和Python使用有关的一些教程，按类别分为不同文件

_intro.md

Python教程

Python是一个新手友好的语言，并且现在机器学习社区深度依赖于Python，C++, Cuda C, R等语言，使得Python的热度稳居第一。本Gist提供Python相关的一些教程，可以直接在Jupyter Notebook中运行。

1. 语言级教程，一般不涉及初级主题；
2. 标准库教程，最常见的标准库基本用法；
3. 第三方库教程，主要是常见的库如numpy，pytorch诸如此类，只涉及基本用法，不考虑新特性

其他内容就不往这个Gist里放了，注意Gist依旧由git进行版本控制，所以可以git clone 到本地，或者直接Google Colab\ Kaggle打开相应的ipynb文件

直接在网页浏览时，由于没有文件列表，可以按Ctrl + F来检索相应的目录，或者点击下面的超链接。

想要参与贡献的直接在评论区留言，有什么问题的也在评论区说 ^.^

目录-语言部分

• asyncio_tutorial.ipynb 协程
• c_extensions_tutorial.ipynb C语言扩展
• concurrency_tutorial.ipynb 并发
• context_managers_tutorial.ipynb 上下文管理器
• design_patterns_tutorial.ipynb 设计模式
• dunder_methods_tutorial.ipynb 内置方法（双下划线方法）
• generics_tutorial.ipynb 泛型和类型注解
• iterators_generators_tutorial.ipynb 生成器与迭代器
• metaprogramming_tutorial.ipynb 元编程
• pattern_matching_tutorial.ipynb 模式匹配
• python_internals_performance_tutorial.ipynb 性能分析
• snippets外链 个人日常开发中有用的一些代码片段

目录-库部分

• standard_library_tutorial.ipynb 标准库简易教程
• numpy_tutorial.ipynb 数学运算基石numpy
• pandas_tutorial.ipynb 结构化数据处理pandas
• matplotlib_tutorial.ipynb 绘图库matplotlib
• seaborn_tutorial.ipynb 绘图库seaborn

目录-具体业务库部分-本教程更多关注机器学习深度学习内容

• scikit_learn_tutorial.ipynb 机器学习库scikit_learn
• boosting_libs_tutorial.ipynb 梯度提升库XGBoost LightGBM CatBoost
• pytorch_tutorial.ipynb 深度学习库pytorch
• gymnasium_tutorial.ipynb 强化学习环境gymnasium
• stable_baselines3_tutorial.ipynb 强化学习算法实现SB3
• transformers_tutorial.ipynb 便利的预训练模型库Transformers
• opencv_tutorial.ipynb 经典的计算机视觉库opencv
• experiment_tracking_tutorial.ipynb 实验跟踪与可视化TensorBoard, WanDB, MLfow
• hpo_tutorial.ipynb 超参调优Optuna Raytune
• app_deploy_tutorial.ipynb 便捷的部署框架FastAPI Gradio Streamlit
• shap_tutorial.ipynb 模型可解释性SHAP
• langchain_tutorial.ipynb 构建完整AI应用LangChain
• llamaindex_tutorial.ipynb 另一个强大工具LlamaIndex
• faiss_tutorial.ipynb 向量数据库faiss
• MCP外链 MCP协议的使用

app_deploy_tutorial.ipynb

asyncio_tutorial.ipynb

boosting_libs_tutorial.ipynb

c_extensions_tutorial.ipynb

concurrency_tutorial.ipynb

context_managers_tutorial.ipynb

design_patterns_tutorial.ipynb

dunder_methods_tutorial.ipynb

experiment_tracking_tutorial.ipynb

faiss_tutorial.ipynb

generics_tutorial.ipynb

gymnasium_tutorial.ipynb

hpo_tutorial.ipynb

iterators_generators_tutorial.ipynb

langchain_tutorial.ipynb

llamaindex_tutorial.ipynb

matplotlib_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib - Python 数据可视化基础教程\n",
    "\n",
    "欢迎来到 Matplotlib 教程！Matplotlib 是 Python 中最基础、最广泛使用的数据可视化库。它提供了一个灵活的平台，可以创建各种静态、动态和交互式的图表。\n",
    "\n",
    "**为什么 Matplotlib 对 ML/DL/数据科学很重要？**\n",
    "\n",
    "1.  **探索性数据分析 (EDA)**：可视化是理解数据分布、关系和模式的关键步骤。\n",
    "2.  **结果展示**：清晰地展示模型性能、比较结果或呈现发现。\n",
    "3.  **调试与监控**：绘制损失曲线、激活值分布等，有助于监控模型训练过程。\n",
    "4.  **生态系统基础**：许多其他高级可视化库（如 Seaborn, Pandas 的绘图功能）都构建在 Matplotlib 之上。\n",
    "\n",
    "**Matplotlib 的两种主要接口：**\n",
    "\n",
    "*   **`pyplot` 接口**：基于状态机的接口（类似于 MATLAB）。它简单易用，适合快速绘图。\n",
    "*   **面向对象 (OO) 接口**：提供更多的控制和灵活性，特别适合创建复杂的图形和在 GUI 应用中嵌入图形。\n",
    "\n",
    "本教程将主要使用 `pyplot` 接口入门，并简要介绍 OO 接口。\n",
    "\n",
    "**本教程将涵盖：**\n",
    "\n",
    "1.  基本绘图 (`plot`)\n",
    "2.  常用图表类型 (散点图, 条形图, 直方图)\n",
    "3.  自定义图形元素 (标题, 标签, 图例, 颜色, 样式)\n",
    "4.  子图 (Subplots)\n",
    "5.  图像显示 (`imshow`)\n",
    "6.  保存图形 (`savefig`)\n",
    "7.  面向对象接口简介"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备工作：导入 Matplotlib 和 NumPy\n",
    "\n",
    "按照惯例，我们将 `matplotlib.pyplot` 导入为 `plt`。通常也需要 NumPy 来生成数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 这行是 Jupyter Notebook 的魔法命令，通常确保图形在单元格内显示\n",
    "%matplotlib inline\n",
    "\n",
    "print(\"Matplotlib and NumPy imported.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 基本绘图 (`plot`)\n",
    "\n",
    "`plt.plot()` 是最基础的绘图函数，通常用于绘制线图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 准备数据\n",
    "x = np.linspace(0, 10, 100) # 0 到 10 之间生成 100 个点\n",
    "y_sin = np.sin(x)\n",
    "y_cos = np.cos(x)\n",
    "\n",
    "# 绘制简单的线图\n",
    "plt.plot(x, y_sin)\n",
    "plt.xlabel(\"x axis label\")\n",
    "plt.ylabel(\"sin(x)\")\n",
    "plt.title(\"Simple Sine Wave\")\n",
    "plt.grid(True) # 添加网格线\n",
    "plt.show() # 显示图形 (在脚本中必需，在Jupyter中通常可选)\n",
    "\n",
    "# 在同一张图上绘制多条线\n",
    "plt.plot(x, y_sin, label='Sine') # 添加标签用于图例\n",
    "plt.plot(x, y_cos, label='Cosine')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.title(\"Sine and Cosine Waves\")\n",
    "plt.legend() # 显示图例\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 常用图表类型\n",
    "\n",
    "除了线图，Matplotlib 还可以轻松创建其他常见图表。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- 散点图 (Scatter Plot) ---\n",
    "x_scatter = np.random.rand(50) # 50个随机 x\n",
    "y_scatter = np.random.rand(50) # 50个随机 y\n",
    "colors = np.random.rand(50)    # 随机颜色值\n",
    "sizes = 100 * np.random.rand(50) # 随机大小\n",
    "\n",
    "plt.scatter(x_scatter, y_scatter, c=colors, s=sizes, alpha=0.7, cmap='viridis')\n",
    "# c: 颜色, s: 大小, alpha: 透明度, cmap: 颜色映射\n",
    "plt.xlabel(\"X value\")\n",
    "plt.ylabel(\"Y value\")\n",
    "plt.title(\"Scatter Plot Example\")\n",
    "plt.colorbar() # 显示颜色条\n",
    "plt.show()\n",
    "\n",
    "# --- 条形图 (Bar Chart) ---\n",
    "categories = ['Category A', 'Category B', 'Category C']\n",
    "values = [10, 25, 15]\n",
    "\n",
    "plt.bar(categories, values, color=['red', 'green', 'blue'])\n",
    "plt.xlabel(\"Categories\")\n",
    "plt.ylabel(\"Values\")\n",
    "plt.title(\"Bar Chart Example\")\n",
    "plt.show()\n",
    "\n",
    "# 水平条形图\n",
    "plt.barh(categories, values, color='skyblue')\n",
    "plt.xlabel(\"Values\")\n",
    "plt.ylabel(\"Categories\")\n",
    "plt.title(\"Horizontal Bar Chart Example\")\n",
    "plt.show()\n",
    "\n",
    "# --- 直方图 (Histogram) ---\n",
    "data_hist = np.random.randn(1000) # 1000个标准正态分布随机数\n",
    "\n",
    "plt.hist(data_hist, bins=30, color='purple', alpha=0.7, edgecolor='black')\n",
    "# bins: 分箱数量\n",
    "plt.xlabel(\"Value\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Histogram Example\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 自定义图形元素\n",
    "\n",
    "Matplotlib 允许对图形的几乎所有方面进行详细定制。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0, 5, 20)\n",
    "y1 = x**2\n",
    "y2 = x**3\n",
    "\n",
    "plt.plot(x, y1,\n",
    "         color='blue',       # 线条颜色\n",
    "         linestyle='--',     # 线条样式 ('-', '--', '-.', ':')\n",
    "         linewidth=2,        # 线条宽度\n",
    "         marker='o',         # 数据点标记样式 ('o', 's', '^', '+', '*')\n",
    "         markersize=5,       # 标记大小\n",
    "         label='y = x^2')    # 图例标签\n",
    "\n",
    "plt.plot(x, y2,\n",
    "         color='#FF8C00',    # 使用十六进制颜色码 (暗橙色)\n",
    "         linestyle=':',      # 点线\n",
    "         linewidth=2,\n",
    "         marker='s',         # 方块标记\n",
    "         markersize=6,\n",
    "         label='y = x^3')\n",
    "\n",
    "# 设置坐标轴范围\n",
    "plt.xlim(0, 5)\n",
    "plt.ylim(0, 130)\n",
    "\n",
    "# 添加标题和标签\n",
    "plt.title(\"Customized Plot\", fontsize=16)\n",
    "plt.xlabel(\"X Axis\", fontsize=12)\n",
    "plt.ylabel(\"Y Axis\", fontsize=12)\n",
    "\n",
    "# 添加网格和图例\n",
    "plt.grid(True, linestyle='-.', alpha=0.5)\n",
    "plt.legend(loc='upper left', fontsize=10) # loc 指定图例位置\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 子图 (Subplots)\n",
    "\n",
    "经常需要在同一个图形窗口 (Figure) 中绘制多个子图 (Axes)。有两种主要方式：\n",
    "1.  `plt.subplot()` (状态机方法)\n",
    "2.  `plt.subplots()` (面向对象方法 - 更推荐)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- 方法 1: plt.subplot() ---\n",
    "plt.figure(figsize=(10, 4)) # 创建一个图形窗口, figsize 指定大小 (英寸)\n",
    "\n",
    "plt.subplot(1, 2, 1) # (nrows, ncols, index), index 从 1 开始\n",
    "plt.plot(x, y_sin, 'r--') # 红色虚线\n",
    "plt.title('Sine Wave (subplot 1)')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(x, y_cos, 'g*-') # 绿色星号实线\n",
    "plt.title('Cosine Wave (subplot 2)')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.suptitle(\"Using plt.subplot()\", fontsize=14) # 整个 Figure 的大标题\n",
    "plt.tight_layout() # 自动调整子图参数以适应图形区域\n",
    "plt.show()\n",
    "\n",
    "# --- 方法 2: plt.subplots() (更推荐) ---\n",
    "# 它会创建一个 Figure 和一个包含 Axes 对象的 NumPy 数组\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 4)) # 返回 figure 和 axes 数组\n",
    "\n",
    "# axes 是一个 NumPy 数组，包含子图对象\n",
    "# print(f\"Type of fig: {type(fig)}\")\n",
    "# print(f\"Type of axes: {type(axes)}, shape: {axes.shape}\")\n",
    "\n",
    "# 在第一个子图 (axes[0]) 上绘制\n",
    "axes[0].plot(x, y_sin, 'b-.') # 蓝色点划线\n",
    "axes[0].set_title('Sine Wave (axes[0])')\n",
    "axes[0].set_xlabel('x')\n",
    "axes[0].set_ylabel('sin(x)')\n",
    "axes[0].grid(True)\n",
    "\n",
    "# 在第二个子图 (axes[1]) 上绘制\n",
    "axes[1].plot(x, y_cos, 'm:') # 洋红色点线\n",
    "axes[1].set_title('Cosine Wave (axes[1])')\n",
    "axes[1].set_xlabel('x')\n",
    "axes[1].set_ylabel('cos(x)')\n",
    "axes[1].grid(True)\n",
    "\n",
    "fig.suptitle(\"Using plt.subplots()\", fontsize=14)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 示例：2x2 子图\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(8, 8))\n",
    "axes[0, 0].plot(x, x, 'r')\n",
    "axes[0, 0].set_title('y = x')\n",
    "axes[0, 1].plot(x, x**2, 'g')\n",
    "axes[0, 1].set_title('y = x^2')\n",
    "axes[1, 0].plot(x, np.exp(x), 'b')\n",
    "axes[1, 0].set_title('y = e^x')\n",
    "axes[1, 1].plot(x, np.log1p(x), 'k') # log(1+x)\n",
    "axes[1, 1].set_title('y = log(1+x)')\n",
    "\n",
    "for ax_row in axes:\n",
    "    for ax in ax_row:\n",
    "        ax.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 图像显示 (`imshow`)\n",
    "\n",
    "`plt.imshow()` 用于将 NumPy 数组显示为图像。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建一个简单的灰度图像数据 (例如，一个棋盘格)\n",
    "img_data = np.zeros((10, 10))\n",
    "img_data[::2, ::2] = 1  # 偶数行偶数列设为1\n",
    "img_data[1::2, 1::2] = 1 # 奇数行奇数列设为1\n",
    "\n",
    "plt.imshow(img_data, cmap='gray') # cmap='gray' 指定灰度颜色映射\n",
    "plt.title(\"Simple Grayscale Image (Checkerboard)\")\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "\n",
    "# 创建一个随机彩色图像数据 (3通道 RGB)\n",
    "img_color = np.random.rand(10, 10, 3) # 10x10 像素, 3个颜色通道 (RGB)\n",
    "\n",
    "plt.imshow(img_color)\n",
    "plt.title(\"Random Color Image\")\n",
    "# plt.axis('off') # 可以关闭坐标轴\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. 保存图形 (`savefig`)\n",
    "\n",
    "可以将绘制的图形保存到文件中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "x = np.linspace(0, 2 * np.pi, 50)\n",
    "y = np.sin(x)\n",
    "\n",
    "plt.figure() # 创建一个新的图形\n",
    "plt.plot(x, y)\n",
    "plt.title(\"Plot to be Saved\")\n",
    "plt.xlabel(\"Radians\")\n",
    "plt.ylabel(\"sin(x)\")\n",
    "plt.grid(True)\n",
    "\n",
    "output_filename = \"sine_wave_plot.png\"\n",
    "plt.savefig(output_filename, dpi=150, bbox_inches='tight') \n",
    "# dpi: 分辨率 (dots per inch)\n",
    "# bbox_inches='tight': 尝试裁剪掉空白边缘\n",
    "\n",
    "print(f\"Plot saved to {output_filename}\")\n",
    "plt.show() # 仍然可以在 notebook 中显示\n",
    "\n",
    "# 清理\n",
    "if os.path.exists(output_filename):\n",
    "    os.remove(output_filename)\n",
    "    print(f\"Cleaned up {output_filename}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. 面向对象接口简介\n",
    "\n",
    "虽然 `pyplot` 接口方便快捷，但对于更复杂的图形或需要更多控制的情况，面向对象 (OO) 的接口通常更好。核心思想是显式地创建和操作 `Figure` 和 `Axes` 对象。\n",
    "\n",
    "*   **`Figure`**: 整个图形窗口或画布。\n",
    "*   **`Axes`**: 图形中的一个子图区域，包含坐标轴、标签、数据点等。一个 Figure 可以包含多个 Axes。\n",
    "\n",
    "`plt.subplots()` 是创建 Figure 和 Axes 的常用方法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OO 风格示例\n",
    "x_oo = np.linspace(0, 10, 100)\n",
    "y_oo = np.sqrt(x_oo)\n",
    "\n",
    "# 1. 创建 Figure 和 Axes 对象\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "\n",
    "# 2. 使用 Axes 对象的方法进行绘图和设置\n",
    "ax.plot(x_oo, y_oo, color='purple', label='sqrt(x)')\n",
    "ax.set_xlabel(\"X Value\")\n",
    "ax.set_ylabel(\"Square Root\")\n",
    "ax.set_title(\"Object-Oriented Plotting Example\")\n",
    "ax.grid(True)\n",
    "ax.legend()\n",
    "\n",
    "# 3. 显示图形 (pyplot 仍然可以用于显示)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 总结\n",
    "\n",
    "Matplotlib 是 Python 数据可视化的基石。通过 `pyplot` 接口可以快速创建常见的图表，而面向对象的接口提供了更精细的控制。\n",
    "\n",
    "**关键要点：**\n",
    "*   使用 `plt.plot()` 绘制线图，`plt.scatter()`, `plt.bar()`, `plt.hist()` 绘制其他常见图表。\n",
    "*   使用 `plt.xlabel()`, `plt.ylabel()`, `plt.title()`, `plt.legend()` 等添加标签和图例。\n",
    "*   可以通过颜色、线型、标记等参数自定义图形外观。\n",
    "*   使用 `plt.subplots()` 创建包含多个子图的图形。\n",
    "*   使用 `plt.imshow()` 显示图像数据。\n",
    "*   使用 `plt.savefig()` 保存图形。\n",
    "*   理解 `pyplot` 接口和面向对象接口的区别。\n",
    "\n",
    "Matplotlib 非常强大，有很多高级功能（3D绘图、动画、自定义样式等）值得探索。官方文档和示例库 (Gallery) 是非常好的学习资源。同时，Seaborn 库基于 Matplotlib 提供了更高级的统计可视化功能，通常与 Matplotlib 结合使用。"
   ]
  }
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metaprogramming_tutorial.ipynb

numpy_tutorial.ipynb

opencv_tutorial.ipynb

pandas_tutorial.ipynb

pattern_matching_tutorial.ipynb

python_internals_performance_tutorial.ipynb

pytorch_tutorial.ipynb

scikit_learn_tutorial.ipynb

seaborn_tutorial.ipynb

shap_tutorial.ipynb

stable_baselines3_tutorial.ipynb

standard_library_tutorial.ipynb

transformers_tutorial.ipynb

返回顶部

有见解，有问题，或者单纯想盖楼灌水，都可以在这里发表！

因为文档比较多，有时候渲染不出来ipynb是浏览器性能的问题，刷新即可

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