text: from pandas import Series
  import pandas as pd
  
  # Lets create a Series (array of data and data labels, its index)
  obj = Series([3, 6, 9, 12])
  
  # Lets show the values
  obj.values
  
  # Lets show the index
  obj.index
  
  # Now lets create a Series with an index
  # WW2 casualties
  ww2_cas = Series([8700000, 4300000, 3000000, 2100000, 400000], index=[
                   'USSR', 'Germany', 'China', 'Japan', 'USA'])
  
  # Now we can use index values to select Series values
  ww2_cas['USA']
  
  # Can also check with array operations
  
  # Check who had casualties greater than 4 million
  ww2_cas[ww2_cas > 4000000]
  
  # Can treat Series as ordered dictionary
  
  # Check if USSR is in Series
  'USSR' in ww2_cas
  
  # Can convert Series into Python dictionary
  ww2_dict = ww2_cas.to_dict()
  
  # Can convert back into a Series
  WW2_Series = Series(ww2_dict)
  
  # Passing a dictionary the index will have the dict keys in order
  countries = ['China', 'Germany', 'Japan', 'USA', 'USSR', 'Argentina']
  
  # Lets redefine a Series
  obj2 = Series(ww2_dict, index=countries)
  
  # We can use isnull and notnull to find missing data
  pd.isnull(obj2)
  # obj2.isnull()
  
  # Same for the opposite
  pd.notnull(obj2)
  # obj2.notnull()
  
  # Now we can add and pandas automatically aligns data by index
  WW2_Series + obj2
  
  # We can give Series names
  obj2.name = "World War 2 Casualties"
  
  # We can also name index
  obj2.index.name = 'Countries'
  