00001pyecharts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4e4ce9e8-4dcf-4fd3-9b2a-d60385dda1bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('20240406', '20240407114051', '20240331')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts.globals import CurrentConfig, NotebookType\n",
    "CurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_LAB\n",
    "import sys\n",
    "sys.path.append(\"D:\\yxp\")\n",
    "from need_import import *\n",
    "d7 = str(list(pd.date_range(ds, freq='-1D', periods=7))[-1].date()).replace('-', '')\n",
    "ds, dstime, d7"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "144133c1-5978-4019-aec3-f9013cb791cd",
   "metadata": {},
   "source": [
    "# 中文文档"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5e8f5a33-da56-4806-b472-4925fb34f4f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://pyecharts.org/#/zh-cn/intro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7e5770a-ccc1-4473-9e86-6fd17fc2469d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cb9bb46d-c30a-4989-a73c-84900100f8b9",
   "metadata": {},
   "source": [
    "# 柱状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9919eb1f-6d8f-4b10-a701-e5d332d8046a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Series1</th>\n",
       "      <th>Series2</th>\n",
       "      <th>Series3</th>\n",
       "      <th>Series4</th>\n",
       "      <th>Series5</th>\n",
       "      <th>合计</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Category 1</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>200</td>\n",
       "      <td>300</td>\n",
       "      <td>-300</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Category 2</td>\n",
       "      <td>100</td>\n",
       "      <td>110</td>\n",
       "      <td>250</td>\n",
       "      <td>460</td>\n",
       "      <td>-460</td>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Category 3</td>\n",
       "      <td>200</td>\n",
       "      <td>90</td>\n",
       "      <td>200</td>\n",
       "      <td>490</td>\n",
       "      <td>-490</td>\n",
       "      <td>490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Category 4</td>\n",
       "      <td>350</td>\n",
       "      <td>50</td>\n",
       "      <td>60</td>\n",
       "      <td>460</td>\n",
       "      <td>-460</td>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Category 5</td>\n",
       "      <td>50</td>\n",
       "      <td>150</td>\n",
       "      <td>400</td>\n",
       "      <td>600</td>\n",
       "      <td>-600</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Category 6</td>\n",
       "      <td>250</td>\n",
       "      <td>50</td>\n",
       "      <td>260</td>\n",
       "      <td>560</td>\n",
       "      <td>-560</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Category  Series1  Series2  Series3  Series4  Series5   合计\n",
       "0  Category 1       50       50      200      300     -300  300\n",
       "1  Category 2      100      110      250      460     -460  460\n",
       "2  Category 3      200       90      200      490     -490  490\n",
       "3  Category 4      350       50       60      460     -460  460\n",
       "4  Category 5       50      150      400      600     -600  600\n",
       "5  Category 6      250       50      260      560     -560  560"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data = [['Category 1', 50, 50, 200, 300,-300],\n",
    "                         ['Category 2', 100, 110, 250, 460, -460],\n",
    "                         ['Category 3', 200, 90, 200, 490, -490],\n",
    "                         ['Category 4', 350, 50, 60, 460, -460],\n",
    "                         ['Category 5', 50, 150, 400, 600, -600],\n",
    "                         ['Category 6', 250, 50, 260, 560, -560]],\n",
    "                columns = ['Category', 'Series1', 'Series2', 'Series3', 'Series4','Series5']\n",
    "                )\n",
    "df['合计'] = df.iloc[:, 1:].sum(axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cfc37fab-bccb-4813-a0da-95def8bfa97d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\yxp_ipy\\\\03 可视化\\\\pyecharts\\\\01柱状图.html'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.Category.to_list()\n",
    "y1 = df.Series1.to_list()\n",
    "y2 = df.Series2.to_list()\n",
    "y3 = df.Series3.to_list()\n",
    "y4 = df.Series4.to_list()\n",
    "y5 = df.Series5.to_list()\n",
    "y6 = df.合计.to_list()\n",
    "\n",
    "\n",
    "from pyecharts.charts import Bar\n",
    "# 类的实例化,实例化一个柱状图的对象\n",
    "bar = Bar(\n",
    "    # 初始化配置项\n",
    "    init_opts = opts.InitOpts(\n",
    "        page_title='01柱状图'  # 网页标题\n",
    "        # ,width = '1100px'\n",
    "        # ,height = '700px'\n",
    "    )\n",
    ")\n",
    "# 添加横轴和纵轴的数据\n",
    "bar.add_xaxis(xaxis_data=x)\n",
    "\n",
    "bar.add_yaxis(series_name='Series1'\n",
    "              ,y_axis=y1 \n",
    "              ,color='#028599' # 绿色\n",
    "             )   \n",
    "\n",
    "bar.add_yaxis(series_name='Series2'\n",
    "              ,y_axis=y2 \n",
    "              ,color='#FF3F5F' # 红色\n",
    "             )  \n",
    "\n",
    "bar.add_yaxis(series_name='Series3'\n",
    "              ,y_axis=y3\n",
    "              ,color='#FEB834'  # 黄色\n",
    "             )  \n",
    "\n",
    "# 设置图表全局配置项\n",
    "bar.set_global_opts(\n",
    "    # 图表标题\n",
    "    title_opts=opts.TitleOpts(is_show=True, \n",
    "                              title='这是一个柱状图', \n",
    "                              pos_left='center'\n",
    "                             )\n",
    "    # 图例\n",
    "    ,legend_opts=opts.LegendOpts(is_show=True, \n",
    "                                pos_top=30,  # 图例离上边界 30\n",
    "                                border_color='transparent'# 图例边界颜色为 透明\n",
    "                               \n",
    "                               )\n",
    "    \n",
    "    # X 坐标轴配置项\n",
    "    ,xaxis_opts=opts.AxisOpts(type_='category'     # 'value': 数值轴，适用于连续数据。\n",
    "                                                  # 'category': 类目轴，适用于离散的类目数据，为该类型时必须通过 data 设置类目数据。\n",
    "                                                  # 'time': 时间轴，适用于连续的时序数据，与数值轴相比时间轴带有时间的格式化，在刻度计算上也有所不同，\n",
    "                                                  # 例如会根据跨度的范围来决定使用月，星期，日还是小时范围的刻度。\n",
    "                                                  # 'log' 对数轴。适用于对数数据。\n",
    "                             ,name = 'X坐标轴名称' # 轴名称\n",
    "                             ,is_show = True      # 是否显示x轴\n",
    "                             ,name_location='center'  # 'start', 'middle' 或者 'center','end'\n",
    "                             ,name_gap = 35      # 坐标轴名称与轴线之间的距离。\n",
    "                             # 轴名称文字样式配置\n",
    "                             ,name_textstyle_opts=opts.TextStyleOpts(# color='black'       # 文字颜色，使用默认\n",
    "                                                                     # font_style='normal' # 字体风格\n",
    "                                                                     # ,font_weight='normal'   # 字体粗细\n",
    "                                                                     # ,font_family='Microsoft YaHei'        # 字体 'serif' , 'monospace', 'Arial', 'Courier New', 'Microsoft YaHei',\n",
    "                                                                     font_size=15  # 字体大小\n",
    "                                                                    \n",
    "                                                                    \n",
    "                                                                    )\n",
    "                            \n",
    "                            \n",
    "                            \n",
    "                            )\n",
    "    #  Y 坐标轴配置项\n",
    "    ,yaxis_opts=opts.AxisOpts(type_='value'     # 'value': 数值轴，适用于连续数据。\n",
    "                                                  # 'category': 类目轴，适用于离散的类目数据，为该类型时必须通过 data 设置类目数据。\n",
    "                                                  # 'time': 时间轴，适用于连续的时序数据，与数值轴相比时间轴带有时间的格式化，在刻度计算上也有所不同，\n",
    "                                                  # 例如会根据跨度的范围来决定使用月，星期，日还是小时范围的刻度。\n",
    "                                                  # 'log' 对数轴。适用于对数数据。\n",
    "                             ,name = 'Y坐标轴名称' # 轴名称\n",
    "                             ,is_show = True      # 是否显示\n",
    "                             ,name_location='center'  # 'start', 'middle' 或者 'center','end'\n",
    "                             ,name_gap = 35      # 坐标轴名称与轴线之间的距离。\n",
    "                             # 轴名称文字样式配置\n",
    "                             ,name_textstyle_opts=opts.TextStyleOpts(# color='black'       # 文字颜色,使用默认\n",
    "                                                                     # font_style='normal' # 字体风格\n",
    "                                                                     # font_weight='normal'   # 字体粗细\n",
    "                                                                     # ,font_family='Microsoft YaHei'        # 字体 'serif' , 'monospace', 'Arial', 'Courier New', 'Microsoft YaHei',\n",
    "                                                                     font_size=15  # 字体大小\n",
    "                                                                    \n",
    "                                                                    \n",
    "                                                                    )\n",
    "                             )\n",
    "    \n",
    "                   \n",
    ")\n",
    "\n",
    "# 设置系列配置项\n",
    "bar.set_series_opts(\n",
    "    # 数据标签配置项\n",
    "    label_opts=opts.LabelOpts(\n",
    "        is_show = True,\n",
    "        position = 'top'\n",
    "    )\n",
    "    \n",
    "    # 标记线配置项\n",
    "    ,markline_opts=opts.MarkLineOpts(\n",
    "        data=[\n",
    "                 opts.MarkLineItem(y=240, \n",
    "                                   name=\"yAxis=240\", \n",
    "                                   # 线样式配置项\n",
    "                                   linestyle_opts = opts.LineStyleOpts(color='#5470C6' # 线的颜色\n",
    "                                                                       ,type_='dashed'  # 线型\n",
    "                                                                      \n",
    "                                                                      )\n",
    "                                  \n",
    "                                  )\n",
    "            \n",
    "            \n",
    "                ,opts.MarkLineItem(y=150, name=\"yAxis=150\")\n",
    "             \n",
    "             \n",
    "             ]\n",
    "    )\n",
    ")\n",
    "\n",
    "# 在jupyter 中显示\n",
    "bar.load_javascript()\n",
    "# jupyter渲染的图片往往不够清晰，导出再截图\n",
    "html_name = '01柱状图.html'\n",
    "bar.render(html_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "af808225-01f5-4d1e-9b18-d6637f4b6f73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"950\"\n",
       "            height=\"520\"\n",
       "            src=\"01柱状图.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1ca427b8160>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# render_notebook()显示不出来，就用下面这种\n",
    "from IPython.display import IFrame\n",
    "IFrame(src=html_name, width=950, height=520)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddde455e-fecf-4429-ac7c-96e09854d5cb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ec25fb80-fd6e-4ba4-a797-c42c3ace1a3f",
   "metadata": {},
   "source": [
    "# 条形图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b62822f0-b312-42ba-a0db-6ff45ab5c015",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Series1</th>\n",
       "      <th>Series2</th>\n",
       "      <th>Series3</th>\n",
       "      <th>Series4</th>\n",
       "      <th>Series5</th>\n",
       "      <th>合计</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Category 1</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>200</td>\n",
       "      <td>300</td>\n",
       "      <td>-300</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Category 2</td>\n",
       "      <td>100</td>\n",
       "      <td>110</td>\n",
       "      <td>250</td>\n",
       "      <td>460</td>\n",
       "      <td>-460</td>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Category 3</td>\n",
       "      <td>200</td>\n",
       "      <td>90</td>\n",
       "      <td>200</td>\n",
       "      <td>490</td>\n",
       "      <td>-490</td>\n",
       "      <td>490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Category 4</td>\n",
       "      <td>350</td>\n",
       "      <td>50</td>\n",
       "      <td>60</td>\n",
       "      <td>460</td>\n",
       "      <td>-460</td>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Category 5</td>\n",
       "      <td>50</td>\n",
       "      <td>150</td>\n",
       "      <td>400</td>\n",
       "      <td>600</td>\n",
       "      <td>-600</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Category 6</td>\n",
       "      <td>250</td>\n",
       "      <td>50</td>\n",
       "      <td>260</td>\n",
       "      <td>560</td>\n",
       "      <td>-560</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Category  Series1  Series2  Series3  Series4  Series5   合计\n",
       "0  Category 1       50       50      200      300     -300  300\n",
       "1  Category 2      100      110      250      460     -460  460\n",
       "2  Category 3      200       90      200      490     -490  490\n",
       "3  Category 4      350       50       60      460     -460  460\n",
       "4  Category 5       50      150      400      600     -600  600\n",
       "5  Category 6      250       50      260      560     -560  560"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data = [['Category 1', 50, 50, 200, 300,-300],\n",
    "                         ['Category 2', 100, 110, 250, 460, -460],\n",
    "                         ['Category 3', 200, 90, 200, 490, -490],\n",
    "                         ['Category 4', 350, 50, 60, 460, -460],\n",
    "                         ['Category 5', 50, 150, 400, 600, -600],\n",
    "                         ['Category 6', 250, 50, 260, 560, -560]],\n",
    "                columns = ['Category', 'Series1', 'Series2', 'Series3', 'Series4','Series5']\n",
    "                )\n",
    "df['合计'] = df.iloc[:, 1:].sum(axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c5f4f4b9-70d9-4d6d-852e-b1659289471f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\yxp_ipy\\\\03 可视化\\\\pyecharts\\\\02条形图.html'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.Category.to_list()\n",
    "y1 = df.Series1.to_list()\n",
    "y2 = df.Series2.to_list()\n",
    "y3 = df.Series3.to_list()\n",
    "y4 = df.Series4.to_list()\n",
    "y5 = df.Series5.to_list()\n",
    "y6 = df.合计.to_list()\n",
    "\n",
    "from pyecharts.charts import Bar\n",
    "bar = Bar(\n",
    "    # 初始化配置项\n",
    "    init_opts = opts.InitOpts(\n",
    "        page_title='02条形图'  # 网页标题\n",
    "        # ,width = '1100px'\n",
    "        # ,height = '700px'\n",
    "    )\n",
    ")\n",
    "# 添加横轴和纵轴的数据\n",
    "bar.add_xaxis(xaxis_data=x)\n",
    "\n",
    "\n",
    "bar.add_yaxis(series_name='Series1'\n",
    "              ,y_axis=y1 \n",
    "              ,color='#028599' # 绿色\n",
    "             )   \n",
    "\n",
    "bar.add_yaxis(series_name='Series2'\n",
    "              ,y_axis=y2 \n",
    "              ,color='#FF3F5F' # 红色\n",
    "             )  \n",
    "\n",
    "bar.add_yaxis(series_name='Series3'\n",
    "              ,y_axis=y3\n",
    "              ,color='#FEB834'  # 黄色\n",
    "             )  \n",
    "\n",
    "\n",
    "bar.reversal_axis()\n",
    "\n",
    "\n",
    "# 设置图表全局配置项\n",
    "bar.set_global_opts(\n",
    "    # 图表标题\n",
    "    title_opts=opts.TitleOpts(is_show=True, \n",
    "                              title='这是一个条形图', \n",
    "                              pos_left='center'\n",
    "                             )\n",
    "    # 图例\n",
    "    ,legend_opts=opts.LegendOpts(is_show=True, \n",
    "                                pos_top=30,  # 图例离上边界 30\n",
    "                                border_color='transparent'# 图例边界颜色为 透明\n",
    "                               \n",
    "                               )  \n",
    "     # X 坐标轴配置项\n",
    "    ,xaxis_opts=opts.AxisOpts(type_='value'       # 'value': 数值轴，适用于连续数据。\n",
    "                                                  # 'category': 类目轴，适用于离散的类目数据，为该类型时必须通过 data 设置类目数据。\n",
    "                                                  # 'time': 时间轴，适用于连续的时序数据，与数值轴相比时间轴带有时间的格式化，在刻度计算上也有所不同，\n",
    "                                                  # 例如会根据跨度的范围来决定使用月，星期，日还是小时范围的刻度。\n",
    "                                                  # 'log' 对数轴。适用于对数数据。\n",
    "                             ,name = 'y坐标轴名称' # 轴名称\n",
    "                             ,is_show = True      # 是否显示x轴\n",
    "                             ,name_location='center'  # 'start', 'middle' 或者 'center','end'\n",
    "                             ,name_gap = 35      # 坐标轴名称与轴线之间的距离。\n",
    "                             # 轴名称文字样式配置\n",
    "                             ,name_textstyle_opts=opts.TextStyleOpts(# color='black'       # 文字颜色，使用默认\n",
    "                                                                     # font_style='normal' # 字体风格\n",
    "                                                                     # ,font_weight='normal'   # 字体粗细\n",
    "                                                                     # ,font_family='Microsoft YaHei'        # 字体 'serif' , 'monospace', 'Arial', 'Courier New', 'Microsoft YaHei',\n",
    "                                                                     font_size=15  # 字体大小\n",
    "                                                                    \n",
    "                                                                    \n",
    "                                                                    )\n",
    "                             )\n",
    "                   \n",
    ")\n",
    "\n",
    "# 设置系列配置项\n",
    "bar.set_series_opts(\n",
    "    # 数据标签配置项\n",
    "    label_opts=opts.LabelOpts(\n",
    "        is_show = True,\n",
    "        position = 'right'\n",
    "    )\n",
    "    \n",
    "    # 标记线配置项\n",
    "    ,markline_opts=opts.MarkLineOpts(\n",
    "        data=[\n",
    "                 opts.MarkLineItem(x=240, \n",
    "                                   name=\"xAxis=240\", \n",
    "                                   # 线样式配置项\n",
    "                                   linestyle_opts = opts.LineStyleOpts(color='#5470C6' # 线的颜色\n",
    "                                                                       ,type_='dashed'  # 线型\n",
    "                                                                      \n",
    "                                                                      )\n",
    "                                  \n",
    "                                  ) \n",
    "             \n",
    "             ]\n",
    "    )\n",
    ")\n",
    "    \n",
    "\n",
    "\n",
    "# 在jupyter 中显示\n",
    "bar.load_javascript()\n",
    "# jupyter渲染的图片往往不够清晰，导出再截图\n",
    "html_name = '02条形图.html'\n",
    "bar.render(html_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "af4041c0-709c-4d40-b71b-ba7f55bffdf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"950\"\n",
       "            height=\"520\"\n",
       "            src=\"02条形图.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1ca7f1c7b20>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# render_notebook()显示不出来，就用下面这种# bar.render_notebook()\n",
    "from IPython.display import IFrame\n",
    "IFrame(src=html_name, width=950, height=520)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ab72912-490b-4791-af52-b9da69116c55",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0fdf55dc-b44d-432f-b5c8-659ae7fe0c8b",
   "metadata": {},
   "source": [
    "# 堆叠柱状图  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b8b4f6ff-e934-4e11-ab85-b264bfc8aea9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Series1</th>\n",
       "      <th>Series2</th>\n",
       "      <th>Series3</th>\n",
       "      <th>合计</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Category 1</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>200</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Category 2</td>\n",
       "      <td>100</td>\n",
       "      <td>110</td>\n",
       "      <td>250</td>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Category 3</td>\n",
       "      <td>200</td>\n",
       "      <td>90</td>\n",
       "      <td>200</td>\n",
       "      <td>490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Category 4</td>\n",
       "      <td>350</td>\n",
       "      <td>50</td>\n",
       "      <td>60</td>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Category 5</td>\n",
       "      <td>50</td>\n",
       "      <td>150</td>\n",
       "      <td>400</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Category 6</td>\n",
       "      <td>250</td>\n",
       "      <td>50</td>\n",
       "      <td>260</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Category  Series1  Series2  Series3   合计\n",
       "0  Category 1       50       50      200  300\n",
       "1  Category 2      100      110      250  460\n",
       "2  Category 3      200       90      200  490\n",
       "3  Category 4      350       50       60  460\n",
       "4  Category 5       50      150      400  600\n",
       "5  Category 6      250       50      260  560"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data = [['Category 1', 50, 50, 200, 300],\n",
    "                         ['Category 2', 100, 110, 250, 460],\n",
    "                         ['Category 3', 200, 90, 200, 490],\n",
    "                         ['Category 4', 350, 50, 60, 460],\n",
    "                         ['Category 5', 50, 150, 400, 600],\n",
    "                         ['Category 6', 250, 50, 260, 560]],\n",
    "                columns = ['Category', 'Series1', 'Series2', 'Series3', '合计']\n",
    "                )\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1e7e2c5c-fedf-488a-9f11-80476a8342fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\yxp_ipy\\\\03 可视化\\\\pyecharts\\\\03堆叠柱状图.html'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.Category.to_list()\n",
    "y1 = df.Series1.to_list()\n",
    "y2 = df.Series2.to_list()\n",
    "y3 = df.Series3.to_list()\n",
    "合计 = df.合计.to_list()\n",
    "\n",
    "\n",
    "bar = Bar(\n",
    "    # 初始化配置项\n",
    "    init_opts = opts.InitOpts(\n",
    "        page_title='03堆叠柱状图'  # 网页标题\n",
    "        # ,width = '1100px'\n",
    "        # ,height = '700px'\n",
    "    )\n",
    ")\n",
    "bar.add_xaxis(x)\n",
    "# stack的值相同的就会堆叠放置\n",
    "bar.add_yaxis(\"Series1\", y1, stack=\"stack1\", color='#028599', bar_width=40)\n",
    "bar.add_yaxis(\"Series2\", y2, stack=\"stack1\", color='#FF3F5F', bar_width=40)\n",
    "bar.add_yaxis(\"Series3\", y3, stack=\"stack1\", color='#FEB834', bar_width=40)\n",
    "bar.add_yaxis(\"合计\",    合计, stack=\"stack2\", bar_width=40)\n",
    "\n",
    "# 设置图表全局配置项\n",
    "bar.set_global_opts(\n",
    "    # 图表标题\n",
    "    title_opts=opts.TitleOpts(is_show=True, \n",
    "                              title='这是一个堆叠的状图', \n",
    "                              pos_left='center'\n",
    "                             )\n",
    "    # 图例\n",
    "    ,legend_opts=opts.LegendOpts(is_show=True, \n",
    "                                pos_top=30,  # 图例离上边界 30\n",
    "                                border_color='transparent'# 图例边界颜色为 透明\n",
    "                               \n",
    "                               )\n",
    "    \n",
    "    # X 坐标轴配置项\n",
    "    ,xaxis_opts=opts.AxisOpts(type_='category'     # 'value': 数值轴，适用于连续数据。\n",
    "                                                  # 'category': 类目轴，适用于离散的类目数据，为该类型时必须通过 data 设置类目数据。\n",
    "                                                  # 'time': 时间轴，适用于连续的时序数据，与数值轴相比时间轴带有时间的格式化，在刻度计算上也有所不同，\n",
    "                                                  # 例如会根据跨度的范围来决定使用月，星期，日还是小时范围的刻度。\n",
    "                                                  # 'log' 对数轴。适用于对数数据。\n",
    "                             ,name = 'X坐标轴名称' # 轴名称\n",
    "                             ,is_show = True      # 是否显示x轴\n",
    "                             ,name_location='center'  # 'start', 'middle' 或者 'center','end'\n",
    "                             ,name_gap = 35      # 坐标轴名称与轴线之间的距离。\n",
    "                             # 轴名称文字样式配置\n",
    "                             ,name_textstyle_opts=opts.TextStyleOpts(# color='black'       # 文字颜色，使用默认\n",
    "                                                                     # font_style='normal' # 字体风格\n",
    "                                                                     # ,font_weight='normal'   # 字体粗细\n",
    "                                                                     # ,font_family='Microsoft YaHei'        # 字体 'serif' , 'monospace', 'Arial', 'Courier New', 'Microsoft YaHei',\n",
    "                                                                     font_size=15  # 字体大小\n",
    "                                                                    \n",
    "                                                                    \n",
    "                                                                    )\n",
    "                            \n",
    "                            \n",
    "                            \n",
    "                            )\n",
    "    #  Y 坐标轴配置项\n",
    "    ,yaxis_opts=opts.AxisOpts(type_='value'     # 'value': 数值轴，适用于连续数据。\n",
    "                                                  # 'category': 类目轴，适用于离散的类目数据，为该类型时必须通过 data 设置类目数据。\n",
    "                                                  # 'time': 时间轴，适用于连续的时序数据，与数值轴相比时间轴带有时间的格式化，在刻度计算上也有所不同，\n",
    "                                                  # 例如会根据跨度的范围来决定使用月，星期，日还是小时范围的刻度。\n",
    "                                                  # 'log' 对数轴。适用于对数数据。\n",
    "                             ,name = 'Y坐标轴名称' # 轴名称\n",
    "                             ,is_show = True      # 是否显示\n",
    "                             ,name_location='center'  # 'start', 'middle' 或者 'center','end'\n",
    "                             ,name_gap = 35       # 坐标轴名称与轴线之间的距离。\n",
    "                             # ,max_=700            # y轴的最大值  \n",
    "                              \n",
    "                             # 轴名称文字样式配置\n",
    "                             ,name_textstyle_opts=opts.TextStyleOpts(# color='black'       # 文字颜色,使用默认\n",
    "                                                                     # font_style='normal' # 字体风格\n",
    "                                                                     # font_weight='normal'   # 字体粗细\n",
    "                                                                     # ,font_family='Microsoft YaHei'        # 字体 'serif' , 'monospace', 'Arial', 'Courier New', 'Microsoft YaHei',\n",
    "                                                                     font_size=15  # 字体大小\n",
    "                                                                    \n",
    "                                 \n",
    "                            \n",
    "                                                                    \n",
    "                                                                    )\n",
    "                             )\n",
    "    \n",
    "                   \n",
    ")\n",
    "\n",
    "# 设置系列配置项\n",
    "bar.set_series_opts(\n",
    "    # 数据标签配置项\n",
    "    label_opts=opts.LabelOpts(\n",
    "        is_show = True,\n",
    "        #position = 'bottom'\n",
    "    )\n",
    ")\n",
    "\n",
    "\n",
    "bar.load_javascript() \n",
    "# jupyter渲染的图片往往不够清晰，导出再截图\n",
    "html_name = '03堆叠柱状图.html'\n",
    "bar.render(html_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2e5a8986-e8d7-4f3b-b055-a2ed3baa8816",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"950\"\n",
       "            height=\"520\"\n",
       "            src=\"03堆叠柱状图.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1ca7f207c40>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# render_notebook()显示不出来，就用下面这种# bar.render_notebook()\n",
    "from IPython.display import IFrame\n",
    "IFrame(src=html_name, width=950, height=520)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49f7b29b-a5d7-4de0-a1b9-7391076d2160",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 堆叠条形图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0fd87fe8-16e9-49bd-a11d-63c7d69f8c4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Series1</th>\n",
       "      <th>Series2</th>\n",
       "      <th>Series3</th>\n",
       "      <th>合计</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Category 1</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>200</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Category 2</td>\n",
       "      <td>100</td>\n",
       "      <td>110</td>\n",
       "      <td>250</td>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Category 3</td>\n",
       "      <td>200</td>\n",
       "      <td>90</td>\n",
       "      <td>200</td>\n",
       "      <td>490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Category 4</td>\n",
       "      <td>350</td>\n",
       "      <td>50</td>\n",
       "      <td>60</td>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Category 5</td>\n",
       "      <td>50</td>\n",
       "      <td>150</td>\n",
       "      <td>400</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Category 6</td>\n",
       "      <td>250</td>\n",
       "      <td>50</td>\n",
       "      <td>260</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Category  Series1  Series2  Series3   合计\n",
       "0  Category 1       50       50      200  300\n",
       "1  Category 2      100      110      250  460\n",
       "2  Category 3      200       90      200  490\n",
       "3  Category 4      350       50       60  460\n",
       "4  Category 5       50      150      400  600\n",
       "5  Category 6      250       50      260  560"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data = [['Category 1', 50, 50, 200, 300],\n",
    "                         ['Category 2', 100, 110, 250, 460],\n",
    "                         ['Category 3', 200, 90, 200, 490],\n",
    "                         ['Category 4', 350, 50, 60, 460],\n",
    "                         ['Category 5', 50, 150, 400, 600],\n",
    "                         ['Category 6', 250, 50, 260, 560]],\n",
    "                columns = ['Category', 'Series1', 'Series2', 'Series3', '合计']\n",
    "                )\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "58062fed-9849-4446-869f-60c0ff1b3bca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"950\"\n",
       "            height=\"520\"\n",
       "            src=\"04堆叠条形图.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1ca42819af0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.Category.to_list()\n",
    "y1 = df.Series1.to_list()\n",
    "y2 = df.Series2.to_list()\n",
    "y3 = df.Series3.to_list()\n",
    "合计 = df.合计.to_list()\n",
    "\n",
    "from pyecharts.charts import Bar\n",
    "bar = Bar(\n",
    "    # 初始化配置项\n",
    "    init_opts = opts.InitOpts(\n",
    "        page_title='04堆叠条形图'  # 网页标题\n",
    "        # ,width = '1100px'\n",
    "        # ,height = '700px'\n",
    "    )\n",
    ")\n",
    "# 添加横轴和纵轴的数据\n",
    "bar.add_xaxis(xaxis_data=x)\n",
    "bar.add_yaxis(series_name='Series1', y_axis=y1,  stack='1', color='#028599')   # 绿色\n",
    "bar.add_yaxis(series_name='Series2', y_axis=y2,  stack='1', color='#FF3F5F')  # 红色\n",
    "bar.add_yaxis(series_name='Series3', y_axis=y3,  stack='1', color='#FEB834')  # 黄色\n",
    "\n",
    "bar.reversal_axis()\n",
    "\n",
    "\n",
    "# 设置图表全局配置项\n",
    "bar.set_global_opts(\n",
    "    # 图表标题\n",
    "    title_opts=opts.TitleOpts(is_show=True, \n",
    "                              title='这是一个堆叠条形图', \n",
    "                              pos_left='center'\n",
    "                             )\n",
    "    # 图例\n",
    "    ,legend_opts=opts.LegendOpts(is_show=True, \n",
    "                                pos_top=30,  # 图例离上边界 30\n",
    "                                border_color='transparent'# 图例边界颜色为 透明\n",
    "                               \n",
    "                               )  \n",
    "     # X 坐标轴配置项\n",
    "    ,xaxis_opts=opts.AxisOpts(type_='value'       # 'value': 数值轴，适用于连续数据。\n",
    "                                                  # 'category': 类目轴，适用于离散的类目数据，为该类型时必须通过 data 设置类目数据。\n",
    "                                                  # 'time': 时间轴，适用于连续的时序数据，与数值轴相比时间轴带有时间的格式化，在刻度计算上也有所不同，\n",
    "                                                  # 例如会根据跨度的范围来决定使用月，星期，日还是小时范围的刻度。\n",
    "                                                  # 'log' 对数轴。适用于对数数据。\n",
    "                             ,name = 'y坐标轴名称' # 轴名称\n",
    "                             ,is_show = True      # 是否显示x轴\n",
    "                             ,name_location='center'  # 'start', 'middle' 或者 'center','end'\n",
    "                             ,name_gap = 35      # 坐标轴名称与轴线之间的距离。\n",
    "                             # 轴名称文字样式配置\n",
    "                             ,name_textstyle_opts=opts.TextStyleOpts(# color='black'       # 文字颜色，使用默认\n",
    "                                                                     # font_style='normal' # 字体风格\n",
    "                                                                     # ,font_weight='normal'   # 字体粗细\n",
    "                                                                     # ,font_family='Microsoft YaHei'        # 字体 'serif' , 'monospace', 'Arial', 'Courier New', 'Microsoft YaHei',\n",
    "                                                                     font_size=15  # 字体大小\n",
    "                                                                    \n",
    "                                                                    \n",
    "                                                                    )\n",
    "                             )\n",
    "                   \n",
    ")\n",
    "\n",
    "# 设置系列配置项\n",
    "bar.set_series_opts(\n",
    "    # 数据标签配置项\n",
    "    label_opts=opts.LabelOpts(\n",
    "        is_show = True,\n",
    "       #  position = 'right'\n",
    "    )\n",
    ")\n",
    "    \n",
    "\n",
    "\n",
    "# 在jupyter 中显示\n",
    "bar.load_javascript()\n",
    "# jupyter渲染的图片往往不够清晰，导出再截图\n",
    "html_name = '04堆叠条形图.html'\n",
    "bar.render(html_name)\n",
    "\n",
    "# render_notebook()显示不出来，就用下面这种# bar.render_notebook()\n",
    "from IPython.display import IFrame\n",
    "IFrame(src=html_name, width=950, height=520)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4495f86b-7479-4335-8f6b-4e1d90d636a3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "91f71a53-b8ba-4340-9ca7-e9a6d68bceff",
   "metadata": {},
   "source": [
    "# 对比条形图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0ad00f8b-5a85-4389-bdc3-a4e92a9823a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"950\"\n",
       "            height=\"520\"\n",
       "            src=\"05对比条形图.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1ca7f207df0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts.charts import Bar\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.commons.utils import JsCode\n",
    "\n",
    "# 准备数据\n",
    "x_data = ['A1', 'B1', 'C1', 'D1', 'E1', 'F1']\n",
    "y_data1 = [20, 30, 40, 50, 60, 100]\n",
    "y_data2 = [15, 25, 35, 45, 55, 100]\n",
    "\n",
    "# 创建Bar实例\n",
    "bar = Bar(\n",
    "    # 初始化配置项\n",
    "    init_opts = opts.InitOpts(\n",
    "        page_title='06对比条形图'  # 网页标题\n",
    "        # ,width = '1100px'\n",
    "        # ,height = '700px'\n",
    "    )\n",
    "\n",
    ")\n",
    "\n",
    "# 添加数据\n",
    "bar.add_xaxis(x_data)\n",
    "bar.add_yaxis(\"Series1\", \n",
    "              y_data1, \n",
    "              stack=\"stack1\", \n",
    "              color='#028599', \n",
    "              label_opts=opts.LabelOpts(formatter=\"{c}\")) # c代表限制的是 值\n",
    "\n",
    "\n",
    "bar.add_yaxis(\"Series2\", \n",
    "              [-i for i in y_data2], \n",
    "              color='#FF3F5F', \n",
    "              stack=\"stack1\",  \n",
    "              label_opts=opts.LabelOpts(is_show=True,\n",
    "                                           # 利用回调函数去除负号\n",
    "                                           formatter=JsCode(\n",
    "                                               \"\"\"\n",
    "                                               function(params) {\n",
    "                                                   return -(params.data)\n",
    "                                               }\n",
    "                                               \"\"\"\n",
    "\n",
    "                                           )\n",
    "\n",
    "\n",
    "                                          ))\n",
    "bar.reversal_axis()\n",
    "\n",
    "# 设置全局配置项\n",
    "bar.set_global_opts(\n",
    "    # 图表标题\n",
    "    title_opts=opts.TitleOpts(is_show=True, \n",
    "                              title='这是一个对比条形图', \n",
    "                              pos_left='center'\n",
    "                             ),\n",
    "    # 图例\n",
    "    legend_opts=opts.LegendOpts(is_show=True, \n",
    "                                pos_top=30,  # 图例离上边界 30\n",
    "                                border_color='transparent'# 图例边界颜色为 透明\n",
    "                               \n",
    "                               ),  \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    # x坐标轴不显示\n",
    "    xaxis_opts=opts.AxisOpts(is_show=False),\n",
    "    \n",
    "    # y轴\n",
    "    yaxis_opts=opts.AxisOpts(is_show=True, \n",
    "                            # 坐标轴刻度线配置项\n",
    "                            axisline_opts = opts.AxisLineOpts(is_show=False),\n",
    "                             \n",
    "                            # 坐标轴刻度配置项\n",
    "                            axistick_opts = opts.AxisTickOpts(is_show=False),\n",
    "                             \n",
    "                            # 分割线配置项\n",
    "                            splitline_opts=opts.SplitLineOpts(is_show=True)\n",
    "                            )\n",
    ")\n",
    "\n",
    "# 渲染图表\n",
    "bar.load_javascript()\n",
    "html_name = '05对比条形图.html'\n",
    "bar.render(html_name)\n",
    "\n",
    "# render_notebook()显示不出来，就用下面这种# bar.render_notebook()\n",
    "from IPython.display import IFrame\n",
    "IFrame(src=html_name, width=950, height=520)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df8dc527-3d9e-454e-8838-c7463fbf6fea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c228a5c0-b1ef-4f24-ae8b-bec23872dbbf",
   "metadata": {},
   "source": [
    "# 饼图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "06109afb-ceb0-4293-b697-4af6f2190cdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>种类</th>\n",
       "      <th>数值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>熊猫</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>蟒蛇</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>老虎</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>兔子</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>大象</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>河马</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>狮子</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   种类   数值\n",
       "5  熊猫  124\n",
       "1  蟒蛇   95\n",
       "2  老虎   85\n",
       "4  兔子   80\n",
       "3  大象   77\n",
       "0  河马   68\n",
       "6  狮子   25"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data=[['河马', 68],\n",
    "                         ['蟒蛇', 95],\n",
    "                         ['老虎', 85],\n",
    "                         ['大象', 77],\n",
    "                         ['兔子', 80],\n",
    "                         ['熊猫', 124],\n",
    "                         ['狮子', 25]]\n",
    "                  \n",
    "                  ,columns=['种类', '数值']\n",
    "                 \n",
    "                 )\n",
    "df = df.sort_values('数值', ascending=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6d8d3e0c-347b-49d3-8cd1-e7c4490d0620",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    0.223827\n",
       "1    0.171480\n",
       "2    0.153430\n",
       "4    0.144404\n",
       "3    0.138989\n",
       "0    0.122744\n",
       "6    0.045126\n",
       "Name: 数值, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.数值/df.数值.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d8683b90-2997-4c6f-b812-6709a50601b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['熊猫', 124],\n",
       " ['蟒蛇', 95],\n",
       " ['老虎', 85],\n",
       " ['兔子', 80],\n",
       " ['大象', 77],\n",
       " ['河马', 68],\n",
       " ['狮子', 25]]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "181e356b-cab9-44a5-a0b2-b66242eb082e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"950\"\n",
       "            height=\"520\"\n",
       "            src=\"饼图.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1ca4274c4f0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Pie\n",
    "\n",
    "pie = Pie(\n",
    "    # 初始化配置项\n",
    "    init_opts = opts.InitOpts(\n",
    "        page_title='05饼图'  # 网页标题\n",
    "        # ,width = '1100px'\n",
    "        # ,height = '700px'\n",
    "    )\n",
    "\n",
    ")\n",
    "\n",
    "pie.add(series_name='',data_pair=df.values.tolist()\n",
    "        # radius=0.7\n",
    "        # ,rosetype='radius' # 是否变成玫瑰图\n",
    "        ,percent_precision=1 # 百分比小数位数\n",
    "       \n",
    "       )\n",
    "\n",
    "# 全局配置项\n",
    "pie.set_global_opts(\n",
    "\n",
    "    # 图表标题\n",
    "    title_opts=opts.TitleOpts(is_show=True, \n",
    "                              title='这是一个饼图', \n",
    "                              pos_left='center'\n",
    "                             ),\n",
    "    # 图例\n",
    "    legend_opts=opts.LegendOpts(is_show=True \n",
    "                                ,pos_top=150  # 图例离上边界 30\n",
    "                                ,pos_left=50\n",
    "                                ,orient = 'vertical'   #  可选：'horizontal', 'vertical'#\n",
    "                                ,border_color='transparent'# 图例边界颜色为 透明\n",
    "                               \n",
    "                               ),  \n",
    "\n",
    ")\n",
    "\n",
    "\n",
    "# 设置系列配置项\n",
    "pie.set_series_opts(\n",
    "    # 数据标签配置项\n",
    "    label_opts=opts.LabelOpts(\n",
    "        is_show = True,\n",
    "        # position = 'top',\n",
    "        #{a}（系列名称），{b}（数据项名称），{c}（数值）, {d}（百分比）\n",
    "        formatter=\"{b}：{c} \\n\\n{d}%\"\n",
    "        \n",
    "    )\n",
    ")\n",
    "\n",
    "pie.load_javascript()\n",
    "html_name = '饼图.html'\n",
    "pie.render(html_name)\n",
    "\n",
    "# render_notebook()显示不出来，就用下面这种# bar.render_notebook()\n",
    "from IPython.display import IFrame\n",
    "IFrame(src=html_name, width=950, height=520)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f539d3a-48ca-4f25-bbb8-d4ecd2cbbb1c",
   "metadata": {},
   "source": [
    "# 玫瑰图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "edaad43a-f91a-4f1f-845a-71d68d2eb837",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>种类</th>\n",
       "      <th>数值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>熊猫</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>蟒蛇</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>老虎</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>兔子</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>大象</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>河马</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>狮子</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   种类   数值\n",
       "5  熊猫  124\n",
       "1  蟒蛇   95\n",
       "2  老虎   85\n",
       "4  兔子   80\n",
       "3  大象   77\n",
       "0  河马   68\n",
       "6  狮子   25"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data=[['河马', 68],\n",
    "                         ['蟒蛇', 95],\n",
    "                         ['老虎', 85],\n",
    "                         ['大象', 77],\n",
    "                         ['兔子', 80],\n",
    "                         ['熊猫', 124],\n",
    "                         ['狮子', 25]]\n",
    "                  \n",
    "                  ,columns=['种类', '数值']\n",
    "                 \n",
    "                 )\n",
    "df = df.sort_values('数值', ascending=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7aa14ab8-0f8f-4f20-834b-78ae6d701663",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['熊猫', 124],\n",
       " ['蟒蛇', 95],\n",
       " ['老虎', 85],\n",
       " ['兔子', 80],\n",
       " ['大象', 77],\n",
       " ['河马', 68],\n",
       " ['狮子', 25]]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f374f2d6-3df1-4085-9be2-df9151b11df2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"950\"\n",
       "            height=\"520\"\n",
       "            src=\"玫瑰图.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1ca427f66a0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pie = Pie(\n",
    "    # 初始化配置项\n",
    "    init_opts = opts.InitOpts(\n",
    "        page_title='玫瑰图'  # 网页标题\n",
    "        # ,width = '1100px'\n",
    "        # ,height = '700px'\n",
    "    )\n",
    "\n",
    ")\n",
    "\n",
    "pie.add(series_name='',data_pair=df.values.tolist()\n",
    "        # radius=0.7\n",
    "        ,rosetype='radius' # 是否变成玫瑰图\n",
    "        ,percent_precision=1 # 百分比小数位数\n",
    "       \n",
    "       )\n",
    "\n",
    "# 全局配置项\n",
    "pie.set_global_opts(\n",
    "\n",
    "    # 图表标题\n",
    "    title_opts=opts.TitleOpts(is_show=True, \n",
    "                              title='这是一个玫瑰图', \n",
    "                              pos_left='center'\n",
    "                             ),\n",
    "    # 图例\n",
    "    legend_opts=opts.LegendOpts(is_show=True \n",
    "                                ,pos_top=150  # 图例离上边界 30\n",
    "                                ,pos_left=50\n",
    "                                ,orient = 'vertical'   #  可选：'horizontal', 'vertical'#\n",
    "                                ,border_color='transparent'# 图例边界颜色为 透明\n",
    "                               \n",
    "                               ),  \n",
    "\n",
    ")\n",
    "\n",
    "\n",
    "# 设置系列配置项\n",
    "pie.set_series_opts(\n",
    "    # 数据标签配置项\n",
    "    label_opts=opts.LabelOpts(\n",
    "        is_show = True,\n",
    "        # position = 'top',\n",
    "        #{a}（系列名称），{b}（数据项名称），{c}（数值）, {d}（百分比）\n",
    "        formatter=\"{b}：{c} \\n\\n{d}%\"\n",
    "        \n",
    "    )\n",
    ")\n",
    "pie.render('玫瑰图.html')\n",
    "pie.load_javascript()\n",
    "html_name = '玫瑰图.html'\n",
    "pie.render(html_name)\n",
    "from IPython.display import IFrame\n",
    "IFrame(src=html_name, width=950, height=520)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ed21f2b-2660-490d-b86b-5b22f7a07d7b",
   "metadata": {},
   "source": [
    "# 圆环图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "27ba238d-6720-4dbf-8cf6-c4cb5162c164",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>种类</th>\n",
       "      <th>数值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>熊猫</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>蟒蛇</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>老虎</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>兔子</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>大象</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>河马</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>狮子</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   种类   数值\n",
       "5  熊猫  124\n",
       "1  蟒蛇   95\n",
       "2  老虎   85\n",
       "4  兔子   80\n",
       "3  大象   77\n",
       "0  河马   68\n",
       "6  狮子   25"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data=[['河马', 68],\n",
    "                         ['蟒蛇', 95],\n",
    "                         ['老虎', 85],\n",
    "                         ['大象', 77],\n",
    "                         ['兔子', 80],\n",
    "                         ['熊猫', 124],\n",
    "                         ['狮子', 25]]\n",
    "                  \n",
    "                  ,columns=['种类', '数值']\n",
    "                 \n",
    "                 )\n",
    "df = df.sort_values('数值', ascending=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "042aceec-67b0-444c-990b-eb9b3493233b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"950\"\n",
       "            height=\"520\"\n",
       "            src=\"圆环图.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1ca427f69a0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pie = Pie(\n",
    "    # 初始化配置项\n",
    "    init_opts = opts.InitOpts(\n",
    "        page_title='圆环图'  # 网页标题\n",
    "        # ,width = '1100px'\n",
    "        # ,height = '700px'\n",
    "    )\n",
    "\n",
    ")\n",
    "\n",
    "pie.add(series_name='',data_pair=df.values.tolist()\n",
    "        # 饼图的半径，数组的第一项是内半径，第二项是外半径\n",
    "        ,radius=[\"50%\", \"75%\"]\n",
    "        ,center=[\"50%\", \"50%\"]\n",
    "        #,rosetype='radius' # 是否变成玫瑰图\n",
    "        ,percent_precision=1 # 百分比小数位数\n",
    "       \n",
    "       )\n",
    "\n",
    "# 全局配置项\n",
    "pie.set_global_opts(\n",
    "\n",
    "    # 图表标题\n",
    "    title_opts=opts.TitleOpts(is_show=True, \n",
    "                              title='这是一个圆环图', \n",
    "                              pos_left='center'\n",
    "                             ),\n",
    "    # 图例\n",
    "    legend_opts=opts.LegendOpts(is_show=True \n",
    "                                ,pos_top=150  # 图例离上边界 30\n",
    "                                ,pos_left=50\n",
    "                                ,orient = 'vertical'   #  可选：'horizontal', 'vertical'#\n",
    "                                ,border_color='transparent'# 图例边界颜色为 透明\n",
    "                               \n",
    "                               ),  \n",
    "\n",
    ")\n",
    "\n",
    "\n",
    "# 设置系列配置项\n",
    "pie.set_series_opts(\n",
    "    # 数据标签配置项\n",
    "    label_opts=opts.LabelOpts(\n",
    "        is_show = True,\n",
    "        # position = 'top',\n",
    "        #{a}（系列名称），{b}（数据项名称），{c}（数值）, {d}（百分比）\n",
    "        formatter=\"{b}：{c} \\n\\n{d}%\"\n",
    "        \n",
    "    )\n",
    ")\n",
    "\n",
    "pie.load_javascript()\n",
    "html_name = '圆环图.html'\n",
    "pie.render(html_name)\n",
    "from IPython.display import IFrame\n",
    "IFrame(src=html_name, width=950, height=520)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5359d393-e67c-4a5f-8914-1937dea41da3",
   "metadata": {},
   "source": [
    "# 散点图"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "231f3d69-99e9-441f-9eb2-d1304a78d0b7",
   "metadata": {},
   "source": [
    "## x是数值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4ac977d9-3e34-4829-91f6-b8ecb96df429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"950\"\n",
       "            height=\"520\"\n",
       "            src=\"散点图1.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1ca42741d30>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.linspace(-10, 10, 100).tolist()\n",
    "y1 = np.sin(x).tolist()\n",
    "y2 = np.cos(x).tolist()\n",
    "\n",
    "from pyecharts.charts import Scatter\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.commons.utils import JsCode\n",
    "\n",
    "sc = Scatter(    # 初始化配置项\n",
    "    init_opts = opts.InitOpts(\n",
    "        page_title='散点图'  # 网页标题\n",
    "        # ,width = '1100px'\n",
    "        # ,height = '700px'\n",
    "    )\n",
    ")\n",
    "\n",
    "sc.add_xaxis(xaxis_data=x)\n",
    "sc.add_yaxis(series_name='sin', y_axis=y1, color='#028599' )\n",
    "sc.add_yaxis(series_name='tanh', y_axis=y2, color='#FF3F5F')\n",
    "\n",
    "# 全局配置项\n",
    "sc.set_global_opts(\n",
    "    \n",
    "    # 图表标题\n",
    "    title_opts=opts.TitleOpts(is_show=True, \n",
    "                              title='这是一个散点图', \n",
    "                              pos_left='center'\n",
    "                             ),\n",
    "    # 图例\n",
    "    legend_opts=opts.LegendOpts(is_show=True \n",
    "                                ,pos_top=30  # 图例离上边界 30\n",
    "                                # ,pos_left=50\n",
    "                                # ,orient = 'vertical'   #  可选：'horizontal', 'vertical'#\n",
    "                                ,border_color='transparent'# 图例边界颜色为 透明\n",
    "                               \n",
    "                               ),  \n",
    "    \n",
    "    # x轴配置项\n",
    "    xaxis_opts=opts.AxisOpts(\n",
    "        # x轴数值为数值型\n",
    "        type_=\"value\",     #  如果x是数值型，这里要指定为 value\n",
    "    )\n",
    ")\n",
    "\n",
    "# 系列配置项\n",
    "sc.set_series_opts(\n",
    "    # 标签配置项\n",
    "    label_opts=opts.LabelOpts(\n",
    "        is_show=False\n",
    "    )\n",
    ")\n",
    "\n",
    "sc.load_javascript()\n",
    "\n",
    "html_name = '散点图1.html'\n",
    "sc.render(html_name)\n",
    "from IPython.display import IFrame\n",
    "IFrame(src=html_name, width=950, height=520)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "532a00ae-2b88-4473-88db-59b115036bdb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "46b3b138-33fc-4ce1-84c2-e8121c8cd956",
   "metadata": {},
   "source": [
    "## x是类别"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0409ec67-1f3b-4635-978e-b3a829a5bafc",
   "metadata": {},
   "source": [
    "### 原坐标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5410c2c7-2fcf-45b9-b7ff-2d8cf3089b65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>种类</th>\n",
       "      <th>数值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>熊猫</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>蟒蛇</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>老虎</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>兔子</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>大象</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>河马</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>狮子</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   种类   数值\n",
       "5  熊猫  124\n",
       "1  蟒蛇   95\n",
       "2  老虎   85\n",
       "4  兔子   80\n",
       "3  大象   77\n",
       "0  河马   68\n",
       "6  狮子   25"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data=[['河马', 68],\n",
    "                         ['蟒蛇', 95],\n",
    "                         ['老虎', 85],\n",
    "                         ['大象', 77],\n",
    "                         ['兔子', 80],\n",
    "                         ['熊猫', 124],\n",
    "                         ['狮子', 25]]\n",
    "                  \n",
    "                  ,columns=['种类', '数值']\n",
    "                 \n",
    "                 )\n",
    "df = df.sort_values('数值', ascending=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4ce54e5a-4339-4042-b32d-402fd6b85f70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"950\"\n",
       "            height=\"520\"\n",
       "            src=\"散点图2.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1ca426c68e0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.种类.to_list()\n",
    "y = df.数值.to_list()\n",
    "\n",
    "\n",
    "from pyecharts.charts import Scatter\n",
    "from pyecharts import options as opts\n",
    "\n",
    "sc = Scatter(    # 初始化配置项\n",
    "    init_opts = opts.InitOpts(\n",
    "        page_title='散点图'  # 网页标题\n",
    "        # ,width = '1100px'\n",
    "        # ,height = '700px'\n",
    "    )\n",
    ")\n",
    "\n",
    "sc.add_xaxis(xaxis_data=x)\n",
    "sc.add_yaxis(series_name='', \n",
    "             y_axis=[list(z) for z in zip(y, x)],\n",
    "             color='#028599', # 绿色\n",
    "             symbol_size=50,# 散点大小\n",
    "            \n",
    "    )\n",
    "\n",
    "# 全局配置项\n",
    "sc.set_global_opts(\n",
    "    \n",
    "    # 图表标题\n",
    "    title_opts=opts.TitleOpts(is_show=True, \n",
    "                              title='这是一个散点图', \n",
    "                              pos_left='center'\n",
    "                             ),\n",
    "    # 图例\n",
    "    legend_opts=opts.LegendOpts(is_show=True \n",
    "                                ,pos_top=30  # 图例离上边界 30\n",
    "                                # ,pos_left=50\n",
    "                                # ,orient = 'vertical'   #  可选：'horizontal', 'vertical'#\n",
    "                                ,border_color='transparent'# 图例边界颜色为 透明\n",
    "                               \n",
    "                               ),  \n",
    "    \n",
    "    # x轴配置项\n",
    "    xaxis_opts=opts.AxisOpts( \n",
    "        is_show=False,\n",
    "        # x轴数值为种类\n",
    "        type_=\"category\",     #  如果x是数值型，这里要指定为 value\n",
    "    ),\n",
    "    \n",
    "    # tooltip_opts=opts.TooltipOpts( is_show=True,\n",
    "    #         formatter=JsCode(\n",
    "    #             \"function (params) {return params.value[0] + ' : ' + params.value[1];}\"\n",
    "    #         )\n",
    "    \n",
    "    # ),\n",
    "    visualmap_opts=opts.VisualMapOpts( \n",
    "        is_show=False,  # -------  是否显示映射的标识\n",
    "        type_=\"color\", \n",
    "        max_=150, \n",
    "        min_=20, \n",
    "        dimension=1,\n",
    "        range_color=['#028599',  '#FEB834','#FF3F5F',]# 颜色梯度，绿色 黄 红\n",
    "        ),\n",
    "    \n",
    "\n",
    ")\n",
    "\n",
    "# 系列配置项\n",
    "sc.set_series_opts(\n",
    "    # 标签配置项\n",
    "    label_opts=opts.LabelOpts(\n",
    "         is_show=True,\n",
    "         # position = 'top',\n",
    "         font_size=8, # 字体大小\n",
    "         formatter=JsCode(\n",
    "                     \"function (params) {return params.value[0] + ' : ' + params.value[1];}\"\n",
    "                 ),\n",
    "        \n",
    "    )\n",
    ")\n",
    "\n",
    "sc.load_javascript()\n",
    "\n",
    "html_name = '散点图2.html'\n",
    "sc.render(html_name)\n",
    "from IPython.display import IFrame\n",
    "IFrame(src=html_name, width=950, height=520)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1053a00-c307-4c67-80ce-92bd3a377b6b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d637a5c7-af53-4dd8-9147-aa08a178ed39",
   "metadata": {},
   "source": [
    "### 标记线\n",
    "- 使用标记线，然后隐藏原来的坐标轴即可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "86037516-5b4a-4602-bbf6-2475766acc50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>种类</th>\n",
       "      <th>数值1</th>\n",
       "      <th>数值2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>熊猫</td>\n",
       "      <td>124</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>蟒蛇</td>\n",
       "      <td>95</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>老虎</td>\n",
       "      <td>85</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>兔子</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>大象</td>\n",
       "      <td>77</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>河马</td>\n",
       "      <td>68</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>狮子</td>\n",
       "      <td>25</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   种类  数值1  数值2\n",
       "5  熊猫  124   37\n",
       "1  蟒蛇   95   30\n",
       "2  老虎   85   10\n",
       "4  兔子   80  100\n",
       "3  大象   77   50\n",
       "0  河马   68   79\n",
       "6  狮子   25   59"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data=[['河马', 68, 79],\n",
    "                         ['蟒蛇', 95, 30],\n",
    "                         ['老虎', 85, 10],\n",
    "                         ['大象', 77, 50],\n",
    "                         ['兔子', 80, 100],\n",
    "                         ['熊猫', 124, 37],\n",
    "                         ['狮子', 25, 59]]\n",
    "                  \n",
    "                  ,columns=['种类', '数值1', '数值2']\n",
    "                 \n",
    "                 )\n",
    "df = df.sort_values('数值1', ascending=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "782003a0-e702-46cb-955d-1d64481614ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"950\"\n",
       "            height=\"520\"\n",
       "            src=\"散点图3.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1ca42819130>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.种类.to_list()\n",
    "y1 = df.数值1.to_list()\n",
    "y2 = df.数值2.to_list()\n",
    "\n",
    "from pyecharts.charts import Scatter\n",
    "from pyecharts import options as opts\n",
    "\n",
    "sc = Scatter(    # 初始化配置项\n",
    "    init_opts = opts.InitOpts(\n",
    "        page_title='散点图'  # 网页标题\n",
    "        # ,width = '1100px'\n",
    "        # ,height = '700px'\n",
    "    )\n",
    ")\n",
    "\n",
    "sc.add_xaxis(xaxis_data=y1)\n",
    "sc.add_yaxis(series_name='', \n",
    "             y_axis=[list(z) for z in zip(y2, x)],\n",
    "             #y_axis=y2,\n",
    "             color='#028599', # 绿色\n",
    "             symbol_size=55,# 散点大小\n",
    "            \n",
    "    )\n",
    "\n",
    "# 全局配置项\n",
    "sc.set_global_opts(\n",
    "    \n",
    "    # 图表标题\n",
    "    title_opts=opts.TitleOpts(is_show=True, \n",
    "                              title='这是一个散点图', \n",
    "                              pos_left='center'\n",
    "                             ),\n",
    "    # 图例\n",
    "    legend_opts=opts.LegendOpts(is_show=True \n",
    "                                ,pos_top=30  # 图例离上边界 30\n",
    "                                # ,pos_left=50\n",
    "                                # ,orient = 'vertical'   #  可选：'horizontal', 'vertical'#\n",
    "                                ,border_color='transparent'# 图例边界颜色为 透明\n",
    "                               \n",
    "                               ),  \n",
    "    \n",
    "    # x轴配置项\n",
    "    xaxis_opts=opts.AxisOpts( \n",
    "        is_show=True,\n",
    "        # x轴数值为种类\n",
    "        type_=\"value\",     #  如果x是数值型，这里要指定为 value\n",
    "    ),\n",
    "    # x轴配置项\n",
    "    yaxis_opts=opts.AxisOpts( \n",
    "        is_show=True,\n",
    "        # x轴数值为种类\n",
    "        type_=\"value\",     #  如果x是数值型，这里要指定为 value\n",
    "    ),\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    # tooltip_opts=opts.TooltipOpts( is_show=True,\n",
    "    #         formatter=JsCode(\n",
    "    #             \"function (params) {return params.value[0] + ' : ' + params.value[1];}\"\n",
    "    #         )\n",
    "    \n",
    "    # ),\n",
    "    visualmap_opts=opts.VisualMapOpts( \n",
    "        is_show=False,  # -------  是否显示映射的标识\n",
    "        type_=\"color\", \n",
    "        max_=150, \n",
    "        min_=20, \n",
    "        dimension=1,\n",
    "        range_color=['#028599',  '#FEB834','#FF3F5F',]# 颜色梯度，绿色 黄 红\n",
    "        ),\n",
    "    \n",
    "\n",
    ")\n",
    "\n",
    "# 系列配置项\n",
    "sc.set_series_opts(\n",
    "    # 标签配置项\n",
    "    label_opts=opts.LabelOpts(\n",
    "         is_show=True,\n",
    "         # position = 'top',\n",
    "         font_size=8, # 字体大小\n",
    "        \n",
    "         \n",
    "         # params.value[0]指的是传入的y1，，就是x 轴接受到的东西\n",
    "         # params.value[1]指的是传入的y2，y轴接受的东西\n",
    "         # params.value[2]指的是传入的x，就是Y轴接受的标签\n",
    "         formatter=JsCode(\n",
    "                     '''function (params) {\n",
    "                         return params.value[2]+','+\n",
    "                                params.value[0]+','+\n",
    "                                params.value[1];\n",
    "                       }'''\n",
    "                 ),       \n",
    "    ),\n",
    "    \n",
    "    # 标记线配置项\n",
    "    markline_opts=opts.MarkLineOpts(\n",
    "        data=[\n",
    "                 opts.MarkLineItem(x=80, \n",
    "                                   name=\"xAxis=80\", \n",
    "                                   # 线样式配置项\n",
    "                                   linestyle_opts = opts.LineStyleOpts(color='#5470C6' # 线的颜色\n",
    "                                                                       ,type_='dashed'  # 线型\n",
    "                                                                      \n",
    "                                                                      )\n",
    "                                  \n",
    "                                  ),\n",
    "                 opts.MarkLineItem(y=50, \n",
    "                                   name=\"yAxis=50\", \n",
    "                                   # 线样式配置项\n",
    "                                   linestyle_opts = opts.LineStyleOpts(color='#5470C6' # 线的颜色\n",
    "                                                                       ,type_='dashed'  # 线型\n",
    "                                                                      \n",
    "                                                                      )\n",
    "                                  \n",
    "                                  ) \n",
    "             \n",
    "             ]\n",
    "        \n",
    "    )\n",
    "    \n",
    "    \n",
    ")\n",
    "\n",
    "sc.load_javascript()\n",
    "\n",
    "html_name = '散点图3.html'\n",
    "sc.render(html_name)\n",
    "from IPython.display import IFrame\n",
    "IFrame(src=html_name, width=950, height=520)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45123953-ee9a-4524-a72f-ed3a1141d335",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "116855e9-8208-4e49-b88f-d024f950641b",
   "metadata": {},
   "source": [
    "# 气泡图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "70b3a6dd-4681-4184-91af-cd2b97aa9427",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>种类</th>\n",
       "      <th>数值1</th>\n",
       "      <th>数值2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>熊猫</td>\n",
       "      <td>124</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>蟒蛇</td>\n",
       "      <td>95</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>老虎</td>\n",
       "      <td>85</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>兔子</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>大象</td>\n",
       "      <td>77</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>河马</td>\n",
       "      <td>68</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>狮子</td>\n",
       "      <td>25</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   种类  数值1  数值2\n",
       "5  熊猫  124   37\n",
       "1  蟒蛇   95   30\n",
       "2  老虎   85   10\n",
       "4  兔子   80  100\n",
       "3  大象   77   50\n",
       "0  河马   68   79\n",
       "6  狮子   25   59"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data=[['河马', 68, 79],\n",
    "                         ['蟒蛇', 95, 30],\n",
    "                         ['老虎', 85, 10],\n",
    "                         ['大象', 77, 50],\n",
    "                         ['兔子', 80, 100],\n",
    "                         ['熊猫', 124, 37],\n",
    "                         ['狮子', 25, 59]]\n",
    "                  \n",
    "                  ,columns=['种类', '数值1', '数值2']\n",
    "                 \n",
    "                 )\n",
    "df = df.sort_values('数值1', ascending=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "259186a7-6475-4f47-b69b-0863301bde1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"950\"\n",
       "            height=\"520\"\n",
       "            src=\"散点图4.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1ca4b834790>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.种类.to_list()\n",
    "y1 = df.数值1.to_list()\n",
    "y2 = df.数值2.to_list()\n",
    "\n",
    "from pyecharts.charts import Scatter\n",
    "from pyecharts import options as opts\n",
    "\n",
    "sc = Scatter(    # 初始化配置项\n",
    "    init_opts = opts.InitOpts(\n",
    "        page_title='散点图'  # 网页标题\n",
    "        # ,width = '1100px'\n",
    "        # ,height = '700px'\n",
    "    )\n",
    ")\n",
    "\n",
    "sc.add_xaxis(xaxis_data=y1)\n",
    "sc.add_yaxis(series_name='', \n",
    "             y_axis=[list(z) for z in zip(y2, x)],\n",
    "             #y_axis=y2,\n",
    "             color='#028599', # 绿色\n",
    "             symbol_size=55,# 散点大小\n",
    "            \n",
    "    )\n",
    "\n",
    "# 全局配置项\n",
    "sc.set_global_opts(\n",
    "    \n",
    "    # 图表标题\n",
    "    title_opts=opts.TitleOpts(is_show=True, \n",
    "                              title='这是一个散点图', \n",
    "                              pos_left='center'\n",
    "                             ),\n",
    "    # 图例\n",
    "    legend_opts=opts.LegendOpts(is_show=True \n",
    "                                ,pos_top=30  # 图例离上边界 30\n",
    "                                # ,pos_left=50\n",
    "                                # ,orient = 'vertical'   #  可选：'horizontal', 'vertical'#\n",
    "                                ,border_color='transparent'# 图例边界颜色为 透明\n",
    "                               \n",
    "                               ),  \n",
    "    \n",
    "    # x轴配置项\n",
    "    xaxis_opts=opts.AxisOpts( \n",
    "        is_show=True,\n",
    "        # x轴数值为种类\n",
    "        type_=\"value\",     #  如果x是数值型，这里要指定为 value\n",
    "    ),\n",
    "    # x轴配置项\n",
    "    yaxis_opts=opts.AxisOpts( \n",
    "        is_show=True,\n",
    "        # x轴数值为种类\n",
    "        type_=\"value\",     #  如果x是数值型，这里要指定为 value\n",
    "    ),\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    # tooltip_opts=opts.TooltipOpts( is_show=True,\n",
    "    #         formatter=JsCode(\n",
    "    #             \"function (params) {return params.value[0] + ' : ' + params.value[1];}\"\n",
    "    #         )\n",
    "    \n",
    "    # ),\n",
    "    visualmap_opts=opts.VisualMapOpts( \n",
    "        is_show=False,  # -------  是否显示映射的标识\n",
    "        type_=\"size\", \n",
    "        max_=150, \n",
    "        min_=20, \n",
    "        dimension=1,\n",
    "        # range_color=['#028599',  '#FEB834','#FF3F5F',]# 颜色梯度，绿色 黄 红\n",
    "        ),\n",
    "    \n",
    "\n",
    ")\n",
    "\n",
    "# 系列配置项\n",
    "sc.set_series_opts(\n",
    "    # 标签配置项\n",
    "    label_opts=opts.LabelOpts(\n",
    "         is_show=True,\n",
    "         position = 'top',\n",
    "         font_size=8, # 字体大小\n",
    "        \n",
    "         \n",
    "         # params.value[0]指的是传入的y1，，就是x 轴接受到的东西\n",
    "         # params.value[1]指的是传入的y2，y轴接受的东西\n",
    "         # params.value[2]指的是传入的x，就是Y轴接受的标签\n",
    "         formatter=JsCode(\n",
    "                     '''function (params) {\n",
    "                         return params.value[2]+','+\n",
    "                                params.value[0]+','+\n",
    "                                params.value[1];\n",
    "                       }'''\n",
    "                 ),       \n",
    "    ),\n",
    "    \n",
    "    # 标记线配置项\n",
    "    markline_opts=opts.MarkLineOpts(\n",
    "        data=[\n",
    "                 opts.MarkLineItem(x=80, \n",
    "                                   name=\"xAxis=80\", \n",
    "                                   # 线样式配置项\n",
    "                                   linestyle_opts = opts.LineStyleOpts(color='#5470C6' # 线的颜色\n",
    "                                                                       ,type_='dashed'  # 线型\n",
    "                                                                      \n",
    "                                                                      )\n",
    "                                  \n",
    "                                  ),\n",
    "                 opts.MarkLineItem(y=50, \n",
    "                                   name=\"yAxis=50\", \n",
    "                                   # 线样式配置项\n",
    "                                   linestyle_opts = opts.LineStyleOpts(color='#5470C6' # 线的颜色\n",
    "                                                                       ,type_='dashed'  # 线型\n",
    "                                                                      \n",
    "                                                                      )\n",
    "                                  \n",
    "                                  ) \n",
    "             \n",
    "             ]\n",
    "        \n",
    "    )\n",
    "    \n",
    "    \n",
    ")\n",
    "\n",
    "sc.load_javascript()\n",
    "\n",
    "html_name = '散点图4.html'\n",
    "sc.render(html_name)\n",
    "from IPython.display import IFrame\n",
    "IFrame(src=html_name, width=950, height=520)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68183733-a751-4216-b43c-40cc677de387",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "82952fb0-e8e2-4ed4-b3d9-8deba86d67de",
   "metadata": {},
   "source": [
    "# 雷达图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "a3bfadde-6985-429b-8fa7-64e7e0e14092",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data = [['骑行', 32, 12],\n",
    "                         ['足球', 32, 12],\n",
    "                         ['酷跑', 28, 22],\n",
    "                         ['健身', 12, 21],\n",
    "                         ['攀岩', 15, 28]],\n",
    "                 \n",
    "                  columns = ['种类', '数值1', '数值2']\n",
    "                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "5134c1c7-0e49-444a-b437-84d42d4b4e89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>种类</th>\n",
       "      <th>数值1</th>\n",
       "      <th>数值2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>骑行</td>\n",
       "      <td>32</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>足球</td>\n",
       "      <td>32</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>酷跑</td>\n",
       "      <td>28</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>健身</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>攀岩</td>\n",
       "      <td>15</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   种类  数值1  数值2\n",
       "0  骑行   32   12\n",
       "1  足球   32   12\n",
       "2  酷跑   28   22\n",
       "3  健身   12   21\n",
       "4  攀岩   15   28"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "77933c90-38ac-4852-9e80-a6231426c92d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['骑行', '足球', '酷跑', '健身', '攀岩']"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.种类.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "0f5522b9-2703-42ee-8d93-6aab2cf3b9b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>数值1</th>\n",
       "      <th>数值2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   数值1  数值2\n",
       "0   32   12\n",
       "1   32   12\n",
       "2   28   22\n",
       "3   12   21\n",
       "4   15   28"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "5c987211-7793-4712-aefe-398c8453bf34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[32, 32, 28, 21, 28]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.iloc[:, 1:].max(axis=1).to_list()\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "1eba1b17-1709-4ed7-bbee-c9be4987944f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"950\"\n",
       "            height=\"520\"\n",
       "            src=\"雷达图.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1ca817dffa0>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyecharts.options as opts\n",
    "from pyecharts.charts import Radar\n",
    "(\n",
    "    Radar(init_opts=opts.InitOpts(page_title='雷达图' ))\n",
    "    .add_schema(\n",
    "        schema=[\n",
    "            opts.RadarIndicatorItem(name=df.种类.to_list()[0]+'（50）', max_= 50),\n",
    "            opts.RadarIndicatorItem(name=df.种类.to_list()[1]+'（50）', max_= 50),\n",
    "            opts.RadarIndicatorItem(name=df.种类.to_list()[2]+'（50）', max_= 50),\n",
    "            opts.RadarIndicatorItem(name=df.种类.to_list()[3]+'（50）', max_= 50),\n",
    "            opts.RadarIndicatorItem(name=df.种类.to_list()[4]+'（50）', max_= 50),\n",
    "        ],\n",
    "        splitarea_opt=opts.SplitAreaOpts(\n",
    "            is_show=True, \n",
    "            areastyle_opts=opts.AreaStyleOpts(opacity=0.1)\n",
    "        ),\n",
    "        # 几个维度的字体\n",
    "        textstyle_opts=opts.TextStyleOpts(font_size=22, color=\"\"),\n",
    "        \n",
    "        \n",
    "    )\n",
    "    .add(\n",
    "        series_name=\"数值1\",\n",
    "        data=[df.数值1.to_list()],\n",
    "        color='#028599',\n",
    "        linestyle_opts=opts.LineStyleOpts(width=3), # 线宽\n",
    "        areastyle_opts=opts.AreaStyleOpts(opacity=0.2)  # 图像填充透明度\n",
    "    )\n",
    "    .add(\n",
    "        series_name=\"数值2\",\n",
    "        data=[df.数值2.to_list()],\n",
    "        color='#FF3F5F',\n",
    "        linestyle_opts=opts.LineStyleOpts(width=3), \n",
    "        areastyle_opts=opts.AreaStyleOpts(opacity=0) # 不填充\n",
    "    )\n",
    "    # 系列配置项\n",
    "    .set_series_opts(\n",
    "    \n",
    "        # 数据标签配置项\n",
    "        label_opts=opts.LabelOpts(\n",
    "            is_show = True,\n",
    "            position = 'top'\n",
    "        )\n",
    "    \n",
    "    \n",
    "    )\n",
    "    \n",
    "    # 全局配置项\n",
    "    .set_global_opts(\n",
    "        \n",
    "        # 图表标题\n",
    "        title_opts=opts.TitleOpts(is_show=False, \n",
    "                                  title='这是一个雷达图', \n",
    "                                  pos_left='center'\n",
    "                                 ),\n",
    "        # 图例\n",
    "        legend_opts=opts.LegendOpts(is_show=True \n",
    "                                    # ,pos_top=0  # 图例离上边界 30\n",
    "                                    # ,pos_left=350\n",
    "                                    ,pos_bottom=1\n",
    "                                    ,orient = 'horizontal'   #  可选：'horizontal', 'vertical'#\n",
    "                                    ,border_color='transparent'# 图例边界颜色为 透明\n",
    "\n",
    "                               ),  \n",
    "    \n",
    "    )\n",
    "    .render(\"雷达图.html\")\n",
    ")\n",
    "\n",
    "from IPython.display import IFrame\n",
    "IFrame(src='雷达图.html', width=950, height=520)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c628bdc-c0b4-483e-a644-1694581644ed",
   "metadata": {},
   "source": [
    "# 漏斗图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "936e9e5b-e5ef-4536-83f2-9d6c34b4d971",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>步骤</th>\n",
       "      <th>人数</th>\n",
       "      <th>相对上一步的转化率</th>\n",
       "      <th>相对第一步的转化率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>展现</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>点击</td>\n",
       "      <td>90</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>访问</td>\n",
       "      <td>80</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>咨询</td>\n",
       "      <td>50</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>订单</td>\n",
       "      <td>10</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   步骤   人数  相对上一步的转化率  相对第一步的转化率\n",
       "0  展现  100        NaN        1.0\n",
       "1  点击   90   0.900000        0.9\n",
       "2  访问   80   0.888889        0.8\n",
       "3  咨询   50   0.625000        0.5\n",
       "4  订单   10   0.200000        0.1"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data = [['展现', 100], ['点击', 90], ['访问', 80], ['咨询', 50], ['订单', 10]],\n",
    "             columns = ['步骤', '人数'\n",
    "                       \n",
    "                       ]\n",
    "            \n",
    "            \n",
    "            )\n",
    "df['相对上一步的转化率'] = df.人数/df.人数.shift(1)\n",
    "\n",
    "df['相对第一步的转化率'] = df.人数/100\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "e77f51f3-ee99-4355-8814-4ae80e099ae6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['展现', 100], ['点击', 90], ['访问', 80], ['咨询', 50], ['订单', 10]]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df[['步骤', '人数']].values.tolist()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "28196dfe-8505-40db-89ce-fcf5461f30fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"950\"\n",
       "            height=\"520\"\n",
       "            src=\"漏斗图.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1ca44cdfcd0>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyecharts.options as opts\n",
    "from pyecharts.charts import Funnel\n",
    "\n",
    "(\n",
    "    Funnel(init_opts=opts.InitOpts(page_title='漏斗图' ))\n",
    "    .add(\n",
    "        series_name=\"\",\n",
    "        data_pair=data,\n",
    "        gap=2,\n",
    "        # tooltip_opts=opts.TooltipOpts(trigger=\"item\", formatter=\"{a} <br/>{b} : {c}%\"),\n",
    "        label_opts=opts.LabelOpts(is_show=True, position=\"inside\",\n",
    "                                           formatter=\"{b} : {c}\",\n",
    "                                 font_size=12\n",
    "                                 ),\n",
    "        \n",
    "        \n",
    "        \n",
    "        itemstyle_opts=opts.ItemStyleOpts(border_color=\"#fff\", border_width=1),\n",
    "    )\n",
    "    .set_global_opts(title_opts=opts.TitleOpts(title=\"漏斗图\", subtitle=\"纯属虚构\"))\n",
    "    .render(\"漏斗图.html\")\n",
    ")\n",
    "from IPython.display import IFrame\n",
    "IFrame(src='漏斗图.html', width=950, height=520)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc3fcb36-e66a-4b9a-840b-1851fb8048b6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a01018bc-72c1-42a0-9f9d-cdbe06ca7b52",
   "metadata": {},
   "source": [
    "# 桑基图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "cfb4a21c-a6ba-43de-a340-387a43a151fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"950\"\n",
       "            height=\"520\"\n",
       "            src=\"桑基图.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1ca81783c70>"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Sankey\n",
    "\n",
    "nodes = [\n",
    "    {\"name\": \"category1\"},\n",
    "    {\"name\": \"category2\"},\n",
    "    {\"name\": \"category3\"},\n",
    "    {\"name\": \"category4\"},\n",
    "    {\"name\": \"category5\"},\n",
    "    {\"name\": \"category6\"},\n",
    "]\n",
    "\n",
    "links = [\n",
    "    {\"source\": \"category1\", \"target\": \"category2\", \"value\": 10},\n",
    "    {\"source\": \"category2\", \"target\": \"category3\", \"value\": 15},\n",
    "    {\"source\": \"category3\", \"target\": \"category4\", \"value\": 20},\n",
    "    {\"source\": \"category5\", \"target\": \"category6\", \"value\": 25},\n",
    "]\n",
    "c = (\n",
    "    Sankey(init_opts=opts.InitOpts(page_title='桑基图' ))\n",
    "    .add(\n",
    "        \"sankey\",\n",
    "        nodes,\n",
    "        links,\n",
    "        linestyle_opt=opts.LineStyleOpts(opacity=0.2, curve=0.5, color=\"source\"),\n",
    "        label_opts=opts.LabelOpts(position=\"right\",formatter=\"{b}\"),\n",
    "    )\n",
    "    .set_global_opts(title_opts=opts.TitleOpts(title=\"\"),\n",
    "                     legend_opts=opts.LegendOpts(is_show=False)\n",
    "                    )\n",
    "    \n",
    "    .render(\"桑基图.html\")\n",
    ")\n",
    "from IPython.display import IFrame\n",
    "IFrame(src='桑基图.html', width=950, height=520)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ffc13fc-650c-4b99-95d3-597420ba4f03",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "423a2117-5a6b-402a-a746-e630fdbbd901",
   "metadata": {},
   "source": [
    "# 词云图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "3bde6a32-7a1e-45ac-ad42-c808e716bccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>词</th>\n",
       "      <th>数值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>花鸟市场</td>\n",
       "      <td>1446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>汽车</td>\n",
       "      <td>928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>视频</td>\n",
       "      <td>906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>电视</td>\n",
       "      <td>825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lover Boy 88</td>\n",
       "      <td>514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>动漫</td>\n",
       "      <td>486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>音乐</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>直播</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>广播电台</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>戏曲曲艺</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>演出票务</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>给陌生的你听</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>资讯</td>\n",
       "      <td>1437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>商业财经</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>娱乐八卦</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>军事</td>\n",
       "      <td>331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>科技资讯</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>社会时政</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>时尚</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>网络奇闻</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               词    数值\n",
       "0           花鸟市场  1446\n",
       "1             汽车   928\n",
       "2             视频   906\n",
       "3             电视   825\n",
       "4   Lover Boy 88   514\n",
       "5             动漫   486\n",
       "6             音乐    53\n",
       "7             直播   163\n",
       "8           广播电台    86\n",
       "9           戏曲曲艺    17\n",
       "10          演出票务     6\n",
       "11        给陌生的你听     1\n",
       "12            资讯  1437\n",
       "13          商业财经   422\n",
       "14          娱乐八卦   353\n",
       "15            军事   331\n",
       "16          科技资讯   313\n",
       "17          社会时政   307\n",
       "18            时尚    43\n",
       "19          网络奇闻    15"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([\n",
    "    (\"花鸟市场\", 1446),\n",
    "    (\"汽车\", 928),\n",
    "    (\"视频\", 906),\n",
    "    (\"电视\", 825),\n",
    "    (\"Lover Boy 88\", 514),\n",
    "    (\"动漫\", 486),\n",
    "    (\"音乐\", 53),\n",
    "    (\"直播\", 163),\n",
    "    (\"广播电台\", 86),\n",
    "    (\"戏曲曲艺\", 17),\n",
    "    (\"演出票务\", 6),\n",
    "    (\"给陌生的你听\", 1),\n",
    "    (\"资讯\", 1437),\n",
    "    (\"商业财经\", 422),\n",
    "    (\"娱乐八卦\", 353),\n",
    "    (\"军事\", 331),\n",
    "    (\"科技资讯\", 313),\n",
    "    (\"社会时政\", 307),\n",
    "    (\"时尚\", 43),\n",
    "    (\"网络奇闻\", 15)\n",
    "])\n",
    "df.columns = ['词', '数值']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "4cdbbd5c-a4df-46fd-8aa9-0eb4e32dae19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['花鸟市场', 1446],\n",
       "       ['汽车', 928],\n",
       "       ['视频', 906],\n",
       "       ['电视', 825],\n",
       "       ['Lover Boy 88', 514],\n",
       "       ['动漫', 486],\n",
       "       ['音乐', 53],\n",
       "       ['直播', 163],\n",
       "       ['广播电台', 86],\n",
       "       ['戏曲曲艺', 17],\n",
       "       ['演出票务', 6],\n",
       "       ['给陌生的你听', 1],\n",
       "       ['资讯', 1437],\n",
       "       ['商业财经', 422],\n",
       "       ['娱乐八卦', 353],\n",
       "       ['军事', 331],\n",
       "       ['科技资讯', 313],\n",
       "       ['社会时政', 307],\n",
       "       ['时尚', 43],\n",
       "       ['网络奇闻', 15]], dtype=object)"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values.to_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "01c5e2e9-371a-4a47-b59f-5182046c2f06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"950\"\n",
       "            height=\"520\"\n",
       "            src=\"词云图.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1ca81e15d90>"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import WordCloud\n",
    "\n",
    "words = \n",
    "c = (\n",
    "    WordCloud()\n",
    "    .add(\"\", words, word_size_range=[12, 55])\n",
    "    .set_global_opts(title_opts=opts.TitleOpts(title=\"WordCloud-自定义图片\"))\n",
    "    .render(\"词云图.html\")\n",
    ")\n",
    "\n",
    "\n",
    "from IPython.display import IFrame\n",
    "IFrame(src='词云图.html', width=950, height=520)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3b3f4e1-4cf7-4cef-afda-9e2078bcf202",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc2a0241-8b1e-4eef-b8f2-a22e8fdd1a8a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fbdb41d3-0620-4bf8-b6f7-ebc52e38ac79",
   "metadata": {},
   "source": [
    "# 组合图"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af4fbcaa-a865-47bf-a9dd-3b55983a9bc8",
   "metadata": {},
   "source": [
    "## 柱状图+线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65fea0a4-233b-4745-a818-f38da16929cb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6aff677-d329-4ac8-8dbc-ff696158d091",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35609242-2d92-4a25-ae55-527afae5b1bb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "67aa7d83-0763-42e4-a12d-cbb27a80b6a1",
   "metadata": {},
   "source": [
    "## 散点图+线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2a04642b-7677-4544-960d-952fbb6eeb3a",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'Series1'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Input \u001b[1;32mIn [34]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyecharts\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcharts\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Scatter, Line\n\u001b[0;32m      2\u001b[0m sc1 \u001b[38;5;241m=\u001b[39m Scatter()\n\u001b[1;32m----> 3\u001b[0m sc1\u001b[38;5;241m.\u001b[39madd_xaxis(xaxis_data\u001b[38;5;241m=\u001b[39m\u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSeries1\u001b[49m\u001b[38;5;241m.\u001b[39mto_list())\n\u001b[0;32m      4\u001b[0m sc1\u001b[38;5;241m.\u001b[39madd_yaxis(series_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mA\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m      5\u001b[0m              y_axis \u001b[38;5;241m=\u001b[39mdf\u001b[38;5;241m.\u001b[39mSeries2\u001b[38;5;241m.\u001b[39mto_list(),\n\u001b[0;32m      6\u001b[0m              symbol_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m,\n\u001b[0;32m      7\u001b[0m              label_opts\u001b[38;5;241m=\u001b[39mopts\u001b[38;5;241m.\u001b[39mLabelOpts(is_show\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[0;32m      8\u001b[0m             )\n\u001b[0;32m      9\u001b[0m sc1\u001b[38;5;241m.\u001b[39mset_global_opts(\n\u001b[0;32m     10\u001b[0m         xaxis_opts\u001b[38;5;241m=\u001b[39mopts\u001b[38;5;241m.\u001b[39mAxisOpts(\n\u001b[0;32m     11\u001b[0m             type_\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m,     \u001b[38;5;66;03m#  如果x是数值型，这里要指定为 value\u001b[39;00m\n\u001b[0;32m     12\u001b[0m         )\n\u001b[0;32m     13\u001b[0m )\n",
      "File \u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\core\\generic.py:5575\u001b[0m, in \u001b[0;36mNDFrame.__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5568\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m   5569\u001b[0m     name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_internal_names_set\n\u001b[0;32m   5570\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metadata\n\u001b[0;32m   5571\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_accessors\n\u001b[0;32m   5572\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info_axis\u001b[38;5;241m.\u001b[39m_can_hold_identifiers_and_holds_name(name)\n\u001b[0;32m   5573\u001b[0m ):\n\u001b[0;32m   5574\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[name]\n\u001b[1;32m-> 5575\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getattribute__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'Series1'"
     ]
    }
   ],
   "source": [
    "from pyecharts.charts import Scatter, Line\n",
    "sc1 = Scatter()\n",
    "sc1.add_xaxis(xaxis_data=df.Series1.to_list())\n",
    "sc1.add_yaxis(series_name = 'A',\n",
    "             y_axis =df.Series2.to_list(),\n",
    "             symbol_size=20,\n",
    "             label_opts=opts.LabelOpts(is_show=False),\n",
    "            )\n",
    "sc1.set_global_opts(\n",
    "        xaxis_opts=opts.AxisOpts(\n",
    "            type_=\"value\",     #  如果x是数值型，这里要指定为 value\n",
    "        )\n",
    ")\n",
    "\n",
    "sc2 = Scatter()\n",
    "sc2.add_xaxis(xaxis_data=df.Series2.to_list())\n",
    "sc2.add_yaxis(series_name = 'B',\n",
    "             y_axis =df.Series3.to_list(),\n",
    "             symbol_size=20,\n",
    "             label_opts=opts.LabelOpts(is_show=False),\n",
    "            )\n",
    "sc2.set_global_opts(\n",
    "        xaxis_opts=opts.AxisOpts(\n",
    "            type_=\"value\",     #  如果x是数值型，这里要指定为 value\n",
    "        )\n",
    ")\n",
    "\n",
    "\n",
    "x = np.linspace(0, 500, 20).tolist()\n",
    "y = (np.sin(x)*200+200).tolist()\n",
    "line1 = Line()\n",
    "line1.add_xaxis(x)\n",
    "line1.add_yaxis('C',y,label_opts=opts.LabelOpts(is_show=False), is_smooth=True)\n",
    "\n",
    "sc1.overlap(sc2)\n",
    "sc1.overlap(line1)\n",
    "sc1.load_javascript()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "701adcc6-b32f-4fc0-aef7-dd3cec7e456d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc1.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78ada46d-26ea-4d1a-acee-5496895e5a7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "words = [\n",
    "    (\"花鸟市场\", 1446),\n",
    "    (\"汽车\", 928),\n",
    "    (\"视频\", 906),\n",
    "    (\"电视\", 825),\n",
    "    (\"Lover Boy 88\", 514),\n",
    "    (\"动漫\", 486),\n",
    "    (\"音乐\", 53),\n",
    "    (\"直播\", 163),\n",
    "    (\"广播电台\", 86),\n",
    "    (\"戏曲曲艺\", 17),\n",
    "    (\"演出票务\", 6),\n",
    "    (\"给陌生的你听\", 1),\n",
    "    (\"资讯\", 1437),\n",
    "    (\"商业财经\", 422),\n",
    "    (\"娱乐八卦\", 353),\n",
    "    (\"军事\", 331),\n",
    "    (\"科技资讯\", 313),\n",
    "    (\"社会时政\", 307),\n",
    "    (\"时尚\", 43),\n",
    "    (\"网络奇闻\", 15),\n",
    "    (\"旅游出行\", 438),\n",
    "    (\"景点类型\", 957),\n",
    "    (\"国内游\", 927),\n",
    "    (\"远途出行方式\", 908),\n",
    "    (\"酒店\", 693),\n",
    "    (\"关注景点\", 611),\n",
    "    (\"旅游网站偏好\", 512),\n",
    "    (\"出国游\", 382),\n",
    "    (\"交通票务\", 312),\n",
    "    (\"旅游方式\", 187),\n",
    "    (\"旅游主题\", 163),\n",
    "    (\"港澳台\", 104),\n",
    "    (\"本地周边游\", 3),\n",
    "    (\"小卖家\", 1331),\n",
    "    (\"全日制学校\", 941),\n",
    "    (\"基础教育科目\", 585),\n",
    "    (\"考试培训\", 473),\n",
    "    (\"语言学习\", 358),\n",
    "    (\"留学\", 246),\n",
    "    (\"K12课程培训\", 207),\n",
    "    (\"艺术培训\", 194),\n",
    "    (\"技能培训\", 104),\n",
    "    (\"IT培训\", 87),\n",
    "    (\"高等教育专业\", 63),\n",
    "    (\"家教\", 48),\n",
    "    (\"体育培训\", 23),\n",
    "    (\"职场培训\", 5),\n",
    "    (\"金融财经\", 1328),\n",
    "    (\"银行\", 765),\n",
    "    (\"股票\", 452),\n",
    "    (\"保险\", 415),\n",
    "    (\"贷款\", 253),\n",
    "    (\"基金\", 211),\n",
    "    (\"信用卡\", 180),\n",
    "    (\"外汇\", 138),\n",
    "    (\"P2P\", 116),\n",
    "    (\"贵金属\", 98),\n",
    "    (\"债券\", 93),\n",
    "    (\"网络理财\", 92),\n",
    "    (\"信托\", 90),\n",
    "    (\"征信\", 76),\n",
    "    (\"期货\", 76),\n",
    "    (\"公积金\", 40),\n",
    "    (\"银行理财\", 36),\n",
    "    (\"银行业务\", 30),\n",
    "    (\"典当\", 7),\n",
    "    (\"海外置业\", 1),\n",
    "    (\"汽车\", 1309),\n",
    "    (\"汽车档次\", 965),\n",
    "    (\"汽车品牌\", 900),\n",
    "    (\"汽车车型\", 727),\n",
    "    (\"购车阶段\", 461),\n",
    "    (\"二手车\", 309),\n",
    "    (\"汽车美容\", 260),\n",
    "    (\"新能源汽车\", 173),\n",
    "    (\"汽车维修\", 155),\n",
    "    (\"租车服务\", 136),\n",
    "    (\"车展\", 121),\n",
    "    (\"违章查询\", 76),\n",
    "    (\"汽车改装\", 62),\n",
    "    (\"汽车用品\", 37),\n",
    "    (\"路况查询\", 32),\n",
    "    (\"汽车保险\", 28),\n",
    "    (\"陪驾代驾\", 4),\n",
    "    (\"网络购物\", 1275),\n",
    "    (\"做我的猫\", 1088),\n",
    "    (\"只想要你知道\", 907),\n",
    "    (\"团购\", 837),\n",
    "    (\"比价\", 201),\n",
    "    (\"海淘\", 195),\n",
    "    (\"移动APP购物\", 179),\n",
    "    (\"支付方式\", 119),\n",
    "    (\"代购\", 43),\n",
    "    (\"体育健身\", 1234),\n",
    "    (\"体育赛事项目\", 802),\n",
    "    (\"运动项目\", 405),\n",
    "    (\"体育类赛事\", 337),\n",
    "    (\"健身项目\", 199),\n",
    "    (\"健身房健身\", 78),\n",
    "    (\"运动健身\", 77),\n",
    "    (\"家庭健身\", 36),\n",
    "    (\"健身器械\", 29),\n",
    "    (\"办公室健身\", 3),\n",
    "    (\"商务服务\", 1201),\n",
    "    (\"法律咨询\", 508),\n",
    "    (\"化工材料\", 147),\n",
    "    (\"广告服务\", 125),\n",
    "    (\"会计审计\", 115),\n",
    "    (\"人员招聘\", 101),\n",
    "    (\"印刷打印\", 66),\n",
    "    (\"知识产权\", 32),\n",
    "    (\"翻译\", 22),\n",
    "    (\"安全安保\", 9),\n",
    "    (\"公关服务\", 8),\n",
    "    (\"商旅服务\", 2),\n",
    "    (\"展会服务\", 2),\n",
    "    (\"特许经营\", 1),\n",
    "    (\"休闲爱好\", 1169),\n",
    "    (\"收藏\", 412),\n",
    "    (\"摄影\", 393),\n",
    "    (\"温泉\", 230),\n",
    "    (\"博彩彩票\", 211),\n",
    "    (\"美术\", 207),\n",
    "    (\"书法\", 139),\n",
    "    (\"DIY手工\", 75),\n",
    "    (\"舞蹈\", 23),\n",
    "    (\"钓鱼\", 21),\n",
    "    (\"棋牌桌游\", 17),\n",
    "    (\"KTV\", 6),\n",
    "    (\"密室\", 5),\n",
    "    (\"采摘\", 4),\n",
    "    (\"电玩\", 1),\n",
    "    (\"真人CS\", 1),\n",
    "    (\"轰趴\", 1),\n",
    "    (\"家电数码\", 1111),\n",
    "    (\"手机\", 885),\n",
    "    (\"电脑\", 543),\n",
    "    (\"大家电\", 321),\n",
    "    (\"家电关注品牌\", 253),\n",
    "    (\"网络设备\", 162),\n",
    "    (\"摄影器材\", 149),\n",
    "    (\"影音设备\", 133),\n",
    "    (\"办公数码设备\", 113),\n",
    "    (\"生活电器\", 67),\n",
    "    (\"厨房电器\", 54),\n",
    "    (\"智能设备\", 45),\n",
    "    (\"个人护理电器\", 22),\n",
    "    (\"服饰鞋包\", 1047),\n",
    "    (\"服装\", 566),\n",
    "    (\"饰品\", 289),\n",
    "    (\"鞋\", 184),\n",
    "    (\"箱包\", 168),\n",
    "    (\"奢侈品\", 137),\n",
    "    (\"母婴亲子\", 1041),\n",
    "    (\"孕婴保健\", 505),\n",
    "    (\"母婴社区\", 299),\n",
    "    (\"早教\", 103),\n",
    "    (\"奶粉辅食\", 66),\n",
    "    (\"童车童床\", 41),\n",
    "    (\"关注品牌\", 271),\n",
    "    (\"宝宝玩乐\", 30),\n",
    "    (\"母婴护理服务\", 25),\n",
    "    (\"纸尿裤湿巾\", 16),\n",
    "    (\"妈妈用品\", 15),\n",
    "    (\"宝宝起名\", 12),\n",
    "    (\"童装童鞋\", 9),\n",
    "    (\"胎教\", 8),\n",
    "    (\"宝宝安全\", 1),\n",
    "    (\"宝宝洗护用品\", 1),\n",
    "    (\"软件应用\", 1018),\n",
    "    (\"系统工具\", 896),\n",
    "    (\"理财购物\", 440),\n",
    "    (\"生活实用\", 365),\n",
    "    (\"影音图像\", 256),\n",
    "    (\"社交通讯\", 214),\n",
    "    (\"手机美化\", 39),\n",
    "    (\"办公学习\", 28),\n",
    "    (\"应用市场\", 23),\n",
    "    (\"母婴育儿\", 14),\n",
    "    (\"游戏\", 946),\n",
    "    (\"手机游戏\", 565),\n",
    "    (\"PC游戏\", 353),\n",
    "    (\"网页游戏\", 254),\n",
    "    (\"游戏机\", 188),\n",
    "    (\"模拟辅助\", 166),\n",
    "    (\"个护美容\", 942),\n",
    "    (\"护肤品\", 177),\n",
    "    (\"彩妆\", 133),\n",
    "    (\"美发\", 80),\n",
    "    (\"香水\", 50),\n",
    "    (\"个人护理\", 46),\n",
    "    (\"美甲\", 26),\n",
    "    (\"SPA美体\", 21),\n",
    "    (\"花鸟萌宠\", 914),\n",
    "    (\"绿植花卉\", 311),\n",
    "    (\"狗\", 257),\n",
    "    (\"其他宠物\", 131),\n",
    "    (\"水族\", 125),\n",
    "    (\"猫\", 122),\n",
    "    (\"动物\", 81),\n",
    "    (\"鸟\", 67),\n",
    "    (\"宠物用品\", 41),\n",
    "    (\"宠物服务\", 26),\n",
    "    (\"书籍阅读\", 913),\n",
    "    (\"网络小说\", 483),\n",
    "    (\"关注书籍\", 128),\n",
    "    (\"文学\", 105),\n",
    "    (\"报刊杂志\", 77),\n",
    "    (\"人文社科\", 22),\n",
    "    (\"建材家居\", 907),\n",
    "    (\"装修建材\", 644),\n",
    "    (\"家具\", 273),\n",
    "    (\"家居风格\", 187),\n",
    "    (\"家居家装关注品牌\", 140),\n",
    "    (\"家纺\", 107),\n",
    "    (\"厨具\", 47),\n",
    "    (\"灯具\", 43),\n",
    "    (\"家居饰品\", 29),\n",
    "    (\"家居日常用品\", 10),\n",
    "    (\"生活服务\", 883),\n",
    "    (\"物流配送\", 536),\n",
    "    (\"家政服务\", 108),\n",
    "    (\"摄影服务\", 49),\n",
    "    (\"搬家服务\", 38),\n",
    "    (\"物业维修\", 37),\n",
    "    (\"婚庆服务\", 24),\n",
    "    (\"二手回收\", 24),\n",
    "    (\"鲜花配送\", 3),\n",
    "    (\"维修服务\", 3),\n",
    "    (\"殡葬服务\", 1),\n",
    "    (\"求职创业\", 874),\n",
    "    (\"创业\", 363),\n",
    "    (\"目标职位\", 162),\n",
    "    (\"目标行业\", 50),\n",
    "    (\"兼职\", 21),\n",
    "    (\"期望年薪\", 20),\n",
    "    (\"实习\", 16),\n",
    "    (\"雇主类型\", 10),\n",
    "    (\"星座运势\", 789),\n",
    "    (\"星座\", 316),\n",
    "    (\"算命\", 303),\n",
    "    (\"解梦\", 196),\n",
    "    (\"风水\", 93),\n",
    "    (\"面相分析\", 47),\n",
    "    (\"手相\", 32),\n",
    "    (\"公益\", 90),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f45a907-45bc-4e34-9402-50fc926ce54d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import base64\n",
    "\n",
    "img_path = r'1.png';\n",
    "with open(img_path, 'rb') as file:\n",
    "    image_data1 = file.read()\n",
    "image = base64.b64encode(image_data1).decode('utf8')\n",
    "data = {\"key\": [\"image\"], \"value\": [image]}\n",
    "# 转成 json 字符串\n",
    "json_str = json.dumps(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "381e9f39-482d-4697-a2c1-daaa794bc3f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "json_str[0:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd3fcdc9-1a47-4ad8-8f74-aa70e74df7e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "json.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfeed396-593c-4f6b-a8ae-72eff5f9a500",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import WordCloud\n",
    "\n",
    "\n",
    "\n",
    "# with open(\"wordcloud_image_base64.json\", \"r\", encoding=\"utf-8\") as f:\n",
    "#     j = json.load(f)\n",
    "\n",
    "\n",
    "c = (\n",
    "    WordCloud()\n",
    "    .add(\"\", words, word_size_range=[12, 55], mask_image=json_str[\"base64\"])\n",
    "    .set_global_opts(title_opts=opts.TitleOpts(title=\"WordCloud-自定义图片\"))\n",
    "    .render(\"wordcloud_custom_mask_image.html\")\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76334160-d16b-403e-8a38-55980a7c4efd",
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = '''\n",
    "冠县国美\n",
    "冠百购物中心\n",
    "莘县成和\n",
    "陵县家庭号购物中心\n",
    "德百家电澳德乐店\n",
    "庆云供销大厦\n",
    "武城盐百购物广场\n",
    "武城金蝶电子\n",
    "武城好运来购物\n",
    "德百家电大楼店\n",
    "'''.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b98886e-ea36-4475-884f-46c7770ebe10",
   "metadata": {},
   "outputs": [],
   "source": [
    "s2 = '''\n",
    "聊城百大三联\n",
    "冠县国美\n",
    "武城盐百购物广场\n",
    "陵县家庭号购物中心\n",
    "聊城金鼎\n",
    "惠民信誉楼\n",
    "莘县成和\n",
    "开发区振华超市\n",
    "冠百购物中心\n",
    "临清商博瑞\n",
    "高唐双隆购物广场\n",
    "垦利县信誉楼\n",
    "德百家电大楼店\n",
    "庆云供销大厦\n",
    "高唐商博瑞\n",
    "\n",
    "'''.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52f90091-897f-4f79-8f81-910c5bc8fdb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "s3 = '''\n",
    "冠县国美\n",
    "德百家电大楼店\n",
    "垦利县信誉楼\n",
    "\n",
    "'''.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15d6600e-02d2-4789-97d4-945608099cdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "s4 = '''\n",
    "聊城百大三联\n",
    "冠县国美\n",
    "武城盐百购物广场\n",
    "陵县家庭号购物中心\n",
    "聊城金鼎\n",
    "惠民信誉楼\n",
    "莘县成和\n",
    "开发区振华超市\n",
    "冠百购物中心\n",
    "临清商博瑞\n",
    "高唐双隆购物广场\n",
    "垦利县信誉楼\n",
    "德百家电大楼店\n",
    "庆云供销大厦\n",
    "高唐商博瑞\n",
    "德州武城德百\n",
    "夏津大鹏影库\n",
    "莘县张寨\n",
    "\n",
    "'''.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7093c0d-969f-4607-9e70-c3c70a75652a",
   "metadata": {},
   "outputs": [],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b999cd31-6056-40f0-a8d0-1f34a7e83b4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in pd.Series(s1+s2+s3+s4).value_counts().head(5).index:\n",
    "    print(i, end='、')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c6fa665-aa5e-4609-9b43-3364ee1d2538",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64c9752b-9783-4bf3-8d83-e8568de8a5d1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7a02444-186f-4a81-8315-c3f7b0a3b79b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11585019-8ba7-4156-b93a-db33d02db8ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22ecb375-b7ac-4668-88d7-1427eee3a2b5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db5c74dd-f53f-4493-9582-d555414f16bc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6928ee37-5f02-488d-aae7-d0bc2553c3d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8b4e8e5-c54e-4068-b4e9-81e30f7ddf42",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "toc-autonumbering": true
 },
 "nbformat": 4,
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}