tubular.dates.BetweenDatesTransformer¶

class tubular.dates.BetweenDatesTransformer(column_lower, column_between, column_upper, new_column_name, lower_inclusive=True, upper_inclusive=True, **kwargs)[source]¶

Bases: tubular.base.BaseTransformer

Transformer to generate a boolean column indicating if one date is between two others.

If not all column_lower values are less than or equal to column_upper when transform is run then a warning will be raised.

Parameters

column_lower (str) – Name of column containing the lower date range values.
column_between (str) – Name of column to check if it’s values fall between column_lower and column_upper.
column_upper (str) – Name of column containing the upper date range values.
new_column_name (str) – Name for new column to be added to X.
lower_inclusive (bool, defualt = True) – If lower_inclusive is True the comparison to column_lower will be column_lower <= column_between, otherwise the comparison will be column_lower < column_between.
upper_inclusive (bool, defualt = True) – If upper_inclusive is True the comparison to column_upper will be column_between <= column_upper, otherwise the comparison will be column_between < column_upper.
**kwargs – Arbitrary keyword arguments passed onto BaseTransformer.__init__().

column_lower¶

Name of date column to subtract. This attribute is not for use in any method, use ‘columns’ instead. Here only as a fix to allow string representation of transformer.

Type: str

column_upper¶

Name of date column to subtract from. This attribute is not for use in any method, use ‘columns instead. Here only as a fix to allow string representation of transformer.

Type: str

column_between¶

Name of column to check if it’s values fall between column_lower and column_upper. This attribute is not for use in any method, use ‘columns instead. Here only as a fix to allow string representation of transformer.

Type: str

columns¶

Contains the names of the columns to compare in the order [column_lower, column_between column_upper].

Type: list

new_column_name¶

new_column_name argument passed when initialising the transformer.

Type: str

lower_inclusive¶

lower_inclusive argument passed when initialising the transformer.

Type: bool

upper_inclusive¶

upper_inclusive argument passed when initialising the transformer.

Type: bool

__init__(column_lower, column_between, column_upper, new_column_name, lower_inclusive=True, upper_inclusive=True, **kwargs) → None[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(column_lower, column_between, …)	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 - creates column indicating if middle date is between the other two.

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 - creates column indicating if middle date is between the other two.

If not all column_lower values are less than or equal to column_upper when transform is run then a warning will be raised.

Parameters: X (pd.DataFrame) – Data to transform.
Returns: X – Input X with additional column (self.new_column_name) added. This column is boolean and indicates if the middle column is between the other 2.
Return type: pd.DataFrame