Python & Algorithmique

note = float(input("Entrez votre note sur 20 : "))

if note >= 16:
    mention = "Très Bien"
elif note >= 14:
    mention = "Bien"
elif note >= 12:
    mention = "Assez Bien"
elif note >= 10:
    mention = "Passable"
else:
    mention = "Insuffisant — Ajourné"

print(f"Note : {note}/20 → Mention : {mention}")

age = 22; possede_cni = True
if age >= 18 and possede_cni:
    print("Peut voter aux élections")

est_etudiant = True; est_personnel = False
if est_etudiant or est_personnel:
    print("Accès au campus MUST autorisé")

pluie = False
if not pluie:
    print("Beau temps à Toliara — sortie possible !")

age = int(input("Âge du spectateur : "))
a_carte_must = input("Carte MUST ? (oui/non) : ").lower() == "oui"

if age < 6:
    tarif = 0; categorie = "Gratuit"
elif age < 12:
    tarif = 1000 if a_carte_must else 2000
    categorie = "Tarif enfant"
elif age < 18:
    tarif = 3000; categorie = "Tarif jeune"
else:
    tarif = 4000 if a_carte_must else 5000
    categorie = "Tarif adulte"

print(f"{categorie} — Tarif : {tarif:,} Ar")

# Saisie sécurisée
note = -1
while not (0 <= note <= 20):
    note = float(input("Note entre 0 et 20 : "))
print(f"Note enregistrée : {note}/20")

# Compte à rebours
compteur = 10
while compteur > 0:
    print(f"T-{compteur}...")
    compteur -= 1
print("Décollage !")

regions = ["Toliara", "Antananarivo", "Tamatave", "Fianarantsoa"]
for region in regions:
    print(f"Région : {region}")

for i in range(1, 11):
    print(i, end=" ")  # 1 2 3...10

for i in range(0, 101, 10):
    print(i, end=" ")  # 0, 10, 20...100

for i in range(10, 0, -1):
    print(i, end=" ")  # 10, 9, 8...1

# break
for n in range(1, 100):
    if n * n > 500:
        print(f"Premier n avec n² > 500 : n={n}, n²={n*n}")
        break

# continue
for i in range(1, 11):
    if i % 2 == 0:
        continue
    print(i, end=" ")  # 1 3 5 7 9

# else sur for : exécuté si PAS de break
for n in range(2, 10):
    if 97 % n == 0:
        print(f"97 divisible par {n}")
        break
else:
    print("97 est premier !")

notes = [15, 12, 18, 9, 14, 11, 16]
print(notes[0])   # 15
print(notes[-1])  # 16 (dernier)
print(notes[-3])  # 14 (3ème en partant de la fin)
notes[0] = 16     # Modification
print(notes)      # [16, 12, 18, 9, 14, 11, 16]

# Slicing
print(notes[1:4])     # [12, 18, 9]
print(notes[:3])      # [15, 12, 18] (3 premiers)
print(notes[::2])     # Un élément sur deux
print(notes[::-1])    # Inversé

carres = [x**2 for x in range(1, 11)]
# [1, 4, 9, 16, 25, 36, 49, 64, 81, 100]

notes = [15, 12, 18, 9, 14, 11, 16]
admis = [n for n in notes if n >= 10]
mentions = [n for n in notes if n >= 14]

# Celsius → Fahrenheit
celsius = [0, 10, 20, 30, 40]
fahrenheit = [c * 9/5 + 32 for c in celsius]
# [32.0, 50.0, 68.0, 86.0, 104.0]

def saluer(nom, ville="Toliara"):
    """Retourne un message de salutation."""
    return f"Bonjour {nom}, bienvenue à {ville} !"

print(saluer("Marie"))
print(saluer("Paul", "Tana"))
print(saluer(ville="Mada", nom="Jean"))

# Retourner plusieurs valeurs
def stats_basiques(lst):
    return min(lst), max(lst), sum(lst)/len(lst)

mini, maxi, moy = stats_basiques([15, 12, 18, 9, 14])

# Portée LEGB
TAUX_TVA = 0.20  # Globale
def prix_ttc(prix_ht):
    tva = prix_ht * TAUX_TVA
    return prix_ht + tva

# Écrire fichier texte
with open("journal.txt", "w", encoding="utf-8") as f:
    f.write("Lundi 01/07/2026 : cours Python MUST\n")
    f.write("Mardi 02/07/2026 : TP data analysis\n")

# Lire ligne par ligne
with open("journal.txt", "r", encoding="utf-8") as f:
    for num, ligne in enumerate(f, 1):
        print(f"Ligne {num}: {ligne.rstrip()}")

# CSV
import csv
with open("etudiants.csv", "w", newline="", encoding="utf-8") as f:
    csv.writer(f).writerows([
        ["nom","filiere","note"],
        ["RAKOTO","DATA",15],
        ["RASOA","IoT",12],
    ])

# JSON
import json
config = {"filiere":"DATA","annee":2026,"campus":"Toliara"}
with open("config.json","w",encoding="utf-8") as f:
    json.dump(config, f, ensure_ascii=False, indent=2)

try:
    valeur = int(input("Entrez un entier : "))
    resultat = 100 / valeur
except ValueError:
    print("Saisie invalide — entrez un nombre entier")
except ZeroDivisionError:
    print("Division par zéro impossible")
except Exception as e:
    print(f"Erreur inattendue : {e}")
else:
    print(f"Résultat : {resultat:.2f}")
finally:
    print("Bloc toujours exécuté — nettoyage")

# Logging
import logging
logging.basicConfig(level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(message)s")
logging.info("Programme démarré")
logging.warning("Fichier manquant")
logging.error("Connexion échouée")

import os, math, random
from datetime import datetime, date
from collections import Counter

print(os.getcwd())
os.makedirs("data/raw", exist_ok=True)

print(math.sqrt(144))  # 12.0
print(random.randint(1, 6))

now = datetime.now()
print(now.strftime("%d/%m/%Y %H:%M:%S"))

texte = "data science data python data analytics"
freq = Counter(texte.split())
print(freq.most_common(3))

notes = {"Alice":15, "Bob":9, "Charlie":12, "Dia":17}
admis = {n:v for n,v in notes.items() if v >= 10}
inverse = {v:n for n,v in notes.items()}

annuaire = {
    "RAKOTO Jean": {"tel":"+261 34 12 34 56", "ville":"Toliara"},
    "RASOA Marie": {"tel":"+261 32 98 76 54", "ville":"Tana"},
}
print(annuaire["RAKOTO Jean"]["tel"])

from collections import Counter
freq = Counter(texte.split())

class Etudiant:
    nb_total = 0
    def __init__(self, nom, filiere):
        self.nom = nom
        self.filiere = filiere
        self.notes = []
        Etudiant.nb_total += 1
    def ajouter_note(self, note):
        if 0 <= note <= 20:
            self.notes.append(note)
    @property
    def moyenne(self):
        return sum(self.notes)/len(self.notes) if self.notes else None
    def __str__(self):
        moy = f"{self.moyenne:.1f}" if self.moyenne else "—"
        return f"[{self.filiere}] {self.nom} — Moy:{moy}/20"

class EtudiantBoursier(Etudiant):
    def __init__(self, nom, filiere, bourse_ar):
        super().__init__(nom, filiere)
        self.bourse_ar = bourse_ar

import numpy as np

a = np.array([1, 2, 3, 4, 5])
M = np.array([[1,2,3],[4,5,6]])
zeros = np.zeros((3, 4))
x = np.arange(0, 10, 0.5)

# Opérations vectorisées
print(a * 2)       # [2 4 6 8 10]
print(a ** 2)      # [1 4 9 16 25]
print(a.mean())    # 3.0
print(a.std())     # 1.41

# Monte Carlo — estimation de pi
N = 1_000_000
pts = np.random.uniform(-1, 1, (N, 2))
dans_cercle = (pts[:,0]**2 + pts[:,1]**2 <= 1).sum()
print(f"π = {4*dans_cercle/N:.5f}")

import pandas as pd

df = pd.DataFrame({
    "nom": ["RAKOTO","RASOA","RABE","RAFOLO","HERY"],
    "ville": ["Toliara","Tana","Toliara","Mahajanga","Toliara"],
    "filiere": ["DATA","IoT","DATA","MGT","IoT"],
    "note": [15, 12, 18, 9, 14]
})

print(df.shape)
print(df.dtypes)
print(df.describe())
print(df.isnull().sum())

# Filtrage
df[df["note"] >= 12]
df[(df["ville"]=="Toliara") & (df["note"]>10)]

# Colonne calculée
df["mention"] = df["note"].apply(
    lambda n: "TB" if n>=16 else "B" if n>=14 else "P" if n>=10 else "F")

import matplotlib.pyplot as plt
import numpy as np

fig, axes = plt.subplots(2, 2, figsize=(12, 8))

data = np.random.normal(28, 5, 200)
axes[0,0].hist(data, bins=20, color="#0A7C6E", edgecolor="white")
axes[0,0].axvline(data.mean(), color="red", ls="--", label="Moyenne")
axes[0,0].set_title("Distribution températures")

axes[0,1].bar(["DATA","IoT","MGT"], [40,35,25],
              color=["#0A7C6E","#1A237E","#B45309"])
axes[0,1].set_title("Répartition étudiants")

x = np.random.randn(200)
axes[1,0].scatter(x, x+np.random.randn(200)*0.5, alpha=0.5)

axes[1,1].boxplot([np.random.normal(13,3,50), np.random.normal(12,4,50)],
                  labels=["S1","S2"])
plt.tight_layout()
plt.savefig("dashboard_must.png", dpi=150, bbox_inches="tight")

# Initialisation
git init
git clone URL

# Workflow quotidien
git status
git add fichier.py
git commit -m "feat: add stats_lib"
git push origin main
git pull

# Branches
git checkout -b feature/visualisation
git merge feature/visualisation

# Conventions de commit
# feat: nouvelle fonctionnalité
# fix: correction de bug
# docs: mise à jour documentation

import requests, pandas as pd, matplotlib.pyplot as plt

r = requests.get("https://api.open-meteo.com/v1/forecast", params={
    "latitude": -23.35,
    "longitude": 43.67,
    "daily": "temperature_2m_max,temperature_2m_min,precipitation_sum",
    "timezone": "Indian/Antananarivo",
    "forecast_days": 7
})
r.raise_for_status()

data = r.json()["daily"]
df = pd.DataFrame(data)
df.columns = ["date", "t_max", "t_min", "pluie_mm"]
df.to_csv("meteo_toliara_7j.csv", index=False)

plt.figure(figsize=(10, 4))
plt.fill_between(df["date"], df["t_min"], df["t_max"], alpha=0.3)
plt.plot(df["date"], df["t_max"], "o-", label="T max")
plt.plot(df["date"], df["t_min"], "o-", label="T min")
plt.title("Prévisions Toliara — 7 jours")
plt.xticks(rotation=45)
plt.legend()
plt.tight_layout()
plt.savefig("meteo_toliara_7j.png", dpi=150)