matplot_img_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 解决坐标轴负刻度乱码问题\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "# 解决中文乱码问题\n",
    "plt.rcParams['font.sans-serif'] = ['Simhei']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0, 2 * np.pi, 50)\n",
    "y = np.random.random(50)\n",
    "plt.scatter(x, y)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.randint(1, 100, 100)\n",
    "y = np.random.rand(100)\n",
    "\n",
    "size = np.random.rand(100) * 500\n",
    "color = np.random.rand(100)\n",
    "\n",
    "plt.rc('figure', figsize=(10, 4))\n",
    "plt.scatter(\n",
    "    x, y, size, color,\n",
    "    alpha=.3,\n",
    "    # linewidths=2\n",
    ")\n",
    "plt.colorbar()  # 显示色条\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "size = 5\n",
    "a = np.random.random(size)\n",
    "x = np.arange(size)\n",
    "\n",
    "plt.bar(x, a)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "size = 5\n",
    "a = np.random.random(size)\n",
    "b = np.random.random(size)\n",
    "c = np.random.random(size)\n",
    "x = np.arange(size)\n",
    "\n",
    "total_width, n = 0.8, 3\n",
    "width = total_width / n\n",
    "x = x - (total_width - width) / 2\n",
    "\n",
    "plt.bar(x, a, width=width, label='a')\n",
    "plt.bar(x + width, b, width=width, label='b')\n",
    "plt.bar(x + width * 2, c, width=width, label='c')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 5\n",
    "men_means = (20, 35, 30, 27, 35)\n",
    "women_means = (25, 30, 28, 10, 35)\n",
    "ind = np.arange(n)\n",
    "width = 0.35\n",
    "\n",
    "p1 = plt.bar(ind, men_means, width, color='#d62728')\n",
    "p2 = plt.bar(ind, women_means, width, bottom=men_means)\n",
    "plt.ylabel('Scores')\n",
    "plt.title('Score by group and gender')\n",
    "plt.xticks(ind, (\"G1\", \"G2\", \"G3\", \"G4\", \"G5\"))\n",
    "plt.yticks(np.arange(0, 81, 10))\n",
    "\n",
    "plt.legend((p1[0], p2[0]), ('Men', 'Women'))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 5\n",
    "men_means = (20, 35, 30, 27, 35)\n",
    "women_means = (25, 30, 28, 10, 35)\n",
    "\n",
    "men_std = (2, 3, 4, 1, 2)\n",
    "women_std = (3, 5, 2, 3, 3)\n",
    "\n",
    "ind = np.arange(n)\n",
    "width = 0.35\n",
    "\n",
    "plt.bar(ind, men_means, width, color='#d62728', label='men', yerr=men_std)\n",
    "plt.bar(ind, women_means, width, bottom=men_means, label='women', yerr=women_std)\n",
    "\n",
    "plt.ylabel('Scores')\n",
    "plt.title('Score by group and gender')\n",
    "plt.xticks(ind, (\"G1\", \"G2\", \"G3\", \"G4\", \"G5\"))\n",
    "plt.yticks(np.arange(0, 81, 10))\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"直方图\"\"\"\n",
    "x = np.random.randn(100)\n",
    "plt.hist(x, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.randn(100)\n",
    "plt.hist(x, 10, orientation='horizontal')  # horizontal 水平的  vertical 垂直的\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python\\Python37\\lib\\site-packages\\ipykernel_launcher.py:11: MatplotlibDeprecationWarning: scipy.stats.norm.pdf\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    }
   ],
   "source": [
    "# 返回指定均值和标准差的正态分布的概率分布函数\n",
    "import matplotlib.mlab as mlab\n",
    "\n",
    "mu = 100  # 均值\n",
    "sigma = 5  # 标准差\n",
    "x = mu + sigma * np.random.randn(1000)\n",
    "\n",
    "n, bins, patches = plt.hist(x, 50, density=1)\n",
    "\n",
    "# 添加拟合曲线\n",
    "y = mlab.normpdf(bins, mu, sigma)\n",
    "plt.plot(bins, y, '--')\n",
    "plt.xlabel('Smarts')\n",
    "plt.ylabel('Probability density')\n",
    "plt.title(r'Histogram of IQ: $\\mu=100$, $\\sigma=15$')\n",
    "\n",
    "# 调整间距 防止 ylabel 被覆盖\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 饼图 \n",
    "labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'\n",
    "sizes = [15, 30, 45, 10]\n",
    "explode = (0, 0.1, 0, 0)  # 突出Hogs\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)\n",
    "\n",
    "# 相等的纵横比，确保饼是圆的\n",
    "ax.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-31-01dd43de87de>, line 28)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-31-01dd43de87de>\"\u001b[1;36m, line \u001b[1;32m28\u001b[0m\n\u001b[1;33m    plt.show()\u001b[0m\n\u001b[1;37m      ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "# 箱线图  小提琴图\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_excel(r'1-3月份学员数据.xlsx')\n",
    "df = df.iloc[1:, 1:]\n",
    "data = df.values\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(14, 4))\n",
    "\n",
    "axes[0].violinplot(data, \n",
    "                   vert=False,\n",
    "                   shomeans=True,\n",
    "                   showmedians=True)\n",
    "axes[0].set_title('Violin plot')\n",
    "\n",
    "axes[1].boxplot(data, sym='r*', meanline=False, showmeans=True)\n",
    "axes[1].set_title('Box plot')\n",
    "\n",
    "# 添加网格\n",
    "for ax in axes:\n",
    "    ax.yaxis.grid(True)\n",
    "    ax.set_ylim(0, 50)\n",
    "    ax.set_xticks([y + 1 for y in range(len(data))])\n",
    "    ax.set_xlabel('不同分馆')\n",
    "    ax.set_ylabel('观测')\n",
    "    \n",
    "plt.step(axes, xticklabels=['1', '2', '3'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 注解显示\n",
    "x = np.linspace(0, 10, 1000)\n",
    "y = np.sin(x)\n",
    "plt.plot(x, y, label='$sin(x)$', color='red', linewidth=3)\n",
    "\n",
    "plt.text(2, 0, '文本在（2,0）这个点', family='Simhei', fontsize=10)\n",
    "plt.arrow(4, -0.5, 0.5, 0.5)  # 两点连成线\n",
    "plt.annotate('注解', xy=(0.5, 0.5), xytext=(1, -0.25), arrowprops=dict(arrowstyle='->'))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'Solarize_Light2', 'tableau-colorblind10', '_classic_test']\n"
     ]
    }
   ],
   "source": [
    "print(plt.style.available)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('seaborn-dark')  # 全局样式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 临时样式  使用with代码块\n",
    "with plt.style.context(('grayscale')):\n",
    "    plt.plot(np.sin(np.linspace(0, 2 * np.pi)), 'r-o')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aaa</th>\n",
       "      <td>0.644432</td>\n",
       "      <td>1.570763</td>\n",
       "      <td>0.723178</td>\n",
       "      <td>0.618787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bbb</th>\n",
       "      <td>0.721627</td>\n",
       "      <td>0.653262</td>\n",
       "      <td>1.520680</td>\n",
       "      <td>0.933523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ccc</th>\n",
       "      <td>1.335850</td>\n",
       "      <td>0.576316</td>\n",
       "      <td>0.056712</td>\n",
       "      <td>1.769443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ddd</th>\n",
       "      <td>0.707586</td>\n",
       "      <td>0.682389</td>\n",
       "      <td>0.351531</td>\n",
       "      <td>1.237966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eee</th>\n",
       "      <td>0.603974</td>\n",
       "      <td>0.748069</td>\n",
       "      <td>0.415961</td>\n",
       "      <td>1.333073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            A         B         C         D\n",
       "aaa  0.644432  1.570763  0.723178  0.618787\n",
       "bbb  0.721627  0.653262  1.520680  0.933523\n",
       "ccc  1.335850  0.576316  0.056712  1.769443\n",
       "ddd  0.707586  0.682389  0.351531  1.237966\n",
       "eee  0.603974  0.748069  0.415961  1.333073"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 热力图\n",
    "import numpy as np\n",
    "from pandas import DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "index = ['aaa', 'bbb', 'ccc', 'ddd', 'eee']\n",
    "cols = ['A', 'B', 'C', 'D']\n",
    "df = DataFrame(abs(np.random.randn(5, 4)), index=index, columns=cols)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolor(df)\n",
    "plt.yticks(np.arange(0.5, len(df.index), 1), df.index)\n",
    "plt.xticks(np.arange(0.5, len(df.columns), 1), df.columns)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.heatmap(df, annot=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "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": "3.7.0"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}