plotly python Sankey Plot

Question

I am trying to create a Sankey Diagram in python. The idea is to show the change of size of each Topic month on month.This is my pandas sample DataFrame. There are more Topic and also each Topic has more month and year. That makes the dataframe tall.

 df
    year    month   Topic   Document_Size   
  0 2022    1        0.0            63  
  1 2022    1        1.0            120 
  2 2022    1        2.0            106 
  3 2022    2        0.0            70  
  4 2022    2        1.0            42  
  5 2022    2        2.0            45  
  6 2022    3        0.0            78  
  7 2022    3        1.0            14  
  8 2022    3        2.0            84

I have prepared the following from plotly demo. I am missing the values that will go to the variables node_label, source_node, target_node so that the following code works. I am not getting the correct plot output

 node_label = ?
 source_node = ?
 target_node = ?
 values = df['Document_Size']

from webcolors import hex_to_rgb
%matplotlib inline

from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import plotly.graph_objects as go # Import the graphical object

fig = go.Figure( 
data=[go.Sankey( # The plot we are interest
    # This part is for the node information
    node = dict( 
        label = node_label
    ),
    # This part is for the link information
    link = dict(
        source = source_node,
        target = target_node,
        value = values
    ))])

 # With this save the plots 
 plot(fig,
 image_filename='sankey_plot_1', 
 image='png', 
 image_width=5000, 
 image_height=3000)

 # And shows the plot
 fig.show()

Answer 1

• reusing this answer sankey from dataframe
• restructure dataframe so that it has structure used in answer

Document_Size	source	target
63	2022 01 0.0	2022 02 0.0
120	2022 01 1.0	2022 02 1.0
106	2022 01 2.0	2022 02 2.0
70	2022 02 0.0	2022 03 0.0
42	2022 02 1.0	2022 03 1.0
45	2022 02 2.0	2022 03 2.0

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

df = pd.read_csv(
    io.StringIO(
        """    year    month   Topic   Document_Size   
  0 2022    1        0.0            63  
  1 2022    1        1.0            120 
  2 2022    1        2.0            106 
  3 2022    2        0.0            70  
  4 2022    2        1.0            42  
  5 2022    2        2.0            45  
  6 2022    3        0.0            78  
  7 2022    3        1.0            14  
  8 2022    3        2.0            84"""
    ),
    sep="\s+",
)


# data for year and month
df["date"] = pd.to_datetime(df.assign(day=1).loc[:, ["year", "month", "day"]])

# index dataframe ready for constructing dataframe of source and target
df = df.drop(columns=["year", "month"]).set_index(["date", "Topic"])

dates = df.index.get_level_values(0).unique()

# for each pair of current date and next date, construct segment of source /' target data
df_sankey = pd.concat(
    [df.loc[s].assign(source=s, target=t) for s, t in zip(dates, dates[1:])]
)

df_sankey["source"] = df_sankey["source"].dt.strftime(
    "%Y %m "
) + df_sankey.index.astype(str)
df_sankey["target"] = df_sankey["target"].dt.strftime(
    "%Y %m "
) + df_sankey.index.astype(str)

nodes = np.unique(df_sankey[["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_sankey["source"]],
            "target": nodes.loc[df_sankey["target"]],
            "value": df_sankey["Document_Size"],
        },
    )
)

output