – The name of the 1st column in the operation.
column2_name (str) – The name of the 2nd column in the operation.
new_column_name (str) – The name of the new column that the output is assigned to.
pd_method_kwargs (dict, default = {'axis':0}) – Dictionary of method kwargs to be passed to pandas.DataFrame method. Must contain an entry for axis, set to either 1 or 0.
**kwargs – Arbitrary keyword arguments passed onto BaseTransformer.__init__().

pd_method_name¶

The name of the pandas.DataFrame method to be called.

Type: str

columns¶

list containing two string items: [column1_name, column2_name] The first will be operated upon by the chosen pandas method using the second.

Type: list

column2_name¶

The name of the 2nd column in the operation.

Type: str

new_column_name¶

The name of the new column that the output is assigned to.

Type: str

pd_method_kwargs¶

Dictionary of method kwargs to be passed to pandas.DataFrame method.

Type: dict

__init__(pd_method_name, columns, new_column_name, pd_method_kwargs={'axis': 0}, **kwargs) → None[source]¶: Performs input checks not done in either DataFrameMethodTransformer.__init__ or BaseTransformer.__init__.

Methods

`__init__`(pd_method_name, columns, …[, …])	Performs input checks not done in either DataFrameMethodTransformer.__init__ or BaseTransformer.__init__.
`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.
`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.
`columns_set_or_check`(X)	Function to check or set columns attribute.
`fit`(X[, y])	Base transformer fit method, checks X and y types.
`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)	Transform input data by applying the chosen method to the two specified columns.

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)¶

Base transformer fit method, checks X and y types. Currently only pandas DataFrames are allowed for X and DataFrames or Series for y.

Fit calls the columns_set_or_check method which will set the columns attribute to all columns in X, if it is None.

Parameters

X (pd.DataFrame) – Data to fit the transformer on.
y (None or pd.DataFrame or pd.Series, default = None) – Optional argument only required for the transformer to work with sklearn pipelines.

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)[source]¶: Transform input data by applying the chosen method to the two specified columns.

X (pd.DataFrame): Data to transform.

pd.DataFrame: Input X with an additional column.