> For the complete documentation index, see [llms.txt](https://pyhub01.gitbook.io/python-complete-tutorial/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://pyhub01.gitbook.io/python-complete-tutorial/big-data-and-data-visualization/parallel-coordinates.md).

# Parallel coordinates

Parallel coordinates

When you are trying to find the relationship between multiple variables, Parallel coordinates is a good choice.

Parallel coordinates can clearly observe the distribution of data. For example, we can run Parallel coordinates on the iris data set.

![iris dataset with parallel\_coordinates](https://2417198446-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F04q6gn1bES1RZU067ZeW%2Fuploads%2Fgit-blob-bad73861ee656f1a201d06a708aebb16394a5738%2Fimage.png?alt=media)

```python
import seaborn
iris_data = seaborn.load_dataset('iris')
 
from pandas.plotting import parallel_coordinates
parallel_coordinates( iris_data, 'species',
                      color = ('#55627080', '#4ECDC480', '#C7F46480')
                      )

import matplotlib.pyplot as plt
plt.title('iris dataset with parallel_coordinates')
# Mandatory run display interface
plt.show()
```

After running Parallel coordinates on the iris data set, you can clearly observe the relationship between sepal\_length, sepal\_width, petal\_length, petal\_width and species.

For example, the petal\_length of virginica is larger than the other two varieties. Then when you encounter an iris plant in the future, if its petal\_length is larger, then it is likely to be a virginica.

## Statistics

Start time of this page: December 20, 2021

Completion time of this page: December 20, 2021