1. Read in the sensors dataset and identify the variables:

>>> sensors = pd.read_csv('data/sensors.csv')
>>> sensors

2. The only variable placed correctly in a vertical column is Group. The Property column appears to have three unique variables, Pressure, Temperature, and Flow. The rest of the columns 2012 to 2016 are themselves a single variable, which we can sensibly name Year. It isn't possible to restructure this kind of messy data with a single DataFrame method. Let's begin with the melt method to pivot the years into their own column:

>>> sensors.melt(id_vars=['Group', 'Property'], var_name='Year') 
           .head(6)

3. This takes care of one of our issues. Let's use the pivot_table method to pivot the Property column into new column names:

>>> sensors.melt(id_vars=['Group', 'Property'], var_name='Year') 
           .pivot_table(index=['Group', 'Year'],
                        columns='Property', values='value') 
           .reset_index() 
           .rename_axis(None, axis='columns')