A parallel coordinate plot is used to compare data values which are of completely different types or magnitudes within a single visualization. The values are normalized and then presented as points on a line, or a profile, with one point per data column. This makes parallel coordinate plots similar in appearance to line charts, but the way data is translated into a plot is very different. The visualization is useful also for examining patterns.

The normalized values are expressed as a percentage. The lowest value in a data column is always set to 0%, and the highest value is set to 100%. Values in between are recalculated accordingly. The normalization makes it possible to visualize columns containing values of completely different magnitudes. For example, a column with values between 0 and 1 and a column with values between 0 and 10000 can be visualized at the same time in the parallel coordinate plot.

The values can represent aggregated data or non-aggregated data for the particular data point. An aggregated value could be, for example, a sum, an average or the first value in a data column.

Important axes are the Line by axis and the Color by axis. These axes are used to select the columns whose values you want to represent as lines and you can modify them from the legend.

For example, the parallel coordinate plot above is based on customers' total purchase amounts at different store departments. Each customer is represented by a line, so buying patterns can be examined. To illustrate, see the line below. This customer seems to be a high spender in the first place at the furniture, garden and groceries departments, but also at the electronics department. The toys purchases are relatively low though.

Note: The scale of the various columns is totally separate, so do not compare the height of the line in one column to the height of the line in another column. For example, the actual amount the customer spent at the toys department above can be higher than the amount spent at any other department.

Categorical column values are also possible to visualize. Note the gender column furthest to the right where male and female customers are split into different values on the percentage scale.