{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 自己生成数据，绘制出正态分布的左半部分数据的直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.random.standard_normal(10000000)\n",
    "# 截取小于平均值的数据\n",
    "x = x[x<np.mean(x)]\n",
    "\n",
    "# 创建画布\n",
    "plt.figure(figsize=(20,8))\n",
    "# 绘制图像\n",
    "plt.hist(x, bins=1000)\n",
    "# 显示图像a\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## axes绘图区域的命名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 231 µs, sys: 1 µs, total: 232 µs\n",
      "Wall time: 237 µs\n"
     ]
    },
    {
     "data": {
      "image/png": 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xqvrgVuzL+nt+P7tl+3rW/0AMYZq9nwP8eHR9HrhqoL2nMZcZm9e+zvZgs91qrmcZ+nndeTjNvncAzwc+leTzSd4xwL7T7j0LE/etqoeA7yX5EvBoVX1xq/YGPgS8K8lDrL+XOZM7TJO8ZBvNmLO9RfvOaLZbzbU3TElSc94wJUnNGXpJas7QS1Jzhl6SmjP0ktScoZek5gy9JDX3v2mrvmw1ELN/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建多个绘图区域\n",
    "fig, ax = plt.subplots(nrows=1,ncols=2)\n",
    "\n",
    "# 设置画布的title\n",
    "fig.suptitle('abc')\n",
    "\n",
    "# 设置第一个绘图区域（第一幅图像）的title\n",
    "%time  ax[0].set_title('123')\n",
    "\n",
    "# 显示图像\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}