jupytext_version: 1.1.1

The example below visualizes a breakdown of sales (corresponding to sector width) and call success rate (corresponding to sector color) by region, county and salesperson level. For example, when exploring the data you can see that although the East region is behaving poorly, the Tyler county is still above average -- however, its performance is reduced by the poor success rate of salesperson GT.

In the right subplot which has a `maxdepth` of two levels, click on a sector to see its breakdown to lower levels.

import plotly.graph_objects as go

from plotly.subplots import make_subplots

import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/sales_success.csv')

print(df.head())

levels = ['salesperson', 'county', 'region'] # levels used for the hierarchical chart

color_columns = ['sales', 'calls']

value_column = 'sales'

def build_hierarchical_dataframe(df, levels, value_column, color_columns=None):

"""

df_all_trees = pd.DataFrame(columns=['id', 'parent', 'value', 'color'])

for i, level in enumerate(levels):

df_tree = pd.DataFrame(columns=['id', 'parent', 'value', 'color'])

dfg = df.groupby(levels[i:]).sum(numerical_only=True)

dfg = dfg.reset_index()

df_tree['id'] = dfg[level].copy()

if i < len(levels) - 1:

df_tree['parent'] = dfg[levels[i+1]].copy()

else:

df_tree['parent'] = 'total'

df_tree['value'] = dfg[value_column]

df_tree['color'] = dfg[color_columns[0]] / dfg[color_columns[1]]

df_all_trees = df_all_trees.append(df_tree, ignore_index=True)

total = pd.Series(dict(id='total', parent='',

value=df[value_column].sum(),

color=df[color_columns[0]].sum() / df[color_columns[1]].sum()))

df_all_trees = df_all_trees.append(total, ignore_index=True)

return df_all_trees

df_all_trees = build_hierarchical_dataframe(df, levels, value_column, color_columns)

average_score = df['sales'].sum() / df['calls'].sum()

fig = make_subplots(1, 2, specs=[[{"type": "domain"}, {"type": "domain"}]],)

fig.add_trace(go.Treemap(

labels=df_all_trees['id'],

parents=df_all_trees['parent'],

values=df_all_trees['value'],

branchvalues='total',

marker=dict(

colors=df_all_trees['color'],

colorscale='RdBu',

cmid=average_score),

hovertemplate='<b>%{label} </b> <br> Sales: %{value}<br> Success rate: %{color:.2f}',

name=''

), 1, 1)

fig.add_trace(go.Treemap(

labels=df_all_trees['id'],

parents=df_all_trees['parent'],

values=df_all_trees['value'],

branchvalues='total',

marker=dict(

colors=df_all_trees['color'],

colorscale='RdBu',

cmid=average_score),

hovertemplate='<b>%{label} </b> <br> Sales: %{value}<br> Success rate: %{color:.2f}',

maxdepth=2

), 1, 2)

fig.update_layout(margin=dict(t=10, b=10, r=10, l=10))

fig.show()