Add source example files

source/plotting/agg-pie.py

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+import pandas as pd
+from matplotlib import use, pyplot
+
+# Utiliser tkinter
+use("TkAgg")
+pyplot.style.use('ggplot')
+pyplot.rcParams["font.family"] = "Cabin"
+
+# Données à afficher
+data = pd.DataFrame(data={
+    "product": ["pomme", "poire", "banane", "pêche"],
+    "price": [1.99, 2.49, 2.99, 3.49]
+})
+
+
+def sector_label(pct: float) -> str:
+    value = pct * sum(data["price"]) / 100
+    return f"{value:.1f}€\n{pct:.1f}%"
+
+
+# Générer un graphique dans Matplotlib
+axis = data.set_index("product").plot.pie(y="price", title="Prix", autopct=sector_label)
+# Afficher le dernier graphique généré
+axis.figure.savefig("eda-matplotlib-pie-themed.png")
+pyplot.show()

source/plotting/agg-test.py

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+import matplotlib
+import pandas as pd
+from matplotlib import pyplot
+from matplotlib.axes import Axes
+
+matplotlib.use("TkAgg")
+pyplot.style.use('ggplot')
+pyplot.rcParams["font.family"] = "Cabin"
+
+data = pd.DataFrame(data={
+    "product": ["pomme", "poire", "banane", "pêche"],
+    "price": [1.99, 2.49, 2.99, 3.49],
+    "wpu": [200, 180, 140, 200]
+})
+# Générer un graphique dans Matplotlib
+data.plot.bar(x="product", y=["price", "wpu"], rot=0.0, secondary_y="wpu", legend="reverse", title="Prix et poids unitaires")
+prices, weights = pyplot.gcf().axes  # type: Axes
+prices.legend(bbox_to_anchor=(0.0, 1.1), loc="upper left")
+weights.legend(bbox_to_anchor=(1.0, 1.1), loc="upper right")
+# Il est difficile de personnaliser le contenu du graphique
+options: dict = {"fontsize": 8, "color": "w", "rotation": 90, "label_type": "center"}
+prices.bar_label(prices.containers[0], labels=[f"{p}€" for p in data["price"]], **options)
+weights.bar_label(weights.containers[0], labels=[f"{p}g" for p in data["wpu"]], **options)
+pyplot.gcf().savefig("eda-matplotlib-bar-labeled.png")

source/plotting/charts/bar_chart.py

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+from typing import Iterable
+
+from matplotlib import pyplot as plot
+from matplotlib.axes import Axes
+from matplotlib.figure import Figure
+import matplotlib
+
+if __name__ == '__main__':
+    matplotlib.use("QtCairo")
+    labels: Iterable[str] = ("Janvier", "Février", "Mars", "Avril")
+    values: Iterable[float] = (50, 11.4, 91, 66.1)
+    figure: Figure
+    axis: Axes
+    figure, axis = plot.subplots(nrows=1, ncols=1)
+    axis.bar(x=labels, height=values, color="#00ff00")
+    circle = plot.Circle((2, 20), 2, color="#FF0000", fill=False)
+    axis.set_ylabel("Précipitations (mm)")
+    axis.set_xlabel("Mois")
+    axis.set_title("Précipitations pour 2022")
+    axis.add_artist(circle)
+    plot.show()

source/plotting/charts/bar_chart_save.py

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+from typing import Iterable
+
+from matplotlib import pyplot as plot
+from matplotlib.axes import Axes
+from matplotlib.figure import Figure
+import matplotlib
+
+if __name__ == '__main__':
+    matplotlib.use("QtCairo")
+    labels: Iterable[str] = ("Janvier", "Février", "Mars", "Avril")
+    values: Iterable[float] = (50, 11.4, 91, 66.1)
+    figure: Figure
+    axis: Axes
+    figure, axis = plot.subplots(nrows=1, ncols=1)
+    axis.bar(x=labels, height=values, color="#00ff00")
+    axis.set_ylabel("Précipitations (mm)")
+    axis.set_xlabel("Mois")
+    axis.set_title("Précipitations pour 2022")
+    figure.savefig("bar-chart.png", transparent=True)

source/plotting/charts/pie_chart.py

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+"""
+Display sales of various cities in a sunburst chart.
+
+Given the cities can have recurring parents, we can have
+a sunburst chart with 2 rings, the centermost ring for the
+country, and the outmost ring to have sales per city.
+"""
+import matplotlib
+import pandas as pd
+from matplotlib import pyplot
+
+if __name__ == '__main__':
+    matplotlib.use("QtCairo")
+    df = pd.DataFrame(data={
+        "country": ["France", "France", "Spain", "Spain"],
+        "city": ["Montpellier", "Bordeaux", "Madrid", "Valencia"],
+        "sales": [150_000, 127_000, 97_200, 137_250]
+    })
+    df.set_index(["country", "city"], inplace=True)
+    total: int = df["sales"].sum()
+    print(df)
+    axes = df.plot.pie(subplots=True, autopct=lambda x: f"{x * total / 100:.0f} {x:.2f}%")
+    pyplot.show()
+

source/plotting/charts/plotly_bar.py

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+import pandas as pd
+from plotly.express import bar
+
+
+if __name__ == '__main__':
+    df = pd.DataFrame(data={"label": ["Citron", "Pomme", "Mangue"], "price": [1.99, 3.97, 6.8]})
+    plot = bar(df, x="label", y="price")
+    plot.show()

source/plotting/charts/plotly_iris_scatter.py

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+import pandas as pd
+from plotly import express as px
+
+data = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data", header=None,
+                   names=["sepal-length", "sepal-width", "petal-length", "petal-width", "class"])
+
+plot = px.scatter(data, x="sepal-length", y="sepal-width", size="petal-width", color="class", template="seaborn",
+                  title="Iris flowers dataset",
+                  labels={"sepal-length": "Sepal length", "sepal-width": "Sepal width", "petal-width": "Petal width", "class": "Class"})
+plot.layout.update({"font": {"family": "Cabin", "size": 13}})
+plot.show()

source/plotting/charts/plotly_sunburst.py

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+"""
+Display sales of various cities in a sunburst chart.
+
+Given the cities can have recurring parents, we can have
+a sunburst chart with 2 rings, the centermost ring for the
+country, and the outmost ring to have sales per city.
+"""
+import pandas as pd
+import plotly
+from plotly.express import sunburst
+
+if __name__ == '__main__':
+    df = pd.DataFrame(data={
+        "country": ["France", "France", "Spain", "Spain", "England", "England", "England"],
+        "city": ["Montpellier", "Bordeaux", "Madrid", "Valencia", "London", "Manchester", "Bristol"],
+        "sales": [150_000, 127_000, 97_200, 137_250, 200_000, 180_000, 150_000]
+    })
+    plot = sunburst(df, path=["country", "city"], values="sales", title="Sales by country and city", template="ggplot2",
+                    color_discrete_sequence=plotly.colors.qualitative.Dark2)
+    plot.layout.update({"font": {"family": "Cabin", "size": 13}})
+    plot.show()

source/plotting/dash/simple_dashboard.py

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+import dash
+import pandas as pd
+from dash import Dash, html, dash_table, dcc
+from plotly.express import line
+
+
+data = pd.read_excel("france-communes.xlsx")
+dept = data.groupby("departement").sum("population")
+app = Dash("demo")
+app.layout = html.Div(children=[
+    html.H1(children="Dashboard"),
+    dash_table.DataTable(data=data.to_dict("records"), page_size=10),
+    dcc.Graph(id="dept-population", figure=line(dept, x=None, y="population")),
+    dcc.Dropdown(id="color-select", options=["#ff0000", "#00ff00"])
+])
+
+
+@app.callback(dash.Output("dept-population", "figure"), dash.Input("color-select", "value"))
+def update_dept_population(color):
+    return line(dept, x=None, y="population", color_discrete_sequence=[color])
+
+
+if __name__ == '__main__':
+    app.run(debug=True)

source/plotting/gui/base-window.ui

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+<?xml version="1.0" encoding="UTF-8"?>
+<ui version="4.0">
+ <class>MainWindow</class>
+ <widget class="QMainWindow" name="MainWindow">
+  <property name="geometry">
+   <rect>
+    <x>0</x>
+    <y>0</y>
+    <width>800</width>
+    <height>600</height>
+   </rect>
+  </property>
+  <property name="windowTitle">
+   <string>MainWindow</string>
+  </property>
+  <widget class="QWidget" name="centralwidget">
+   <layout class="QVBoxLayout" name="verticalLayout">
+    <item>
+     <layout class="QHBoxLayout" name="horizontalLayout">
+      <item>
+       <widget class="QPushButton" name="refresh_button">
+        <property name="text">
+         <string>Refresh</string>
+        </property>
+       </widget>
+      </item>
+     </layout>
+    </item>
+   </layout>
+  </widget>
+  <widget class="QMenuBar" name="menubar">
+   <property name="geometry">
+    <rect>
+     <x>0</x>
+     <y>0</y>
+     <width>800</width>
+     <height>34</height>
+    </rect>
+   </property>
+  </widget>
+  <widget class="QStatusBar" name="statusbar"/>
+ </widget>
+ <resources/>
+ <connections/>
+</ui>

source/plotting/gui/base_graph.py

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+import pandas as pd
+from PySide6.QtCore import QObject, Slot
+from PySide6.QtUiTools import QUiLoader
+from PySide6.QtWidgets import QWidget, QMainWindow, QApplication, QPushButton
+from matplotlib.axes import Axes
+from matplotlib.backends.backend_qt import FigureCanvasQT
+from matplotlib.backends.backend_qtcairo import FigureCanvasQTCairo
+from matplotlib.figure import Figure
+
+
+class Main(QObject):
+    """Classe de gestion d'affichage de diagramme dans pyside."""
+    area: QWidget = None
+    window: QMainWindow | QWidget = None
+    canvas: FigureCanvasQT = None
+    refresh_button: QPushButton = None
+    plot1: Axes = None
+    data = pd.DataFrame({"age": [25, 45, 65], "prenom": ["Pierre", "Paul", "Jacques"]})
+
+    def __init__(self):
+        super().__init__()
+        self.window = QUiLoader().load("base-window.ui")
+        self.area = self.window.centralWidget()
+        self.refresh_button = self.window.refresh_button
+        # Créer un canevas compatible avec Matplotlib
+        self.canvas = FigureCanvasQTCairo()
+        self.canvas.figure = self.create_figures()
+        self.area.layout().addWidget(self.canvas)
+        self.show_bar()
+        # Répondre à un événement sur le bouton
+        self.refresh_button.clicked.connect(self.on_refresh_button_click)
+
+    @Slot()
+    def on_refresh_button_click(self):
+        """Rafraîchir le graphique."""
+        self.show_line()
+        self.canvas.draw()
+
+    def create_figures(self) -> Figure:
+        """Générer une image avec un graphique."""
+        figure = Figure(dpi=96)
+        self.plot1 = figure.add_subplot(111)  # 1 colonne, 1 ligne, position 1
+        return figure
+
+    def show_bar(self):
+        """Mettre à jour le canevas avec un diagramme en barres."""
+        self.canvas.figure.clear()  # retirer tous les graphiques
+        self.plot1 = self.canvas.figure.add_subplot(111)  # 1 colonne, 1 ligne, position 1
+        self.data.plot.bar(x="prenom", y="age", ax=self.plot1, rot=0)
+
+    def show_line(self):
+        """Mettre à jour le canevas avec un diagramme en lignes."""
+        self.canvas.figure.clear()  # retirer tous les graphiques
+        self.plot1 = self.canvas.figure.add_subplot(111)  # 1 colonne, 1 ligne, position 1
+        self.data.plot.line(x="prenom", y="age", ax=self.plot1, color="#ffa820")
+
+
+
+if __name__ == '__main__':
+    application = QApplication()
+    main = Main()
+    main.window.show()
+    application.exec()

source/plotting/gui/quick_threading.py

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+from __future__ import annotations
+
+import time
+
+from PySide6.QtCore import QObject, Slot, QThreadPool
+from PySide6.QtUiTools import QUiLoader
+from PySide6.QtWidgets import QWidget, QMainWindow, QApplication, QPushButton
+
+
+class Main(QObject):
+    """Classe de gestion de threads dans Pyside."""
+    area: QWidget = None
+    window: QMainWindow | QWidget = None
+    refresh_button: QPushButton = None
+    thread_pool = QThreadPool()
+    counter: int = 0
+
+    def __init__(self):
+        super().__init__()
+        self.window = QUiLoader().load("base-window.ui")
+        self.area = self.window.centralWidget()
+        self.refresh_button = self.window.refresh_button
+        # Répondre à un événement sur le bouton
+        self.refresh_button.clicked.connect(self.on_refresh_button_click)
+
+    @Slot()
+    def on_refresh_button_click(self):
+        """Rafraîchir le graphique."""
+        self.refresh_button.setDisabled(True)
+        self.thread_pool.start(self.counter_updater)
+
+    def counter_updater(self):
+        """Thread."""
+        while True:
+            self.counter += 1
+            self.refresh_button.setText(f"{self.counter}")
+            time.sleep(1.0)
+
+
+if __name__ == '__main__':
+    application = QApplication()
+    main = Main()
+    main.window.show()
+    application.exec()

source/plotting/plotly-bar-series.py

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+import pandas as pd
+from plotly import express
+
+
+values = pd.Series(
+    data=[1.99, 2.49, 2.99, 3.49],
+    index=["pomme", "poire", "banane", "peche"],
+    name="price"
+)
+# Générer un graphique
+figure = express.bar(values, x=None, y="price")
+figure.show()

source/plotting/plotly-barcolors.py

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+import pandas as pd
+from plotly import express as px
+
+data = pd.DataFrame(data={
+    "product": ["pomme", "poire", "banane", "pêche"],
+    "price": [1.99, 2.49, 2.99, 3.49], "wpu": [200, 180, 140, 200]
+})
+figure = px.bar(data, y="price", x="product", title="Prix", color="price", color_continuous_scale=["red", "orange", "yellow", "#8F0"])
+figure.layout.update({"template": "seaborn", "title": "Prix au kilo", "font": {"family": "Cabin", "size": 13}})
+figure.show()

source/plotting/plotly-compose.py

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+import pandas as pd
+from plotly.graph_objs import Figure, Bar
+
+data = pd.DataFrame(data={
+    "product": ["pomme", "poire", "banane", "pêche"],
+    "price": [1.99, 2.49, 2.99, 3.49], "wpu": [200, 180, 140, 200]
+})
+figure = Figure([
+    Bar(name="Prix", x=data["product"], y=data["price"], yaxis="y", offsetgroup=1),
+    Bar(name="Poids", x=data["product"], y=data["wpu"], yaxis="y2", offsetgroup=2),
+])
+# Afficher le dernier graphique généré
+figure.layout.update({
+    "template": "seaborn",
+    "title": "Prix et poids unitaires", "font": {"family": "Cabin", "size": 13},
+    "yaxis": {"title": "Prix (€)"}, "yaxis2": {"title": "Poids (g)", "overlaying": "y", "side": "right"}
+})
+figure.show()

source/plotting/plotly-piecolors.py

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+import pandas as pd
+from plotly import express as px
+
+data = pd.DataFrame(data={
+    "product": ["pomme", "poire", "banane", "pêche"],
+    "price": [1.99, 2.49, 2.99, 3.49], "wpu": [200, 180, 140, 200]
+})
+figure = px.pie(data, values="price", names="product", title="Prix", color_discrete_sequence=["red", "orange", "yellow", "#8F0"])
+figure.layout.update({"template": "seaborn", "title": "Prix au kilo", "font": {"family": "Cabin", "size": 13}})
+figure.show()

source/plotting/plotly-pielabel.py

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+import pandas as pd
+from plotly import express as px
+
+data = pd.DataFrame(data={
+    "product": ["pomme", "poire", "banane", "pêche"],
+    "price": [1.99, 2.49, 2.99, 3.49], "wpu": [200, 180, 140, 200]
+})
+figure = px.pie(data, values="price", names="product", title="Prix")
+# Afficher le dernier graphique généré
+figure.layout.update({
+    "template": "seaborn",
+    "title": "Prix au kilo", "font": {"family": "Cabin", "size": 13},
+})
+figure.update_traces(**{"textinfo": "label+value", "texttemplate": "%{label}<br>%{value:.2f}€"})
+figure.show()

source/plotting/plotly-test.py

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+import pandas as pd
+from plotly import express
+
+data = pd.DataFrame(data={
+    "product": ["pomme", "poire", "banane", "pêche"],
+    "price": [1.99, 2.49, 2.99, 3.49],
+    "wpu": [200, 180, 140, 200]
+})
+# Générer un graphique. Plotly express ne prend en charge qu'un seul graphique par figure
+figure = express.bar(data, x="product", y="price", title="Prix", template="seaborn",
+                     labels={"price": "Prix", "product": "Produit"})
+figure.layout.update({"font": {"family": "Cabin", "size": 13}})
+figure.show()