41. Pandas读取Excel绘制直方图.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas读取Excel绘制直方图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***直方图(Histogram)：***  \n",
    "直方图是数值数据分布的精确图形表示，是一个连续变量（定量变量）的概率分布的估计，它是一种条形图。   \n",
    "为了构建直方图，第一步是将值的范围分段，即将整个值的范围分成一系列间隔，然后计算每个间隔中有多少值。 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 读取数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "波斯顿房价数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"./datas/boston-house-prices/housing.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
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       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
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       "      <td>7.07</td>\n",
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       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
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       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "      <td>22.4</td>\n",
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       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
       "      <td>20.6</td>\n",
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       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
       "      <td>23.9</td>\n",
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       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.9</td>\n",
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       "<p>506 rows × 14 columns</p>\n",
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      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  \\\n",
       "0    0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296   \n",
       "1    0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242   \n",
       "2    0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242   \n",
       "3    0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222   \n",
       "4    0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...  ...   \n",
       "501  0.06263   0.0  11.93     0  0.573  6.593  69.1  2.4786    1  273   \n",
       "502  0.04527   0.0  11.93     0  0.573  6.120  76.7  2.2875    1  273   \n",
       "503  0.06076   0.0  11.93     0  0.573  6.976  91.0  2.1675    1  273   \n",
       "504  0.10959   0.0  11.93     0  0.573  6.794  89.3  2.3889    1  273   \n",
       "505  0.04741   0.0  11.93     0  0.573  6.030  80.8  2.5050    1  273   \n",
       "\n",
       "     PTRATIO       B  LSTAT  MEDV  \n",
       "0       15.3  396.90   4.98  24.0  \n",
       "1       17.8  396.90   9.14  21.6  \n",
       "2       17.8  392.83   4.03  34.7  \n",
       "3       18.7  394.63   2.94  33.4  \n",
       "4       18.7  396.90   5.33  36.2  \n",
       "..       ...     ...    ...   ...  \n",
       "501     21.0  391.99   9.67  22.4  \n",
       "502     21.0  396.90   9.08  20.6  \n",
       "503     21.0  396.90   5.64  23.9  \n",
       "504     21.0  393.45   6.48  22.0  \n",
       "505     21.0  396.90   7.88  11.9  \n",
       "\n",
       "[506 rows x 14 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   CRIM     506 non-null    float64\n",
      " 1   ZN       506 non-null    float64\n",
      " 2   INDUS    506 non-null    float64\n",
      " 3   CHAS     506 non-null    int64  \n",
      " 4   NOX      506 non-null    float64\n",
      " 5   RM       506 non-null    float64\n",
      " 6   AGE      506 non-null    float64\n",
      " 7   DIS      506 non-null    float64\n",
      " 8   RAD      506 non-null    int64  \n",
      " 9   TAX      506 non-null    int64  \n",
      " 10  PTRATIO  506 non-null    float64\n",
      " 11  B        506 non-null    float64\n",
      " 12  LSTAT    506 non-null    float64\n",
      " 13  MEDV     506 non-null    float64\n",
      "dtypes: float64(11), int64(3)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      24.0\n",
       "1      21.6\n",
       "2      34.7\n",
       "3      33.4\n",
       "4      36.2\n",
       "       ... \n",
       "501    22.4\n",
       "502    20.6\n",
       "503    23.9\n",
       "504    22.0\n",
       "505    11.9\n",
       "Name: MEDV, Length: 506, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"MEDV\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 使用matplotlib画直方图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "matplotlib直方图文档：https://matplotlib.org/3.2.0/api/_as_gen/matplotlib.pyplot.hist.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "plt.hist(df[\"MEDV\"], bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 使用pyecharts画直方图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pyecharts直方图文档：http://gallery.pyecharts.org/#/Bar/bar_histogram  \n",
    "numpy直方图文档：https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 需要自己计算有多少个间隔、以及每个间隔有多少个值\n",
    "hist,bin_edges =  np.histogram(df[\"MEDV\"], bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.  ,  5.45,  5.9 ,  6.35,  6.8 ,  7.25,  7.7 ,  8.15,  8.6 ,\n",
       "        9.05,  9.5 ,  9.95, 10.4 , 10.85, 11.3 , 11.75, 12.2 , 12.65,\n",
       "       13.1 , 13.55, 14.  , 14.45, 14.9 , 15.35, 15.8 , 16.25, 16.7 ,\n",
       "       17.15, 17.6 , 18.05, 18.5 , 18.95, 19.4 , 19.85, 20.3 , 20.75,\n",
       "       21.2 , 21.65, 22.1 , 22.55, 23.  , 23.45, 23.9 , 24.35, 24.8 ,\n",
       "       25.25, 25.7 , 26.15, 26.6 , 27.05, 27.5 , 27.95, 28.4 , 28.85,\n",
       "       29.3 , 29.75, 30.2 , 30.65, 31.1 , 31.55, 32.  , 32.45, 32.9 ,\n",
       "       33.35, 33.8 , 34.25, 34.7 , 35.15, 35.6 , 36.05, 36.5 , 36.95,\n",
       "       37.4 , 37.85, 38.3 , 38.75, 39.2 , 39.65, 40.1 , 40.55, 41.  ,\n",
       "       41.45, 41.9 , 42.35, 42.8 , 43.25, 43.7 , 44.15, 44.6 , 45.05,\n",
       "       45.5 , 45.95, 46.4 , 46.85, 47.3 , 47.75, 48.2 , 48.65, 49.1 ,\n",
       "       49.55, 50.  ])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 这是每个间隔的分割点\n",
    "bin_edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(bin_edges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  1,  1,  0,  5,  2,  1,  6,  3,  0,  3,  3,  5,  3,  4,  6,  3,\n",
       "        5, 14,  9,  9,  6, 11,  8,  6,  8,  6, 10,  9,  9, 15, 13, 20, 16,\n",
       "       19, 10, 14, 19, 13, 15, 21, 16,  9, 12, 14,  1,  0,  4,  5,  2,  6,\n",
       "        5,  5,  4,  3,  6,  2,  3,  4,  3,  4,  3,  6,  2,  1,  1,  5,  3,\n",
       "        1,  4,  1,  3,  1,  1,  1,  0,  0,  1,  0,  0,  1,  1,  1,  1,  1,\n",
       "        1,  2,  0,  1,  1,  0,  1,  1,  0,  0,  0,  2,  1,  0, 16],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 这是间隔的计数\n",
    "hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(hist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 对bin_edges的解释，为什么是101个？比hist计数多1个？\n",
    "\n",
    "举例：如果bins是[1, 2, 3, 4]，那么会分成3个区间：[1, 2)、[2, 3)、[3, 4]；  \n",
    "其中bins的第一个值是数组的最小值，bins的最后一个元素是数组的最大值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    506.000000\n",
       "mean      22.532806\n",
       "std        9.197104\n",
       "min        5.000000\n",
       "25%       17.025000\n",
       "50%       21.200000\n",
       "75%       25.000000\n",
       "max       50.000000\n",
       "Name: MEDV, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 注意观察，min是bins的第一个值，max是bins的最后一个元素\n",
    "df[\"MEDV\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45,\n",
       "       0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45,\n",
       "       0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45,\n",
       "       0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45,\n",
       "       0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45,\n",
       "       0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45,\n",
       "       0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45,\n",
       "       0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45,\n",
       "       0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45,\n",
       "       0.45])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看bins每一个值和前一个值的差值，可以看到这是等分的数据\n",
    "np.diff(bin_edges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 这些间隔的数目，刚好等于计数hist的数目\n",
    "len(np.diff(bin_edges))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pyecharts的直方图使用bar实现\n",
    "# 取bins[:-1]，意思是用每个区间的左边元素作为x轴的值\n",
    "bar = (\n",
    "    Bar()\n",
    "    .add_xaxis([str(x) for x in bin_edges[:-1]])\n",
    "    .add_yaxis(\"价格分布\", [float(x) for x in hist], category_gap=0)\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"波斯顿房价-价格分布-直方图\", pos_left=\"center\"),\n",
    "        legend_opts=opts.LegendOpts(is_show=False)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
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       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"c552da67584643c2a067d0a088fb3b41\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
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       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
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       "    \"color\": [\n",
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       "                \"margin\": 8\n",
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       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u4ef7\\u683c\\u5206\\u5e03\"\n",
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       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
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       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
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       "        },\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
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       "    },\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
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       "            \"splitNumber\": 5,\n",
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       "                \"lineStyle\": {\n",
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       "                    \"width\": 1,\n",
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       "                \"26.150000000000002\",\n",
       "                \"26.6\",\n",
       "                \"27.05\",\n",
       "                \"27.5\",\n",
       "                \"27.95\",\n",
       "                \"28.400000000000002\",\n",
       "                \"28.85\",\n",
       "                \"29.3\",\n",
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       "                \"30.2\",\n",
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       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u6ce2\\u65af\\u987f\\u623f\\u4ef7-\\u4ef7\\u683c\\u5206\\u5e03-\\u76f4\\u65b9\\u56fe\",\n",
       "            \"left\": \"center\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
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       "};\n",
       "                chart_c552da67584643c2a067d0a088fb3b41.setOption(option_c552da67584643c2a067d0a088fb3b41);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x240525b8e48>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bar.render_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>小作业：</b>   \n",
    "获取你们产品的销量数据、价格数据，提取得到一个一数组，画一个直方图看一下数据分布"
   ]
  }
 ],
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