tubular.dates.DateDiffLeapYearTransformer

class tubular.dates.DateDiffLeapYearTransformer(column_lower, column_upper, new_column_name, drop_cols, missing_replacement=None, **kwargs)

Bases: tubular.base.BaseTransformer

Transformer to calculate the number of years between two dates.

Parameters

• column_lower (str) – Name of date column to subtract.

• column_upper (str) – Name of date column to subtract from.

• new_column_name (str) – Name for the new year column.

• drop_cols (bool) – Flag for whether to drop the original columns.

• missing_replacement (int/float/str) – Value to output if either the lower date value or the upper date value are missing. Default value is None.

• **kwargs – Arbitrary keyword arguments passed onto BaseTransformer.init method.

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

columns

List containing column names for transformation in format [column_lower, column_upper]

Type

list

new_column_name

Column name for the year column calculated in the transform method.

Type

str

drop_cols

Indicator whether to drop old columns during transform method.

Type

bool

__init__(column_lower, column_upper, new_column_name, drop_cols, missing_replacement=None, **kwargs) → None

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(column_lower, column_upper, …[, …])	Initialize self.
calculate_age(row)	Function to calculate age from two date columns in a pd.DataFrame.
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)	Calculate year gap between the two provided columns.

calculate_age(row)

Function to calculate age from two date columns in a pd.DataFrame.

This function, although slower than the np.timedelta64 solution (or something similar), accounts for leap years to accurately calculate age for all values.

Parameters

row (pd.Series) – Named pandas series, with lower_date_name and upper_date_name as index values.

Returns

age – Year gap between the upper and lower date values passes.

Return type

int

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)

Calculate year gap between the two provided columns.

New column is created under the ‘new_column_name’, and optionally removes the old date columns.

Parameters

X (pd.DataFrame) – Data containing column_upper and column_lower.

Returns

X – Transformed data with new_column_name column.

Return type

pd.DataFrame