Imputing Missing Values

py_entitymatching.impute_table(table, exclude_attrs=None, missing_val='NaN', strategy='mean', axis=0, val_all_nans=0, verbose=True)[source]

Impute table containing missing values.

Parameters:
  • table (DataFrame) – DataFrame which values should be imputed.
  • exclude_attrs (List) – list of attribute names to be excluded from imputing (defaults to None).
  • missing_val (string or int) – The placeholder for the missing values. All occurrences of missing_values will be imputed. For missing values encoded as np.nan, use the string value ‘NaN’ (defaults to ‘NaN’).
  • strategy (string) – String that specifies on how to impute values. Valid strings: ‘mean’, ‘median’, ‘most_frequent’ (defaults to ‘mean’).
  • axis (int) – axis=1 along rows, and axis=0 along columns (defaults to 0).
  • val_all_nans (float) – Value to fill in if all the values in the column are NaN.
Returns:

Imputed DataFrame.
Raises:

AssertionError – If table is not of type pandas DataFrame.