شرح بسيط لـ Python Matplotlib Seaborn (مع رسوم بيانية وكود حقيقي)

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

sns.set_theme(
    style="whitegrid",
    context="notebook",
    palette="viridis",
    font_scale=1.05,
)

df = pd.read_csv("ventes_2024.csv", parse_dates=["date"])
print(df.info())
print(df.describe())

fig, ax = plt.subplots(figsize=(10, 5))
sns.histplot(data=df, x="vente_eur", kde=True, bins=40, color="#7c3aed", ax=ax)

mean_vente = df["vente_eur"].mean()
median_vente = df["vente_eur"].median()
ax.axvline(mean_vente, color="red", linestyle="--", linewidth=2, label=f"Moyenne : {mean_vente:.0f} EUR")
ax.axvline(median_vente, color="orange", linestyle="--", linewidth=2, label=f"Mediane : {median_vente:.0f} EUR")

ax.set_title("Distribution du panier moyen", fontweight="bold")
ax.set_xlabel("Montant de la commande (EUR)")
ax.set_ylabel("Nombre de transactions")
ax.legend()
sns.despine()
plt.tight_layout()
plt.show()

monthly = df.groupby("mois").agg(
    ca=("vente_eur", "sum"),
    nb_cmd=("vente_eur", "count"),
).reset_index()

fig, ax = plt.subplots(figsize=(11, 5))
sns.lineplot(data=monthly, x="mois", y="ca", marker="o", linewidth=2.5, color="#7c3aed", ax=ax)
ax.fill_between(monthly["mois"], monthly["ca"], alpha=0.15, color="#7c3aed")

best_month = monthly.loc[monthly["ca"].idxmax()]
ax.annotate(
    f"Pic : {best_month['ca']:,.0f} EUR",
    xy=(best_month["mois"], best_month["ca"]),
    xytext=(best_month["mois"], best_month["ca"] + 1500),
    ha="center", fontsize=11, fontweight="bold", color="red",
    arrowprops=dict(arrowstyle="->", color="red")
)

ax.set_title("Evolution mensuelle du chiffre d'affaires 2024", fontweight="bold")
ax.set_xlabel("Mois"); ax.set_ylabel("CA (EUR)")
ax.set_xticks(range(1, 13))
sns.despine()
plt.tight_layout()
plt.show()

cat_perf = df.groupby("categorie").agg(
    ca=("vente_eur", "sum"),
    marge=("marge_eur", "sum"),
).reset_index().sort_values("ca", ascending=True)

fig, ax = plt.subplots(figsize=(10, 5))
y_pos = range(len(cat_perf))
ax.barh(y_pos, cat_perf["ca"], color="#a78bfa", label="CA", alpha=0.8)
ax.barh(y_pos, cat_perf["marge"], color="#7c3aed", label="Marge", alpha=0.9)

for i, (ca, marge) in enumerate(zip(cat_perf["ca"], cat_perf["marge"])):
    ax.text(ca + 2000, i, f"{ca:,.0f} EUR", va="center", fontsize=10, fontweight="bold")

ax.set_yticks(y_pos)
ax.set_yticklabels(cat_perf["categorie"])
ax.set_title("CA et marges par categorie produit", fontweight="bold")
ax.set_xlabel("Montant (EUR)")
ax.legend(loc="lower right")
sns.despine()
plt.tight_layout()
plt.show()

fig, ax = plt.subplots(figsize=(11, 5))
order = df.groupby("magasin")["vente_eur"].median().sort_values(ascending=False).index

sns.boxplot(data=df, x="magasin", y="vente_eur", order=order,
            palette="viridis", hue="magasin", legend=False, ax=ax)
sns.stripplot(data=df, x="magasin", y="vente_eur", order=order,
              color="black", alpha=0.15, size=2, ax=ax)

ax.set_title("Distribution des paniers par magasin (trie par mediane)", fontweight="bold")
ax.set_xlabel("Magasin"); ax.set_ylabel("Panier (EUR)")
sns.despine()
plt.tight_layout()
plt.show()

numeric_cols = ["vente_eur", "marge_eur", "nb_articles", "mois", "trimestre"]
corr = df[numeric_cols].corr()

fig, ax = plt.subplots(figsize=(8, 6))
sns.heatmap(corr, annot=True, fmt=".2f", cmap="vlag", center=0,
            square=True, linewidths=0.5, cbar_kws={"shrink": 0.7}, ax=ax)
ax.set_title("Matrice de correlation des variables numeriques", fontweight="bold")
plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 2 * np.pi, 100)
y = np.sin(x)

plt.plot(x, y)
plt.title("Ma premiere fonction sinus")
plt.xlabel("x (radians)")
plt.ylabel("sin(x)")
plt.grid(True)
plt.show()

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 2 * np.pi, 100)

y = np.sin(x)

plt.plot(x, y)

plt.title("Ma premiere fonction sinus")
plt.xlabel("x (radians)")
plt.ylabel("sin(x)")
plt.grid(True)

import pandas as pd
import matplotlib.pyplot as plt

data = {
    "Mois": ["Jan", "Fev", "Mar", "Avr", "Mai", "Juin",
             "Juil", "Aout", "Sep", "Oct", "Nov", "Dec"],
    "Produit A": [120, 135, 148, 160, 175, 210,
                   250, 245, 200, 170, 140, 290],
    "Produit B": [80, 85, 90, 95, 100, 110,
                   130, 135, 120, 100, 90, 180],
    "Produit C": [50, 55, 60, 70, 85, 100,
                   120, 115, 95, 75, 60, 150],
}
df = pd.DataFrame(data)
print(df.head())

Mois  Produit A  Produit B  Produit C
0  Jan        120         80         50
1  Fev        135         85         55
2  Mar        148         90         60
3  Avr        160         95         70
4  Mai        175        100         85

fig, ax = plt.subplots(figsize=(10, 5))

ax.plot(df["Mois"], df["Produit A"], color="purple", linewidth=2)
ax.set_title("Ventes mensuelles du Produit A", fontsize=14)
ax.set_xlabel("Mois")
ax.set_ylabel("Unites vendues")
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

fig, ax = plt.subplots(figsize=(12, 6))

ax.plot(df["Mois"], df["Produit A"], label="Produit A", linewidth=2, marker="o")
ax.plot(df["Mois"], df["Produit B"], label="Produit B", linewidth=2, marker="s")
ax.plot(df["Mois"], df["Produit C"], label="Produit C", linewidth=2, marker="^")

ax.set_title("Ventes mensuelles par produit", fontsize=14, pad=15)
ax.set_xlabel("Mois", fontsize=12)
ax.set_ylabel("Unites vendues", fontsize=12)
ax.legend(loc="upper left", fontsize=11)
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

# إذا كان الملف في نفس المجلد
df = pd.read_csv("ventes.csv")

# إذا كان الملف على الويب
url = "https://raw.githubusercontent.com/exemple/data/main/ventes.csv"
df = pd.read_csv(url)

# بفاصل آخر (فاصلة منقوطة)
df = pd.read_csv("ventes.csv", sep=";")

# مع تحليل تلقائي للتواريخ
df = pd.read_csv("ventes.csv", parse_dates=["date"])

print(df.head())
print(df.dtypes)

df.set_index("Mois").plot(figsize=(10, 5), marker="o")
plt.title("Ventes par produit (style Pandas)")
plt.ylabel("Unites")
plt.grid(True, alpha=0.3)
plt.show()