How can we create a Chord Diagram with a dataframe object?

I found this generic code online.

import pandas as pd
import holoviews as hv
from holoviews import opts, dim
from bokeh.sampledata.les_mis import data

hv.extension('bokeh')
hv.output(size=200)

links = pd.DataFrame(data['links'])
print(links.head(3))
hv.Chord(links)

nodes = hv.Dataset(pd.DataFrame(data['nodes']), 'index')
nodes.data.head()

chord = hv.Chord((links, nodes)).select(value=(5, None))
chord.opts(
    opts.Chord(cmap='Category20', edge_cmap='Category20', edge_color=dim('source').str(), 
               labels='name', node_color=dim('index').str()))

That makes this, which looks nice.

[![enter image description here][1]][1]

The sample data is sourced from here.

https://holoviews.org/reference/elements/bokeh/Chord.html

Apparently, 'links' is a pandas dataframe and 'nodes' is a holoviews dataset, and the type is like this.

<class 'pandas.core.frame.DataFrame'>
<class 'holoviews.core.data.Dataset'>

So, my question is this...how can I feed a dataframe into a Chord Diagram? Here is my sample dataframe. Also, I don't know how to incorporate the <class 'holoviews.core.data.Dataset'> into the mix.

I think your data does not match the requirements of this function. Let me explain why I think so?

The Chord-function expects at least on dataset (this can be a pandas DataFrame) with three columns, but all elements are numbers.

   source  target  value
0       1       0      1
1       2       0      8
2       3       0     10

A second dataset is optional. This can take strings in the second columns to add labels for example.

    index     name  group
0      0         a      0
1      1         b      0
2      2         c      0

Basic Example

Your given data looks like this.

    Measure     Country Value
0   Arrivals    Greece  1590
1   Arrivals    Spain   1455
2   Arrivals    France  1345
3   Arrivals    Iceland 1100
4   Arrivals    Iceland 1850
5   Departures  America 2100
6   Departures  Ireland 1000
7   Departures  America 950
8   Departures  Ireland 1200
9   Departures  Japan   1050

You can bring your date in the basic form, if you replace the strings in your DataFrame df by numbers like this:

_df = df.copy()
values = list(_df.Measure.unique())+list(_df.Country.unique())
d = {value: i for i, value in enumerate(values)}

def str2num(s):
    return d[s]

_df.Measure = _df.Measure.apply(str2num)
_df.Country = _df.Country.apply(str2num)

>>> df
    Measure Country Value
0   0   2   1590
1   0   3   1455
2   0   4   1345
3   0   5   1100
4   0   5   1850
5   1   6   2100
6   1   7   1000
7   1   6   950
8   1   7   1200
9   1   8   1050

Now your data matches the basic conditions and you can create a Chord diagram.

chord = hv.Chord(_df).select(value=(5, None))
chord.opts(
    opts.Chord(cmap='Category20', edge_cmap='Category20', 
               edge_color=dim('Measure').str(), 
               labels='Country', 
               node_color=dim('index').str()))

As you can see, all the conection lines only have one of two colors. This is because in the Measure column are only two elements. Therefor I think, this is not what you want.

Modificated Example

Let's Modify your data a tiny bit:

_list = list(df.Country.values)
new_df = pd.DataFrame({'From':_list, 'To':_list[3:]+_list[:3], 'Value':df.Value})
>>> new_df
       From      To Value
0    Greece Iceland  1590
1     Spain Iceland  1455
2    France America  1345
3   Iceland Ireland  1100
4   Iceland America  1850
5   America Ireland  2100
6   Ireland   Japan  1000
7   America  Greece   950
8   Ireland   Spain  1200
9     Japan  France  1050

and:

node = pd.DataFrame()
for i, value in enumerate(df.Measure.unique()):
    _list = list(df[df['Measure']==value].Country.unique())
    node = pd.concat([node, pd.DataFrame({'Name':_list, 'Group':i})], ignore_index=True)
>>> node
    Name    Group
0   Greece  0
1   Spain   0
2   France  0
3   Iceland 0
4   America 1
5   Ireland 1
6   Japan   1

Now we have to replace the strings in new_df again and can call the Chord-function again.

values = list(df.Country.unique())
d = {value: i for i, value in enumerate(values)}

def str2num(s):
    return d[s]

new_df.From = new_df.From.apply(str2num)
new_df.To = new_df.To.apply(str2num)

hv.Chord(new_df)
nodes = hv.Dataset(pd.DataFrame(node), 'index')
chord = hv.Chord((new_df, nodes)).select(value=(5, None))
chord.opts(
    opts.Chord(cmap='Category20', edge_cmap='Category20', edge_color=dim('From').str(), 
               labels='Name', node_color=dim('index').str()
              )
)

The are now two groups added to the HoverTool.