watex.utils.to_numeric_dtypes#

watex.utils.to_numeric_dtypes(arr, *, columns=None, return_feature_types=False, missing_values=nan, pop_cat_features=False, sanitize_columns=False, regex=None, verbose=False)[source]#

Convert array to dataframe and coerce arguments to appropriate dtypes.

Parameters:

  • arr (Ndarray or Dataframe, shape (M=samples, N=features)) – Array of dataframe to create

  • columns (list of str, optional) – Usefull to create a dataframe when array is given. Be aware to fit the number of array columns (shape[1])

  • return_feature_types (bool, default=False,) – return the list of numerical and categorial features

  • missing_values (float:) – Replace the missing or empty string if exist in the dataframe.

  • pop_cat_features (bool, default=False,) – remove removes the categorial features from the DataFrame.

  • sanitize_columns (bool, default=False,) –

    remove undesirable character in the data columns using the default argument of regex parameters.

    New in version 0.1.9.

  • regex (re object,) –

    Regular expresion object used to polish the data columns.

    the default is:

    >>> import re
>>> re.compile (r'[_#&.)(*@!_,;\s-]\s*', flags=re.IGNORECASE)

    New in version 0.1.9.

  • verbose (bool, default=False,) – outputs a message by listing the categorial items dropped from the dataframe if exists.

Returns:

df or (df, nf, cf) – also return nf and cf if return_feature_types is set to``True``.

Return type:

Dataframe of values casted to numeric types

Examples

>>> from watex.datasets.dload import load_bagoue
>>> from watex.utils.funcutils import to_numeric_dtypes
>>> X, y = load_bagoue (as_frame =True )
>>> X0 =X[['shape', 'power', 'magnitude']]
>>> X0.dtypes
... shape        object
    power        object
    magnitude    object
    dtype: object
>>> df = to_numeric_dtypes(X0)
>>> df.dtypes
... shape         object
    power        float64
    magnitude    float64
    dtype: object