text: Title: Add the ability to test whether data is changing/jumping beyond a tolerance 
  Body: # Brief Description
  
  Following up on #703, this issue seeks to introduce the ability to test whether in each column in a data frame the change between every two rows is within a user-defined range of values.
  
  I would like to propose..
  # Example API
  ```python
  import pandas as pd
  import numpy as np
  
  # Create a random data frame
  df = pd.DataFrame(data=[["2015-01-01 00:00:00", -0.76, 2, 2, 1.2],
                          ["2015-01-01 01:00:00", -0.73, 2, 4, 1.1],
                          ["2015-01-01 02:00:00", -0.71, 2, 4, 1.1],
                          ["2015-01-01 03:00:00", -0.68, 2, 32, 1.1],
                          ["2015-01-01 04:00:00", -0.65, 2, 2, 1.0],
                          ["2015-01-01 05:00:00", -0.76, 2, 2, 1.2],
                          ["2015-01-01 06:00:00", -0.73, 2, 4, 1.1],
                          ["2015-01-01 07:00:00", -0.71, 2, 4, 1.1],
                          ["2015-01-01 08:00:00", -0.68, 2, 32, 1.1],
                          ["2015-01-01 09:00:00", -0.65, 2, 2, 1.0],
                          ["2015-01-01 10:00:00", -0.76, 2, 2, 1.2],
                          ["2015-01-01 11:00:00", -0.73, 2, 4, 1.1],
                          ["2015-01-01 12:00:00", -0.71, 2, 4, 1.1],
                          ["2015-01-01 13:00:00", -0.68, 2, 32, 1.1],
                          ["2015-01-01 14:00:00", -0.65, 2, 2, 1.0],
                          ["2015-01-01 15:00:00", -0.76, 2, 2, 1.2],
                          ["2015-01-01 16:00:00", -0.73, 2, 4, 1.1],
                          ["2015-01-01 17:00:00", -0.71, 2, 4, 1.1],
                          ["2015-01-01 18:00:00", -0.68, 2, 32, 1.1],
                          ["2015-01-01 19:00:00", -0.65, 2, 2, 1.0],
                          ["2015-01-01 20:00:00", -0.76, 2, 2, 1.2],
                          ["2015-01-01 21:00:00", -0.73, 2, 4, 1.1],
                          ["2015-01-01 22:00:00", -0.71, 2, 4, 1.1],
                          ["2015-01-01 23:00:00", -0.68, 2, 32, 1.1],
                          ["2015-01-02 00:00:00", -0.65, 2, 2, 1.0]],
                    columns=['DateTime', 'column1', 'column2', 'column3', 'column4'])
  
  # Set the index
  df["DateTime"] = pd.to_datetime(df["DateTime"])
  df.set_index("DateTime", inplace=True)
  
  # Flag functionality 
  df1 = df.copy(deep=True)
  
  def flag_jumps(
          df_input: pd.DataFrame,
          tolerance: float = 10**-2
  ) -> pd.DataFrame:
      """
      Returns a data frame the size of the input
  
      Flags rows in each column where tolerance is violated
      :param df_input: input data frame to test for jumps
      :param tolerance: acceptable value of tolerance
      :return: data frame with flags indicating whether tolerance has been violated or not
      """
      # Calculate the difference between every two rows
      df2 = df_input.diff()
  
      # Check for tolerance violation
      df3 = df2.gt(tolerance)
  
      return df3
  
  jump_flags = flag_jumps(df1, tolerance=2)
  print(jump_flags)
  ```
  