How do I make a Sankey diagram with Plotly with one layer that goes only one level?

Question

I want to make a Sankey diagram that splits into different levels (obviously), but one of these levels should stop after one, because the further steps do not apply. Much like this:

import pandas as pd

pd.DataFrame({
    'kind': ['not an animal', 'animal', 'animal', 'animal', 'animal'],
    'animal': ['?', 'cat', 'cat', 'dog', 'cat'],
    'sex': ['?', 'female', 'female', 'male', 'male'],
    'status': ['?', 'domesticated', 'domesticated', 'wild', 'domesticated'],
    'count': [8, 10, 11, 14, 6]
})

    kind            animal  sex     status          count
0   not an animal   ?       ?       ?               8
1   animal          cat     female  domesticated    10
2   animal          cat     female  domesticated    11
3   animal          dog     male    wild            14
4   animal          cat     male    domesticated    6

'Not an animal" shouldn't split on further levels since they do not apply. It should look like this:

Answer 1

• re-using structure that I used in this answer plotly sankey graph data formatting
• re-structure data frame in question to:

	source	target	count
0	animal	cat	27
1	animal	dog	14
2	cat	female	21
3	cat	male	6
4	dog	male	14
5	female	domesticated	21
6	male	domesticated	6
7	male	wild	14
8	not an animal	?	8

• then it becomes a case of building arrays of nodes and links

full code

import pandas as pd
import numpy as np
import plotly.graph_objects as go
import io

df2 = pd.read_csv(
    io.StringIO(
        """    kind            animal  sex     status          count
0   not an animal   ?       ?       ?               8
1   animal          cat     female  domesticated    10
2   animal          cat     female  domesticated    11
3   animal          dog     male    wild            14
4   animal          cat     male    domesticated    6"""
    ),
    sep="\s\s+",
    engine="python",
)

df = (
    pd.concat(
        [
            df2.loc[:, [c1, c2] + ["count"]].rename(
                columns={c1: "source", c2: "target"}
            )
            for c1, c2 in zip(df2.columns[:-1], df2.columns[1:-1])
        ]
    )
    .loc[lambda d: ~d["source"].eq("?")]
    .groupby(["source", "target"], as_index=False)
    .sum()
)

nodes = np.unique(df[["source", "target"]], axis=None)
nodes = pd.Series(index=nodes, data=range(len(nodes)))

go.Figure(
    go.Sankey(
        node={"label": nodes.index},
        link={
            "source": nodes.loc[df["source"]],
            "target": nodes.loc[df["target"]],
            "value": df["count"],
        },
    )
)

data frame structuring in stages

col_pairs = [[c1, c2] for c1, c2 in zip(df2.columns[:-1], df2.columns[1:-1])]
# reconstruct as source / target pairs
df = pd.concat(
    [
        df2.loc[:, cols + ["count"]].rename(
            columns={cols[0]: "source", cols[1]: "target"}
        )
        for cols in col_pairs
    ]
)

# filter out where source is unknown
df = df.loc[~df["source"].eq("?")]
# aggregate to limit links in sankey
df = df.groupby(["source", "target"], as_index=False).sum()