Tabular Data Imputation Methods

This page provides an overview of the data imputation methods available in the AI-DAPT Data Cleaning Engine for tabular data. Each method replaces missing values in a DataFrame while preserving its original structure.

All methods operate on numerical columns.

backward_forward_fill

Performs backward fill followed by forward fill imputation.

Parameters

  • df (pd.DataFrame) Input DataFrame containing numerical values.

mean_fill

Imputes missing values using the mean of each column.

Parameters

  • df (pd.DataFrame) Input DataFrame containing numerical values.

median_fill

Imputes missing values using the median of each column.

Parameters

  • df (pd.DataFrame) Input DataFrame containing numerical values.

mode_fill

Imputes missing values using the most frequent value (mode) of each column.

Parameters

  • df (pd.DataFrame) Input DataFrame containing numerical values.

linear_interp_fill

Fills missing values using linear interpolation.

Parameters

  • df (pd.DataFrame) Input DataFrame containing numerical values.

polynomial_interp_fill

Fills missing values using polynomial interpolation.

Parameters

  • df (pd.DataFrame) Input DataFrame containing numerical values.

spline_interp_fill

Fills missing values using spline interpolation.

Parameters

  • df (pd.DataFrame) Input DataFrame containing numerical values.

nearest_fill

Fills missing values using nearest-neighbor interpolation.

Parameters

  • df (pd.DataFrame) Input DataFrame containing numerical values.

pad_fill

Fills missing values using forward filling (padding).

Parameters

  • df (pd.DataFrame) Input DataFrame containing numerical values.