matplotlib_learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "x1 = np.linspace(0, 2 * np.pi, 200)\n",
    "y1 = np.sin(x1)\n",
    "plt.plot(x1, y1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x1c9f75aed48>]"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "x2 = np.linspace(0, 2 * np.pi, 200)\n",
    "y2 = np.cos(x2)\n",
    "plt.plot(x2, y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出空图，设置左下宽高\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0.1, 0.1, 1, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x1c9f6daff88>]"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "x = np.linspace(0, 4 * np.pi, 200)\n",
    "ax.plot(x, np.sin(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "fig.add_axes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0.0, 5.0, 0.02)\n",
    "y = np.exp(-x) * np.cos(2 * np.pi * x)\n",
    "plt.plot(x, y)\n",
    "plt.grid(color='green')  # 显示网格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x1c9f758ad88>]"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "ax.grid(color='gray', linestyle='--')\n",
    "ax.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "ax.grid?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(-1, 1.5)"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 解决中文显示与负号显示问题\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "ax.grid(color='gray', linestyle='--')\n",
    "ax.plot(x, y)\n",
    "\n",
    "# 设置轴名称\n",
    "ax.set_xlabel('x axis')\n",
    "ax.set_ylabel('纵坐标')\n",
    "\n",
    "# 设置轴取值范围\n",
    "ax.set_xlim(-1, 6)\n",
    "ax.set_ylim(-1, 1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.axis.XTick at 0x1c9f6eee988>,\n <matplotlib.axis.XTick at 0x1c9f6fbce88>,\n <matplotlib.axis.XTick at 0x1c9f75ae348>,\n <matplotlib.axis.XTick at 0x1c9f59dd808>,\n <matplotlib.axis.XTick at 0x1c9f59dd188>,\n <matplotlib.axis.XTick at 0x1c9f5b00788>,\n <matplotlib.axis.XTick at 0x1c9f56674c8>,\n <matplotlib.axis.XTick at 0x1c9f59d9888>,\n <matplotlib.axis.XTick at 0x1c9f5b07448>,\n <matplotlib.axis.XTick at 0x1c9f5669108>,\n <matplotlib.axis.XTick at 0x1c9f5a4bf08>]"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 864x576 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 8))\n",
    "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "ax.grid(color='gray', linestyle='--')\n",
    "ax.plot(x, y)\n",
    "\n",
    "ax.set_xlabel('x axis')\n",
    "ax.set_xlim(0, 5)\n",
    "\n",
    "# 设置轴刻度\n",
    "ax.set_xticks(np.linspace(0, 5, 11))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes()\n",
    "ax.plot(np.random.rand(50))\n",
    "\n",
    "# 不显示Y轴刻线与标示\n",
    "ax.yaxis.set_major_locator(plt.NullLocator())\n",
    "# 不显示X轴标示\n",
    "ax.xaxis.set_major_formatter(plt.NullFormatter())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 576x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#综合练习\n",
    "from matplotlib.ticker import (MultipleLocator, FormatStrFormatter)\n",
    "\n",
    "# 生成两个数组\n",
    "t = np.linspace(0, 100, 100)\n",
    "s = 9.8 * np.power(t, 2) / 2\n",
    "\n",
    "# 绘制曲线\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "ax.plot(t, s, color='red')\n",
    "\n",
    "ax.set_ylabel('位移s/m')\n",
    "ax.set_xlim(0, 100)\n",
    "ax.set_xlabel('时刻t/s')\n",
    "\n",
    "# 以20的倍数为X轴的主刻线\n",
    "xmajor_locator = MultipleLocator(20)\n",
    "# 主标示显示格式\n",
    "xmajor_formatter = FormatStrFormatter('%1d')\n",
    "# 副刻线对象，5的倍数\n",
    "xminor_locator = MultipleLocator(5)\n",
    "\n",
    "# 弓10000的位数为Y轴的主刻线\n",
    "ymajor_locator = MultipleLocator(10000)\n",
    "ymajor_formatter = FormatStrFormatter('%1.1f')\n",
    "yminor_locator = MultipleLocator(5000)\n",
    "\n",
    "# 设置X轴\n",
    "ax.xaxis.set_major_locator(xmajor_locator)\n",
    "ax.xaxis.set_major_formatter(xmajor_formatter)\n",
    "ax.xaxis.set_minor_locator(xminor_locator)\n",
    "\n",
    "# 设置Y轴\n",
    "ax.yaxis.set_major_locator(ymajor_locator)\n",
    "ax.yaxis.set_major_formatter(ymajor_formatter)\n",
    "ax.yaxis.set_minor_locator(yminor_locator)\n",
    "\n",
    "# 设置网络\n",
    "ax.grid(True, which='major', color='blue')\n",
    "ax.grid(True, which='minor', linestyle='--')\n",
    "\n",
    "# 循环设置X轴的刻度，字号为16\n",
    "for tick in ax.xaxis.get_major_ticks():\n",
    "    tick.label1.set_fontsize(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 比较plt.subplot和plt.subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 9 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 3, sharex='col', sharey='row')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(3, 3)"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "ax2 = fig.add_axes([0.6, 0.5, 0.2, 0.3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "plt.subplot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [],
   "source": [
    "plt.subplots?"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "data": {
      "text/plain": "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n0         0       3    male  22.0      1      0   7.2500        S  Third   \n1         1       1  female  38.0      1      0  71.2833        C  First   \n2         1       3  female  26.0      0      0   7.9250        S  Third   \n3         1       1  female  35.0      1      0  53.1000        S  First   \n4         0       3    male  35.0      0      0   8.0500        S  Third   \n\n     who  adult_male deck  embark_town alive  alone  \n0    man        True  NaN  Southampton    no  False  \n1  woman       False    C    Cherbourg   yes  False  \n2  woman       False  NaN  Southampton   yes   True  \n3  woman       False    C  Southampton   yes  False  \n4    man        True  NaN  Southampton    no   True  ",
      "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>survived</th>\n      <th>pclass</th>\n      <th>sex</th>\n      <th>age</th>\n      <th>sibsp</th>\n      <th>parch</th>\n      <th>fare</th>\n      <th>embarked</th>\n      <th>class</th>\n      <th>who</th>\n      <th>adult_male</th>\n      <th>deck</th>\n      <th>embark_town</th>\n      <th>alive</th>\n      <th>alone</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>3</td>\n      <td>male</td>\n      <td>22.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>7.2500</td>\n      <td>S</td>\n      <td>Third</td>\n      <td>man</td>\n      <td>True</td>\n      <td>NaN</td>\n      <td>Southampton</td>\n      <td>no</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>female</td>\n      <td>38.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>71.2833</td>\n      <td>C</td>\n      <td>First</td>\n      <td>woman</td>\n      <td>False</td>\n      <td>C</td>\n      <td>Cherbourg</td>\n      <td>yes</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>3</td>\n      <td>female</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>7.9250</td>\n      <td>S</td>\n      <td>Third</td>\n      <td>woman</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>Southampton</td>\n      <td>yes</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>female</td>\n      <td>35.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>53.1000</td>\n      <td>S</td>\n      <td>First</td>\n      <td>woman</td>\n      <td>False</td>\n      <td>C</td>\n      <td>Southampton</td>\n      <td>yes</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>3</td>\n      <td>male</td>\n      <td>35.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8.0500</td>\n      <td>S</td>\n      <td>Third</td>\n      <td>man</td>\n      <td>True</td>\n      <td>NaN</td>\n      <td>Southampton</td>\n      <td>no</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "titanic = sns.load_dataset('titanic')\n",
    "titanic.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "sex\nfemale    0.742038\nmale      0.188908\nName: survived, dtype: float64"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby('sex')['survived'].mean()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "data": {
      "text/plain": "sex     class \nfemale  First     0.968085\n        Second    0.921053\n        Third     0.500000\nmale    First     0.368852\n        Second    0.157407\n        Third     0.135447\nName: survived, dtype: float64"
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby(['sex', 'class'])['survived'].agg('mean')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "data": {
      "text/plain": "0      (18.0, 60.0]\n1      (18.0, 60.0]\n2      (18.0, 60.0]\n3      (18.0, 60.0]\n4      (18.0, 60.0]\n           ...     \n886    (18.0, 60.0]\n887    (18.0, 60.0]\n888             NaN\n889    (18.0, 60.0]\n890    (18.0, 60.0]\nName: age, Length: 891, dtype: category\nCategories (3, interval[int64]): [(0, 18] < (18, 60] < (60, 99]]"
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按年龄分割样本\n",
    "ages = pd.cut(titanic['age'], [0, 18, 60, 99])\n",
    "ages"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "data": {
      "text/plain": "class               First    Second     Third\nsex    age                                   \nfemale (0, 18]   0.909091  1.000000  0.511628\n       (18, 60]  0.972222  0.900000  0.413793\n       (60, 99]  1.000000       NaN  1.000000\nmale   (0, 18]   0.800000  0.600000  0.215686\n       (18, 60]  0.416667  0.061728  0.136364\n       (60, 99]  0.083333  0.333333  0.000000",
      "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>class</th>\n      <th>First</th>\n      <th>Second</th>\n      <th>Third</th>\n    </tr>\n    <tr>\n      <th>sex</th>\n      <th>age</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">female</th>\n      <th>(0, 18]</th>\n      <td>0.909091</td>\n      <td>1.000000</td>\n      <td>0.511628</td>\n    </tr>\n    <tr>\n      <th>(18, 60]</th>\n      <td>0.972222</td>\n      <td>0.900000</td>\n      <td>0.413793</td>\n    </tr>\n    <tr>\n      <th>(60, 99]</th>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">male</th>\n      <th>(0, 18]</th>\n      <td>0.800000</td>\n      <td>0.600000</td>\n      <td>0.215686</td>\n    </tr>\n    <tr>\n      <th>(18, 60]</th>\n      <td>0.416667</td>\n      <td>0.061728</td>\n      <td>0.136364</td>\n    </tr>\n    <tr>\n      <th>(60, 99]</th>\n      <td>0.083333</td>\n      <td>0.333333</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 制作数据透视表\n",
    "titanic.pivot_table('survived', index=['sex', ages], columns='class')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
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