Categorical data

I broadly classify data into dates, continuous values, and categorical values. In this section, we will explore quantifying and visualizing categorical data.

How to do it…

1. Pick out the columns with data types that are object:

   >>> fueleco.select_dtypes(object).columns
   Index(['drive', 'eng_dscr', 'fuelType', 'fuelType1', 'make', 'model',
          'mpgData', 'trany', 'VClass', 'guzzler', 'trans_dscr', 'tCharger',
          'sCharger', 'atvType', 'fuelType2', 'rangeA', 'evMotor', 'mfrCode',
          'c240Dscr', 'c240bDscr', 'createdOn', 'modifiedOn', 'startStop'],
         dtype='object')
   

2. Use .nunique to determine the cardinality:

   >>> fueleco.drive.nunique()
   7
   

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