cryptodatapy.transform.impute
=============================

.. py:module:: cryptodatapy.transform.impute


Classes
-------

.. autoapisummary::

   cryptodatapy.transform.impute.Impute


Module Contents
---------------

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

   Handles missing values.


   .. py:attribute:: filtered_df


   .. py:attribute:: plot
      :value: False



   .. py:attribute:: plot_series
      :value: ('BTC', 'close')



   .. py:attribute:: imputed_df
      :value: None



   .. py:method:: 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.
      :rtype: pd.DataFrame - MultiIndex



   .. py:method:: interpolate(method: str = 'linear', order: Optional[int] = None, axis: int = 0, limit: Optional[int] = None) -> pandas.DataFrame

      Imputes missing values by interpolating using various methods.

      :param method:           ‘polynomial’, ‘krogh’, ‘piecewise_polynomial’, ‘pchip’, ‘akima’, ‘cubicspline’}, default spline
                     Interpolation method to use.
      :type method: str, {'linear', ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘spline’, ‘barycentric’,
      :param order: Order of polynomial or spline.
      :type order: int, optional, default None
      :param axis: Axis to interpolate along.
      :type axis: {{0 or ‘index’, 1 or ‘columns’, None}}, default None
      :param limit: Maximum number of consecutive NaNs to fill. Must be greater than 0.
      :type limit: int, optional, default None

      :returns: **imputed_df** -- DataFrame MultiIndex with DatetimeIndex (level 0), ticker (level 1) and fields (cols) with imputed values
                using interpolation method.
      :rtype: pd.DataFrame - MultiIndex



   .. py:method:: fcst(yhat_df: pandas.DataFrame) -> pandas.DataFrame

      Imputes missing values with forecasts from outlier detection algorithm.

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

      :returns: **imputed_df** -- DataFrame MultiIndex with DatetimeIndex (level 0), ticker (level 1) and fields (cols) with imputed values
                using forecasts from outlier detection method.
      :rtype: pd.DataFrame - MultiIndex



   .. py:method:: plot_imputed() -> None

      Plots filtered time series.