build/lib/AutoMxL/__main__.py
def explore(self, verbose=False):
        """data exploration and features type identification

        Note :  if you disagree with automated identification, you can directly modify d_features attribute

        Create self.d_features : dict {x : list of variables names}
            - date: date features
            - identifier: identifier features
            - verbatim: verbatim features
            - boolean: boolean features
            - categorical: categorical features
            - numerical: numerical features
            - NA: features which contains NA values
            - low_variance: list of the features with low variance and unique values

        Parameters
        ----------
        verbose : boolean (Default False)
            Get logging information
        """
        if verbose:
            start_time = time()
            print_title1('Explore')

        df_local = self.copy()
        if self.target is not None:
            df_local = df_local.drop(self.target, axis=1)

        # call std_audit_dataset function
        self.d_features = explore(
            df_local, verbose=verbose)

        self.step = 'explore'

        # created attributes display
        if verbose:
            color_print("\nCreated attributes :  d_features (dict) ")
            print("Keys :")
            print("  -> date")
            print("  -> identifier")
            print("  -> verbatim")
            print("  -> boolean")
            print("  -> categorical")
            print("  -> numerical")
            print("  -> date")
            print("  -> NA")
            print("  -> low_variance")
            print('\n\t\t>>>', 'explore execution time:', round(time() - start_time, 4), 'secs. <<<')
data exploration and features type identification

        Note :  if you disagree with automated identification, you can directly modify d_features attribute

        Create self.d_features : dict {x : list of variables names}
            - date: date features
            - identifier: identifier features
            - verbatim: verbatim features
            - boolean: boolean features
            - categorical: categorical features
            - numerical: numerical features
            - NA: features which contains NA values
            - low_variance: list of the features with low variance and unique values

        Parameters
        ----------
        verbose : boolean (Default False)
            Get logging information
        
data exploration and features type identification
Note :  if you disagree with automated identification, you can directly modify d_features attribute



Parameters

verbose : boolean (Default False)
Get logging information
data exploration and features type identification Note : if you disagree with automated identification you can directly modify d_features attribute Parameters verbose : boolean ( Default False ) Get logging information
def explore(self, verbose=False):
        if verbose:
            start_time = time()
            print_title1('Explore')
        df_local = self.copy()
        if self.target is not None:
            df_local = df_local.drop(self.target, axis=1)
        self.d_features = explore(
            df_local, verbose=verbose)
        self.step = 'explore'
        if verbose:
            color_print("\nCreated attributes :  d_features (dict) ")
            print("Keys :")
            print("  -> date")
            print("  -> identifier")
            print("  -> verbatim")
            print("  -> boolean")
            print("  -> categorical")
            print("  -> numerical")
            print("  -> date")
            print("  -> NA")
            print("  -> low_variance")
            print('\n\t\t>>>', 'explore execution time:', round(time() - start_time, 4), 'secs. <<<')
def explore ( self , verbose = False ) : if verbose : start_time = time ( ) print_title1 ( 'Explore' ) df_local = self . copy ( ) if self . target is not None : df_local = df_local . drop ( self . target , axis = 1 ) self . d_features = explore ( df_local , verbose = verbose ) self . step = 'explore' if verbose : color_print ( "\nCreated attributes :  d_features (dict) " ) print ( "Keys :" ) print ( "  -> date" ) print ( "  -> identifier" ) print ( "  -> verbatim" ) print ( "  -> boolean" ) print ( "  -> categorical" ) print ( "  -> numerical" ) print ( "  -> date" ) print ( "  -> NA" ) print ( "  -> low_variance" ) print ( '\n\t\t>>>' , 'explore execution time:' , round ( time ( ) - start_time , 4 ) , 'secs. <<<' )
data exploration and features type identification
Note :  if you disagree with automated identification, you can directly modify d_features attribute
data exploration and features type identification Note : if you disagree with automated identification you can directly modify d_features attribute
"""data exploration and features type identification

        Note :  if you disagree with automated identification, you can directly modify d_features attribute

        Create self.d_features : dict {x : list of variables names}
            - date: date features
            - identifier: identifier features
            - verbatim: verbatim features
            - boolean: boolean features
            - categorical: categorical features
            - numerical: numerical features
            - NA: features which contains NA values
            - low_variance: list of the features with low variance and unique values

        Parameters
        ----------
        verbose : boolean (Default False)
            Get logging information
        """ # call std_audit_dataset function # created attributes display
self
verbose