old_contents: """
  Created on 29 July 2012
  @author: Lisa Simpson
  """
  
  class DatabaseManager(object):
      """
      Create and manage a new sqlite database.
      """
  
  """
  Created: 01/20/2015
  Author: Sebastian Raschka
  """
  
  def minmax_scaling(df, columns, min_val=0, max_val=1):
      """
      Min max scaling for pandas DataFrames
  
      Parameters
      ----------
      df : pandas DataFrame object.
  
      columns : array-like, shape = [n_columns]
        Array-like with pandas DataFrame column names, e.g., ['col1', 'col2', ...]
  
      min_val : `int` or `float`, optional (default=`0`)
        minimum value after rescaling.
  
      min_val : `int` or `float`, optional (default=`1`)
        maximum value after rescaling.
  
      Returns
      ----------
  
      df_new: pandas DataFrame object.
        Copy of the DataFrame with rescaled columns.
  
      """
      df_new = df.copy()
      for c in columns:
          df_new[c] = (df_new[c] - df_new[c].min(axis=0)) / (df_new[c].max(axis=0) - df_new[c].min(axis=0))
  
          if not min_val == 0 and not max_val==1:
              df_new[c] = df_new[c] * (max_val - min_val) + min_val
  
      return df_new
  
  
  
  def standardizing(df, columns):
      """
      Standardizing columns in pandas DataFrames.
  
      Parameters
      ----------
      df : pandas DataFrame object.
  
      columns : array-like, shape = [n_columns]
        Array-like with pandas DataFrame column names, e.g., ['col1', 'col2', ...]
  
      Returns
      ----------
  
      df_new: pandas DataFrame object.
        Copy of the DataFrame with standardized columns.
  
      """
      df_new = df.copy()
      for c in columns:
          df_new[c] = (df_new[c] - df_new[c].mean(axis=0)) / (df_new[c].std(axis=0))
  
      return df_new
new_contents: """
  Created: 01/20/2015
  Author: Sebastian Raschka
  """
  
  def minmax_scaling(df, columns, min_val=0, max_val=1):
      """
      Min max scaling for pandas DataFrames
  
      Parameters
      ----------
      df : pandas DataFrame object.
  
      columns : array-like, shape = [n_columns]
        Array-like with pandas DataFrame column names, e.g., ['col1', 'col2', ...]
  
      min_val : `int` or `float`, optional (default=`0`)
        minimum value after rescaling.
  
      min_val : `int` or `float`, optional (default=`1`)
        maximum value after rescaling.
  
      Returns
      ----------
  
      df_new: pandas DataFrame object.
        Copy of the DataFrame with rescaled columns.
  
      """
      df_new = df.copy()
      for c in columns:
          df_new[c] = (df_new[c] - df_new[c].min(axis=0)) / (df_new[c].max(axis=0) - df_new[c].min(axis=0))
  
          if not min_val == 0 and not max_val==1:
              df_new[c] = df_new[c] * (max_val - min_val) + min_val
  
      return df_new
  
  
  
  def standardizing(df, columns):
      """
      Standardizing columns in pandas DataFrames.
  
      Parameters
      ----------
      df : pandas DataFrame object.
  
      columns : array-like, shape = [n_columns]
        Array-like with pandas DataFrame column names, e.g., ['col1', 'col2', ...]
  
      Returns
      ----------
  
      df_new: pandas DataFrame object.
        Copy of the DataFrame with standardized columns.
  
      """
      df_new = df.copy()
      for c in columns:
          df_new[c] = (df_new[c] - df_new[c].mean(axis=0)) / (df_new[c].std(axis=0))
  
      return df_new
  
subject: Update scaling.py