tubular.base.DataFrameMethodTransformer¶

class tubular.base.DataFrameMethodTransformer(new_column_name, pd_method_name, columns, pd_method_kwargs={}, drop_original=False, **kwargs)[source]¶

Bases: tubular.base.BaseTransformer

Tranformer that applies a pandas.DataFrame method.

Transformer assigns the output of the method to a new column or columns. It is possible to supply other key word arguments to the transform method, which will be passed to the pandas.DataFrame method being called.

Be aware it is possible to supply incompatible arguments to init that will only be identified when transform is run. This is because there are many combinations of method, input and output sizes. Additionally some methods may only work as expected when called in transform with specific key word arguments.

Parameters

new_column_name (str or list of str) – The name of the column or columns to be assigned to the output of running the pandas method in transform.
pd_method_name (str) – The name of the pandas.DataFrame method to call.
columns (None or list or str) – Columns to apply the transformer to. If a str is passed this is put into a list. Value passed in columns is saved in the columns attribute on the object. Note this has no default value so the user has to specify the columns when initialising the transformer. This is avoid likely when the user forget to set columns, in this case all columns would be picked up when super transform runs.
pd_method_kwargs (dict, default = {}) – A dictionary of keyword arguments to be passed to the pd.DataFrame method when it is called.
drop_original (bool, default = False) – Should original columns be dropped?
**kwargs – Arbitrary keyword arguments passed onto BaseTransformer.__init__().

new_column_name¶

The name of the column or columns to be assigned to the output of running the pandas method in transform.

Type: str or list of str

pd_method_name¶

The name of the pandas.DataFrame method to call.

Type: str

__init__(new_column_name, pd_method_name, columns, pd_method_kwargs={}, drop_original=False, **kwargs) → None[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(new_column_name, pd_method_name, …)	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.
`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 pandas DataFrame (X) using the given pandas.DataFrame method and assign the output back to column or columns in X.

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 pandas DataFrame (X) using the given pandas.DataFrame method and assign the output back to column or columns in X.

Any keyword arguments set in the pd_method_kwargs attribute are passed onto the pandas DataFrame method when calling it.

Parameters: X (pd.DataFrame) – Data to transform.
Returns: X – Input X with additional column or columns (self.new_column_name) added. These contain the output of running the pandas DataFrame method.
Return type: pd.DataFrame