Exemples de code Python

Chaque exemple est un extrait Matplotlib complet et autonome. Dans les bonnes versions, les lignes ajoutées ou corrigées sont mises en évidence.

Règles de complétude

Les règles 1 à 6 vérifient si le graphique contient assez d’information pour être compris à lui seul.

Règle 1 : Titre clair

Le titre doit dire au lecteur de quoi parle le graphique avant même qu’il examine les marques visuelles.

Code incomplet

import matplotlib.pyplot as plt
 
months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]
revenue = [18, 20, 21, 23, 25, 28]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.plot(months, revenue, marker="o")
ax.set_xlabel("Month")
ax.set_ylabel("Revenue (k EUR)")
plt.show()

Code corrigé

import matplotlib.pyplot as plt
 
months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]
revenue = [18, 20, 21, 23, 25, 28]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.plot(months, revenue, marker="o")
ax.set_title("Monthly revenue increased from Jan to Jun 2026")
ax.set_xlabel("Month")
ax.set_ylabel("Revenue (k EUR)")
plt.show()

Règle 2 : Étiquettes d’axes

Les étiquettes d’axes et les unités lèvent toute ambiguïté sur ce qui est mesuré.

Code incomplet

import matplotlib.pyplot as plt
 
ad_spend = [8, 12, 16, 20, 24, 30, 34, 38]
signups = [110, 140, 175, 205, 245, 310, 335, 390]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.scatter(ad_spend, signups)
ax.set_title("Campaign performance")
plt.show()

Code corrigé

import matplotlib.pyplot as plt
 
ad_spend = [8, 12, 16, 20, 24, 30, 34, 38]
signups = [110, 140, 175, 205, 245, 310, 335, 390]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.scatter(ad_spend, signups)
ax.set_title("Campaign performance")
ax.set_xlabel("Ad spend (k EUR)")
ax.set_ylabel("New signups")
plt.show()

Règle 3 : Clarté des unités et de l’échelle

Les grandes valeurs doivent être mises à l’échelle et étiquetées pour que le lecteur sache immédiatement ce que signifient les nombres.

Code incomplet

import matplotlib.pyplot as plt
 
years = [2022, 2023, 2024, 2025]
revenue = [1250000, 1480000, 1730000, 1910000]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.plot(years, revenue, marker="o")
ax.set_title("Revenue trend")
ax.set_ylabel("Revenue")
plt.show()

Code corrigé

import matplotlib.pyplot as plt
 
years = [2022, 2023, 2024, 2025]
revenue = [1250000, 1480000, 1730000, 1910000]
revenue_millions = [value / 1_000_000 for value in revenue]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.plot(years, revenue_millions, marker="o")
ax.set_title("Revenue trend")
ax.set_xlabel("Year")
ax.set_ylabel("Revenue (million EUR)")
plt.show()

Règle 4 : Clarté de la légende

Dès qu’il y a plusieurs séries, il faut des libellés clairs pour que le lecteur identifie chaque ligne.

Code incomplet

import matplotlib.pyplot as plt
 
years = [2021, 2022, 2023, 2024, 2025]
series = {
    "Basic": [12, 14, 16, 18, 20],
    "Pro": [9, 13, 17, 22, 28],
    "Enterprise": [6, 8, 12, 17, 25],
}
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
for values in series.values():
    ax.plot(years, values, marker="o")
ax.set_title("Subscriptions by plan")
ax.set_ylabel("Subscriptions (k)")
plt.show()

Code corrigé

import matplotlib.pyplot as plt
 
years = [2021, 2022, 2023, 2024, 2025]
series = {
    "Basic": [12, 14, 16, 18, 20],
    "Pro": [9, 13, 17, 22, 28],
    "Enterprise": [6, 8, 12, 17, 25],
}
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
for label, values in series.items():
    ax.plot(years, values, marker="o", label=label)
ax.legend(loc="center left", bbox_to_anchor=(1.02, 0.5))
ax.set_title("Subscriptions by plan")
ax.set_ylabel("Subscriptions (k)")
plt.show()

Règle 5 : Contexte d’annotation

Quand un événement ou une idée clé explique le motif, il doit être visible dans le graphique.

Code incomplet

import matplotlib.pyplot as plt
 
weeks = [1, 2, 3, 4, 5, 6, 7, 8]
adoption = [12, 15, 18, 23, 34, 39, 43, 46]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.plot(weeks, adoption, marker="o")
ax.set_title("Feature adoption")
ax.set_xlabel("Week")
ax.set_ylabel("Adoption (%)")
plt.show()

Code corrigé

import matplotlib.pyplot as plt
 
weeks = [1, 2, 3, 4, 5, 6, 7, 8]
adoption = [12, 15, 18, 23, 34, 39, 43, 46]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.plot(weeks, adoption, marker="o")
ax.annotate("Onboarding email launched", xy=(5, 34), xytext=(3.2, 42), arrowprops={"arrowstyle": "->"})
ax.set_title("Feature adoption increased after onboarding email")
ax.set_xlabel("Week")
ax.set_ylabel("Adoption (%)")
plt.show()

Règle 6 : Indices d’incertitude

Les estimations doivent montrer l’incertitude lorsque celle-ci compte pour l’interprétation.

Code incomplet

import matplotlib.pyplot as plt
 
groups = ["A", "B", "C", "D"]
mean = [52, 57, 61, 55]
error = [4, 7, 3, 6]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.bar(groups, mean)
ax.set_title("Average test result")
ax.set_ylabel("Score")
plt.show()

Code corrigé

import matplotlib.pyplot as plt
 
groups = ["A", "B", "C", "D"]
mean = [52, 57, 61, 55]
error = [4, 7, 3, 6]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.bar(groups, mean, yerr=error, capsize=6)
ax.set_title("Average test result with uncertainty")
ax.set_ylabel("Score")
plt.show()

Règles de lisibilité

Les règles 7 à 16 vérifient si le graphique peut être lu, parcouru et comparé sans effort inutile.

Règle 7 : Étiquettes lisibles

Des libellés longs ne doivent ni se chevaucher ni obliger le lecteur à déchiffrer un axe saturé.

Code incomplet

import matplotlib.pyplot as plt
 
segments = [
    "Returning enterprise customers",
    "New small business customers",
    "One-time promotional buyers",
    "Students using education plan",
    "Trial users awaiting onboarding",
]
counts = [180, 145, 96, 125, 72]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.bar(segments, counts)
ax.set_title("Customers by segment")
plt.show()

Code corrigé

import matplotlib.pyplot as plt
import textwrap
 
segments = [
    "Returning enterprise customers",
    "New small business customers",
    "One-time promotional buyers",
    "Students using education plan",
    "Trial users awaiting onboarding",
]
counts = [180, 145, 96, 125, 72]
labels = [textwrap.fill(s, 22) for s in segments]
pairs = sorted(zip(counts, labels))
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.barh([label for _, label in pairs], [count for count, _ in pairs])
ax.set_title("Customers by segment")
ax.set_xlabel("Customers")
plt.show()

Règle 8 : Accessibilité des couleurs

Les graphiques doivent rester lisibles pour les personnes ayant des déficiences de vision des couleurs.

Code incomplet

import matplotlib.pyplot as plt
 
segments = ["Home", "Food", "Auto", "Health", "Shopping"]
spend = [42, 26, 18, 12, 8]
colors = ["#d7191c", "#fdae61", "#ffffbf", "#a6d96a", "#1a9641"]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.bar(segments, spend, color=colors)
ax.set_title("Household spend by category")
ax.set_ylabel("Spend (%)")
plt.show()

Code corrigé

import matplotlib.pyplot as plt
 
segments = ["Home", "Food", "Auto", "Health", "Shopping"]
spend = [42, 26, 18, 12, 8]
colors = ["#0072B2", "#E69F00", "#56B4E9", "#009E73", "#CC79A7"]
hatches = ["", "//", "\\", "..", "xx"]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
bars = ax.bar(segments, spend, color=colors)
for bar, hatch in zip(bars, hatches):
    bar.set_hatch(hatch)
ax.set_title("Household spend by category")
ax.set_ylabel("Spend (%)")
plt.show()

Règle 9 : Étiquetage direct

Quand les lignes peuvent être étiquetées directement, le lecteur ne devrait pas avoir à faire des allers-retours entre le tracé et la légende.

Code incomplet

import matplotlib.pyplot as plt
 
years = [2021, 2022, 2023, 2024, 2025]
series = {
    "Basic": [12, 14, 16, 18, 20],
    "Pro": [9, 13, 17, 22, 28],
    "Enterprise": [6, 8, 12, 17, 25],
    "Education": [4, 7, 10, 13, 19],
}
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
for label, values in series.items():
    ax.plot(years, values, marker="o", label=label)
ax.legend()
ax.set_title("Subscriptions by plan")
ax.set_ylabel("Subscriptions (k)")
plt.show()

Code corrigé

import matplotlib.pyplot as plt
 
years = [2021, 2022, 2023, 2024, 2025]
series = {
    "Basic": [12, 14, 16, 18, 20],
    "Pro": [9, 13, 17, 22, 28],
    "Enterprise": [6, 8, 12, 17, 25],
    "Education": [4, 7, 10, 13, 19],
}
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
for label, values in series.items():
    ax.plot(years, values, marker="o")
    ax.text(years[-1] + 0.05, values[-1], label, va="center")
ax.set_xlim(2021, 2025.9)
ax.set_title("Subscriptions by plan")
ax.set_ylabel("Subscriptions (k)")
plt.show()

Règle 10 : Éviter le chartjunk

La décoration ne doit jamais entrer en concurrence avec les données.

Code incomplet

import matplotlib.pyplot as plt
 
regions = ["North", "South", "East", "West"]
profit = [18, 12, 15, 21]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.set_facecolor("#f3d9a5")
ax.bar(regions, profit, edgecolor="black", linewidth=2)
ax.grid(True, axis="both")
ax.set_title("!!! PROFIT !!!")
ax.set_ylabel("Profit (k EUR)")
plt.show()

Code corrigé

import matplotlib.pyplot as plt
 
regions = ["North", "South", "East", "West"]
profit = [18, 12, 15, 21]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.bar(regions, profit)
ax.grid(True, axis="y")
ax.set_title("Profit by region")
ax.set_ylabel("Profit (k EUR)")
plt.show()

Règle 11 : Trop de catégories

Trop de catégories rendent la comparaison lente et encombrée.

Code incomplet

import matplotlib.pyplot as plt
 
categories = [f"C{i}" for i in range(1, 19)]
values = [34, 28, 26, 22, 20, 18, 16, 12, 11, 10, 9, 8, 7, 6, 5, 5, 4, 3]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.bar(categories, values)
ax.set_title("Requests by category")
ax.set_ylabel("Requests")
plt.show()

Code corrigé

import matplotlib.pyplot as plt
 
categories = [f"C{i}" for i in range(1, 19)]
values = [34, 28, 26, 22, 20, 18, 16, 12, 11, 10, 9, 8, 7, 6, 5, 5, 4, 3]
top_labels = categories[:7] + ["Other"]
top_values = values[:7] + [sum(values[7:])]
pairs = sorted(zip(top_values, top_labels))
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.barh([label for _, label in pairs], [value for value, _ in pairs])
ax.set_title("Requests by category")
ax.set_xlabel("Requests")
plt.show()

Règle 12 : Trier les barres catégorielles

Des barres triées rendent le classement et la comparaison beaucoup plus rapides.

Code incomplet

import matplotlib.pyplot as plt
 
teams = ["Ops", "Sales", "Support", "Product", "Finance"]
hours = [42, 18, 31, 24, 36]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.barh(teams, hours)
ax.set_title("Average resolution time")
ax.set_xlabel("Hours")
plt.show()

Code corrigé

import matplotlib.pyplot as plt
 
teams = ["Ops", "Sales", "Support", "Product", "Finance"]
hours = [42, 18, 31, 24, 36]
pairs = sorted(zip(hours, teams))
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.barh([team for _, team in pairs], [hour for hour, _ in pairs])
ax.set_title("Average resolution time")
ax.set_xlabel("Hours")
plt.show()

Règle 13 : Surimpression dans les nuages de points

Des points répétés ou très denses doivent révéler la densité au lieu de la masquer.

Code incomplet

import matplotlib.pyplot as plt
import numpy as np
 
rng = np.random.default_rng(42)
centers = np.array([[2, 2], [3, 3], [4, 2.5], [4.5, 4]])
points = np.repeat(centers, [35, 45, 25, 30], axis=0)
points = points + rng.normal(0, 0.04, size=(135, 2))
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.scatter(points[:, 0], points[:, 1], s=28)
ax.set_xlabel("Value score")
ax.set_ylabel("Satisfaction score")
plt.show()

Code corrigé

import matplotlib.pyplot as plt
import numpy as np
 
centers = np.array([[2, 2], [3, 3], [4, 2.5], [4.5, 4]])
counts = np.array([35, 45, 25, 30])
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.scatter(centers[:, 0], centers[:, 1], s=counts * 18, alpha=0.55, edgecolor="black")
ax.set_xlabel("Value score")
ax.set_ylabel("Satisfaction score")
ax.set_title("Point size shows repeated observations")
plt.show()

Règle 14 : Précision décimale

Les libellés doivent éviter les décimales inutiles qui ajoutent du bruit sans ajouter de sens.

Code incomplet

import matplotlib.pyplot as plt
 
products = ["A", "B", "C", "D"]
conversion = [12.3421, 11.9874, 13.2251, 12.7718]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
bars = ax.bar(products, conversion)
ax.bar_label(bars, fmt="%.4f%%")
ax.set_ylim(0, 15)
ax.set_ylabel("Conversion (%)")
plt.show()

Code corrigé

import matplotlib.pyplot as plt
 
products = ["A", "B", "C", "D"]
conversion = [12.3421, 11.9874, 13.2251, 12.7718]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
bars = ax.bar(products, conversion)
ax.bar_label(bars, fmt="%.1f%%")
ax.set_ylim(0, 15)
ax.set_ylabel("Conversion (%)")
plt.show()

Règle 15 : Formatage des dates

Les graduations temporelles doivent être formatées selon un intervalle facile à lire.

Code incomplet

import matplotlib.pyplot as plt
import numpy as np
 
dates = [np.datetime64("2026-01-01") + np.timedelta64(i * 14, "D") for i in range(10)]
visits = [120, 135, 128, 150, 162, 158, 170, 181, 190, 205]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.plot(dates, visits, marker="o")
ax.set_title("Website visits")
ax.set_ylabel("Visits (k)")
plt.show()

Code corrigé

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
 
dates = [np.datetime64("2026-01-01") + np.timedelta64(i * 14, "D") for i in range(10)]
visits = [120, 135, 128, 150, 162, 158, 170, 181, 190, 205]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.plot(dates, visits, marker="o")
ax.set_title("Website visits")
ax.set_ylabel("Visits (k)")
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter("%b %Y"))
ax.tick_params(axis="x", rotation=30)
plt.show()

Règle 16 : Économie visuelle

Il faut assez d’encodage visuel pour expliquer les données, sans ajouter de style redondant.

Code incomplet

import matplotlib.pyplot as plt
 
products = ["A", "B", "C", "D", "E", "F"]
values = [18, 25, 22, 31, 27, 20]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.bar(products, values, hatch="//", edgecolor="black")
ax.plot(products, values, marker="D", color="red")
ax.grid(True, axis="both")
ax.set_title("Product sales with redundant styling")
plt.show()

Code corrigé

import matplotlib.pyplot as plt
 
products = ["A", "B", "C", "D", "E", "F"]
values = [18, 25, 22, 31, 27, 20]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.bar(products, values)
ax.grid(True, axis="y")
ax.set_title("Product sales")
ax.set_ylabel("Sales (k EUR)")
plt.show()

Règles d’intégrité

Les règles 17 à 25 vérifient si le graphique évite les échelles, les encodages ou les comparaisons trompeuses.

Règle 17 : Échelle appropriée

Avec des barres, une base tronquée peut exagérer de petites différences.

Code incomplet

import matplotlib.pyplot as plt
 
branches = ["North", "Central", "South"]
satisfaction = [82, 86, 88]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.bar(branches, satisfaction)
ax.set_ylim(80, 90)
ax.set_title("Customer satisfaction by branch")
ax.set_ylabel("Satisfied customers (%)")
plt.show()

Code corrigé

import matplotlib.pyplot as plt
 
branches = ["North", "Central", "South"]
satisfaction = [82, 86, 88]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.bar(branches, satisfaction)
ax.set_ylim(0, 100)
ax.set_title("Customer satisfaction by branch")
ax.set_ylabel("Satisfied customers (%)")
plt.show()

Règle 18 : Type de graphique adapté

Les comparaisons entre catégories se lisent mieux avec des barres qu’avec une ligne qui suggère à tort une séquence.

Code incomplet

import matplotlib.pyplot as plt
 
products = ["Shoes", "Bags", "Watches", "Perfume", "Jewelry"]
sales = [54, 31, 46, 28, 62]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.plot(products, sales, marker="o")
ax.set_title("Sales by product category")
ax.set_ylabel("Sales (k EUR)")
plt.show()

Code corrigé

import matplotlib.pyplot as plt
 
products = ["Shoes", "Bags", "Watches", "Perfume", "Jewelry"]
sales = [54, 31, 46, 28, 62]
pairs = sorted(zip(sales, products))
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.barh([p for _, p in pairs], [s for s, _ in pairs])
ax.set_title("Sales by product category")
ax.set_xlabel("Sales (k EUR)")
plt.show()

Règle 19 : Qualité de la palette continue

Une couleur continue doit s’appuyer sur une échelle perceptuelle et sur une barre de couleur explicitement étiquetée.

Code incomplet

import matplotlib.pyplot as plt
import numpy as np
 
heat = np.outer(np.linspace(0, 1, 12), np.linspace(0.2, 1, 12))
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
image = ax.imshow(heat, cmap="rainbow")
ax.set_title("Demand intensity")
fig.colorbar(image, ax=ax)
plt.show()

Code corrigé

import matplotlib.pyplot as plt
import numpy as np
 
heat = np.outer(np.linspace(0, 1, 12), np.linspace(0.2, 1, 12))
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
image = ax.imshow(heat, cmap="viridis")
ax.set_title("Demand intensity")
colorbar = fig.colorbar(image, ax=ax)
colorbar.set_label("Orders per store")
plt.show()

Règle 20 : Éviter les doubles axes

Les doubles axes peuvent suggérer des relations qui viennent surtout du choix d’échelle, et non des données.

Code incomplet

import matplotlib.pyplot as plt
 
months = [1, 2, 3, 4, 5, 6]
revenue = [1.2, 1.5, 1.8, 2.1, 2.4, 2.7]
churn = [9, 8, 7, 6, 5, 4]
 
fig, ax1 = plt.subplots(figsize=(6.4, 3.8))
ax2 = ax1.twinx()
ax1.plot(months, revenue, marker="o", label="Revenue")
ax2.plot(months, churn, marker="o", color="red", label="Churn")
ax1.set_ylabel("Revenue (M EUR)")
ax2.set_ylabel("Churn (%)")
plt.show()

Code corrigé

import matplotlib.pyplot as plt
 
months = [1, 2, 3, 4, 5, 6]
revenue = [1.2, 1.5, 1.8, 2.1, 2.4, 2.7]
churn = [9, 8, 7, 6, 5, 4]
 
fig, axes = plt.subplots(2, 1, figsize=(6.4, 4.2), sharex=True)
axes[0].plot(months, revenue, marker="o")
axes[0].set_ylabel("Revenue (M EUR)")
axes[1].plot(months, churn, marker="o", color="red")
axes[1].set_ylabel("Churn (%)")
axes[1].set_xlabel("Month")
plt.show()

Règle 21 : Base des aires

Les aires remplies doivent reposer sur une base honnête, car la surface perçue a un sens.

Code incomplet

import matplotlib.pyplot as plt
 
years = [2020, 2021, 2022, 2023, 2024, 2025]
share = [62, 64, 66, 67, 69, 70]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.fill_between(years, share, 60)
ax.plot(years, share, marker="o")
ax.set_ylim(60, 72)
ax.set_ylabel("Share (%)")
plt.show()

Code corrigé

import matplotlib.pyplot as plt
 
years = [2020, 2021, 2022, 2023, 2024, 2025]
share = [62, 64, 66, 67, 69, 70]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.fill_between(years, share, 0)
ax.plot(years, share, marker="o")
ax.set_ylim(0, 100)
ax.set_ylabel("Share (%)")
plt.show()

Règle 22 : Ratio d’aspect raisonnable

Des ratios d’aspect extrêmes peuvent aplatir ou dramatiser les tendances.

Code incomplet

import matplotlib.pyplot as plt
 
quarters = [1, 2, 3, 4, 5, 6, 7, 8]
index = [12, 14, 15, 16, 18, 19, 21, 22]
 
fig, ax = plt.subplots(figsize=(7.8, 2.2))
ax.plot(quarters, index, marker="o")
ax.set_title("Compressed'aspect ratio hides the trend")
plt.show()

Code corrigé

import matplotlib.pyplot as plt
 
quarters = [1, 2, 3, 4, 5, 6, 7, 8]
index = [12, 14, 15, 16, 18, 19, 21, 22]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.plot(quarters, index, marker="o")
ax.set_title("Balanced'aspect ratio shows the trend clearly")
plt.show()

Règle 23 : Qualité des classes d’histogramme

Un histogramme a besoin d’un nombre de classes suffisant pour révéler la forme de la distribution sans créer de bruit.

Code incomplet

import matplotlib.pyplot as plt
import numpy as np
 
rng = np.random.default_rng(42)
scores = rng.normal(72, 9, 420)
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.hist(scores, bins=3)
ax.set_title("Exam scores")
ax.set_xlabel("Score")
ax.set_ylabel("Students")
plt.show()

Code corrigé

import matplotlib.pyplot as plt
import numpy as np
 
rng = np.random.default_rng(42)
scores = rng.normal(72, 9, 420)
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.hist(scores, bins=18)
ax.set_title("Exam scores")
ax.set_xlabel("Score")
ax.set_ylabel("Students")
plt.show()

Règle 24 : Cohérence des couleurs catégorielles

Une même catégorie doit garder la même couleur dans tout le graphique.

Code incomplet

import matplotlib.pyplot as plt
 
quarters = ["Q1", "Q2", "Q3"]
desktop = [42, 45, 48]
mobile = [31, 34, 38]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.plot(quarters, desktop, marker="o", color="blue", label="Desktop")
ax.plot(quarters, mobile, marker="o", color="orange", label="Mobile")
ax.scatter(["Q2"], [45], color="orange", s=90)
ax.scatter(["Q2"], [34], color="blue", s=90)
ax.legend()
plt.show()

Code corrigé

import matplotlib.pyplot as plt
 
quarters = ["Q1", "Q2", "Q3"]
desktop = [42, 45, 48]
mobile = [31, 34, 38]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.plot(quarters, desktop, marker="o", color="blue", label="Desktop")
ax.plot(quarters, mobile, marker="o", color="orange", label="Mobile")
ax.legend()
ax.set_title("Traffic by device")
ax.set_ylabel("Sessions (k)")
plt.show()

Règle 25 : Référence zéro pour les valeurs divergentes

Les valeurs positives et négatives ont besoin d’une référence zéro clairement visible.

Code incomplet

import matplotlib.pyplot as plt
 
departments = ["Ops", "Sales", "Support", "Product"]
delta = [-8, 12, -5, 9]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.bar(departments, delta)
ax.set_title("Change vs target")
ax.set_ylabel("Change (%)")
plt.show()

Code corrigé

import matplotlib.pyplot as plt
 
departments = ["Ops", "Sales", "Support", "Product"]
delta = [-8, 12, -5, 9]
colors = ["#b42318" if value < 0 else "#2f855a" for value in delta]
 
fig, ax = plt.subplots(figsize=(6.4, 3.8))
ax.bar(departments, delta, color=colors)
ax.axhline(0, color="black", linewidth=1.2)
ax.set_title("Change vs target")
ax.set_ylabel("Change (%)")
plt.show()