2.2 matplotlib箱型图 散点图.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e5c6eaf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bb23b465",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.random.rand(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0f101f25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 91.,  98.,  86., 103., 107.,  96., 116.,  96.,  97., 110.]),\n",
       " array([0.00136718, 0.10117803, 0.20098888, 0.30079973, 0.40061058,\n",
       "        0.50042143, 0.60023228, 0.70004313, 0.79985398, 0.89966483,\n",
       "        0.99947568]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cc98925",
   "metadata": {},
   "source": [
    "# 箱型图 可用于观察数据分布，判断异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4bd5f8dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "664c384b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.random.randn(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f3592029",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x184c9c1f8b0>,\n",
       "  <matplotlib.lines.Line2D at 0x184c9c1f700>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x184c9c1f910>,\n",
       "  <matplotlib.lines.Line2D at 0x184c9c1eb90>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x184c9c1fc10>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x184c9c1e830>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x184c9c1f160>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiIAAAGdCAYAAAAvwBgXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAAATmUlEQVR4nO3db2iV99nA8SsqO00wZkhtqxgXrR1JqXXV/UGLwwy36QNlobQMNpmWTsrQwdA3zV5UfLFlMDsGQ1zH/jhwpQWxDoS1dGVWXzhG0wlzJK2CweCf2jGWE6M9buY8L/qY4Rp9ms4714nn84GDnuT2/l2+ypf7/uXcDdVqtRoAAAmmZQ8AANQvIQIApBEiAEAaIQIApBEiAEAaIQIApBEiAEAaIQIApJmRPcDNjI6OxtmzZ6O5uTkaGhqyxwEAPoRqtRrDw8Mxb968mDbt5tc8ajpEzp49G62trdljAAAfweDgYMyfP/+mx9R0iDQ3N0fE+/+RWbNmJU8DAHwY5XI5Wltbx36O30xNh8i12zGzZs0SIgAwxXyYbRU2qwIAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJCmpj/QDLg9Xb16NY4cORLnzp2LuXPnxqpVq2L69OnZYwEJXBEBJtX+/ftj8eLF0dnZGV/72teis7MzFi9eHPv3788eDUggRIBJs3///njsscdiyZIlcfTo0RgeHo6jR4/GkiVL4rHHHhMjUIcaqtVqNXuIGymXy9HS0hJDQ0OeNQNT3NWrV2Px4sWxZMmSOHDgwHWPBh8dHY2urq44fvx4nDhxwm0amOIm8vPbFRFgUhw5ciQGBgbiu9/97nUREhExbdq06O7ujlOnTsWRI0eSJgQyFBoiu3fvjgcffHDs6bkrVqyI3/3ud0UuCdSoc+fORUTEAw88MO73r3392nFAfSg0RObPnx8/+MEPore3N9544434whe+EF/5ylfir3/9a5HLAjVo7ty5ERFx/Pjxcb9/7evXjgPqw6TvEZk9e3b88Ic/jCeffPL/PdYeEbh92CMC9aMm94hcvXo1XnjhhRgZGYkVK1aMe0ylUolyuXzdC7g9TJ8+PZ599tk4ePBgdHV1XfdbM11dXXHw4MHYuXOnCIE6U/gHmv3lL3+JFStWxHvvvRczZ86Ml156Ke6///5xj+3p6YkdO3YUPRKQ5NFHH419+/bFtm3bYuXKlWNfX7hwYezbty8effTRxOmADIXfmrly5UqcPn06hoaGYt++ffHzn/88Xn/99XFjpFKpRKVSGXtfLpejtbXVrRm4zfhkVbi9TeTWzKTvEVmzZk3ce++98dxzz/2/x9ojAgBTT03uEblmdHT0uqseAED9KnSPSHd3d6xbty4WLFgQw8PD8fzzz8ehQ4filVdeKXJZoMa5NQNcU2iIXLhwIb7xjW/EuXPnoqWlJR588MF45ZVX4otf/GKRywI1bP/+/bFt27YYGBgY+1pbW1s8++yzNqtCHSr01swvfvGLGBgYiEqlEhcuXIjf//73IgTqmIfeAf/JQ++ASeEDzaB+1PRmVaA+eegdMB4hAkwKD70DxiNEgEnhoXfAeIQIMClWrVoVbW1t8f3vfz9GR0ev+97o6Gj09PTEwoULY9WqVUkTAhmECDApPPQOGE/hD70DuMZD74D/5Nd3gUnnk1Xh9jaRn9+uiACTbvr06bF69ersMYAaYI8IAJDGFRFgQi5duhT9/f3/9XkuX74cAwMD0dbWFo2Njbdgsoj29vZoamq6JecCJocQASakv78/li9fnj3GuHp7e2PZsmXZYwATIESACWlvb4/e3t7/+jx9fX2xfv362Lt3b3R0dNyCyd6fDZhahAgwIU1NTbf0qkNHR4erGFDHbFYFANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIUGiI9PT3xmc98Jpqbm+Ouu+6Krq6ueOutt4pcEgCYQgoNkddffz02b94cf/zjH+PVV1+Nf/7zn/GlL30pRkZGilwWAJgiZhR58pdffvm693v27Im77rorent74/Of/3yRSwMAU0ChIfKfhoaGIiJi9uzZ436/UqlEpVIZe18ulydlLgAgx6RtVh0dHY3vfOc78fDDD8cDDzww7jE9PT3R0tIy9mptbZ2s8QCABJMWIps3b47jx4/HCy+8cMNjuru7Y2hoaOw1ODg4WeMBAAkm5dbMli1b4uDBg3H48OGYP3/+DY8rlUpRKpUmYyQAoAYUGiLVajW+/e1vx0svvRSHDh2KhQsXFrkcADDFFBoimzdvjueffz5++9vfRnNzc5w/fz4iIlpaWqKxsbHIpQGAKaDQPSK7d++OoaGhWL16dcydO3fs9eKLLxa5LAAwRRR+awYA4EY8awYASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0hYbI4cOH45FHHol58+ZFQ0NDHDhwoMjlAIApptAQGRkZiaVLl8auXbuKXAYAmKJmFHnydevWxbp164pcAgCYwgoNkYmqVCpRqVTG3pfL5cRpAICi1dRm1Z6enmhpaRl7tba2Zo8EABSopkKku7s7hoaGxl6Dg4PZIwEABaqpWzOlUilKpVL2GADAJKmpKyIAQH0p9IrIxYsX4+TJk2PvT506FceOHYvZs2fHggULilwaAJgCCg2RN954Izo7O8feb926NSIiNmzYEHv27ClyaQBgCig0RFavXh3VarXIJQCAKcweEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIU+hHvQO04ceJEDA8PZ48xpq+v77o/a0Vzc3Pcd9992WNA3RAiUAdOnDgRn/zkJ7PHGNf69euzR/iAt99+W4zAJBEiUAeuXQnZu3dvdHR0JE/zvsuXL8fAwEC0tbVFY2Nj9jgR8f7VmfXr19fUlSO43QkRqCMdHR2xbNmy7DHGPPzww9kjAMlsVgUA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACDNpITIrl27oq2tLe6444743Oc+F3/6058mY1kAoMYVHiIvvvhibN26NbZv3x5vvvlmLF26NL785S/HhQsXil4aAKhxhYfIj370o9i0aVM88cQTcf/998dPf/rTaGpqil/+8pdFLw0A1LhCQ+TKlSvR29sba9as+feC06bFmjVr4ujRo0UuDQBMATOKPPnf/va3uHr1atx9993Xff3uu++O/v7+DxxfqVSiUqmMvS+Xy0WOB3Wj4V/vxUP3TIvGf7wdcdYe9Rtp/Mfb8dA906LhX+9ljwJ1o9AQmaienp7YsWNH9hhw27nj4ul486mZEYefijicPU3t6oiIN5+aGX0XT0fEyuxxoC4UGiJ33nlnTJ8+Pd55553rvv7OO+/EPffc84Hju7u7Y+vWrWPvy+VytLa2Fjki1IX3Zi6IZc9djN/85jfR0d6ePU7N6uvvj69//evxi/9ZkD0K1I1CQ+RjH/tYLF++PF577bXo6uqKiIjR0dF47bXXYsuWLR84vlQqRalUKnIkqEvVGXfEn8+PxuWPfzJi3qeyx6lZl8+Pxp/Pj0Z1xh3Zo0DdKPzWzNatW2PDhg3x6U9/Oj772c/Gj3/84xgZGYknnnii6KUBgBpXeIh89atfjXfffTeeeeaZOH/+fHzqU5+Kl19++QMbWAGA+jMpm1W3bNky7q0YAKC++T0+ACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACDNjOwBgOJdunQpIiLefPPN5En+7fLlyzEwMBBtbW3R2NiYPU5ERPT19WWPAHVHiEAd6O/vj4iITZs2JU8yNTQ3N2ePAHVDiEAd6OrqioiI9vb2aGpqyh3m//T19cX69etj79690dHRkT3OmObm5rjvvvuyx4C6IUSgDtx5553xzW9+M3uMcXV0dMSyZcuyxwCS2KwKAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAmsJC5Hvf+16sXLkympqa4uMf/3hRywAAU1hhIXLlypV4/PHH41vf+lZRSwAAU9yMok68Y8eOiIjYs2dPUUsAAFNcYSHyUVQqlahUKmPvy+Vy4jQAQNFqarNqT09PtLS0jL1aW1uzRwIACjShEHn66aejoaHhpq/+/v6PPEx3d3cMDQ2NvQYHBz/yuQCA2jehWzPbtm2LjRs33vSYRYsWfeRhSqVSlEqlj/zvAYCpZUIhMmfOnJgzZ05RswAAdaawzaqnT5+Ov//973H69Om4evVqHDt2LCIiFi9eHDNnzixqWQBgCiksRJ555pn49a9/Pfb+oYceioiIP/zhD7F69eqilgUAppDCfmtmz549Ua1WP/ASIQDANTX167sAQH0RIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAmsJCZGBgIJ588slYuHBhNDY2xr333hvbt2+PK1euFLUkADDFzCjqxP39/TE6OhrPPfdcLF68OI4fPx6bNm2KkZGR2LlzZ1HLAgBTSGEhsnbt2li7du3Y+0WLFsVbb70Vu3fvFiIAQEQUGCLjGRoaitmzZ9/w+5VKJSqVytj7crk8GWMBAEkmbbPqyZMn4yc/+Uk89dRTNzymp6cnWlpaxl6tra2TNR4AkGDCIfL0009HQ0PDTV/9/f3X/ZszZ87E2rVr4/HHH49Nmzbd8Nzd3d0xNDQ09hocHJz4/wgAmDImfGtm27ZtsXHjxpses2jRorG/nz17Njo7O2PlypXxs5/97Kb/rlQqRalUmuhIAMAUNeEQmTNnTsyZM+dDHXvmzJno7OyM5cuXx69+9auYNs3HlgAA/1bYZtUzZ87E6tWr4xOf+ETs3Lkz3n333bHv3XPPPUUtCwBMIYWFyKuvvhonT56MkydPxvz586/7XrVaLWpZAGAKKexeycaNG6NarY77AgCI8KwZACCREAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACCNEAEA0ggRACDNjOwBgKnl0qVL0d/f/1+fp6+v77o/b4X29vZoamq6ZecDiidEgAnp7++P5cuX37LzrV+//padq7e3N5YtW3bLzgcUT4gAE9Le3h69vb3/9XkuX74cAwMD0dbWFo2NjbdgsvdnA6aWhmq1Ws0e4kbK5XK0tLTE0NBQzJo1K3scAOBDmMjPb5tVAYA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0M7IHuJlrDwYul8vJkwAAH9a1n9vXfo7fTE2HyPDwcEREtLa2Jk8CAEzU8PBwtLS03PSYhuqHyZUko6Ojcfbs2Whubo6GhobscYBbqFwuR2trawwODsasWbOyxwFuoWq1GsPDwzFv3ryYNu3mu0BqOkSA21e5XI6WlpYYGhoSIlDHbFYFANIIEQAgjRABUpRKpdi+fXuUSqXsUYBE9ogAAGlcEQEA0ggRACCNEAEA0ggRACCNEAEm1eHDh+ORRx6JefPmRUNDQxw4cCB7JCCREAEm1cjISCxdujR27dqVPQpQA2r6oXfA7WfdunWxbt267DGAGuGKCACQRogAAGmECACQRogAAGmECACQxm/NAJPq4sWLcfLkybH3p06dimPHjsXs2bNjwYIFiZMBGTx9F5hUhw4dis7Ozg98fcOGDbFnz57JHwhIJUQAgDT2iAAAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJBGiAAAaYQIAJDmfwG6ucrBGxE7iQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b5155ec",
   "metadata": {},
   "source": [
    "# 散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "45a0a8a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = [1,2,3,4,5]\n",
    "y1 = [3,2,1,4,7]\n",
    "x2 = [2,4,6,8,9]\n",
    "y2 = [6,1,4,2,8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3bbec9f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x184cd4e50f0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x1,y1,color='r',marker='>', label='one', s=100)  # s=100 点的大小 \n",
    "plt.scatter(x2,y2,color='b',marker='o', label='two', s=200)  # s可传动态参数\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5980765c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}