text: Title: Discussion: arguments `old_min` and `old_max` should be removed from `min_max_scale`
  Body: ```python
  >>> import pandas as pd
  >>> import janitor
  
  # Use one column dataframe to avoid scaling the entire data or the column data problem
  
  >>> df = pd.Series([0, 1, 2]).to_frame()
  
  # use the minimum and maximum value of data
  
  >>> df.min_max_scale()
       0
  0  0.0
  1  0.5
  2  1.0
  
  # Overwrite the value of data. The result seems wired for the user, but it's ok for the formula view.
  # Question 1: 0 should be scaled or not? 0 is out range of [old_min 1, old_max 2].
  # Question 2: I already define the new_min (0) and new_max of value. Why do there have -1?
  # Question 3: The API differs from sklearn.preprocessing.MinMaxScaler
  
  # Min-Max normalization formula
  # X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
  # X_scaled = X_std * (max - min) + min
  
  >>> df.min_max_scale(old_min=1, old_max=2)
       0
  0 -1.0
  1  0.0
  2  1.0
  ```
  
  In the end, it's hard to trace the `min_max_scale` [committing history](https://github.com/pyjanitor-devs/pyjanitor/commits/dev/janitor/functions/min_max_scale.py) to know the reason why added these options.