import numpy as np

# 創(chuàng)建一個(gè)一維數(shù)組
arr = np.array([1, 2, 3, 4, 5])
print(arr)
# 輸出: [1 2 3 4 5]

# 創(chuàng)建一個(gè)二維數(shù)組
matrix = np.array([[1, 2, 3], [4, 5, 6]])
print(matrix)
# 輸出:
# [[1 2 3]
#  [4 5 6]]

# 數(shù)組的基本運(yùn)算
result = arr + 10
print(result)
# 輸出: [11 12 13 14 15]

from scipy.integrate import quad

# 計(jì)算定積分
def f(x):
    return x**2

integral, error = quad(f, 0, 1)
print(f"Integral: {integral}, Error: {error}")
# 輸出: Integral: 0.3333333333333333, Error: 3.700743415417188e-15

import pandas as pd

# 創(chuàng)建一個(gè)DataFrame
data = {'Name': ['Tom', 'Jerry', 'Mickey'],
        'Age': [5, 7, 8],
        'City': ['New York', 'Paris', 'London']}
df = pd.DataFrame(data)

print(df)
# 輸出:
#     Name  Age       City
# 0    Tom    5   New York
# 1  Jerry    7      Paris
# 2  Mickey    8     London

# 選擇數(shù)據(jù)
print(df['Age'])
# 輸出:
# 0    5
# 1    7
# 2    8
# Name: Age, dtype: int64

import matplotlib.pyplot as plt

# 繪制折線圖
x = [1, 2, 3, 4, 5]
y = [1, 4, 9, 16, 25]
plt.plot(x, y)
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Simple Plot')
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt

# 使用Seaborn繪制散點(diǎn)圖
tips = sns.load_dataset("tips")
sns.scatterplot(x="total_bill", y="tip", data=tips)
plt.show()

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# 加載Iris數(shù)據(jù)集
iris = load_iris()
X = iris.data
y = iris.target

# 劃分訓(xùn)練集和測(cè)試集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 創(chuàng)建KNN分類器
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)

# 預(yù)測(cè)并計(jì)算準(zhǔn)確率
y_pred = knn.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, y_pred)}")
# 輸出: Accuracy: 0.9666666666666667

import sympy as sp

# 定義符號(hào)變量
x = sp.symbols('x')

# 進(jìn)行符號(hào)計(jì)算
expr = x**2 + 2*x + 1
print(f"Expression: {expr}")
# 輸出: Expression: x**2 + 2*x + 1

# 因式分解
factored_expr = sp.factor(expr)
print(f"Factored Expression: {factored_expr}")
# 輸出: Factored Expression: (x + 1)**2

import networkx as nx
import matplotlib.pyplot as plt

# 創(chuàng)建一個(gè)無(wú)向圖
G = nx.Graph()

# 添加節(jié)點(diǎn)和邊
G.add_node(1)
G.add_nodes_from([2, 3, 4, 5])
G.add_edge(1, 2)
G.add_edges_from([(2, 3), (3, 4), (4, 5), (5, 1)])

# 繪制圖形
nx.draw(G, with_labels=True)
plt.show()

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix

# 加載數(shù)據(jù)集
iris = load_iris()
X = iris.data
y = iris.target

# 劃分訓(xùn)練集和測(cè)試集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 創(chuàng)建隨機(jī)森林分類器
clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X_train, y_train)

# 預(yù)測(cè)
y_pred = clf.predict(X_test)

# 輸出分類報(bào)告和混淆矩陣
print("Classification Report:")
print(classification_report(y_test, y_pred))
print("\nConfusion Matrix:")
print(confusion_matrix(y_test, y_pred))