import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10, 100)
y = np.sin(x)
# 狀態(tài)機 API (不推薦用于復(fù)雜項目)
plt.figure()
plt.plot(x, y)
plt.title("Sine Wave")
plt.show()
# 面向?qū)ο?API (強烈推薦，邏輯清晰，易于控制)
fig, ax = plt.subplots(figsize=(8, 5))  # 創(chuàng)建畫布和坐標(biāo)系
ax.plot(x, y, label="Sine")             # 在指定的ax上繪圖
ax.set_title("Sine Wave")               # 設(shè)置屬性
ax.legend()
plt.show()

import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import pandas as pd
# ==========================================
# 全局參數(shù)設(shè)置 (rcParams)
# ==========================================
# 1. 解決中文顯示問題 (Windows/Mac通用方案)
plt.rcParams['font.sans-serif'] = ['SimHei', 'Arial Unicode MS', 'Microsoft YaHei'] # 支持黑體/蘋方/微軟雅黑
plt.rcParams['axes.unicode_minus'] = False  # 正常顯示負號
# 2. 提升圖表默認顏值
plt.rcParams['figure.dpi'] = 120            # 提高默認分辨率
plt.rcParams['axes.spines.top'] = False     # 隱藏頂部邊框
plt.rcParams['axes.spines.right'] = False   # 隱藏右側(cè)邊框
plt.rcParams['axes.grid'] = True            # 默認開啟網(wǎng)格
plt.rcParams['grid.alpha'] = 0.3            # 網(wǎng)格線透明度
plt.rcParams['grid.linestyle'] = '--'       # 網(wǎng)格線型
# 3. 使用內(nèi)置高級樣式 (可選)
# plt.style.use('seaborn-v0_8-whitegrid') # 類似Seaborn的清爽風(fēng)格
# plt.style.use('ggplot')                 # R語言ggplot2風(fēng)格

fig, ax = plt.subplots(figsize=(10, 6))
months = np.arange(1, 13)
sales_A = np.random.randint(100, 300, 12)
sales_B = np.random.randint(150, 350, 12)
# 繪制折線，控制顏色、線型、標(biāo)記
ax.plot(months, sales_A, color='#1f77b4', marker='o', linewidth=2, markersize=6, label='產(chǎn)品A')
ax.plot(months, sales_B, color='#ff7f0e', marker='s', linewidth=2, markersize=6, linestyle='--', label='產(chǎn)品B')
# 填充兩條線之間的區(qū)域 (突出差異)
ax.fill_between(months, sales_A, sales_B, where=(sales_A > sales_B), color='blue', alpha=0.1, interpolate=True)
ax.set_xticks(months)
ax.set_xticklabels([f"{m}月" for m in months])
ax.set_ylabel("銷售額 (萬元)")
ax.set_title("2025年各產(chǎn)品線月度銷售趨勢", fontsize=16, fontweight='bold', pad=15)
ax.legend(loc='upper left', frameon=False) # 去除圖例邊框
plt.tight_layout()
plt.show()

fig, ax = plt.subplots(figsize=(9, 7))
np.random.seed(42)
x = np.random.randn(200)
y = 2.5 * x + np.random.randn(200) * 1.5
colors = np.random.rand(200)  # 顏色映射
sizes = np.random.rand(200) * 500  # 氣泡大小
# 使用 cmap 映射顏色，alpha 控制透明度
scatter = ax.scatter(x, y, c=colors, s=sizes, alpha=0.6, cmap='viridis', edgecolors='w', linewidth=0.5)
# 添加顏色條 (Colorbar)
cbar = fig.colorbar(scatter, ax=ax)
cbar.set_label("用戶活躍度指數(shù)", rotation=270, labelpad=15)
ax.set_xlabel("廣告投入 (萬元)")
ax.set_ylabel("轉(zhuǎn)化率 (%)")
ax.set_title("廣告投入與轉(zhuǎn)化率關(guān)系分析 (氣泡大小代表客單價)")
plt.show()

fig, ax = plt.subplots(figsize=(10, 6))
categories = ['華北', '華東', '華南', '西南', '西北']
q1_sales = [120, 150, 130, 90, 70]
q2_sales = [135, 160, 145, 100, 85]
x = np.arange(len(categories))
width = 0.35
bars1 = ax.bar(x - width/2, q1_sales, width, label='Q1季度', color='#4C72B0', edgecolor='white')
bars2 = ax.bar(x + width/2, q2_sales, width, label='Q2季度', color='#DD8452', edgecolor='white')
# 數(shù)據(jù)標(biāo)簽自動化 (分析師必備技巧)
def add_labels(bars):
    for bar in bars:
        height = bar.get_height()
        ax.annotate(f'{height}',
                    xy=(bar.get_x() + bar.get_width() / 2, height),
                    xytext=(0, 3),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom', fontsize=10)
add_labels(bars1)
add_labels(bars2)
ax.set_xticks(x)
ax.set_xticklabels(categories)
ax.set_ylabel("營收 (百萬)")
ax.set_title("各大區(qū)Q1與Q2營收對比")
ax.legend()
plt.show()

fig, ax = plt.subplots(figsize=(10, 6))
# 生成偏態(tài)分布數(shù)據(jù)
data = np.random.lognormal(mean=2.0, sigma=0.8, size=1000)
# 繪制直方圖 (density=True 使其與密度曲線y軸對齊)
ax.hist(data, bins=50, density=True, alpha=0.6, color='skyblue', edgecolor='black', label='頻數(shù)分布')
# 疊加核密度估計曲線 (KDE) - 需借助scipy
from scipy.stats import gaussian_kde
density = gaussian_kde(data)
x_vals = np.linspace(0, np.max(data), 200)
ax.plot(x_vals, density(x_vals), color='red', linewidth=2, label='KDE密度曲線')
# 添加均值和中位數(shù)輔助線
mean_val, median_val = np.mean(data), np.median(data)
ax.axvline(mean_val, color='green', linestyle='--', label=f'均值: {mean_val:.2f}')
ax.axvline(median_val, color='purple', linestyle='-.', label=f'中位數(shù): {median_val:.2f}')
ax.set_xlabel("用戶停留時長 (秒)")
ax.set_ylabel("概率密度")
ax.set_title("用戶APP停留時長分布 (右偏分布)")
ax.legend()
plt.show()

fig, ax = plt.subplots(figsize=(10, 6))
# 模擬不同部門的薪資數(shù)據(jù) (包含異常值)
data = [
    np.random.normal(15000, 3000, 100).tolist() + [35000, 40000], # 研發(fā)部
    np.random.normal(10000, 2000, 100).tolist() + [25000],        # 市場部
    np.random.normal(8000, 1500, 100).tolist() + [5000, 18000]    # 運營部
]
bp = ax.boxplot(data, patch_artist=True, labels=['研發(fā)部', '市場部', '運營部'],
                showmeans=True, # 顯示均值點
                meanprops={"marker":"D", "markerfacecolor":"red", "markersize":8}) # 均值樣式
# 自定義箱體顏色
colors = ['#8DD3C7', '#FFFFB3', '#BEBADA']
for patch, color in zip(bp['boxes'], colors):
    patch.set_facecolor(color)
ax.set_ylabel("月薪 (元)")
ax.set_title("各部門薪資分布與異常值檢測")
plt.show()

fig, ax = plt.subplots(figsize=(10, 6))
x = np.arange(2015, 2025)
y = [12, 15, 13, 18, 22, 19, 25, 30, 28, 45] # 2024年有突破
# 默認灰色線條
ax.plot(x, y, color='lightgray', marker='o', linewidth=2, markersize=8, zorder=2)
# 高亮最大值
max_idx = np.argmax(y)
ax.plot(x[max_idx], y[max_idx], color='red', marker='o', markersize=12, zorder=3)
# 添加帶箭頭的注釋
ax.annotate(f'歷史峰值: {y[max_idx]}M', 
            xy=(x[max_idx], y[max_idx]), 
            xytext=(x[max_idx]-2, y[max_idx]+5),
            arrowprops=dict(facecolor='black', arrowstyle='->', lw=1.5),
            fontsize=12, fontweight='bold', color='red')
# 添加背景水印或數(shù)據(jù)來源說明
ax.text(0.98, 0.02, '數(shù)據(jù)來源: 內(nèi)部BI系統(tǒng)', transform=ax.transAxes,
        fontsize=9, color='gray', ha='right', va='bottom', alpha=0.7)
ax.set_title("公司年度營收增長趨勢 (2015-2024)")
plt.show()

from matplotlib.ticker import FuncFormatter, MultipleLocator
fig, ax = plt.subplots(figsize=(10, 6))
x = np.arange(10)
y = [1250000, 1450000, 1300000, 1800000, 2200000, 1950000, 2500000, 3000000, 2800000, 4500000]
ax.bar(x, y, color='steelblue')
# 自定義Y軸格式化函數(shù) (轉(zhuǎn)為"萬"單位)
def format_y(value, tick_number):
    return f'{value / 10000:.0f}萬'
ax.yaxis.set_major_formatter(FuncFormatter(format_y))
# 設(shè)置Y軸刻度間隔
ax.yaxis.set_major_locator(MultipleLocator(1000000))
ax.set_xticks(x)
ax.set_title("Y軸自定義格式化示例")
plt.show()

# 創(chuàng)建 2x2 的網(wǎng)格，共享X軸或Y軸
fig, axes = plt.subplots(2, 2, figsize=(12, 10), sharex=True, sharey=True)
# 扁平化axes數(shù)組以便循環(huán)
for i, ax in enumerate(axes.flatten()):
    x = np.random.randn(100)
    y = np.random.randn(100)
    ax.scatter(x, y, alpha=0.6)
    ax.set_title(f'子圖 {i+1}')
fig.suptitle("多子圖共享坐標(biāo)軸示例", fontsize=16, y=1.02)
plt.tight_layout()
plt.show()

import matplotlib.gridspec as gridspec
fig = plt.figure(figsize=(14, 8))
# 定義 3行3列 的網(wǎng)格
gs = gridspec.GridSpec(3, 3, figure=fig, hspace=0.3, wspace=0.3)
# 左側(cè)大圖占據(jù) 3行2列
ax_main = fig.add_subplot(gs[:, :2]) 
# 右上小圖
ax_top_right = fig.add_subplot(gs[0, 2])
# 右中
ax_mid_right = fig.add_subplot(gs[1, 2])
# 右下
ax_bot_right = fig.add_subplot(gs[2, 2])
# 繪制數(shù)據(jù)
x = np.linspace(0, 10, 100)
ax_main.plot(x, np.sin(x), 'b-', linewidth=2)
ax_main.set_title("主視圖：正弦波")
ax_top_right.bar(['A','B','C'], [10, 20, 15], color='coral')
ax_top_right.set_title("分類統(tǒng)計")
ax_mid_right.hist(np.random.randn(200), bins=20, color='lightgreen', edgecolor='black')
ax_mid_right.set_title("分布直方圖")
ax_bot_right.pie([30, 40, 30], labels=['X','Y','Z'], autopct='%1.1f%%', colors=['#ff9999','#66b3ff','#99ff99'])
ax_bot_right.set_title("占比分析")
plt.show()

fig, ax1 = plt.subplots(figsize=(10, 6))
months = np.arange(1, 13)
temperature = [5, 8, 12, 18, 24, 28, 32, 31, 26, 19, 12, 6]
ice_cream_sales = [10, 15, 25, 45, 80, 120, 150, 145, 90, 50, 20, 12]
# 左Y軸 (溫度)
color1 = 'tab:red'
ax1.set_xlabel('月份')
ax1.set_ylabel('平均氣溫 (°C)', color=color1)
ax1.plot(months, temperature, color=color1, marker='o', label='氣溫')
ax1.tick_params(axis='y', labelcolor=color1)
# 實例化第二個Y軸
ax2 = ax1.twinx()  
color2 = 'tab:blue'
ax2.set_ylabel('冰淇淋銷量 (箱)', color=color2)  
ax2.bar(months, ice_cream_sales, color=color2, alpha=0.4, label='銷量')
ax2.tick_params(axis='y', labelcolor=color2)
# 合并圖例 (重要技巧)
lines_1, labels_1 = ax1.get_legend_handles_labels()
lines_2, labels_2 = ax2.get_legend_handles_labels()
ax1.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper left')
plt.title("氣溫與冰淇淋銷量相關(guān)性分析 (雙Y軸)")
fig.tight_layout()
plt.show()

from mpl_toolkits.mplot3d import Axes3D # 導(dǎo)入3D工具包
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
# 定義一個復(fù)雜的3D函數(shù) (如Sinc函數(shù)變形)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R) / (R + 0.1)
# 繪制3D曲面
surf = ax.plot_surface(X, Y, Z, cmap='coolwarm', edgecolor='none', alpha=0.9)
# 添加顏色條
fig.colorbar(surf, ax=ax, shrink=0.5, aspect=10, label='Z Value')
ax.set_zlabel('Z Axis')
ax.set_title("3D Surface Plot")
plt.show()

# 保存為矢量圖 (PDF/SVG) - 論文、PPT首選，無限放大不失真
fig.savefig('report_chart.pdf', format='pdf', bbox_inches='tight')
# 保存為高分辨率位圖 (PNG) - 網(wǎng)頁、公眾號文章首選
fig.savefig('web_chart.png', format='png', dpi=300, bbox_inches='tight', facecolor='white')

import seaborn as sns
# 使用Seaborn繪制復(fù)雜的統(tǒng)計圖
fig, ax = plt.subplots(figsize=(10, 6))
tips = sns.load_dataset("tips")
# Seaborn綁定到Matplotlib的ax上
sns.violinplot(x="day", y="total_bill", hue="smoker", data=tips, split=True, ax=ax, palette="Set2")
# 使用Matplotlib的API進行細節(jié)微調(diào)
ax.set_title("Seaborn與Matplotlib結(jié)合使用", fontsize=14)
ax.set_xlabel("星期")
ax.set_ylabel("賬單總額")
sns.despine(ax=ax) # 移除上右邊框
plt.show()