build/lib/AutoMxL/Preprocessing/Categorical.py
def fit_transform(self, df, l_var=None, target=None, verbose=False):
        """fit and transform dataset categorical features

        Parameters
        ----------
        df : DataFrame
            input dataset
        l_var : list (Default None)
            names of the variables to encode.
            If None, all the categorical and boolean features
        target : string (Default None)
            name of the target for deep_encoder method
        verbose : boolean (Default False)
            Get logging information

        Returns
        -------
        DataFrame : modified dataset
        """
        df_local = df.copy()
        # fit
        self.fit(df_local, l_var, target, verbose)
        df_local = self.transform(df_local, verbose)

        return df_local
fit and transform dataset categorical features

        Parameters
        ----------
        df : DataFrame
            input dataset
        l_var : list (Default None)
            names of the variables to encode.
            If None, all the categorical and boolean features
        target : string (Default None)
            name of the target for deep_encoder method
        verbose : boolean (Default False)
            Get logging information

        Returns
        -------
        DataFrame : modified dataset
        
fit and transform dataset categorical features
Parameters

df : DataFrame
input dataset
l_var : list (Default None)
names of the variables to encode.
If None, all the categorical and boolean features
target : string (Default None)
name of the target for deep_encoder method
verbose : boolean (Default False)
Get logging information

Returns

DataFrame : modified dataset
fit and transform dataset categorical features Parameters df : DataFrame input dataset l_var : list ( Default None ) names of the variables to encode . If None all the categorical and boolean features target : string ( Default None ) name of the target for deep_encoder method verbose : boolean ( Default False ) Get logging information Returns DataFrame : modified dataset
def fit_transform(self, df, l_var=None, target=None, verbose=False):
        df_local = df.copy()
        self.fit(df_local, l_var, target, verbose)
        df_local = self.transform(df_local, verbose)
        return df_local
def fit_transform ( self , df , l_var = None , target = None , verbose = False ) : df_local = df . copy ( ) self . fit ( df_local , l_var , target , verbose ) df_local = self . transform ( df_local , verbose ) return df_local
fit and transform dataset categorical features
Parameters
fit and transform dataset categorical features Parameters
"""fit and transform dataset categorical features

        Parameters
        ----------
        df : DataFrame
            input dataset
        l_var : list (Default None)
            names of the variables to encode.
            If None, all the categorical and boolean features
        target : string (Default None)
            name of the target for deep_encoder method
        verbose : boolean (Default False)
            Get logging information

        Returns
        -------
        DataFrame : modified dataset
        """ # fit
self
df
l_var
target
verbose