Hi Mike, it could be refered to ‘whole food’ or whole form
- “coffee beans wf” would refer to whole, unprocessed coffee beans.
- “rice-grains-wf” would refer to whole rice grains, retaining the bran and germ.
3 Likes
Thank you @adamschroeder for explaining graph_objs, makes sense. And thanks to @Avacsiglo21 and @marieanne for chiming in on the wf question.
The way all of you quickly answered these curiosities is a textbook example how friendly and supportive people are in this forum.
1 Like
Thank you adamschroeder!
@Mike_Purtell I just saw your question, after I accidentally created my chart with import ploty.gaph_objs as go, I was only able to upload it to PyCafe with this version.
3 Likes
@Ester I love how you created two X axes to your graph, with a bar trace and a scatter trace: pretty advanced stuff 
1 Like
I chose the colors from colorhunt.co in the food category and the yellow marker symbolizes money. 
1 Like
For week 13 I created a single-plot dashboard of the scatter polar example provide with this week’s dataset. Steps taken were:
• Converted the pandas code to polars
• Set all column names to upper case and reworded some of the column labels for brevity
• Changed all categories to title case, removed all dashes
• Used ‘simple_white’ template
• Added 2 pull-down driven callbacks, 1 to select categories and 1 to select nutrient columns
• Removed the table, only visualization is the scatter polar
• I tried to post this on PyCafe, my account is not properly setup. I will work on that.
Here is the code:
from dash import Dash, dcc, html, Input, Output
import dash_bootstrap_components as dbc
import polars as pl
import plotly.graph_objects as go
# Download CSV sheet at:
# https://drive.google.com/file/d/1EoFTpSJOIYmVzemoMLj7vMTqeM1zMy0o/view?usp=sharing
citation = (
'Source : Prevalence of processed foods in major US grocery stores ' +
'Authors: Babak Ravandi, Gordana Ispirova, Michael Sebek, Peter Mehler, ' +
'Albert-László Barabási and Giulia Menichetti ' +
'journal: Nature Food, 2025: dio={10.1038/s43016-024-01095-7} ' +
'url = {https://www.nature.com/articles/s43016-024-01095-7}'
)
style_space = {
'border': 'none',
'height': '5px',
'background': 'linear-gradient(to right, #007bff, #ff7b00)',
'margin': '10px 0'
}
df=pl.read_csv('GroceryDB_foods.csv')
df_filtered = (
df
.rename({col: col.upper() for col in df.columns})
.with_columns(
CATEGORY=pl.col('HARMONIZED SINGLE CATEGORY')
.str.to_titlecase()
.str.replace_all('-', ' ')
.str.replace('Milk Milk Substitute', 'Milk or Substitute'),
FAT_TOTAL=pl.col('TOTAL FAT'),
CARBS=pl.col('CARBOHYDRATE'),
SUGAR_TOTAL=pl.col('SUGARS, TOTAL'),
DIETARY_FIBER=pl.col('FIBER, TOTAL DIETARY'),
SAT_FATTY_ACIDS_TOT=pl.col('FATTY ACIDS, TOTAL SATURATED'),
VITAMIN_A=pl.col('TOTAL VITAMIN A'),
VITAMIN_C=pl.col('VITAMIN C'),
)
.select(
'CATEGORY', 'CALCIUM', 'CARBS', 'CHOLESTEROL', 'DIETARY_FIBER',
'FAT_TOTAL', 'IRON', 'PROTEIN', 'SAT_FATTY_ACIDS_TOT', 'SODIUM',
'SUGAR_TOTAL', 'VITAMIN_A', 'VITAMIN_C'
)
.group_by('CATEGORY', maintain_order=True)
.mean()
)
# these lists will be used as callback choices
category_list = sorted(df_filtered['CATEGORY'].unique().to_list())
category_defaults = [
'Drink Shakes Other', 'Drink Juice', 'Drink Tea', 'Drink Soft Energy Mixes']
nutrition_list = sorted([c for c in df_filtered.columns if c!= 'CATEGORY'])
nutrient_defaults = ['DIETARY_FIBER', 'SAT_FATTY_ACIDS_TOT']
#----- DEFINE FUNCTIONS---------------------------------------------------------
def make_fig(df_filtered, selected_cats, selected_nutrients):
df_selected = (
df_filtered
.filter(pl.col('CATEGORY').is_in(selected_cats))
.select(['CATEGORY'] + selected_nutrients)
)
fig = go.Figure()
for nutrient in selected_nutrients:
fig.add_trace(go.Scatterpolar(
r=df_selected[nutrient],
theta=df_selected['CATEGORY'],
fill='toself',
name=nutrient,
))
fig.update_layout(
template='simple_white',
polar=dict(
radialaxis=dict(
visible=False,
showticklabels = False,
)),
showlegend=True,
legend=dict(
title=dict(
text="Average Ingredient Count"
)
),
margin=dict(l=200, r=20, t=0, b=20),
height=900, width=900,
)
return fig
app = Dash(external_stylesheets=[dbc.themes.SANDSTONE])
app.layout = dbc.Container([
html.Hr(style=style_space),
html.H2(
'Scatterpolar Nutrition',
style={
'text-align': 'left'
}
),
html.Hr(style=style_space),
html.Div([
html.P(
f'Citation: {citation}',
),
html.Hr(style=style_space)
]),
html.Hr(style=style_space),
html.Div([
html.P(
f'Use Left pulldown to select category, right pull down to select nutrients',
),
html.Hr(style=style_space)
]),
dbc.Row([
dbc.Col(
[dcc.Dropdown(
category_list,
category_defaults,
id='cat_dropdown',
multi=True,
),
]),
dbc.Col([dcc.Dropdown(
nutrition_list,
nutrient_defaults,
id='nutrient_dropdown',
multi=True,
),]),
]),
html.Div(id='dd-output-container2'),
dbc.Row(
[
dbc.Col(
dcc.Graph(
id='scatterpolar_fig',
style={
'width': '75vh',
'height': '75vh',
},
),
),
]
)
])
@app.callback(
Output('scatterpolar_fig', 'figure'),
Input('cat_dropdown', 'value'),
Input('nutrient_dropdown', 'value'),
)
def update_dashboard(selected_cats, selected_nutrients):
# call back returns string if 1 item is selected, list if multiple items
# are selected. The make_fig requires lists, next 4 lines take care of this
if not type(selected_cats) is list:
selected_cats = [selected_cats]
if not type(selected_nutrients) is list:
selected_nutrients = [selected_nutrients]
scatter_polar = make_fig(df_filtered, selected_cats, selected_nutrients)
return scatter_polar
#----- RUN THE APP -------------------------------------------------------------
if __name__ == "__main__":
app.run(debug=True)
4 Likes
Hi Alex, this is brilliant work. I have downloaded your code, and it ran seamlessly on my system. If my dashboards can ever be as attractive and useful as what you have done, I would be so happy. Based on your work I am looking online at the differences between cosine similarity and Pearson regression which I use from time to time. Fascinating stuff. Have a great weekend.
2 Likes
Wow Mike, I’m grateful for your words, they mean a lot. I get help from AI and many hours of trial and error for the dashboards, and obviously the community feedback is the most valuable thing. We’re on the same page, I love looking for relationships/patterns. I find the scikit-learn library spectacular, and these work sections allow me to find ways to implement those fascinating tools. Of course, sometimes it’s difficult to find something, but I make the attempt. Happy weekend to you too.
1 Like