Add source example files

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()