text: Title: Cleaning non-standard unicode characters
  Body: There is a space between "in %" and it does not change to underscore like "in_%" because this is a non-breaking Latin1 (ISO 8859-1) space (xa0).
  
  ```
  import pandas as pd
  table_GDP = pd.read_html('https://en.wikipedia.org/wiki/Economy_of_the_United_States', match='Nominal GDP')
  df  = table_GDP[0]
  
  import janitor
  df=df.clean_names(strip_underscores=True, case_type='lower')
  
  df.columns
  ```
  
  > Index(['year', 'nominal_gdp_in_bil_us_dollar', 'gdp_per_capita_in_us_dollar',
  >        'gdp_growth_real', 'inflation_rate_in_percent',
  >        'unemployment_in_percent', 'budget_balance_in %_of_gdp_[107]',
  >        'government_debt_held_by_public_in %_of_gdp_[108]',
  >        'current_account_balance_in %_of_gdp'],
  >       dtype='object')
  
  We need to add normalize class to correct this and similar issues faced by the data coming from non-standard data sources.
  
  ```
  from unicodedata import normalize
  def clean_normalize_whitespace(x):
      if isinstance(x, str):
          return normalize('NFKC', x).strip()
      else:
          return x
  ```
  
  Reference:
  https://unicode.org/reports/tr15/#Norm_Forms