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Machine Learning

Machine learning specific functions.

get_features_targets(df, target_column_names, feature_column_names=None)

Get the features and targets as separate DataFrames/Series.

This method does not mutate the original DataFrame.

The behaviour is as such:

  • target_column_names is mandatory.
  • If feature_column_names is present, then we will respect the column names inside there.
  • If feature_column_names is not passed in, then we will assume that the rest of the columns are feature columns, and return them.

Examples:

>>> import pandas as pd
>>> import janitor.ml
>>> df = pd.DataFrame(
...     {"a": [1, 2, 3], "b": [-2, 0, 4], "c": [1.23, 7.89, 4.56]}
... )
>>> X, Y = df.get_features_targets(target_column_names=["a", "c"])
>>> X
   b
0 -2
1  0
2  4
>>> Y
   a     c
0  1  1.23
1  2  7.89
2  3  4.56

Parameters:

Name Type Description Default
df DataFrame

The pandas DataFrame object.

 required
target_column_names Union[str, Union[List, Tuple], Hashable]

Either a column name or an iterable (list or tuple) of column names that are the target(s) to be predicted.

 required
feature_column_names Optional[Union[str, Iterable[str], Hashable]]

The column name or iterable of column names that are the features (a.k.a. predictors) used to predict the targets.

 None

Returns:

Type Description
Tuple[DataFrame, DataFrame]

(X, Y) the feature matrix (X) and the target matrix (Y). Both are pandas DataFrames.
Source code in janitor/ml.py 
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@pf.register_dataframe_method
@deprecated_alias(
    target_columns="target_column_names",
    feature_columns="feature_column_names",
)
def get_features_targets(
    df: pd.DataFrame,
    target_column_names: Union[str, Union[List, Tuple], Hashable],
    feature_column_names: Optional[Union[str, Iterable[str], Hashable]] = None,
) -> Tuple[pd.DataFrame, pd.DataFrame]:
    """Get the features and targets as separate DataFrames/Series.

    This method does not mutate the original DataFrame.

    The behaviour is as such:

    - `target_column_names` is mandatory.
    - If `feature_column_names` is present, then we will respect the column
        names inside there.
    - If `feature_column_names` is not passed in, then we will assume that
    the rest of the columns are feature columns, and return them.

    Examples:
        >>> import pandas as pd
        >>> import janitor.ml
        >>> df = pd.DataFrame(
        ...     {"a": [1, 2, 3], "b": [-2, 0, 4], "c": [1.23, 7.89, 4.56]}
        ... )
        >>> X, Y = df.get_features_targets(target_column_names=["a", "c"])
        >>> X
           b
        0 -2
        1  0
        2  4
        >>> Y
           a     c
        0  1  1.23
        1  2  7.89
        2  3  4.56

    Args:
        df: The pandas DataFrame object.
        target_column_names: Either a column name or an
            iterable (list or tuple) of column names that are the target(s) to
            be predicted.
        feature_column_names: The column name or
            iterable of column names that are the features (a.k.a. predictors)
            used to predict the targets.

    Returns:
        `(X, Y)` the feature matrix (`X`) and the target matrix (`Y`).
            Both are pandas DataFrames.
    """
    Y = df[target_column_names]

    if feature_column_names:
        X = df[feature_column_names]
    else:
        if isinstance(target_column_names, (list, tuple)):  # noqa: W503
            xcols = [c for c in df.columns if c not in target_column_names]
        else:
            xcols = [c for c in df.columns if target_column_names != c]

        X = df[xcols]
    return X, Y