## Plotapi, beautiful by default.

Let plotapi do the heavy lifting – enabling beautiful interactive visualisations with a single line of code (instead of hundreds).

Contents

## Preamble¶

In [1]:
from plotapi import Chord

Chord.set_license("your username", "your license key")

## Introduction¶

Adjusting padding may not sound exciting, but in this case, it can have some interesting effects.

As we can see, we have set our license details in the preamble with Chord.set_license()

## Dataset¶

Chord expects a list of names (list[str]) and a co-occurence matrix (list[list[float]]) as input.

In [2]:
matrix = [
[0, 5, 6, 4, 7, 4],
[5, 0, 5, 4, 6, 5],
[6, 5, 0, 4, 5, 5],
[4, 4, 4, 0, 5, 5],
[7, 6, 5, 5, 0, 4],
[4, 5, 5, 5, 4, 0],
]

names = ["Action", "Adventure", "Comedy", "Drama", "Fantasy", "Thriller"]

It may look more clear if we present this as a table with the columns and indices labelled. This is entirely optional.

In [3]:
import pandas as pd
pd.DataFrame(matrix, columns=names, index=names)
Out[3]:
Action Adventure Comedy Drama Fantasy Thriller
Action 0 5 6 4 7 4
Adventure 5 0 5 4 6 5
Comedy 6 5 0 4 5 5
Drama 4 4 4 0 5 5
Fantasy 7 6 5 5 0 4
Thriller 4 5 5 5 4 0

## Visualisation¶

The padding parameter adjusts the padding between the Chord diagram segments. This value must be a float between or equal to $0.0$ and $1.0$. Values below $0.5$ look better.

Be sure to interact with the visualisation to see what the default settings can do!

In [4]:
Chord(matrix, names, padding=0.5).show()
Plotapi - Chord Diagram

You can do so much more than what's presented in this example, and we'll cover this in later sections. If you want to see the full list of growing features, check out the Plotapi Documentation. and the Plotapi Gallery.