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import numpy as np
numbers = np.array([1, 2, 3])
new_numbers = numbers + 1
print(new_numbers.tolist())

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Here are links to the most important free Python courses with a brief description of their value.

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, confusion_matrix
import seaborn as sns

# Load dataset
data = datasets.load_iris()
X = data.data
y = data.target
feature_names = data.feature_names
target_names = data.target_names

# Split and scale data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Train KNN model with k=5
knn = KNeighborsClassifier(n_neighbors=5, metric='euclidean')
knn.fit(X_train_scaled, y_train)

# Predict and evaluate
y_pred = knn.predict(X_test_scaled)
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy:.2f}")

# Confusion Matrix
cm = confusion_matrix(y_test, y_pred)
plt.figure(figsize=(6, 4))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=target_names, yticklabels=target_names)
plt.title('Confusion Matrix')
plt.ylabel('True Label')
plt.xlabel('Predicted Label')
plt.show()

# Visualize decision boundaries (for first two features only)
plt.figure(figsize=(8, 6))
X_plot = X[:, :2]  # Use only first two features for visualization
X_plot_scaled = scaler.fit_transform(X_plot)
knn_visual = KNeighborsClassifier(n_neighbors=5)
knn_visual.fit(X_plot_scaled, y)
h = 0.02
x_min, x_max = X_plot_scaled[:, 0].min() - 1, X_plot_scaled[:, 0].max() + 1
y_min, y_max = X_plot_scaled[:, 1].min() - 1, X_plot_scaled[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = knn_visual.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, alpha=0.3, cmap=plt.cm.Paired)
for i, color in enumerate(['red', 'green', 'blue']):
    idx = np.where(y == i)
    plt.scatter(X_plot_scaled[idx, 0], X_plot_scaled[idx, 1], c=color, label=target_names[i], edgecolors='k')
plt.xlabel(feature_names[0])
plt.ylabel(feature_names[1])
plt.title('KNN Decision Boundaries (First Two Features)')
plt.legend()
plt.show()

x = [1, 2, 3]
y = x
y.append(4)
print(x)

def func(a, b=[]):
    b.append(a)
    return b

print(func(1))
print(func(2))

print(2 ** 3 ** 2)

list1 = [1, 2, 3]
list2 = list1.copy()
list2[0] = 10
print(list1)

a = [1, 2, 3]
b = a[:]
b[0] = 10
print(a)

x = "hello"
y = "world"
print(x + y)

my_fruits = ["apple", "banana", "cherry", "date"]

Using enumerate() for a clean loop with index

print("--- Looping with default index ---")
for index, fruit in enumerate(my_fruits):
    print(f"Fruit at index {index}: {fruit}")

You can also specify a starting index for the counter

print("\n--- Looping with custom start index (e.g., from 1) ---")
for count, fruit in enumerate(my_fruits, start=1):
    print(f"Fruit number {count}: {fruit}")

--- Looping with default index ---
Fruit at index 0: apple
Fruit at index 1: banana
Fruit at index 2: cherry
Fruit at index 3: date

--- Looping with custom start index (e.g., from 1) ---
Fruit number 1: apple
Fruit number 2: banana
Fruit number 3: cherry
Fruit number 4: date

class Car:
    def init(self, make, model, year):
        self.make = make    # Assign 'make' to the instance's 'make' attribute
        self.model = model  # Assign 'model' to the instance's 'model' attribute
        self.year = year    # Assign 'year' to the instance's 'year' attribute

    def get_description(self):
        return f"This is a {self.year} {self.make} {self.model}."

my_car = Car("Toyota", "Camry", 2020)
your_car = Car("Honda", "Civic", 2022)

print(my_car.get_description())
print(your_car.get_description())

This is a 2020 Toyota Camry.
This is a 2022 Honda Civic.