总结.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  set_style( )，set( )\n",
    "## set_style()用来设置主题，set()用来设置背景，调色板等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BELBu3Tp95CIiovvQWdqWlpZ499139ZGFiIh04NBpIiIjwtImIjIiLG0iIiPC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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")  \n",
    "plt.plot(np.arange(10))  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\", palette=\"muted\", color_codes=True)     #set( )设置主题，调色板更常用  \n",
    "plt.plot(np.arange(10))  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_tips = sns.load_dataset(\"tips\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# distplot( )  kdeplot( )\n",
    "## 直方图、密度曲线图\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/camey/anaconda2/envs/fullpy3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12310ef60>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/camey/anaconda2/envs/fullpy3/lib/python3.6/site-packages/matplotlib/font_manager.py:1241: UserWarning: findfont: Font family ['SimHei'] not found. Falling back to DejaVu Sans.\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n",
      "/Users/camey/anaconda2/envs/fullpy3/lib/python3.6/site-packages/matplotlib/font_manager.py:1241: UserWarning: findfont: Font family ['SimHei'] not found. Falling back to DejaVu Sans.\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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B8rJ01LZ4wlYXSYgNrU4fRn187Lx1ofQUGY0iNl4fjgj8QbL1bmASDz3+vEwt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\nyGAY/EREBsPgJyIyGAY/EZHBfAbWOmIefkC2JwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_iris = sns.load_dataset(\"iris\")\n",
    "fig, axes = plt.subplots(1,2)\n",
    "sns.distplot(df_iris['sepal_length'], ax = axes[0], kde = True, rug = True)        # kde 密度曲线  rug 边际毛毯  \n",
    "sns.kdeplot(df_iris['sepal_length'], ax = axes[1], shade=True)                     # shade  阴影                         \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/camey/anaconda2/envs/fullpy3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122dd8f28>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xj/re7t27r2r31FNPpSpSSiiKAt8fjsD70gswZGUh6y/+EuaPXM9nyGbIsnoN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\nTBAEQYh7opgJgiAIce//AYC/pLDtC9HIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x504 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set( palette=\"muted\", color_codes=True)  \n",
    "rs = np.random.RandomState(10)  \n",
    "d = rs.normal(size=100)  \n",
    "f, axes = plt.subplots(2, 2, figsize=(7, 7), sharex=True)  \n",
    "sns.distplot(d, kde=False, color=\"b\", ax=axes[0, 0])  \n",
    "sns.distplot(d, hist=False, rug=True, color=\"r\", ax=axes[0, 1])  \n",
    "sns.distplot(d, hist=False, color=\"g\", kde_kws={\"shade\": True}, ax=axes[1, 0])  \n",
    "sns.distplot(d, color=\"m\", ax=axes[1, 1])  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 箱型图 boxplot( )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12272d9b0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ubm5wdnZGz549tc/x8/PD6tWrYWtri8DAQOzduxczZza/sZmIiFoynVoUJSUl2LBhAyZO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\nRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = df_iris['species'],y = df_iris['sepal_width'])  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x123330ac8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc('font',family='SimHei',size=13)\n",
    "#fliersize=1将异常点虚化，showmeans=True显示平均值，order表示按x轴显示进行排序\n",
    "sns.boxplot(x='time',y='total_bill',hue='sex',hue_order=['Female','Male'],data=df_tips,showmeans=True,fliersize=1,order=['Dinner','Lunch'])\n",
    "plt.legend(loc='upper right')#图示靠右显示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 联合分布jointplot( )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/camey/anaconda2/envs/fullpy3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1233277b8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(\"total_bill\", \"tip\", df_tips)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/camey/anaconda2/envs/fullpy3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1235c4f98>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nrDF7JQeOZOrieuO5bXMMy3aLUiqlKJQcLNt9vPEpskVFR8zkuvVhwn6DXEER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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(\"total_bill\", \"tip\", df_tips, kind='reg')       \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 热点图heatmap( )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122815fd0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/camey/anaconda2/envs/fullpy3/lib/python3.6/site-packages/matplotlib/font_manager.py:1241: UserWarning: findfont: Font family ['SimHei'] not found. Falling back to DejaVu Sans.\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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FBcnPz09+fn5at26dJOnQoUOujAsAgAbPoTXpefPmOTQGAIDHlNud9zCIGivp3Nxc5ebm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\npAEAMKj/D7IT1vSPjBuKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = df_tips.corr()\n",
    "f, ax = plt.subplots(figsize=(10, 7))\n",
    "plt.xticks(rotation='90')\n",
    "sns.heatmap(corrmat, square=True, linewidths=.5, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 散点图scatter( )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 10)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B9uLe2auq7R3a29vV0dGhI0eOaMOGDTpy5Ig6OjpYNgxpbmNON8WP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\nwBCCFgAAgCH/H+nMxwvJPM6GAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10, 7))\n",
    "plt.scatter(x=df_tips['tip'], y=df_tips['total_bill'], c='r')\n",
    "plt.xlim(0,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25.29</td>\n",
       "      <td>4.71</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.77</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26.88</td>\n",
       "      <td>3.12</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.04</td>\n",
       "      <td>1.96</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.78</td>\n",
       "      <td>3.23</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.27</td>\n",
       "      <td>1.71</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>35.26</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15.42</td>\n",
       "      <td>1.57</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>18.43</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.83</td>\n",
       "      <td>3.02</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>21.58</td>\n",
       "      <td>3.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10.33</td>\n",
       "      <td>1.67</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16.29</td>\n",
       "      <td>3.71</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>16.97</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20.65</td>\n",
       "      <td>3.35</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>17.92</td>\n",
       "      <td>4.08</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20.29</td>\n",
       "      <td>2.75</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>15.77</td>\n",
       "      <td>2.23</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>39.42</td>\n",
       "      <td>7.58</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>19.82</td>\n",
       "      <td>3.18</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>17.81</td>\n",
       "      <td>2.34</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>13.37</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>12.69</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>21.70</td>\n",
       "      <td>4.30</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>19.65</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>28.17</td>\n",
       "      <td>6.50</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>12.90</td>\n",
       "      <td>1.10</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>28.15</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>11.59</td>\n",
       "      <td>1.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>7.74</td>\n",
       "      <td>1.44</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>30.14</td>\n",
       "      <td>3.09</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>12.16</td>\n",
       "      <td>2.20</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>13.42</td>\n",
       "      <td>3.48</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>8.58</td>\n",
       "      <td>1.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>15.98</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>13.42</td>\n",
       "      <td>1.58</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>16.27</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>10.09</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>20.45</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>13.28</td>\n",
       "      <td>2.72</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>22.12</td>\n",
       "      <td>2.88</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>24.01</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>15.69</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>11.61</td>\n",
       "      <td>3.39</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>10.77</td>\n",
       "      <td>1.47</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>15.53</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>10.07</td>\n",
       "      <td>1.25</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>12.60</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>32.83</td>\n",
       "      <td>1.17</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>35.83</td>\n",
       "      <td>4.67</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size\n",
       "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
       "1         10.34  1.66    Male     No   Sun  Dinner     3\n",
       "2         21.01  3.50    Male     No   Sun  Dinner     3\n",
       "3         23.68  3.31    Male     No   Sun  Dinner     2\n",
       "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
       "5         25.29  4.71    Male     No   Sun  Dinner     4\n",
       "6          8.77  2.00    Male     No   Sun  Dinner     2\n",
       "7         26.88  3.12    Male     No   Sun  Dinner     4\n",
       "8         15.04  1.96    Male     No   Sun  Dinner     2\n",
       "9         14.78  3.23    Male     No   Sun  Dinner     2\n",
       "10        10.27  1.71    Male     No   Sun  Dinner     2\n",
       "11        35.26  5.00  Female     No   Sun  Dinner     4\n",
       "12        15.42  1.57    Male     No   Sun  Dinner     2\n",
       "13        18.43  3.00    Male     No   Sun  Dinner     4\n",
       "14        14.83  3.02  Female     No   Sun  Dinner     2\n",
       "15        21.58  3.92    Male     No   Sun  Dinner     2\n",
       "16        10.33  1.67  Female     No   Sun  Dinner     3\n",
       "17        16.29  3.71    Male     No   Sun  Dinner     3\n",
       "18        16.97  3.50  Female     No   Sun  Dinner     3\n",
       "19        20.65  3.35    Male     No   Sat  Dinner     3\n",
       "20        17.92  4.08    Male     No   Sat  Dinner     2\n",
       "21        20.29  2.75  Female     No   Sat  Dinner     2\n",
       "22        15.77  2.23  Female     No   Sat  Dinner     2\n",
       "23        39.42  7.58    Male     No   Sat  Dinner     4\n",
       "24        19.82  3.18    Male     No   Sat  Dinner     2\n",
       "25        17.81  2.34    Male     No   Sat  Dinner     4\n",
       "26        13.37  2.00    Male     No   Sat  Dinner     2\n",
       "27        12.69  2.00    Male     No   Sat  Dinner     2\n",
       "28        21.70  4.30    Male     No   Sat  Dinner     2\n",
       "29        19.65  3.00  Female     No   Sat  Dinner     2\n",
       "..          ...   ...     ...    ...   ...     ...   ...\n",
       "214       28.17  6.50  Female    Yes   Sat  Dinner     3\n",
       "215       12.90  1.10  Female    Yes   Sat  Dinner     2\n",
       "216       28.15  3.00    Male    Yes   Sat  Dinner     5\n",
       "217       11.59  1.50    Male    Yes   Sat  Dinner     2\n",
       "218        7.74  1.44    Male    Yes   Sat  Dinner     2\n",
       "219       30.14  3.09  Female    Yes   Sat  Dinner     4\n",
       "220       12.16  2.20    Male    Yes   Fri   Lunch     2\n",
       "221       13.42  3.48  Female    Yes   Fri   Lunch     2\n",
       "222        8.58  1.92    Male    Yes   Fri   Lunch     1\n",
       "223       15.98  3.00  Female     No   Fri   Lunch     3\n",
       "224       13.42  1.58    Male    Yes   Fri   Lunch     2\n",
       "225       16.27  2.50  Female    Yes   Fri   Lunch     2\n",
       "226       10.09  2.00  Female    Yes   Fri   Lunch     2\n",
       "227       20.45  3.00    Male     No   Sat  Dinner     4\n",
       "228       13.28  2.72    Male     No   Sat  Dinner     2\n",
       "229       22.12  2.88  Female    Yes   Sat  Dinner     2\n",
       "230       24.01  2.00    Male    Yes   Sat  Dinner     4\n",
       "231       15.69  3.00    Male    Yes   Sat  Dinner     3\n",
       "232       11.61  3.39    Male     No   Sat  Dinner     2\n",
       "233       10.77  1.47    Male     No   Sat  Dinner     2\n",
       "234       15.53  3.00    Male    Yes   Sat  Dinner     2\n",
       "235       10.07  1.25    Male     No   Sat  Dinner     2\n",
       "236       12.60  1.00    Male    Yes   Sat  Dinner     2\n",
       "237       32.83  1.17    Male    Yes   Sat  Dinner     2\n",
       "238       35.83  4.67  Female     No   Sat  Dinner     3\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
       "\n",
       "[244 rows x 7 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tips"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pointplot画出变量间的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5]), <a list of 6 Text xticklabel objects>)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ht+0zJF19SbZmnZclwzDk9vjV4vErI8Uuu42ADcBaBG0AQFTYfaRFDy8vV93RXUZSkmy6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\nAAagaAMAAAAG+AN/g3TlMFAEewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped_df = df_tips.groupby('size')['total_bill'].aggregate(np.median).reset_index()\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.pointplot(grouped_df['size'].values, grouped_df.total_bill.values, alpha=0.8)\n",
    "plt.ylabel('Median total_bill', fontsize=12)\n",
    "plt.xlabel('size number', fontsize=12)\n",
    "plt.xticks(rotation='vertical')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pairplot( )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/camey/anaconda2/envs/fullpy3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "/Users/camey/anaconda2/envs/fullpy3/lib/python3.6/site-packages/matplotlib/font_manager.py:1241: UserWarning: findfont: Font family ['SimHei'] not found. Falling back to DejaVu Sans.\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x123e4bd68>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/e3WAjy/1Ylqd1PsC3LUmt81deVwnJJKRTGClvTDiGC9u92r307gVDwu9JzI8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\nIYKKUSBpdbyy7JNtr7sU2g2Nm57KdR7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QwUmhUCgUbQ4VnBQKhULR5lDBSaFQKI4RNm/e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\nCBFyJDkJIYQIOf8PkN6T9fdK8hwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 453.85x360 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df_iris, vars=[\"sepal_width\", \"sepal_length\"],hue='species',palette=\"husl\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# barplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5]), <a list of 6 Text yticklabel objects>)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AMKIkAABGlAQAwIiSAAAYURIAACNKAgBgREkAAIxcfcZ1nGrl9lMuA2W1SnD5WePRMIPE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\n/FEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax=plt.subplots(figsize=(6,6))\n",
    "\n",
    "#orient='h'表示是水平展示的，alpha表示颜色的深浅程度\n",
    "sns.barplot(y=grouped_df['size'].values, x=grouped_df.total_bill.values,orient='h', alpha=0.8, color='red')\n",
    "\n",
    "#设置y轴、X轴的坐标名字与字体大小\n",
    "plt.ylabel('size', fontsize=16)\n",
    "plt.xlabel('total_bill', fontsize=16)\n",
    "\n",
    "#设置X轴的各列下标字体是水平的\n",
    "plt.xticks(rotation='horizontal')\n",
    "\n",
    "#设置Y轴下标的字体大小\n",
    "plt.yticks(fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# bar图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x126164550>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jX3zRhs3mID//bK9/YKCLBQvSaNu2FJMJPvwwkHvvjeSLL5KxGn6LvNSFCr/N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\nEhERQyiARETEEAogERExxP8B7D3CVeZ9gbEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc('font', family='SimHei', size=13)\n",
    "\n",
    "num = np.array([13325, 9403, 9227, 8651])\n",
    "ratio = np.array([0.75, 0.76, 0.72, 0.75])\n",
    "men = num * ratio\n",
    "women = num * (1-ratio)\n",
    "x = ['聊天','支付','团购\\n优惠券','在线视频']\n",
    "\n",
    "width = 0.5\n",
    "idx = np.arange(len(x))\n",
    "plt.bar(idx, men, width, color='red', label='男性用户')\n",
    "plt.bar(idx, women, width, bottom=men, color='yellow', label='女性用户')  #这一块可是设置bottom,top，如果是水平放置的，可以设置right或者left。\n",
    "plt.xlabel('应用类别')\n",
    "plt.ylabel('男女分布')\n",
    "plt.xticks(idx+width/2, x, rotation=40)\n",
    "\n",
    "#bar图上显示数字\n",
    "\n",
    "for a,b in zip(idx,men):\n",
    "\n",
    "    plt.text(a, b+0.05, '%.0f' % b, ha='center', va= 'bottom',fontsize=12)\n",
    "for a,b,c in zip(idx,women,men):\n",
    "    plt.text(a, b+c+0.5, '%.0f' % b, ha='center', va= 'bottom',fontsize=12)\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 双Y轴绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25.29</td>\n",
       "      <td>4.71</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.77</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26.88</td>\n",
       "      <td>3.12</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.04</td>\n",
       "      <td>1.96</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.78</td>\n",
       "      <td>3.23</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.27</td>\n",
       "      <td>1.71</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>35.26</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15.42</td>\n",
       "      <td>1.57</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>18.43</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.83</td>\n",
       "      <td>3.02</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>21.58</td>\n",
       "      <td>3.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10.33</td>\n",
       "      <td>1.67</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16.29</td>\n",
       "      <td>3.71</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>16.97</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20.65</td>\n",
       "      <td>3.35</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>17.92</td>\n",
       "      <td>4.08</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20.29</td>\n",
       "      <td>2.75</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>15.77</td>\n",
       "      <td>2.23</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>39.42</td>\n",
       "      <td>7.58</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>19.82</td>\n",
       "      <td>3.18</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>17.81</td>\n",
       "      <td>2.34</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>13.37</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>12.69</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>21.70</td>\n",
       "      <td>4.30</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>19.65</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>28.17</td>\n",
       "      <td>6.50</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>12.90</td>\n",
       "      <td>1.10</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>28.15</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>11.59</td>\n",
       "      <td>1.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>7.74</td>\n",
       "      <td>1.44</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>30.14</td>\n",
       "      <td>3.09</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>12.16</td>\n",
       "      <td>2.20</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>13.42</td>\n",
       "      <td>3.48</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>8.58</td>\n",
       "      <td>1.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>15.98</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>13.42</td>\n",
       "      <td>1.58</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>16.27</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>10.09</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fri</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>20.45</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>13.28</td>\n",
       "      <td>2.72</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>22.12</td>\n",
       "      <td>2.88</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>24.01</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>15.69</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>11.61</td>\n",
       "      <td>3.39</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>10.77</td>\n",
       "      <td>1.47</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>15.53</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>10.07</td>\n",
       "      <td>1.25</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>12.60</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>32.83</td>\n",
       "      <td>1.17</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>35.83</td>\n",
       "      <td>4.67</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size\n",
       "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
       "1         10.34  1.66    Male     No   Sun  Dinner     3\n",
       "2         21.01  3.50    Male     No   Sun  Dinner     3\n",
       "3         23.68  3.31    Male     No   Sun  Dinner     2\n",
       "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
       "5         25.29  4.71    Male     No   Sun  Dinner     4\n",
       "6          8.77  2.00    Male     No   Sun  Dinner     2\n",
       "7         26.88  3.12    Male     No   Sun  Dinner     4\n",
       "8         15.04  1.96    Male     No   Sun  Dinner     2\n",
       "9         14.78  3.23    Male     No   Sun  Dinner     2\n",
       "10        10.27  1.71    Male     No   Sun  Dinner     2\n",
       "11        35.26  5.00  Female     No   Sun  Dinner     4\n",
       "12        15.42  1.57    Male     No   Sun  Dinner     2\n",
       "13        18.43  3.00    Male     No   Sun  Dinner     4\n",
       "14        14.83  3.02  Female     No   Sun  Dinner     2\n",
       "15        21.58  3.92    Male     No   Sun  Dinner     2\n",
       "16        10.33  1.67  Female     No   Sun  Dinner     3\n",
       "17        16.29  3.71    Male     No   Sun  Dinner     3\n",
       "18        16.97  3.50  Female     No   Sun  Dinner     3\n",
       "19        20.65  3.35    Male     No   Sat  Dinner     3\n",
       "20        17.92  4.08    Male     No   Sat  Dinner     2\n",
       "21        20.29  2.75  Female     No   Sat  Dinner     2\n",
       "22        15.77  2.23  Female     No   Sat  Dinner     2\n",
       "23        39.42  7.58    Male     No   Sat  Dinner     4\n",
       "24        19.82  3.18    Male     No   Sat  Dinner     2\n",
       "25        17.81  2.34    Male     No   Sat  Dinner     4\n",
       "26        13.37  2.00    Male     No   Sat  Dinner     2\n",
       "27        12.69  2.00    Male     No   Sat  Dinner     2\n",
       "28        21.70  4.30    Male     No   Sat  Dinner     2\n",
       "29        19.65  3.00  Female     No   Sat  Dinner     2\n",
       "..          ...   ...     ...    ...   ...     ...   ...\n",
       "214       28.17  6.50  Female    Yes   Sat  Dinner     3\n",
       "215       12.90  1.10  Female    Yes   Sat  Dinner     2\n",
       "216       28.15  3.00    Male    Yes   Sat  Dinner     5\n",
       "217       11.59  1.50    Male    Yes   Sat  Dinner     2\n",
       "218        7.74  1.44    Male    Yes   Sat  Dinner     2\n",
       "219       30.14  3.09  Female    Yes   Sat  Dinner     4\n",
       "220       12.16  2.20    Male    Yes   Fri   Lunch     2\n",
       "221       13.42  3.48  Female    Yes   Fri   Lunch     2\n",
       "222        8.58  1.92    Male    Yes   Fri   Lunch     1\n",
       "223       15.98  3.00  Female     No   Fri   Lunch     3\n",
       "224       13.42  1.58    Male    Yes   Fri   Lunch     2\n",
       "225       16.27  2.50  Female    Yes   Fri   Lunch     2\n",
       "226       10.09  2.00  Female    Yes   Fri   Lunch     2\n",
       "227       20.45  3.00    Male     No   Sat  Dinner     4\n",
       "228       13.28  2.72    Male     No   Sat  Dinner     2\n",
       "229       22.12  2.88  Female    Yes   Sat  Dinner     2\n",
       "230       24.01  2.00    Male    Yes   Sat  Dinner     4\n",
       "231       15.69  3.00    Male    Yes   Sat  Dinner     3\n",
       "232       11.61  3.39    Male     No   Sat  Dinner     2\n",
       "233       10.77  1.47    Male     No   Sat  Dinner     2\n",
       "234       15.53  3.00    Male    Yes   Sat  Dinner     2\n",
       "235       10.07  1.25    Male     No   Sat  Dinner     2\n",
       "236       12.60  1.00    Male    Yes   Sat  Dinner     2\n",
       "237       32.83  1.17    Male    Yes   Sat  Dinner     2\n",
       "238       35.83  4.67  Female     No   Sat  Dinner     3\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
       "\n",
       "[244 rows x 7 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3ePbsGerUqYOEhARkZGTA09PTABIqxiCKKygoCEFBQfR3Pz8/bNu2DfXq1cORI0cQGxuL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\nq6urwXIL6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ZMmYeLEiaitrQWQFSDiuUplZSW6uroAfIjp+3w+RCIRRCIRvjFFocTGZbcB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\nRRAEQTgKUlwEQRCEoyDFRRAEQTiK/wANYUfFabUECQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#读取的时候,将索引列变为chn_name,这样画图时候X轴自动为索引\n",
    "df=df_tips\n",
    "df.index.name='size'#这样x轴的label就变成‘size了’。\n",
    "\n",
    "plt.rc('font', family='SimHei', size=13)\n",
    "plt.figure()\n",
    "df['total_bill'].plot(kind='bar')\n",
    "plt.ylabel('total_bill')\n",
    "plt.title('total_bill')\n",
    "\n",
    "p = df['tip']\n",
    "p.plot(color='black',secondary_y=True,style='--o',linewidth=2)#style--表示虚线，-表示实线\n",
    "plt.ylabel('tip')\n",
    "x=[0,1,2,3,4]#因为x轴是汉字，所以默认对应的数值是从0开始的\n",
    "for a,b in zip(x,p):\n",
    "    plt.text(a+0.1, b+0.02, '%.2f' % b, ha='center', va= 'bottom',fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'size分布')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5L2eu29GrOvpB3e9pP1mIfdEXYZGZG1v/bomIa4Hbu3n9+PgEk5NTs1p3P71pY2Oba12/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\nhoUkqej/AWLLJBdKNhWiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "education=df['size'].value_counts()\n",
    "df_education=pd.DataFrame({'education':education.index[1:],'fre':education.values[1:]})\n",
    "df_education.index=df_education.education\n",
    "plt.figure()\n",
    "df_education.fre.plot(kind='bar')  \n",
    "plt.ylabel('计数')\n",
    "plt.xlabel('size') \n",
    "plt.title('size分布')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 画饼状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#根据value_counts（）结果画饼图\n",
    "phone=df.phone_operator.value_counts()\n",
    "df_phone=pd.DataFrame({'phone_operator':phone.index[1:],'fre':phone.values[1:]})\n",
    "\n",
    "plt.rc('font', family='SimHei', size=13)\n",
    "fig = plt.figure() \n",
    "plt.pie(df_phone.fre,labels=df_phone.phone_operator,autopct='%1.2f%%') #画饼图（数据，数据对应的标签，百分数保留两位小数点） \n",
    "plt.title(\"手机运营商分布\") "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fullpy3",
   "language": "python",
   "name": "fullpy3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}