Python 科学计算必备的八个库

import numpy as np

# 创建一个一维数组
arr = np.array([1, 2, 3, 4, 5])
print(arr)
# 输出: [1 2 3 4 5]

# 创建一个二维数组
matrix = np.array([[1, 2, 3], [4, 5, 6]])
print(matrix)
# 输出:
# [[1 2 3]
#  [4 5 6]]

# 数组的基本运算
result = arr + 10
print(result)
# 输出: [11 12 13 14 15]
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from scipy.integrate import quad

# 计算定积分
def f(x):
    return x**2

integral, error = quad(f, 0, 1)
print(f"Integral: {integral}, Error: {error}")
# 输出: Integral: 0.3333333333333333, Error: 3.700743415417188e-15
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import pandas as pd

# 创建一个DataFrame
data = {'Name': ['Tom', 'Jerry', 'Mickey'],
        'Age': [5, 7, 8],
        'City': ['New York', 'Paris', 'London']}
df = pd.DataFrame(data)

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

# 选择数据
print(df['Age'])
# 输出:
# 0    5
# 1    7
# 2    8
# Name: Age, dtype: int64
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import matplotlib.pyplot as plt

# 绘制折线图
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()
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import seaborn as sns
import matplotlib.pyplot as plt

# 使用Seaborn绘制散点图
tips = sns.load_dataset("tips")
sns.scatterplot(x="total_bill", y="tip", data=tips)
plt.show()
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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

# 加载Iris数据集
iris = load_iris()
X = iris.data
y = iris.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建KNN分类器
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)

# 预测并计算准确率
y_pred = knn.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, y_pred)}")
# 输出: Accuracy: 0.9666666666666667
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import sympy as sp

# 定义符号变量
x = sp.symbols('x')

# 进行符号计算
expr = x**2 + 2*x + 1
print(f"Expression: {expr}")
# 输出: Expression: x**2 + 2*x + 1

# 因式分解
factored_expr = sp.factor(expr)
print(f"Factored Expression: {factored_expr}")
# 输出: Factored Expression: (x + 1)**2
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import networkx as nx
import matplotlib.pyplot as plt

# 创建一个无向图
G = nx.Graph()

# 添加节点和边
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)])

# 绘制图形
nx.draw(G, with_labels=True)
plt.show()
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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

# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 创建随机森林分类器
clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X_train, y_train)

# 预测
y_pred = clf.predict(X_test)

# 输出分类报告和混淆矩阵
print("Classification Report:")
print(classification_report(y_test, y_pred))
print("\nConfusion Matrix:")
print(confusion_matrix(y_test, y_pred))
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