01_demo — Notebook de test

# Vérif kernel Thebe/Binder
import sys, platform
print("Python:", sys.version.split()[0], "|", platform.platform())

import numpy as np, pandas as pd, matplotlib
print("NumPy:", np.__version__, "| Pandas:", pd.__version__, "| Matplotlib:", matplotlib.__version__)

Python: 3.11.13 | Linux-6.6.87.2-microsoft-standard-WSL2-x86_64-with-glibc2.35

NumPy: 2.3.2 | Pandas: 2.3.2 | Matplotlib: 3.10.6

# Initialisation 
import sys, platform
import numpy as np
import pandas as pd

print("Python:", sys.version.split()[0])
print("NumPy:", np.__version__, "| Pandas:", pd.__version__)
print("Platform:", platform.platform())

np.random.seed(0)

Python: 3.11.13
NumPy: 2.3.2 | Pandas: 2.3.2
Platform: Linux-6.6.87.2-microsoft-standard-WSL2-x86_64-with-glibc2.35

01_demo — Notebook de test

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 2*np.pi, 200)
y = np.sin(x)

plt.figure()
plt.plot(x, y)
plt.title("sin(x)")
plt.xlabel("x")
plt.ylabel("y")
plt.show()

01_demo — Notebook de test

import pandas as pd
from IPython.display import display

df = pd.DataFrame({"a": [1, 2, 3, 4], "b": [10, 20, 10, 30]})
display(df)
df.describe()

	a	b
0	1	10
1	2	20
2	3	10
3	4	30

	a	b
count	4.000000	4.000000
mean	2.500000	17.500000
std	1.290994	9.574271
min	1.000000	10.000000
25%	1.750000	10.000000
50%	2.500000	15.000000
75%	3.250000	22.500000
max	4.000000	30.000000

01_demo — Notebook de test

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

X, y = load_iris(return_X_y=True)
Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.2, random_state=0)

clf = LogisticRegression(max_iter=200)
clf.fit(Xtr, ytr)
pred = clf.predict(Xte)
print("Accuracy:", accuracy_score(yte, pred))

Accuracy: 1.0