pip install matplotlib seaborn pandas numpy

import matplotlib.pyplot as plt

# 準(zhǔn)備數(shù)據(jù)
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

# 創(chuàng)建圖形
plt.figure(figsize=(8, 4))

# 繪制折線圖
plt.plot(x, y, marker='o', linestyle='-', color='b', label='線性增長(zhǎng)')

# 添加標(biāo)題和標(biāo)簽
plt.title('簡(jiǎn)單折線圖示例')
plt.xlabel('X軸')
plt.ylabel('Y軸')

# 添加圖例
plt.legend()

# 顯示網(wǎng)格
plt.grid(True)

# 顯示圖形
plt.show()

import matplotlib.pyplot as plt

# 準(zhǔn)備數(shù)據(jù)
categories = ['A', 'B', 'C', 'D']
values = [15, 25, 30, 20]

# 創(chuàng)建圖形
plt.figure(figsize=(8, 5))

# 繪制柱狀圖
bars = plt.bar(categories, values, color=['red', 'green', 'blue', 'orange'])

# 在每個(gè)柱子上方顯示數(shù)值
for bar in bars:
    height = bar.get_height()
    plt.text(bar.get_x() + bar.get_width()/2., height,
             f'{height}', ha='center', va='bottom')

# 添加標(biāo)題和標(biāo)簽
plt.title('產(chǎn)品銷售額比較')
plt.xlabel('產(chǎn)品類別')
plt.ylabel('銷售額(萬(wàn)元)')

plt.show()

import numpy as np
import matplotlib.pyplot as plt

# 生成隨機(jī)數(shù)據(jù)
np.random.seed(42)
x = np.random.rand(50) * 10
y = 2 * x + np.random.randn(50) * 2

# 創(chuàng)建圖形
plt.figure(figsize=(8, 6))

# 繪制散點(diǎn)圖
plt.scatter(x, y, c='purple', alpha=0.7, edgecolors='w', s=100)

# 添加回歸線
m, b = np.polyfit(x, y, 1)
plt.plot(x, m*x + b, color='red', linestyle='--')

# 添加標(biāo)題和標(biāo)簽
plt.title('散點(diǎn)圖與回歸線')
plt.xlabel('自變量X')
plt.ylabel('因變量Y')

plt.grid(True)
plt.show()

import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt

# 準(zhǔn)備數(shù)據(jù)
np.random.seed(42)
data = [np.random.normal(0, std, 100) for std in range(1, 4)]

# 創(chuàng)建圖形
plt.figure(figsize=(8, 5))

# 繪制箱線圖
sns.boxplot(data=data, palette="Set2")

# 添加標(biāo)題
plt.title('不同標(biāo)準(zhǔn)差的正態(tài)分布箱線圖')
plt.xlabel('組別')
plt.ylabel('值')

plt.show()

import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt

# 生成相關(guān)矩陣數(shù)據(jù)
data = np.random.rand(10, 12)

# 創(chuàng)建圖形
plt.figure(figsize=(10, 8))

# 繪制熱力圖
sns.heatmap(data, annot=True, fmt=".2f", cmap="YlGnBu",
            linewidths=.5, cbar_kws={"shrink": .8})

# 添加標(biāo)題
plt.title('相關(guān)矩陣熱力圖')

plt.show()

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# 創(chuàng)建示例DataFrame
np.random.seed(42)
df = pd.DataFrame({
    'A': np.random.randn(100),
    'B': np.random.randn(100) + 2,
    'C': np.random.randn(100) * 2
})

# 繪制直方圖
df.plot.hist(alpha=0.5, bins=20, figsize=(10, 6))
plt.title('多變量分布直方圖')
plt.show()

# 繪制密度圖
df.plot.kde(figsize=(10, 6))
plt.title('核密度估計(jì)圖')
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# 準(zhǔn)備數(shù)據(jù)
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)
y3 = np.sin(x) * np.cos(x)

# 創(chuàng)建2x2的子圖布局
fig, axs = plt.subplots(2, 2, figsize=(12, 8))

# 第一個(gè)子圖：正弦函數(shù)
axs[0, 0].plot(x, y1, 'r-')
axs[0, 0].set_title('正弦函數(shù)')
axs[0, 0].grid(True)

# 第二個(gè)子圖：余弦函數(shù)
axs[0, 1].plot(x, y2, 'b--')
axs[0, 1].set_title('余弦函數(shù)')
axs[0, 1].grid(True)

# 第三個(gè)子圖：正弦余弦乘積
axs[1, 0].plot(x, y3, 'g-.')
axs[1, 0].set_title('正弦余弦乘積')
axs[1, 0].grid(True)

# 第四個(gè)子圖：全部疊加
axs[1, 1].plot(x, y1, 'r-', label='sin(x)')
axs[1, 1].plot(x, y2, 'b--', label='cos(x)')
axs[1, 1].plot(x, y3, 'g-.', label='sin(x)*cos(x)')
axs[1, 1].set_title('函數(shù)比較')
axs[1, 1].legend()
axs[1, 1].grid(True)

# 調(diào)整布局
plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt
import numpy as np

# 使用ggplot樣式
plt.style.use('ggplot')

# 準(zhǔn)備數(shù)據(jù)
x = np.linspace(0, 10, 100)
y = np.exp(x)

# 創(chuàng)建圖形
fig, ax = plt.subplots(figsize=(10, 6))

# 繪制曲線
ax.plot(x, y, 'b-', linewidth=2, label='指數(shù)曲線')

# 自定義坐標(biāo)軸
ax.set_xlim(0, 10)
ax.set_ylim(1, 10000)
ax.set_yscale('log')

# 添加標(biāo)題和標(biāo)簽
ax.set_title('對(duì)數(shù)坐標(biāo)系下的指數(shù)函數(shù)', fontsize=14, fontweight='bold')
ax.set_xlabel('X軸', fontsize=12)
ax.set_ylabel('Y軸(對(duì)數(shù))', fontsize=12)

# 添加圖例
ax.legend(loc='upper left', fontsize=10)

# 添加網(wǎng)格
ax.grid(True, which="both", ls="-", alpha=0.5)

# 添加文本注釋
ax.text(2, 100, r'$y=e^x$', fontsize=14, color='red')

plt.show()

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

# 創(chuàng)建圖形和坐標(biāo)軸
fig, ax = plt.subplots(figsize=(8, 6))
x = np.linspace(0, 2*np.pi, 100)
line, = ax.plot(x, np.sin(x), 'r-', linewidth=2)

# 設(shè)置坐標(biāo)軸范圍
ax.set_xlim(0, 2*np.pi)
ax.set_ylim(-1.5, 1.5)
ax.grid(True)

# 動(dòng)畫(huà)更新函數(shù)
def update(frame):
    line.set_ydata(np.sin(x + frame/10))
    return line,

# 創(chuàng)建動(dòng)畫(huà)
ani = FuncAnimation(fig, update, frames=100, interval=50, blit=True)

plt.title('動(dòng)態(tài)正弦波')
plt.show()

# 若要保存動(dòng)畫(huà)，可以取消下面行的注釋
# ani.save('sine_wave.gif', writer='pillow', fps=20)

# 數(shù)據(jù)可視化完整示例
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# 設(shè)置樣式
plt.style.use('seaborn')
sns.set_palette("husl")

# 1. 折線圖示例
def line_plot_example():
    x = np.linspace(0, 10, 100)
    y1 = np.sin(x)
    y2 = np.cos(x)
    
    plt.figure(figsize=(10, 5))
    plt.plot(x, y1, label='sin(x)', linewidth=2)
    plt.plot(x, y2, label='cos(x)', linestyle='--', linewidth=2)
    
    plt.title('三角函數(shù)比較', fontsize=14)
    plt.xlabel('X值', fontsize=12)
    plt.ylabel('Y值', fontsize=12)
    plt.legend()
    plt.grid(True)
    plt.show()

# 2. 柱狀圖示例
def bar_plot_example():
    categories = ['Q1', 'Q2', 'Q3', 'Q4']
    sales = [23, 45, 18, 34]
    
    plt.figure(figsize=(8, 5))
    bars = plt.bar(categories, sales, color=sns.color_palette())
    
    for bar in bars:
        height = bar.get_height()
        plt.text(bar.get_x() + bar.get_width()/2., height,
                f'{height}', ha='center', va='bottom')
    
    plt.title('季度銷售額', fontsize=14)
    plt.xlabel('季度', fontsize=12)
    plt.ylabel('銷售額(萬(wàn)元)', fontsize=12)
    plt.show()

# 3. 散點(diǎn)圖示例
def scatter_plot_example():
    np.random.seed(42)
    x = np.random.rand(50) * 10
    y = 2.5 * x + np.random.randn(50) * 2
    
    plt.figure(figsize=(8, 6))
    sns.regplot(x=x, y=y, scatter_kws={'s': 100, 'alpha': 0.6})
    
    plt.title('散點(diǎn)圖與回歸線', fontsize=14)
    plt.xlabel('自變量', fontsize=12)
    plt.ylabel('因變量', fontsize=12)
    plt.grid(True)
    plt.show()

# 4. 餅圖示例
def pie_chart_example():
    sizes = [35, 25, 20, 15, 5]
    labels = ['A產(chǎn)品', 'B產(chǎn)品', 'C產(chǎn)品', 'D產(chǎn)品', '其他']
    explode = (0.1, 0, 0, 0, 0)
    
    plt.figure(figsize=(8, 6))
    plt.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',
            shadow=True, startangle=140)
    plt.title('產(chǎn)品市場(chǎng)份額', fontsize=14)
    plt.axis('equal')
    plt.show()

# 5. 箱線圖示例
def box_plot_example():
    np.random.seed(42)
    data = [np.random.normal(0, std, 100) for std in range(1, 5)]
    
    plt.figure(figsize=(8, 5))
    sns.boxplot(data=data, palette="Set3")
    
    plt.title('不同組別的數(shù)據(jù)分布', fontsize=14)
    plt.xlabel('組別', fontsize=12)
    plt.ylabel('值', fontsize=12)
    plt.show()

# 6. 熱力圖示例
def heatmap_example():
    data = np.random.rand(8, 8)
    
    plt.figure(figsize=(8, 6))
    sns.heatmap(data, annot=True, fmt=".2f", cmap="coolwarm",
                linewidths=.5, cbar_kws={"shrink": .8})
    
    plt.title('相關(guān)矩陣熱力圖', fontsize=14)
    plt.show()

# 7. 多子圖示例
def subplots_example():
    x = np.linspace(0, 10, 100)
    y1 = np.sin(x)
    y2 = np.cos(x)
    y3 = np.tan(x)
    y4 = np.exp(x/5)
    
    fig, axs = plt.subplots(2, 2, figsize=(12, 8))
    
    # 第一個(gè)子圖
    axs[0, 0].plot(x, y1, 'r-')
    axs[0, 0].set_title('正弦函數(shù)')
    axs[0, 0].grid(True)
    
    # 第二個(gè)子圖
    axs[0, 1].plot(x, y2, 'b--')
    axs[0, 1].set_title('余弦函數(shù)')
    axs[0, 1].grid(True)
    
    # 第三個(gè)子圖
    axs[1, 0].plot(x, y3, 'g-.')
    axs[1, 0].set_title('正切函數(shù)')
    axs[1, 0].set_ylim(-5, 5)
    axs[1, 0].grid(True)
    
    # 第四個(gè)子圖
    axs[1, 1].plot(x, y4, 'm:')
    axs[1, 1].set_title('指數(shù)函數(shù)')
    axs[1, 1].grid(True)
    
    plt.tight_layout()
    plt.show()

# 8. Pandas集成可視化
def pandas_visualization():
    np.random.seed(42)
    df = pd.DataFrame({
        'A': np.random.randn(1000),
        'B': np.random.randn(1000) + 2,
        'C': np.random.randn(1000) * 2
    })
    
    # 繪制核密度估計(jì)圖
    df.plot.kde(figsize=(10, 6))
    plt.title('多變量核密度估計(jì)', fontsize=14)
    plt.show()
    
    # 繪制散點(diǎn)矩陣圖
    pd.plotting.scatter_matrix(df, figsize=(10, 8), diagonal='kde')
    plt.suptitle('散點(diǎn)矩陣圖', fontsize=14)
    plt.show()

# 執(zhí)行所有示例
if __name__ == "__main__":
    line_plot_example()
    bar_plot_example()
    scatter_plot_example()
    pie_chart_example()
    box_plot_example()
    heatmap_example()
    subplots_example()
    pandas_visualization()