Blog GIS & Territories

In our previous articles, we looked at how to create graphs in QGIS with Data Plotly. Today, we’re going one step further: automate the generation of a graph by range (or any other entity), exporting the results as PNG images, ready to insert into a report.

And good news: no need to code a plugin, we’re going to use the QGIS Python console, with pandas and plotly.

Objective

Automatically create :

• One graph per range, showing the amount of waste by type
• One PNG image file per graph
• Without repeated clicks or manual manipulation

Example used

We have a layer of beach waste records, with the following fields:

1. beach: beach name
2. waste_type: type (plastic, metal…)
3. quantity: number of objects detected

The Python script in QGIS

Here is a script that you can copy into the QGIS Python console:

import os
import pandas as pd
import plotly.express as px

# Dossier de sortie
output_folder = "C:/temp/export_graphiques"
os.makedirs(output_folder, exist_ok=True)

# Couche active
layer = iface.activeLayer()

# Extraction des données
data = []
for f in layer.getFeatures():
    data.append({
        "plage": f["plage"],
        "type_dechet": f["type_dechet"],
        "quantite": f["quantite"]
    })

# Convertir en DataFrame
df = pd.DataFrame(data)

# Générer un graphique par plage
for plage, group in df.groupby("plage"):
    fig = px.bar(
        group,
        x="type_dechet",
        y="quantite",
        title=f"Déchets par type – {plage}",
        labels={"quantite": "Quantité", "type_dechet": "Type de déchet"},
        color="type_dechet"
    )
    fig.update_layout(showlegend=False)
    fig.write_image(os.path.join(output_folder, f"graph_{plage}.png"))

print("✅ Graphiques exportés dans :", output_folder)

Prerequisites

For the script to work, check that :

• Your QGIS Python environment contains pandas , plotly and kaleido
• If not, you can install it via the command prompt:

  • C:\OSGeo4W\OSGeo4W.bat > python3 -m pip install pandas plotly kaleido

Customize

You can modify :

• The grouping field (“range”) to plot by commune, date, etc.
• Chart type: use px.pie, px.line, px.histogram…

Expected results

In your file, you’ll obtain :

graph_Anse_Mourouk.png
graph_Pointe_Coton.png
graph_Graviers.png
...

Each image corresponds to a range, with a clear graphic ready for insertion in a report or atlas.

If you modify the code to produce pie charts:

import plotly.express as px
import os

for plage, group in df.groupby("plage"):
fig = px.pie(
group,
names="type_dechet", # valeurs catégorielles
values="quantite", # valeurs quantitatives
title=f"Répartition des déchets – {plage}",
color="type_dechet"
)
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.write_image(os.path.join(output_folder, f"graph_{plage}.png"))

You get:

To sum up

Although Data Plotly is handy for creating graphs in QGIS, it is not scriptable in Python. However, by extracting data and using plotly, you benefit from :

• total control,
• a reusable script,
• professional visuals.

Coming soon…

In a future article, we’ll look at how to automatically integrate these graphics into a QGIS Atlas, to create fully customized PDF reports.

Si cet article vous a intéressé et que vous pensez qu'il pourrait bénéficier à d'autres personnes, n'hésitez pas à le partager sur vos réseaux sociaux en utilisant les boutons ci-dessous. Votre partage est apprécié !

Atilio Francois