content: import numpy as np
  import pandas as pd
  
  arts = pd.DataFrame()
  
  
  # Clean the dates so you only see numbers.
  def clean_years(value):
      result = value
      chars_to_replace = ["c.", "©", ", CARCC", "no date", "n.d.", " SODRAC", ", CA", " CARCC", ""]
      chars_to_split = ["-", "/"]
      if isinstance(result, str):
          for char in chars_to_split:
              if char in result:
                  result = result.split(char)[1].strip()
          for char in chars_to_replace:
              result = result.replace(char, "")
          if result == "":
              return np.nan
          else:
              return int(result)
      else:
          return result
  
  arts['execution_date'] = arts['execution_date'].apply(clean_years)
  arts.head()
  
  # If a year is lower than 100, then is referred to 1900. For example, 78 is actually 1978, and that needs to be fixed too.
  def clean_year_99(value):
      if value < 100:
          return value + 1900
      else:
          return value
  
  arts["execution_date"] = arts["execution_date"].apply(clean_year_99)
  arts.head()
  