preprocessing

boxcox(method='mle')

Applies the Box-Cox transformation to numeric columns in a panel DataFrame.

Parameters:

Name	Type	Description	Default
method	str	The method used to determine the lambda parameter of the Box-Cox transformation. Supported methods: mle: maximum likelihood estimation pearsonr: Pearson correlation coefficient	'mle'

coerce_dtypes(schema)

Coerces the column datatypes of a DataFrame using the provided schema.

Parameters:

Name	Type	Description	Default
schema	Mapping[str, DataType]	A dictionary-like object mapping column names to the desired data types.	required

deseasonalize_fourier(sp, K, robust=False)

Removes seasonality via residualized regression with Fourier terms.

Parameters:

Name	Type	Description	Default
sp	int	Seasonal period.	required
K	int	Maximum order(s) of Fourier terms. Must be less than sp.	required

Note: part of this transformer uses sklearn under-the-hood: it is not pure Polars and lazy.

detrend(freq, method='linear')

Removes mean or linear trend from numeric columns in a panel DataFrame.

Parameters:

Name	Type	Description	Default
freq	str	Offset alias supported by Polars.	required
method	str	If mean, subtracts mean from each time-series. If linear, subtracts line of best-fit (via OLS) from each time-series. Defaults to linear.	'linear'

diff(order, sp=1, fill_strategy=None)

Difference time-series in panel data given order and seasonal period.

Parameters:

Name	Type	Description	Default
order	int	The order to difference.	required
sp	int	Seasonal periodicity.	1
fill_strategy	Optional[str]	Strategy to fill nulls by. Nulls are not filled if None. Supported strategies include: ["backward", "forward", "mean", "zero"].	None

fractional_diff(d, min_weight=None, window_size=None)

Compute the fractional differential of a time series.

This particular functionality is referenced in Advances in Financial Machine Learning by Marcos Lopez de Prado (2018).

For feature creation purposes, it is suggested that the minimum value of d is used that removes stationarity from the time series. This can be achieved by running the augmented dickey-fuller test on the time series for different values of d and selecting the minimum value that makes the time series stationary.

Parameters:

Name	Type	Description	Default
d	float	The fractional order of the differencing operator.	required
min_weight	float	The minimum weight to use for calculations. If specified, the window size is computed from this value and not needed.	None
window_size	int	The window size of the fractional differencing operator. If specified, the minimum weight is not needed.	None

impute(method)

Performs missing value imputation on numeric columns of a DataFrame grouped by entity.

Parameters:

Name	Type	Description	Default
method	Union[str, int, float]	The imputation method to use. Supported methods are: 'mean': Replace missing values with the mean of the corresponding column. 'median': Replace missing values with the median of the corresponding column. 'fill': Replace missing values with the mean for float columns and the median for integer columns. 'ffill': Forward fill missing values. 'bfill': Backward fill missing values. 'interpolate': Interpolate missing values using linear interpolation. int or float: Replace missing values with the specified constant.	required

lag(lags, fill_strategy=None)

Applies lag transformation to a LazyFrame.

Parameters:

Name	Type	Description	Default
lags	List[int]	A list of lag values to apply.	required
fill_strategy	Optional[str]	Strategy to fill nulls by. Nulls are not filled if None. Supported strategies include: ["backward", "forward", "mean", "zero"].	None

one_hot_encode(drop_first=False)

Encode categorical features as a one-hot numeric array.

Parameters:

Name	Type	Description	Default
drop_first	bool	Drop the first one hot feature.	False

Raises:

Type	Description
ValueError	if X passed into transform_new contains unknown categories.

reindex(drop_duplicates=False)

Reindexes the entity and time columns to have every possible combination of (entity, time).

Parameters:

Name	Type	Description	Default
drop_duplicates	bool	Defaults to False. If True, duplicates are dropped before reindexing.	False

resample(freq, agg_method, impute_method)

Resamples and transforms a DataFrame using the specified frequency, aggregation method, and imputation method.

Parameters:

Name	Type	Description	Default
freq	str	Offset alias supported by Polars.	required
agg_method	str	The aggregation method to use for resampling. Supported values are 'sum', 'mean', and 'median'.	required
impute_method	Union[str, int, float]	The method used for imputing missing values. If a string, supported values are 'ffill' (forward fill) and 'bfill' (backward fill). If an int or float, missing values will be filled with the provided value.	required

roll(window_sizes, stats, freq, fill_strategy=None)

Performs rolling window calculations on specified columns of a DataFrame.

Parameters:

Name	Type	Description	Default
window_sizes	List[int]	A list of integers representing the window sizes for the rolling calculations.	required
stats	List[Literal['mean', 'min', 'max', 'mlm', 'sum', 'std', 'cv']]	A list of statistical measures to calculate for each rolling window. Supported values are: 'mean' for mean 'min' for minimum 'max' for maximum 'mlm' for maximum minus minimum 'sum' for sum 'std' for standard deviation 'cv' for coefficient of variation	required
freq	str	Offset alias supported by Polars.	required
fill_strategy	Optional[str]	Strategy to fill nulls by. Nulls are not filled if None. Supported strategies include: ["backward", "forward", "mean", "zero"].	None

scale(use_mean=True, use_std=True, rescale_bool=False)

Performs scaling and rescaling operations on the numeric columns of a DataFrame.

Parameters:

Name	Type	Description	Default
use_mean	bool	Whether to subtract the mean from the numeric columns. Defaults to True.	True
use_std	bool	Whether to divide the numeric columns by the standard deviation. Defaults to True.	True
rescale_bool	bool	Whether to rescale boolean columns to the range [-1, 1]. Defaults to False.	False

time_to_arange(eager=False)

Coerces time column into arange per entity.

Assumes even-spaced time-series and homogenous start dates.

trim(direction='both')

Trims time-series in panel to have the same start or end dates as the shortest time-series.

Parameters:

Name	Type	Description	Default
direction	Literal['both', 'left', 'right']	Defaults to "both". If "left" trims from start date of the shortest time series); if "right" trims up to the end date of the shortest time-series; or otherwise "both" trims between start and end dates of the shortest time-series	'both'

yeojohnson(brack=(-2, 2))

Applies the Yeo-Johnson transformation to numeric columns in a panel DataFrame.

Parameters:

Name	Type	Description	Default
brack	2 - tuple	The starting interval for a downhill bracket search with optimize.brent. Note that this is in most cases not critical; the final result is allowed to be outside this bracket.	(-2, 2)