cryptodatapy.transform.impute

Classes

Impute

Handles missing values.

Module Contents

class cryptodatapy.transform.impute.Impute(filtered_df: pandas.DataFrame, plot: bool = False, plot_series: tuple = ('BTC', 'close'))

Handles missing values.

filtered_df

plot = False

plot_series = ('BTC', 'close')

imputed_df = None

fwd_fill() → pandas.DataFrame

Imputes missing values by imputing missing values with latest non-missing values.

Returns:

imputed_df – DataFrame MultiIndex with DatetimeIndex (level 0), ticker (level 1) and fields (cols) with imputed values using forward fill method.

Return type:

pd.DataFrame - MultiIndex

interpolate(method: str = 'linear', order: int | None = None, axis: int = 0, limit: int | None = None) → pandas.DataFrame

Imputes missing values by interpolating using various methods.

Parameters:

• method (str, {'linear', ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘spline’, ‘barycentric’,) – ‘polynomial’, ‘krogh’, ‘piecewise_polynomial’, ‘pchip’, ‘akima’, ‘cubicspline’}, default spline Interpolation method to use.

• order (int, optional, default None) – Order of polynomial or spline.

• axis ({{0 or ‘index’, 1 or ‘columns’, None}}, default None) – Axis to interpolate along.

• limit (int, optional, default None) – Maximum number of consecutive NaNs to fill. Must be greater than 0.

Returns:

imputed_df – DataFrame MultiIndex with DatetimeIndex (level 0), ticker (level 1) and fields (cols) with imputed values using interpolation method.

Return type:

pd.DataFrame - MultiIndex

fcst(yhat_df: pandas.DataFrame) → pandas.DataFrame

Imputes missing values with forecasts from outlier detection algorithm.

Parameters:

yhat_df (pd.DataFrame - MultiIndex) – Multiindex dataframe with DatetimeIndex (level 0), tickers (level 1) and fields (cols) with forecasted values.

Returns:

imputed_df – DataFrame MultiIndex with DatetimeIndex (level 0), ticker (level 1) and fields (cols) with imputed values using forecasts from outlier detection method.

Return type:

pd.DataFrame - MultiIndex

plot_imputed() → None

Plots filtered time series.