diff --git "a/PyEcharts.ipynb" "b/PyEcharts.ipynb"
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "2ef26647",
+ "metadata": {},
+ "source": [
+ "## PyEcharts 快速上手"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a6bfabb3",
+ "metadata": {},
+ "source": [
+ "Echarts 是一个由百度开源的数据可视化,凭借着良好的交互性,精巧的图表设计,得到了众多开发者的认可。\n",
+ "\n",
+ "而 Python 是一门富有表达力的语言,很适合用于数据处理。\n",
+ "\n",
+ "当数据分析遇上数据可视化时,pyecharts 诞生了。\n",
+ "\n",
+ "官方文档:https://pyecharts.org/#/zh-cn/intro"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "af9f7c4a",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-25T16:04:01.459188Z",
+ "start_time": "2023-11-25T16:04:01.443414Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ "
\n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts.charts import Bar\n",
+ "\n",
+ "# 创建 条形图对象\n",
+ "bar = Bar()\n",
+ "\n",
+ "# 添加 x轴数据\n",
+ "bar.add_xaxis([\"衬衫\", \"羊毛衫\", \"雪纺衫\", \"裤子\", \"高跟鞋\", \"袜子\"])\n",
+ "\n",
+ "# 添加 y轴数据\n",
+ "bar.add_yaxis(\"商家A\", [5, 20, 36, 10, 75, 90])\n",
+ "\n",
+ "# render 会生成本地 HTML 文件,默认会在当前目录生成 render.html 文件,也可以传入路径参数\n",
+ "# bar.render(\"PyEcharts/mycharts.html\")\n",
+ "\n",
+ "# 我们选择直接在 notebook中显示\n",
+ "bar.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8b3367b1",
+ "metadata": {},
+ "source": [
+ "**pyecharts 所有方法均支持链式调用**\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "4303457d",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-25T16:05:29.882102Z",
+ "start_time": "2023-11-25T16:05:29.869542Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts.charts import Bar\n",
+ "\n",
+ "# 生成 条形图\n",
+ "bar = (\n",
+ " Bar()\n",
+ " .add_xaxis([\"衬衫\", \"羊毛衫\", \"雪纺衫\", \"裤子\", \"高跟鞋\", \"袜子\"])\n",
+ " .add_yaxis(\"商家A\", [5, 20, 36, 10, 75, 90])\n",
+ ")\n",
+ "\n",
+ "# 展示图表\n",
+ "bar.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "86c36c3d",
+ "metadata": {},
+ "source": [
+ "## 初步使用 options"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3fa5c524",
+ "metadata": {},
+ "source": [
+ "**初步使用 options 配置项:在 pyecharts 中,一切皆 Options**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "4a666458",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-25T16:10:08.105924Z",
+ "start_time": "2023-11-25T16:10:08.078865Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts.charts import Bar\n",
+ "from pyecharts import options as opts\n",
+ "\n",
+ "bar = (\n",
+ " Bar()\n",
+ " .add_xaxis([\"衬衫\", \"羊毛衫\", \"雪纺衫\", \"裤子\", \"高跟鞋\", \"袜子\"])\n",
+ " .add_yaxis(\"商家A\", [5, 20, 36, 10, 75, 90])\n",
+ " \n",
+ " # 利用 TitleOpts 对象 配置标题\n",
+ " .set_global_opts(title_opts=opts.TitleOpts(title=\"主标题\", subtitle=\"副标题\"))\n",
+ "\n",
+ " # 也可以直接使用字典 配置标题\n",
+ " # .set_global_opts(title_opts= {\"text\": \"主标题\", \"subtext\": \"副标题\"})\n",
+ ")\n",
+ "bar.render_notebook()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "efe6697d",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-25T16:11:17.285351Z",
+ "start_time": "2023-11-25T16:11:17.261639Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 除了链式调用,我们还可以单独调用方法\n",
+ "\n",
+ "# 创建 条形图对象\n",
+ "bar = Bar()\n",
+ "\n",
+ "# 添加 x轴\n",
+ "bar.add_xaxis([\"衬衫\", \"羊毛衫\", \"雪纺衫\", \"裤子\", \"高跟鞋\", \"袜子\"])\n",
+ "\n",
+ "# 添加 y轴\n",
+ "bar.add_yaxis(\"商家A\", [5, 20, 36, 10, 75, 90])\n",
+ "\n",
+ "# 配置标题\n",
+ "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"主标题\", subtitle=\"副标题\"))\n",
+ "\n",
+ "# 展示\n",
+ "bar.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a365276c",
+ "metadata": {},
+ "source": [
+ "## 配置主题"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1d20d27c",
+ "metadata": {},
+ "source": [
+ "pyecharts 提供了 10+ 种内置主题,开发者也可以定制自己喜欢的主题"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "e88e71a4",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-25T16:14:58.950942Z",
+ "start_time": "2023-11-25T16:14:58.921837Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts.charts import Bar\n",
+ "from pyecharts import options as opts\n",
+ "\n",
+ "# 内置主题类型 可查看 pyecharts.globals.ThemeType\n",
+ "from pyecharts.globals import ThemeType\n",
+ "\n",
+ "bar = (\n",
+ " # 创建条形图,并配置主题\n",
+ " Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))\n",
+ " \n",
+ " # 添加 x轴数据\n",
+ " .add_xaxis([\"衬衫\", \"羊毛衫\", \"雪纺衫\", \"裤子\", \"高跟鞋\", \"袜子\"])\n",
+ " \n",
+ " # 添加 y轴数据1\n",
+ " .add_yaxis(\"商家A\", [5, 20, 36, 10, 75, 90])\n",
+ " \n",
+ " # 添加 y轴数据2\n",
+ " .add_yaxis(\"商家B\", [15, 6, 45, 20, 35, 66])\n",
+ " \n",
+ " # 设置 标题\n",
+ " .set_global_opts(title_opts=opts.TitleOpts(title=\"主标题\", subtitle=\"副标题\"))\n",
+ ")\n",
+ "\n",
+ "bar.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "5caf4efe",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-25T16:16:01.584174Z",
+ "start_time": "2023-11-25T16:16:01.553210Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "bar = (\n",
+ " # 创建条形图,并配置主题\n",
+ " Bar(init_opts=opts.InitOpts(theme=ThemeType.ROMA))\n",
+ " \n",
+ " # 添加 x轴数据\n",
+ " .add_xaxis([\"衬衫\", \"羊毛衫\", \"雪纺衫\", \"裤子\", \"高跟鞋\", \"袜子\"])\n",
+ " \n",
+ " # 添加 y轴数据1\n",
+ " .add_yaxis(\"商家A\", [5, 20, 36, 10, 75, 90])\n",
+ " \n",
+ " # 添加 y轴数据2\n",
+ " .add_yaxis(\"商家B\", [15, 6, 45, 20, 35, 66])\n",
+ " \n",
+ " # 设置 标题\n",
+ " .set_global_opts(title_opts=opts.TitleOpts(title=\"主标题\", subtitle=\"副标题\"))\n",
+ ")\n",
+ "\n",
+ "bar.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "83e7e0ab",
+ "metadata": {},
+ "source": [
+ "## options 配置项"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cdc92966",
+ "metadata": {},
+ "source": [
+ "注意:配置项章节 应该配��� 图表类型章节 阅读"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b26ffec9",
+ "metadata": {},
+ "source": [
+ "### 全局配置项"
+ ]
+ },
+ {
+ "attachments": {
+ "%E6%88%AA%E5%B1%8F2023-11-26%2000.18.26.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "8dd8af1a",
+ "metadata": {},
+ "source": [
+ "**全局配置项可通过 set_global_opts 方法设置**\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "642b0759",
+ "metadata": {},
+ "source": [
+ "#### TitleOpts:标题配置项\n",
+ "> 源代码 class pyecharts.options.TitleOpts 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f2f75231",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class TitleOpts(\n",
+ " \n",
+ " # 主标题文本,支持使用 \\n 换行。\n",
+ " title: Optional[str] = None,\n",
+ "\n",
+ " \n",
+ " # 主标题跳转 URL 链接\n",
+ " title_link: Optional[str] = None,\n",
+ "\n",
+ " \n",
+ " # 主标题跳转链接方式\n",
+ " # 默认值是: blank\n",
+ " # 可选参数: 'self', 'blank'\n",
+ " # 'self' 当前窗口打开; 'blank' 新窗口打开\n",
+ " title_target: Optional[str] = None,\n",
+ "\n",
+ " \n",
+ " # 副标题文本,支持使用 \\n 换行。\n",
+ " subtitle: Optional[str] = None,\n",
+ "\n",
+ " \n",
+ " # 副标题跳转 URL 链接\n",
+ " subtitle_link: Optional[str] = None,\n",
+ "\n",
+ " \n",
+ " # 副标题跳转链接方式\n",
+ " # 默认值是: blank\n",
+ " # 可选参数: 'self', 'blank'\n",
+ " # 'self' 当前窗口打开; 'blank' 新窗口打开\n",
+ " subtitle_target: Optional[str] = None,\n",
+ "\n",
+ " \n",
+ " # title 组件离容器左侧的距离。\n",
+ " # left 的值可以是像 20 这样的具体像素值,可以是像 '20%' 这样相对于容器高宽的百分比,\n",
+ " # 也可以是 'left', 'center', 'right'。\n",
+ " # 如果 left 的值为'left', 'center', 'right',组件会根据相应的位置自动对齐。\n",
+ " pos_left: Optional[str] = None,\n",
+ "\n",
+ " \n",
+ " # title 组件离容器右侧的距离。\n",
+ " # right 的值可以是像 20 这样的具体像素值,可以是像 '20%' 这样相对于容器高宽的百分比。\n",
+ " pos_right: Optional[str] = None,\n",
+ "\n",
+ " \n",
+ " # title 组件离容器上侧的距离。\n",
+ " # top 的值可以是像 20 这样的具体像素值,可以是像 '20%' 这样相对于容器高宽的百分比,\n",
+ " # 也可以是 'top', 'middle', 'bottom'。\n",
+ " # 如果 top 的值为'top', 'middle', 'bottom',组件会根据相应的位置自动对齐。\n",
+ " pos_top: Optional[str] = None,\n",
+ "\n",
+ " \n",
+ " # title 组件离容器下侧的距离。\n",
+ " # bottom 的值可以是像 20 这样的具体像素值,可以是像 '20%' 这样相对于容器高宽的百分比。\n",
+ " pos_bottom: Optional[str] = None,\n",
+ "\n",
+ " \n",
+ " # 标题内边距,单位px,默认各方向内边距为5,接受数组分别设定上右下左边距。\n",
+ " # // 设置内边距为 5\n",
+ " # padding: 5\n",
+ " # // 设置上下的内边距为 5,左右的内边距为 10\n",
+ " # padding: [5, 10]\n",
+ " # // 分别设置四个方向的内边距\n",
+ " # padding: [\n",
+ " # 5, // 上\n",
+ " # 10, // 右\n",
+ " # 5, // 下\n",
+ " # 10, // 左\n",
+ " # ]\n",
+ " padding: Union[Sequence, Numeric] = 5,\n",
+ "\n",
+ " \n",
+ " # 主副标题之间的间距。\n",
+ " item_gap: Numeric = 10,\n",
+ "\n",
+ " \n",
+ " # 主标题字体样式配置项,参考 `series_options.TextStyleOpts`\n",
+ " title_textstyle_opts: Union[TextStyleOpts, dict, None] = None,\n",
+ "\n",
+ " \n",
+ " # 副标题字体样式配置项,参考 `series_options.TextStyleOpts`\n",
+ " subtitle_textstyle_opts: Union[TextStyleOpts, dict, None] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "248c7d37",
+ "metadata": {},
+ "source": [
+ "#### AxisOpts:坐标轴配置项"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f3eb6589",
+ "metadata": {},
+ "source": [
+ "> 源代码 class pyecharts.options.AxisOpts 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bdc13b53",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class AxisOpts(\n",
+ " # 坐标轴类型。可选:\n",
+ " # '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",
+ " type_: Optional[str] = None,\n",
+ "\n",
+ " \n",
+ " # 坐标轴名称。\n",
+ " name: Optional[str] = None,\n",
+ "\n",
+ " \n",
+ " # 是否显示 x 轴。\n",
+ " is_show: bool = True,\n",
+ "\n",
+ " \n",
+ " # 只在数值轴中(type: 'value')有效。\n",
+ " # 是否是脱离 0 值比例。设置成 true 后坐标刻度不会强制包含零刻度。在双数值轴的散点图中比较有用。\n",
+ " # 在设置 min 和 max 之后该配置项无效。\n",
+ " is_scale: bool = False,\n",
+ "\n",
+ " \n",
+ " # 是否反向坐标轴。\n",
+ " is_inverse: bool = False,\n",
+ "\n",
+ " \n",
+ " # 坐标轴名称显示位置。可选:\n",
+ " # 'start', 'middle' 或者 'center','end'\n",
+ " name_location: str = \"end\",\n",
+ "\n",
+ " \n",
+ " # 坐标轴名称与轴线之间的距离。\n",
+ " name_gap: Numeric = 15,\n",
+ "\n",
+ " \n",
+ " # 坐标轴名字旋转,角度值。\n",
+ " name_rotate: Optional[Numeric] = None,\n",
+ "\n",
+ " \n",
+ " # 强制设置坐标轴分割间隔。\n",
+ " # 因为 splitNumber 是预估的值,实际根据策略计算出来的刻度可能无法达到想要的效果,\n",
+ " # 这时候可以使用 interval 配合 min、max 强制设定刻度划分,一般不建议使用。\n",
+ " # 无法在类目轴中使用。在时间轴(type: 'time')中需要传时间戳,在对数轴(type: 'log')中需要传指数值。\n",
+ " interval: Optional[Numeric] = None,\n",
+ "\n",
+ " \n",
+ " # x 轴所在的 grid 的索引,默认位于第一个 grid。\n",
+ " grid_index: Numeric = 0,\n",
+ "\n",
+ " \n",
+ " # x 轴的位置。可选:\n",
+ " # 'top', 'bottom'\n",
+ " # 默认 grid 中的第一个 x 轴在 grid 的下方('bottom'),第二个 x 轴视第一个 x 轴的位置放在另一侧。\n",
+ " position: Optional[str] = None,\n",
+ "\n",
+ " # Y 轴相对于默认位置的偏移,在相同的 position 上有多个 Y 轴的时候有用。\n",
+ " offset: Numeric = 0,\n",
+ "\n",
+ " # 坐标轴的分割段数,需要注意的是这个分割段数只是个预估值,最后实际显示的段数会在这个基础上根据分割后坐标轴刻度显示的易读程度作调整。 \n",
+ " # 默认值是 5\n",
+ " split_number: Numeric = 5,\n",
+ "\n",
+ " # 坐标轴两边留白策略,类目轴和非类目轴的设置和表现不一样。\n",
+ " # 类目轴中 boundaryGap 可以配置为 true 和 false。默认为 true,这时候刻度只是作为分隔线,\n",
+ " # 标签和数据点都会在两个刻度之间的带(band)中间。\n",
+ " # 非类目轴,包括时间,数值,对数轴,boundaryGap是一个两个值的数组,分别表示数据最小值和最大值的延伸范围\n",
+ " # 可以直接设置数值或者相对的百分比,在设置 min 和 max 后无效。 示例:boundaryGap: ['20%', '20%']\n",
+ " boundary_gap: Union[str, bool, None] = None,\n",
+ "\n",
+ " # 坐标轴刻度最小值。\n",
+ " # 可以设置成特殊值 'dataMin',此时取数据在该轴上的最小值作为最小刻度。\n",
+ " # 不设置时会自动计算最小值保证坐标轴刻度的均匀分布。\n",
+ " # 在类目轴中,也可以设置为类目的序数(如类目轴 data: ['类A', '类B', '类C'] 中,序数 2 表示 '类C'。\n",
+ " # 也可以设置为负数,如 -3)。\n",
+ " min_: Union[Numeric, str, None] = None,\n",
+ "\n",
+ " # 坐标轴刻度最大值。\n",
+ " # 可以设置成特殊值 'dataMax',此时取数据在该轴上的最大值作为最大刻度。\n",
+ " # 不设置时会自动计算最大值保证坐标轴刻度的均匀分布。\n",
+ " # 在类目轴中,也可以设置为类目的序数(如类目轴 data: ['类A', '类B', '类C'] 中,序数 2 表示 '类C'。\n",
+ " # 也可以设置为负数,如 -3)。\n",
+ " max_: Union[Numeric, str, None] = None,\n",
+ "\n",
+ " # 自动计算的坐标轴最小间隔大小。\n",
+ " # 例如可以设置成1保证坐标轴分割刻度显示成整数。\n",
+ " # 默认值是 0\n",
+ " min_interval: Numeric = 0,\n",
+ "\n",
+ " # 自动计算的坐标轴最大间隔大小。\n",
+ " # 例如,在时间轴((type: 'time'))可以设置成 3600 * 24 * 1000 保证坐标轴分割刻度最大为一天。\n",
+ " max_interval: Optional[Numeric] = None,\n",
+ "\n",
+ " # 坐标轴刻度线配置项,参考 `global_options.AxisLineOpts`\n",
+ " axisline_opts: Union[AxisLineOpts, dict, None] = None,\n",
+ "\n",
+ " # 坐标轴刻度配置项,参考 `global_options.AxisTickOpts`\n",
+ " axistick_opts: Union[AxisTickOpts, dict, None] = None,\n",
+ "\n",
+ " # 坐标轴标签配置项,参考 `series_options.LabelOpts`\n",
+ " axislabel_opts: Union[LabelOpts, dict, None] = None,\n",
+ "\n",
+ " # 坐标轴指示器配置项,参考 `global_options.AxisPointerOpts`\n",
+ " axispointer_opts: Union[AxisPointerOpts, dict, None] = None,\n",
+ "\n",
+ " # 坐标轴名称的文字样式,参考 `series_options.TextStyleOpts`\n",
+ " name_textstyle_opts: Union[TextStyleOpts, dict, None] = None,\n",
+ "\n",
+ " # 分割区域配置项,参考 `series_options.SplitAreaOpts`\n",
+ " splitarea_opts: Union[SplitAreaOpts, dict, None] = None,\n",
+ "\n",
+ " # 分割线配置项,参考 `series_options.SplitLineOpts`\n",
+ " splitline_opts: Union[SplitLineOpts, dict] = SplitLineOpts(),\n",
+ "\n",
+ " # 坐标轴次刻度线相关设置,参考 `series_options.MinorTickOpts`\n",
+ " minor_tick_opts: Union[MinorTickOpts, dict, None] = None,\n",
+ "\n",
+ " # 坐标轴在 grid 区域中的次分隔线。次分割线会对齐次刻度线 minorTick,参考 `series_options.MinorSplitLineOpts`\n",
+ " minor_split_line_opts: Union[MinorSplitLineOpts, dict, None] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "afa58bda",
+ "metadata": {},
+ "source": [
+ "##### AxisLineOpts: 坐标轴轴线配置项"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d21afc54",
+ "metadata": {},
+ "source": [
+ "> 源代码 class pyecharts.option.AxisLineOpts 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d9b319bd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class AxisLineOpts(\n",
+ " # 是否显示坐标轴轴线。\n",
+ " is_show: bool = True,\n",
+ "\n",
+ " # X 轴或者 Y 轴的轴线是否在另一个轴的 0 刻度上,只有在另一个轴为数值轴且包含 0 刻度时有效。\n",
+ " is_on_zero: bool = True,\n",
+ "\n",
+ " # 当有双轴时,可以用这个属性手动指定,在哪个轴的 0 刻度上。\n",
+ " on_zero_axis_index: int = 0,\n",
+ "\n",
+ " # 轴线两边的箭头。可以是字符串,表示两端使用同样的箭头;或者长度为 2 的字符串数组,分别表示两端的箭头。\n",
+ " # 默认不显示箭头,即 'none'。\n",
+ " # 两端都显示箭头可以设置为 'arrow'。\n",
+ " # 只在末端显示箭头可以设置为 ['none', 'arrow']。\n",
+ " symbol: Optional[str] = None,\n",
+ "\n",
+ " # 坐标轴线风格配置项,参考 `series_optionsLineStyleOpts`\n",
+ " linestyle_opts: Union[LineStyleOpts, dict, None] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9df79a7b",
+ "metadata": {},
+ "source": [
+ "##### AxisTickOpts: 坐标轴刻度配置项"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "08f7cbcf",
+ "metadata": {},
+ "source": [
+ "> 源代码 class pyecharts.option.AxisTickOpts 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d2fcb461",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class AxisTickOpts(\n",
+ " # 是否显示坐标轴刻度。\n",
+ " is_show: bool = True,\n",
+ "\n",
+ " # 类目轴中在 boundaryGap 为 true 的时候有效,可以保证刻度线和标签对齐。\n",
+ " is_align_with_label: bool = False,\n",
+ "\n",
+ " # 坐标轴刻度是否朝内,默认朝外。\n",
+ " is_inside: bool = False,\n",
+ "\n",
+ " # 坐标轴刻度的长度。\n",
+ " length: Optional[Numeric] = None,\n",
+ "\n",
+ " # 坐标轴线风格配置项,参考 `series_optionsLineStyleOpts`\n",
+ " linestyle_opts: Union[LineStyleOpts, dict, None] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8480bed2",
+ "metadata": {},
+ "source": [
+ "##### AxisPointerOpts: 坐标轴指示器配置项\n",
+ "> 源代码 class pyecharts.option.AxisPointerOpts 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0f0a39a7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class AxisPointerOpts(\n",
+ " # 默认显示坐标轴指示器\n",
+ " is_show: bool = True,\n",
+ "\n",
+ " # 不同轴的 axisPointer 可以进行联动,在这里设置。联动表示轴能同步一起活动。\n",
+ " # 轴依据他们的 axisPointer 当前对应的值来联动。\n",
+ " # link 是一个数组,其中每一项表示一个 link group,一个 group 中的坐标轴互相联动。\n",
+ " # 具体使用方式可以参见:https://www.echartsjs.com/option.html#axisPointer.link\n",
+ " link: Sequence[dict] = None,\n",
+ "\n",
+ " # 指示器类型。\n",
+ " # 可选参数如下,默认为 'line'\n",
+ " # 'line' 直线指示器\n",
+ " # 'shadow' 阴影指示器\n",
+ " # 'none' 无指示器\n",
+ " type_: str = \"line\",\n",
+ "\n",
+ " # 坐标轴指示器的文本标签,坐标轴标签配置项,参考 `series_options.LabelOpts`\n",
+ " label: Union[LabelOpts, dict, None] = None,\n",
+ "\n",
+ " # 坐标轴线风格配置项,参考 `series_optionsLineStyleOpts`\n",
+ " linestyle_opts: Union[LineStyleOpts, dict, None] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f5d6fa75",
+ "metadata": {},
+ "source": [
+ "#### InitOpts:初始化配置项\n",
+ "> 源代码 class pyecharts.options.InitOpts 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "20d32329",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class InitOpts(\n",
+ " # 图表画布宽度,css 长度单位。\n",
+ " width: str = \"900px\",\n",
+ "\n",
+ " # 图表画布高度,css 长度单位。\n",
+ " height: str = \"500px\",\n",
+ "\n",
+ " # 图表 ID,图表唯一标识,用于在多图表时区分。\n",
+ " chart_id: Optional[str] = None,\n",
+ "\n",
+ " # 渲染风格,可选 \"canvas\", \"svg\"\n",
+ " # # 参考 `全局变量` 章节\n",
+ " renderer: str = RenderType.CANVAS,\n",
+ "\n",
+ " # 网页标题\n",
+ " page_title: str = \"Awesome-pyecharts\",\n",
+ "\n",
+ " # 图表主题\n",
+ " theme: str = \"white\",\n",
+ "\n",
+ " # 图表背景颜色\n",
+ " bg_color: Optional[str] = None,\n",
+ "\n",
+ " # 远程 js host,如不设置默认为 https://assets.pyecharts.org/assets/\"\n",
+ " # 参考 `全局变量` 章节\n",
+ " js_host: str = \"\",\n",
+ "\n",
+ " # 画图动画初始化配置,参考 `global_options.AnimationOpts`\n",
+ " animation_opts: Union[AnimationOpts, dict] = AnimationOpts(),\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "06aa91f6",
+ "metadata": {},
+ "source": [
+ "#### AnimationOpts:Echarts 画图动画配置项\n",
+ "> 源代码 class pyecharts.options.Animation 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7dca2e57",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class AnimationOpts(\n",
+ " # 是否开启动画,默认为 True 开启。\n",
+ " animation: bool = True,\n",
+ "\n",
+ " # 是否开启动画的阈值,当单个系列显示的图形数量大于这个阈值时会关闭动画。默认 2000。\n",
+ " animation_threshold: Numeric = 2000,\n",
+ "\n",
+ " # 初始动画的时长,默认值为 1000。\n",
+ " # 支持回调函数,可以通过每个数据返回不同的 delay 时间实现更戏剧的初始动画效果:\n",
+ " animation_duration: Union[Numeric, JSFunc] = 1000,\n",
+ "\n",
+ " # 初始动画的缓动效果。\n",
+ " # 不同的缓动效果可以参考,缓动示例 (https://www.echartsjs.com/gallery/editor.html?c=line-easing)。\n",
+ " animation_easing: Union[str] = \"cubicOut\",\n",
+ "\n",
+ " # 初始动画的延迟,默认值为 0。\n",
+ " # 支持回调函数,可以通过每个数据返回不同的 delay 时间实现更戏剧的初始动画效果。\n",
+ " animation_delay: Union[Numeric, JSFunc] = 0,\n",
+ "\n",
+ " # 数据更新动画的时长,默认值为 300。\n",
+ " # 支持回调函数,可以通过每个数据返回不同的 delay 时间实现更戏剧的更新动画效果:\n",
+ " animation_duration_update: Union[Numeric, JSFunc] = 300,\n",
+ "\n",
+ " # 数据更新动画的缓动效果。\n",
+ " # 不同的缓动效果可以参考,缓动示例 (https://www.echartsjs.com/gallery/editor.html?c=line-easing)。\n",
+ " animation_easing_update: Union[Numeric] = \"cubicOut\",\n",
+ "\n",
+ " # 数据更新动画的延迟,默认值为 0。\n",
+ " # 支持回调函数,可以通过每个数据返回不同的 delay 时间实现更戏剧的更新动画效果。\n",
+ " animation_delay_update: Union[Numeric, JSFunc] = 0,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fb4d53fa",
+ "metadata": {},
+ "source": [
+ "#### TooltipOpts:提示框配置项\n",
+ "> 源代码 class pyecharts.options.TooltipOpts 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8e88f3e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class TooltipOpts(\n",
+ " # 是否显示提示框组件,包括提示框浮层和 axisPointer。\n",
+ " is_show: bool = True,\n",
+ "\n",
+ " # 触发类型。可选:\n",
+ " # 'item': 数据项图形触发,主要在散点图,饼图等无类目轴的图表中使用。\n",
+ " # 'axis': 坐标轴触发,主要在柱状图,折线图等会使用类目轴的图表中使用。\n",
+ " # 'none': 什么都不触发\n",
+ " trigger: str = \"item\",\n",
+ "\n",
+ " # 提示框触发的条件,可选:\n",
+ " # 'mousemove': 鼠标移动时触发。\n",
+ " # 'click': 鼠标点击时触发。\n",
+ " # 'mousemove|click': 同时鼠标移动和点击时触发。\n",
+ " # 'none': 不在 'mousemove' 或 'click' 时触发,\n",
+ " trigger_on: str = \"mousemove|click\",\n",
+ "\n",
+ " # 指示器类型。可选\n",
+ " # 'line':直线指示器\n",
+ " # 'shadow':阴影指示器\n",
+ " # 'none':无指示器\n",
+ " # 'cross':十字准星指示器。其实是种简写,表示启用两个正交的轴的 axisPointer。\n",
+ " axis_pointer_type: str = \"line\",\n",
+ "\n",
+ " # 是否显示提示框浮层,默认显示。\n",
+ " # 只需 tooltip 触发事件或显示 axisPointer 而不需要显示内容时可配置该项为 false。\n",
+ " is_show_content: bool = True,\n",
+ "\n",
+ " # 是否永远显示提示框内容,\n",
+ " # 默认情况下在移出可触发提示框区域后一定时间后隐藏,设置为 true 可以保证一直显示提示框内容。\n",
+ " is_always_show_content: bool = False,\n",
+ "\n",
+ " # 浮层显示的延迟,单位为 ms,默认没有延迟,也不建议设置。\n",
+ " show_delay: Numeric = 0,\n",
+ "\n",
+ " # 浮层隐藏的延迟,单位为 ms,在 alwaysShowContent 为 true 的时候无效。\n",
+ " hide_delay: Numeric = 100,\n",
+ "\n",
+ " # 提示框浮层的位置,默认不设置时位置会跟随鼠标的位置。\n",
+ " # 1、通过数组配置:\n",
+ " # 绝对位置,相对于容器左侧 10px, 上侧 10 px ===> position: [10, 10]\n",
+ " # 相对位置,放置在容器正中间 ===> position: ['50%', '50%']\n",
+ " # 2、通过回调函数配置\n",
+ " # 3、固定参数配置:'inside','top','left','right','bottom'\n",
+ " position: Union[str, Sequence, JSFunc] = None,\n",
+ "\n",
+ " # 标签内容格式器,支持字符串模板和回调函数两种形式,字符串模板与回调函数返回的字符串均支持用 \\n 换行。\n",
+ " # 字符串模板 模板变量有:\n",
+ " # {a}:系列名。\n",
+ " # {b}:数据名。\n",
+ " # {c}:数据值。\n",
+ " # {@xxx}:数据中名为 'xxx' 的维度的值,如 {@product} 表示名为 'product'` 的维度的值。\n",
+ " # {@[n]}:数据中维度 n 的值,如{@[3]}` 表示维度 3 的值,从 0 开始计数。\n",
+ " # 示例:formatter: '{b}: {@score}'\n",
+ " # \n",
+ " # 回调函数,回调函数格式:\n",
+ " # (params: Object|Array) => string\n",
+ " # 参数 params 是 formatter 需要的单个数据集。格式如下:\n",
+ " # {\n",
+ " # componentType: 'series',\n",
+ " # // 系列类型\n",
+ " # seriesType: string,\n",
+ " # // 系列在传入的 option.series 中的 index\n",
+ " # seriesIndex: number,\n",
+ " # // 系列名称\n",
+ " # seriesName: string,\n",
+ " # // 数据名,类目名\n",
+ " # name: string,\n",
+ " # // 数据在传入的 data 数组中的 index\n",
+ " # dataIndex: number,\n",
+ " # // 传入的原始数据项\n",
+ " # data: Object,\n",
+ " # // 传入的数据值\n",
+ " # value: number|Array,\n",
+ " # // 数据图形的颜色\n",
+ " # color: string,\n",
+ " # }\n",
+ " formatter: Optional[str] = None,\n",
+ "\n",
+ " # 提示框浮层的背景颜色。\n",
+ " background_color: Optional[str] = None,\n",
+ "\n",
+ " # 提示框浮层的边框颜色。\n",
+ " border_color: Optional[str] = None,\n",
+ "\n",
+ " # 提示框浮层的边框宽。\n",
+ " border_width: Numeric = 0,\n",
+ "\n",
+ " # 文字样式配置项,参考 `series_options.TextStyleOpts`\n",
+ " textstyle_opts: TextStyleOpts = TextStyleOpts(font_size=14),\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "314d7578",
+ "metadata": {},
+ "source": [
+ "### 系列配置项\n",
+ "**系列配置项可通过 set_series_opts 方法设置**\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "199f958b",
+ "metadata": {},
+ "source": [
+ "#### ItemStyleOpts:图元样式配置项\n",
+ "> 源代码 class pyecharts.options.ItemStyleOpts 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "568827d5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class ItemStyleOpts(\n",
+ " # 图形的颜色。\n",
+ " # 颜色可以使用 RGB 表示,比如 'rgb(128, 128, 128)',如果想要加上 alpha 通道表示不透明度,\n",
+ " # 可以使用 RGBA,比如 'rgba(128, 128, 128, 0.5)',也可以使用十六进制格式,比如 '#ccc'。\n",
+ " # 除了纯色之外颜色也支持渐变色和纹理填充\n",
+ " # \n",
+ " # 线性渐变,前四个参数分别是 x0, y0, x2, y2, 范围从 0 - 1,相当于在图形包围盒中的百分比,\n",
+ " # 如果 globalCoord 为 `true`,则该四个值是绝对的像素位置\n",
+ " # color: {\n",
+ " # type: 'linear',\n",
+ " # x: 0,\n",
+ " # y: 0,\n",
+ " # x2: 0,\n",
+ " # y2: 1,\n",
+ " # colorStops: [{\n",
+ " # offset: 0, color: 'red' // 0% 处的颜色\n",
+ " # }, {\n",
+ " # offset: 1, color: 'blue' // 100% 处的颜色\n",
+ " # }],\n",
+ " # global: false // 缺省为 false\n",
+ " # }\n",
+ " # \n",
+ " # 径向渐变,前三个参数分别是圆心 x, y 和半径,取值同线性渐变\n",
+ " # color: {\n",
+ " # type: 'radial',\n",
+ " # x: 0.5,\n",
+ " # y: 0.5,\n",
+ " # r: 0.5,\n",
+ " # colorStops: [{\n",
+ " # offset: 0, color: 'red' // 0% 处的颜色\n",
+ " # }, {\n",
+ " # offset: 1, color: 'blue' // 100% 处的颜色\n",
+ " # }],\n",
+ " # global: false // 缺省为 false\n",
+ " # }\n",
+ " # \n",
+ " # 纹理填充\n",
+ " # color: {\n",
+ " # image: imageDom, // 支持为 HTMLImageElement, HTMLCanvasElement,不支持路径字符串\n",
+ " # repeat: 'repeat' // 是否平铺, 可以是 'repeat-x', 'repeat-y', 'no-repeat'\n",
+ " # }\n",
+ " color: Optional[str] = None,\n",
+ "\n",
+ " # 阴线 图形的颜色。\n",
+ " color0: Optional[str] = None,\n",
+ "\n",
+ " # 图形的描边颜色。支持的颜色格式同 color,不支持回调函数。\n",
+ " border_color: Optional[str] = None,\n",
+ "\n",
+ " # 阴线 图形的描边颜色。\n",
+ " border_color0: Optional[str] = None,\n",
+ "\n",
+ " # 描边宽度,默认不描边。\n",
+ " border_width: Optional[Numeric] = None,\n",
+ "\n",
+ " # 支持 'dashed', 'dotted'。\n",
+ " border_type: Optional[str] = None,\n",
+ "\n",
+ " # 图形透明度。支持从 0 到 1 的数字,为 0 时不绘制该图形。\n",
+ " opacity: Optional[Numeric] = None,\n",
+ "\n",
+ " # 区域的颜色。 \n",
+ " area_color: Optional[str] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8a48af4",
+ "metadata": {},
+ "source": [
+ "#### LabelOpts:标签配置项\n",
+ "> 源代码 class pyecharts.options.LabelOpts 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fa8a0012",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class LabelOpts(\n",
+ " # 是否显示标签。\n",
+ " is_show: bool = True,\n",
+ "\n",
+ " # 标签的位置。可选\n",
+ " # 'top','left','right','bottom','inside','insideLeft','insideRight'\n",
+ " # 'insideTop','insideBottom', 'insideTopLeft','insideBottomLeft'\n",
+ " # 'insideTopRight','insideBottomRight'\n",
+ " position: Union[str, Sequence] = \"top\",\n",
+ "\n",
+ " # 文字的颜色。\n",
+ " # 如果设置为 'auto',则为视觉映射得到的颜色,如系列色。\n",
+ " color: Optional[str] = None,\n",
+ "\n",
+ " # 距离图形元素的距离。当 position 为字符描述值(如 'top'、'insideRight')时候有效。\n",
+ " distance: Union[Numeric, Sequence, None] = None,\n",
+ "\n",
+ " # 文字的字体大小\n",
+ " font_size: Numeric = 12,\n",
+ "\n",
+ " # 文字字体的风格,可选:\n",
+ " # 'normal','italic','oblique'\n",
+ " font_style: Optional[str] = None,\n",
+ "\n",
+ " # 文字字体的粗细,可选:\n",
+ " # 'normal','bold','bolder','lighter'\n",
+ " font_weight: Optional[str] = None,\n",
+ "\n",
+ " # 文字的字体系列\n",
+ " # 还可以是 'serif' , 'monospace', 'Arial', 'Courier New', 'Microsoft YaHei', ...\n",
+ " font_family: Optional[str] = None,\n",
+ "\n",
+ " # 标签旋转。从 -90 度到 90 度。正值是逆时针。\n",
+ " rotate: Optional[Numeric] = None,\n",
+ "\n",
+ " # 刻度标签与轴线之间的距离。\n",
+ " margin: Optional[Numeric] = 8,\n",
+ "\n",
+ " # 坐标轴刻度标签的显示间隔,在类目轴中有效。\n",
+ " # 默认会采用标签不重叠的策略间隔显示标签。\n",
+ " # 可以设置成 0 强制显示所有标签。\n",
+ " # 如果设置为 1,表示『隔一个标签显示一个标签』,如果值为 2,表示隔两个标签显示一个标签,以此类推。\n",
+ " # 可以用数值表示间隔的数据,也可以通过回调函数控制。回调函数格式如下:\n",
+ " # (index:number, value: string) => boolean\n",
+ " # 第一个参数是类目的 index,第二个值是类目名称,如果跳过则返回 false。\n",
+ " interval: Union[Numeric, str, None]= None,\n",
+ "\n",
+ " # 文字水平对齐方式,默认自动。可选:\n",
+ " # 'left','center','right'\n",
+ " horizontal_align: Optional[str] = None,\n",
+ "\n",
+ " # 文字垂直对齐方式,默认自动。可选:\n",
+ " # 'top','middle','bottom'\n",
+ " vertical_align: Optional[str] = None,\n",
+ "\n",
+ " # 标签内容格式器,支持字符串模板和回调函数两种形式,字符串模板与回调函数返回的字符串均支持用 \\n 换行。\n",
+ " # 模板变量有 {a}, {b},{c},{d},{e},分别表示系列名,数据名,数据值等。 \n",
+ " # 在 trigger 为 'axis' 的时候,会有多个系列的数据,此时可以通过 {a0}, {a1}, {a2} 这种后面加索引的方式表示系列的索引。 \n",
+ " # 不同图表类型下的 {a},{b},{c},{d} 含义不一样。 其中变量{a}, {b}, {c}, {d}在不同图表类型下代表数据含义为:\n",
+ "\n",
+ " # 折线(区域)图、柱状(条形)图、K线图 : {a}(系列名称),{b}(类目值),{c}(数值), {d}(无)\n",
+ " # 散点图(气泡)图 : {a}(系列名称),{b}(数据名称),{c}(数值数组), {d}(无)\n",
+ " # 地图 : {a}(系列名称),{b}(区域名称),{c}(合并数值), {d}(无)\n",
+ " # 饼图、仪表盘、漏斗图: {a}(系列名称),{b}(数据项名称),{c}(数值), {d}(百分比)\n",
+ " # 示例:formatter: '{b}: {@score}'\n",
+ " # \n",
+ " # 回调函数,回调函数格式:\n",
+ " # (params: Object|Array) => string\n",
+ " # 参数 params 是 formatter 需要的单个数据集。格式如下:\n",
+ " # {\n",
+ " # componentType: 'series',\n",
+ " # // 系列类型\n",
+ " # seriesType: string,\n",
+ " # // 系列在传入的 option.series 中的 index\n",
+ " # seriesIndex: number,\n",
+ " # // 系列名称\n",
+ " # seriesName: string,\n",
+ " # // 数据名,类目名\n",
+ " # name: string,\n",
+ " # // 数据在传入的 data 数组中的 index\n",
+ " # dataIndex: number,\n",
+ " # // 传入的原始数据项\n",
+ " # data: Object,\n",
+ " # // 传入的数据值\n",
+ " # value: number|Array,\n",
+ " # // 数据图形的颜色\n",
+ " # color: string,\n",
+ " # }\n",
+ " formatter: Optional[str] = None,\n",
+ "\n",
+ " # 在 rich 里面,可以自定义富文本样式。利用富文本样式,可以在标签中做出非常丰富的效果\n",
+ " # 具体配置可以参考一下 https://www.echartsjs.com/tutorial.html#%E5%AF%8C%E6%96%87%E6%9C%AC%E6%A0%87%E7%AD%BE\n",
+ " rich: Optional[dict] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "45dd4b97",
+ "metadata": {},
+ "source": [
+ "#### MarkPointOpts:标记点配置项\n",
+ "> 源代码 class pyecharts.options.MarkPointOpts 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "db9c61ce",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class MarkPointOpts(\n",
+ " # 标记点数据,参考 `series_options.MarkPointItem`\n",
+ " data: Sequence[Union[MarkPointItem, dict]] = None,\n",
+ "\n",
+ " # 标记的图形。\n",
+ " # ECharts 提供的标记类型包括 'circle', 'rect', 'roundRect', 'triangle', \n",
+ " # 'diamond', 'pin', 'arrow', 'none'\n",
+ " # 可以通过 'image://url' 设置为图片,其中 URL 为图片的链接,或者 dataURI。\n",
+ " symbol: Optional[str] = None,\n",
+ "\n",
+ " # 标记的大小,可以设置成诸如 10 这样单一的数字,也可以用数组分开表示宽和高,\n",
+ " # 例如 [20, 10] 表示标记宽为 20,高为 10。\n",
+ " # 如果需要每个数据的图形大小不一样,可以设置为如下格式的回调函数:\n",
+ " # (value: Array|number, params: Object) => number|Array\n",
+ " # 其中第一个参数 value 为 data 中的数据值。第二个参数 params 是其它的数据项参数。\n",
+ " symbol_size: Union[None, Numeric] = None,\n",
+ "\n",
+ " # 标签配置项,参考 `series_options.LabelOpts`\n",
+ " label_opts: LabelOpts = LabelOpts(position=\"inside\", color=\"#fff\"),\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6bdd37e2",
+ "metadata": {},
+ "source": [
+ "##### MarkPointItem:标记点数据项\n",
+ "> 源代码 class pyecharts.options.MarkPointItem 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5eeaf27a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class MarkPointItem(\n",
+ " # 标注名称。\n",
+ " name: Optional[str] = None,\n",
+ "\n",
+ " # 特殊的标注类型,用于标注最大值最小值等。可选:\n",
+ " # 'min' 最大值。\n",
+ " # 'max' 最大值。\n",
+ " # 'average' 平均值。\n",
+ " type_: Optional[str] = None,\n",
+ "\n",
+ " # 在使用 type 时有效,用于指定在哪个维度上指定最大值最小值,可以是 \n",
+ " # 0(xAxis, radiusAxis),\n",
+ " # 1(yAxis, angleAxis),默认使用第一个数值轴所在的维度。\n",
+ " value_index: Optional[Numeric] = None,\n",
+ "\n",
+ " # 在使用 type 时有效,用于指定在哪个维度上指定最大值最小值。这可以是维度的直接名称,\n",
+ " # 例如折线图时可以是 x、angle 等、candlestick 图时可以是 open、close 等维度名称。\n",
+ " value_dim: Optional[str] = None,\n",
+ "\n",
+ " # 标注的坐标。坐标格式视系列的坐标系而定,可以是直角坐标系上的 x, y,\n",
+ " # 也可以是极坐标系上的 radius, angle。例如 [121, 2323]、['aa', 998]。\n",
+ " coord: Optional[Sequence] = None,\n",
+ "\n",
+ " # 相对容器的屏幕 x 坐标,单位像素。\n",
+ " x: Optional[Numeric] = None,\n",
+ "\n",
+ " # 相对容器的屏幕 y 坐标,单位像素。\n",
+ " y: Optional[Numeric] = None,\n",
+ "\n",
+ " # 标注值,可以不设。\n",
+ " value: Optional[Numeric] = None,\n",
+ "\n",
+ " # 标记的图形。\n",
+ " # ECharts 提供的标记类型包括 'circle', 'rect', 'roundRect', 'triangle', \n",
+ " # 'diamond', 'pin', 'arrow', 'none'\n",
+ " # 可以通过 'image://url' 设置为图片,其中 URL 为图片的链接,或者 dataURI。\n",
+ " symbol: Optional[str] = None,\n",
+ "\n",
+ " # 标记的大小,可以设置成诸如 10 这样单一的数字,也可以用数组分开表示宽和高,\n",
+ " # 例如 [20, 10] 表示标记宽为 20,高为 10。\n",
+ " symbol_size: Union[Numeric, Sequence] = None,\n",
+ "\n",
+ " # 标记点样式配置项,参考 `series_options.ItemStyleOpts`\n",
+ " itemstyle_opts: Union[ItemStyleOpts, dict, None] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a950d518",
+ "metadata": {},
+ "source": [
+ "#### MarkLineOpts:标记线配置项\n",
+ "> 源代码 class pyecharts.options.MarkLineOpts 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4c930dd6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class MarkLineOpts(\n",
+ " # 图形是否不响应和触发鼠标事件,默认为 false,即响应和触发鼠标事件。\n",
+ " is_silent: bool = False,\n",
+ "\n",
+ " # 标记线数据,参考 `series_options.MarkLineItem`\n",
+ " data: Sequence[Union[MarkLineItem, dict]] = None,\n",
+ "\n",
+ " # 标线两端的标记类型,可以是一个数组分别指定两端,也可以是单个统一指定,具体格式见 data.symbol。\n",
+ " symbol: Optional[str] = None,\n",
+ "\n",
+ " # 标线两端的标记大小,可以是一个数组分别指定两端,也可以是单个统一指定。\n",
+ " symbol_size: Union[None, Numeric] = None,\n",
+ "\n",
+ " # 标线数值的精度,在显示平均值线的时候有用。\n",
+ " precision: int = 2,\n",
+ "\n",
+ " # 标签配置项,参考 `series_options.LabelOpts`\n",
+ " label_opts: LabelOpts = LabelOpts(),\n",
+ "\n",
+ " # 标记线样式配置项,参考 `series_options.LineStyleOpts`\n",
+ " linestyle_opts: Union[LineStyleOpts, dict, None] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "36777020",
+ "metadata": {},
+ "source": [
+ "##### MarkLineItem:标记线数据项\n",
+ "> 源代码 class pyecharts.options.MarkLineItem 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "df9e540f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class MarkLineItem(\n",
+ " # 标注名称。\n",
+ " name: Optional[str] = None,\n",
+ "\n",
+ " # 特殊的标注类型,用于标注最大值最小值等。可选:\n",
+ " # 'min' 最大值。\n",
+ " # 'max' 最大值。\n",
+ " # 'average' 平均值。\n",
+ " type_: Optional[str] = None,\n",
+ "\n",
+ " # 相对容器的屏幕 x 坐标,单位像素。\n",
+ " x: Union[str, Numeric, None] = None,\n",
+ "\n",
+ " # x 数据坐标。\n",
+ " xcoord: Union[str, Numeric, None] = None,\n",
+ "\n",
+ " # 相对容器的屏幕 y 坐标,单位像素。\n",
+ " y: Union[str, Numeric, None] = None,\n",
+ "\n",
+ " # y 数据坐标。\n",
+ " ycoord: Union[str, Numeric, None] = None,\n",
+ "\n",
+ " # 在使用 type 时有效,用于指定在哪个维度上指定最大值最小值,可以是 \n",
+ " # 0(xAxis, radiusAxis),\n",
+ " # 1(yAxis, angleAxis),默认使用第一个数值轴所在的维度。\n",
+ " value_index: Optional[Numeric] = None,\n",
+ "\n",
+ " # 在使用 type 时有效,用于指定在哪个维度上指定最大值最小值。这可以是维度的直接名称,\n",
+ " # 例如折线图时可以是 x、angle 等、candlestick 图时可以是 open、close 等维度名称。\n",
+ " value_dim: Optional[str] = None,\n",
+ "\n",
+ " # 起点或终点的坐标。坐标格式视系列的坐标系而定,可以是直角坐标系上的 x, y,\n",
+ " # 也可以是极坐标系上的 radius, angle。\n",
+ " coord: Optional[Sequence] = None,\n",
+ "\n",
+ " # 终点标记的图形。\n",
+ " # ECharts 提供的标记类型包括 'circle', 'rect', 'roundRect', 'triangle',\n",
+ " # 'diamond', 'pin', 'arrow', 'none'\n",
+ " # 可以通过 'image://url' 设置为图片,其中 URL 为图片的链接,或者 dataURI。\n",
+ " symbol: Optional[str] = None,\n",
+ "\n",
+ " # 标记的大小,可以设置成诸如 10 这样单一的数字,也可以用数组分开表示宽和高,\n",
+ " # 例如 [20, 10] 表示标记宽为 20,高为 10。\n",
+ " symbol_size: Optional[Numeric] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fcf8a73e",
+ "metadata": {},
+ "source": [
+ "#### TextStyleOpts:文字样式配置项\n",
+ "> 源代码 class pyecharts.options.TextStyleOpts 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "11262543",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class TextStyleOpts(\n",
+ " # 文字颜色。\n",
+ " color: Optional[str] = None,\n",
+ "\n",
+ " # 文字字体的风格\n",
+ " # 可选:'normal','italic','oblique'\n",
+ " font_style: Optional[str] = None,\n",
+ "\n",
+ " # 主标题文字字体的粗细,可选:\n",
+ " # 'normal','bold','bolder','lighter'\n",
+ " font_weight: Optional[str] = None,\n",
+ "\n",
+ " # 文字的字体系列\n",
+ " # 还可以是 'serif' , 'monospace', 'Arial', 'Courier New', 'Microsoft YaHei', ...\n",
+ " font_family: Optional[str] = None,\n",
+ "\n",
+ " # 文字的字体大小\n",
+ " font_size: Optional[Numeric] = None,\n",
+ "\n",
+ " # 文字水平对齐方式,默认自动\n",
+ " align: Optional[str] = None,\n",
+ "\n",
+ " # 文字垂直对齐方式,默认自动\n",
+ " vertical_align: Optional[str] = None,\n",
+ "\n",
+ " # 行高\n",
+ " line_height: Optional[str] = None,\n",
+ "\n",
+ " # 文字块背景色。可以是直接的颜色值,例如:'#123234', 'red', 'rgba(0,23,11,0.3)'\n",
+ " background_color: Optional[str] = None,\n",
+ "\n",
+ " # 文字块边框颜色\n",
+ " border_color: Optional[str] = None,\n",
+ "\n",
+ " # 文字块边框宽度\n",
+ " border_width: Optional[Numeric] = None,\n",
+ "\n",
+ " # 文字块的圆角\n",
+ " border_radius: Union[Numeric, Sequence, None] = None,\n",
+ "\n",
+ " # 文字块的内边距 \n",
+ " # 例如 padding: [3, 4, 5, 6]:表示 [上, 右, 下, 左] 的边距\n",
+ " # 例如 padding: 4:表示 padding: [4, 4, 4, 4]\n",
+ " # 例如 padding: [3, 4]:表示 padding: [3, 4, 3, 4]\n",
+ " padding: Union[Numeric, Sequence, None] = None,\n",
+ "\n",
+ " # 文字块的背景阴影颜色\n",
+ " shadow_color: Optional[str] = None,\n",
+ "\n",
+ " # 文字块的背景阴影长度\n",
+ " shadow_blur: Optional[Numeric] = None,\n",
+ "\n",
+ " # 文字块的宽度\n",
+ " width: Optional[str] = None,\n",
+ "\n",
+ " # 文字块的高度\n",
+ " height: Optional[str] = None,\n",
+ "\n",
+ " # 在 rich 里面,可以自定义富文本样式。利用富文本样式,可以在标签中做出非常丰富的效果\n",
+ " # 具体配置可以参考一下 https://www.echartsjs.com/tutorial.html#%E5%AF%8C%E6%96%87%E6%9C%AC%E6%A0%87%E7%AD%BE\n",
+ " rich: Optional[dict] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d99c7817",
+ "metadata": {},
+ "source": [
+ "#### LineStyleOpts:线样式配置项\n",
+ "> 源代码 class pyecharts.options.LineStyleOpts 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "37cf84a0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class LineStyleOpts(\n",
+ " # 是否显示\n",
+ " is_show: bool = True,\n",
+ "\n",
+ " # 线宽。\n",
+ " width: Numeric = 1,\n",
+ "\n",
+ " # 图形透明度。支持从 0 到 1 的数字,为 0 时不绘制该图形。\n",
+ " opacity: Numeric = 1,\n",
+ "\n",
+ " # 线的弯曲度,0 表示完全不弯曲\n",
+ " curve: Numeric = 0,\n",
+ "\n",
+ " # 线的类型。可选:\n",
+ " # 'solid', 'dashed', 'dotted'\n",
+ " type_: str = \"solid\",\n",
+ "\n",
+ " # 线的颜色。\n",
+ " # 颜色可以使用 RGB 表示,比如 'rgb(128, 128, 128)',如果想要加上 alpha 通道表示不透明度,\n",
+ " # 可以使用 RGBA,比如 'rgba(128, 128, 128, 0.5)',也可以使用十六进制格式,比如 '#ccc'。\n",
+ " # 除了纯色之外颜色也支持渐变色和纹理填充\n",
+ " # \n",
+ " # 线性渐变,前四个参数分别是 x0, y0, x2, y2, 范围从 0 - 1,相当于在图形包围盒中的百分比,\n",
+ " # 如果 globalCoord 为 `true`,则该四个值是绝对的像素位置\n",
+ " # color: {\n",
+ " # type: 'linear',\n",
+ " # x: 0,\n",
+ " # y: 0,\n",
+ " # x2: 0,\n",
+ " # y2: 1,\n",
+ " # colorStops: [{\n",
+ " # offset: 0, color: 'red' // 0% 处的颜色\n",
+ " # }, {\n",
+ " # offset: 1, color: 'blue' // 100% 处的颜色\n",
+ " # }],\n",
+ " # global: false // 缺省为 false\n",
+ " # }\n",
+ " # \n",
+ " # 径向渐变,前三个参数分别是圆心 x, y 和半径,取值同线性渐变\n",
+ " # color: {\n",
+ " # type: 'radial',\n",
+ " # x: 0.5,\n",
+ " # y: 0.5,\n",
+ " # r: 0.5,\n",
+ " # colorStops: [{\n",
+ " # offset: 0, color: 'red' // 0% 处的颜色\n",
+ " # }, {\n",
+ " # offset: 1, color: 'blue' // 100% 处的颜色\n",
+ " # }],\n",
+ " # global: false // 缺省为 false\n",
+ " # }\n",
+ " # \n",
+ " # 纹理填充\n",
+ " # color: {\n",
+ " # image: imageDom, // 支持为 HTMLImageElement, HTMLCanvasElement,不支持路径字符串\n",
+ " # repeat: 'repeat' // 是否平铺, 可以是 'repeat-x', 'repeat-y', 'no-repeat'\n",
+ " # }\n",
+ " color: Union[str, Sequence, None] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4854cb83",
+ "metadata": {},
+ "source": [
+ "#### AreaStyleOpts:区域填充样式配置项\n",
+ "> 源代码 class pyecharts.options.AreaStyleOpts 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7d43738a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class AreaStyleOpts(\n",
+ " # 图形透明度。支持从 0 到 1 的数字,为 0 时不绘制该图形。\n",
+ " opacity: Optional[Numeric] = 0,\n",
+ " # 填充的颜色。\n",
+ " # 颜色可以使用 RGB 表示,比如 'rgb(128, 128, 128)',如果想要加上 alpha 通道表示不透明度,\n",
+ " # 可以使用 RGBA,比如 'rgba(128, 128, 128, 0.5)',也可以使用十六进制格式,比如 '#ccc'。\n",
+ " # 除了纯色之外颜色也支持渐变色和纹理填充\n",
+ " # \n",
+ " # 线性渐变,前四个参数分别是 x0, y0, x2, y2, 范围从 0 - 1,相当于在图形包围盒中的百分比,\n",
+ " # 如果 globalCoord 为 `true`,则该四个值是绝对的像素位置\n",
+ " # color: {\n",
+ " # type: 'linear',\n",
+ " # x: 0,\n",
+ " # y: 0,\n",
+ " # x2: 0,\n",
+ " # y2: 1,\n",
+ " # colorStops: [{\n",
+ " # offset: 0, color: 'red' // 0% 处的颜色\n",
+ " # }, {\n",
+ " # offset: 1, color: 'blue' // 100% 处的颜色\n",
+ " # }],\n",
+ " # global: false // 缺省为 false\n",
+ " # }\n",
+ " # \n",
+ " # 径向渐变,前三个参数分别是圆心 x, y 和半径,取值同线性渐变\n",
+ " # color: {\n",
+ " # type: 'radial',\n",
+ " # x: 0.5,\n",
+ " # y: 0.5,\n",
+ " # r: 0.5,\n",
+ " # colorStops: [{\n",
+ " # offset: 0, color: 'red' // 0% 处的颜色\n",
+ " # }, {\n",
+ " # offset: 1, color: 'blue' // 100% 处的颜色\n",
+ " # }],\n",
+ " # global: false // 缺省为 false\n",
+ " # }\n",
+ " # \n",
+ " # 纹理填充\n",
+ " # color: {\n",
+ " # image: imageDom, // 支持为 HTMLImageElement, HTMLCanvasElement,不支持路径字符串\n",
+ " # repeat: 'repeat' // 是否平铺, 可以是 'repeat-x', 'repeat-y', 'no-repeat'\n",
+ " # }\n",
+ " color: Optional[str] = None\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c5fb675e",
+ "metadata": {},
+ "source": [
+ "#### SplitAreaOpts:分隔区域配置项\n",
+ "> 源代码 class pyecharts.options.SplitAreaOpts 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "014634df",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class SplitAreaOpts(\n",
+ " # 是否显示分隔区域。\n",
+ " is_show=True, \n",
+ " # 分隔区域的样式配置项,参考 `series_options.AreaStyleOpts`\n",
+ " areastyle_opts: AreaStyleOpts = AreaStyleOpts()\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "23b41ae2",
+ "metadata": {},
+ "source": [
+ "#### MinorTickOpts:次级刻度配置项\n",
+ "> 源代码 class pyecharts.options.MinorTickOpts 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c828c9cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class MinorTickOpts(\n",
+ " # 是否显示次刻度线。\n",
+ " is_show: bool = False,\n",
+ "\n",
+ " # 次刻度线分割数,默认会分割成 5 段\n",
+ " split_number: Numeric = 5,\n",
+ "\n",
+ " # 次刻度线的长度。\n",
+ " length: Numeric = 3,\n",
+ "\n",
+ " # 次刻度线的样式\n",
+ " linestyle_opts: Union[LineStyleOpts, dict, None] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6f9e9659",
+ "metadata": {},
+ "source": [
+ "#### MinorSplitLineOpts:次级分割线配置项\n",
+ "> 源代码 class pyecharts.options.MinorSplitLineOpts 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "612964f0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class MinorSplitLineOpts(\n",
+ " # 是否显示次分隔线。默认不显示。\n",
+ " is_show: bool = False,\n",
+ "\n",
+ " # 次分隔线线宽。\n",
+ " width: Numeric = 1,\n",
+ "\n",
+ " # 次分隔线线的类型。可选:'solid','dashed','dotted'\n",
+ " type_: str = \"solid\",\n",
+ "\n",
+ " # 图形透明度。支持从 0 到 1 的数字,为 0 时不绘制该图形。\n",
+ " opacity: Union[Numeric, None] = None,\n",
+ "\n",
+ " # 线的样式\n",
+ " linestyle_opts: Union[LineStyleOpts, dict, None] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d7e0a46b",
+ "metadata": {},
+ "source": [
+ "## 图表类型"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b3349423",
+ "metadata": {},
+ "source": [
+ "### 条图 Bar"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5dde2f30",
+ "metadata": {},
+ "source": [
+ "\n",
+ "> 源代码 class pyecharts.charts.Bar(RectChart) 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bd307d1c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Bar(\n",
+ " # 初始化配置项,参考 `global_options.InitOpts`\n",
+ " init_opts: opts.InitOpts = opts.InitOpts()\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8570312",
+ "metadata": {},
+ "source": [
+ "> 源代码 func pyecharts.charts.Bar.add_yaxis 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e601734",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def add_yaxis(\n",
+ " # 系列名称,用于 tooltip 的显示,legend 的图例筛选。\n",
+ " series_name: str,\n",
+ "\n",
+ " # 系列数据\n",
+ " y_axis: Sequence[Numeric, opts.BarItem, dict],\n",
+ "\n",
+ " # 是否选中图例\n",
+ " is_selected: bool = True,\n",
+ "\n",
+ " # 使用的 x 轴的 index,在单个图表实例中存在多个 x 轴的时候有用。\n",
+ " xaxis_index: Optional[Numeric] = None,\n",
+ "\n",
+ " # 使用的 y 轴的 index,在单个图表实例中存在多个 y 轴的时候有用。\n",
+ " yaxis_index: Optional[Numeric] = None,\n",
+ "\n",
+ " # 是否启用图例 hover 时的联动高亮\n",
+ " is_legend_hover_link: bool = True,\n",
+ "\n",
+ " # 系列 label 颜色\n",
+ " color: Optional[str] = None,\n",
+ "\n",
+ " # 是否显示柱条的背景色。通过 backgroundStyle 配置背景样式。\n",
+ " is_show_background: bool = False,\n",
+ "\n",
+ " # 每一个柱条的背景样式。需要将 showBackground 设置为 true 时才有效。\n",
+ " background_style: types.Union[types.BarBackground, dict, None] = None,\n",
+ "\n",
+ " # 数据堆叠,同个类目轴上系列配置相同的 stack 值可以堆叠放置。\n",
+ " stack: Optional[str] = None,\n",
+ "\n",
+ " # 柱条的宽度,不设时自适应。\n",
+ " # 可以是绝对值例如 40 或者百分数例如 '60%'。百分数基于自动计算出的每一类目的宽度。\n",
+ " # 在同一坐标系上,此属性会被多个 'bar' 系列共享。此属性应设置于此坐标系中最后一个 'bar' 系列上才会生效,并且是对此坐标系中所有 'bar' 系列生效。\n",
+ " bar_width: types.Union[types.Numeric, str] = None,\n",
+ "\n",
+ " # 柱条的最大宽度。比 barWidth 优先级高。\n",
+ " bar_max_width: types.Union[types.Numeric, str] = None,\n",
+ "\n",
+ " # 柱条的最小宽度。在直角坐标系中,默认值是 1。否则默认值是 null。比 barWidth 优先级高。\n",
+ " bar_min_width: types.Union[types.Numeric, str] = None,\n",
+ "\n",
+ " # 柱条最小高度,可用于防止某数据项的值过小而影响交互。\n",
+ " bar_min_height: types.Numeric = 0,\n",
+ "\n",
+ " # 同一系列的柱间距离,默认为类目间距的 20%,可设固定值\n",
+ " category_gap: Union[Numeric, str] = \"20%\",\n",
+ "\n",
+ " # 不同系列的柱间距离,为百分比(如 '30%',表示柱子宽度的 30%)。\n",
+ " # 如果想要两个系列的柱子重叠,可以设置 gap 为 '-100%'。这在用柱子做背景的时候有用。\n",
+ " gap: Optional[str] = \"30%\",\n",
+ "\n",
+ " # 是否开启大数据量优化,在数据图形特别多而出现卡顿时候可以开启。\n",
+ " # 开启后配合 largeThreshold 在数据量大于指定阈值的时候对绘制进行优化。\n",
+ " # 缺点:优化后不能自定义设置单个数据项的样式。\n",
+ " is_large: bool = False,\n",
+ "\n",
+ " # 开启绘制优化的阈值。\n",
+ " large_threshold: types.Numeric = 400,\n",
+ "\n",
+ " # 使用 dimensions 定义 series.data 或者 dataset.source 的每个维度的信息。\n",
+ " # 注意:如果使用了 dataset,那么可以在 dataset.source 的第一行/列中给出 dimension 名称。\n",
+ " # 于是就不用在这里指定 dimension。\n",
+ " # 但是,如果在这里指定了 dimensions,那么 ECharts 不再会自动从 dataset.source 的第一行/列中获取维度信息。\n",
+ " dimensions: types.Union[types.Sequence, None] = None,\n",
+ "\n",
+ " # 当使用 dataset 时,seriesLayoutBy 指定了 dataset 中用行还是列对应到系列上,也就是说,系列“排布”到 dataset 的行还是列上。可取值:\n",
+ " # 'column':默认,dataset 的列对应于系列,从而 dataset 中每一列是一个维度(dimension)。\n",
+ " # 'row':dataset 的行对应于系列,从而 dataset 中每一行是一个维度(dimension)。\n",
+ " series_layout_by: str = \"column\",\n",
+ "\n",
+ " # 如果 series.data 没有指定,并且 dataset 存在,那么就会使用 dataset。\n",
+ " # datasetIndex 指定本系列使用那个 dataset。\n",
+ " dataset_index: types.Numeric = 0,\n",
+ "\n",
+ " # 是否裁剪超出坐标系部分的图形。柱状图:裁掉所有超出坐标系的部分,但是依然保留柱子的宽度\n",
+ " is_clip: bool = True,\n",
+ "\n",
+ " # 柱状图所有图形的 zlevel 值。\n",
+ " z_level: types.Numeric = 0,\n",
+ "\n",
+ " # 柱状图组件的所有图形的z值。控制图形的前后顺序。\n",
+ " # z值小的图形会被z值大的图形覆盖。\n",
+ " # z相比zlevel优先级更低,而且不会创建新的 Canvas。\n",
+ " z: types.Numeric = 2,\n",
+ "\n",
+ " # 标签配置项,参考 `series_options.LabelOpts`\n",
+ " label_opts: Union[opts.LabelOpts, dict] = opts.LabelOpts(),\n",
+ "\n",
+ " # 标记点配置项,参考 `series_options.MarkPointOpts`\n",
+ " markpoint_opts: Union[opts.MarkPointOpts, dict, None] = None,\n",
+ "\n",
+ " # 标记线配置项,参考 `series_options.MarkLineOpts`\n",
+ " markline_opts: Union[opts.MarkLineOpts, dict, None] = None,\n",
+ "\n",
+ " # 提示框组件配置项,参考 `series_options.TooltipOpts`\n",
+ " tooltip_opts: Union[opts.TooltipOpts, dict, None] = None,\n",
+ "\n",
+ " # 图元样式配置项,参考 `series_options.ItemStyleOpts`\n",
+ " itemstyle_opts: Union[opts.ItemStyleOpts, dict, None] = None,\n",
+ "\n",
+ " # 可以定义 data 的哪个维度被编码成什么。\n",
+ " encode: types.Union[types.JSFunc, dict, None] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ba72f7ed",
+ "metadata": {},
+ "source": [
+ "#### BarItem:柱状图数据项"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "20f0b61b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class BarItem(\n",
+ " # 数据项名称。\n",
+ " name: Optional[str] = None,\n",
+ "\n",
+ " # 单个数据项的数值。\n",
+ " value: Optional[Numeric] = None,\n",
+ "\n",
+ " # 单个柱条文本的样式设置,参考 `series_options.LabelOpts`。\n",
+ " label_opts: Union[LabelOpts, dict, None] = None,\n",
+ "\n",
+ " # 图元样式配置项,参考 `series_options.ItemStyleOpts`\n",
+ " itemstyle_opts: Union[ItemStyleOpts, dict, None] = None,\n",
+ "\n",
+ " # 提示框组件配置项,参考 `series_options.TooltipOpts`\n",
+ " tooltip_opts: Union[TooltipOpts, dict, None] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b47a4d6a",
+ "metadata": {},
+ "source": [
+ "#### BarBackgroundStyleOpts:柱状图背景样式配置"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ff5b7408",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class BarBackgroundStyleOpts(\n",
+ " # 柱条的颜色。\n",
+ " color: str = \"rgba(180, 180, 180, 0.2)\",\n",
+ "\n",
+ " # 柱条的描边颜色。\n",
+ " border_color: str = \"#000\",\n",
+ "\n",
+ " # 柱条的描边宽度,默认不描边。\n",
+ " border_width: Numeric = 0,\n",
+ "\n",
+ " # 柱条的描边类型,默认为实线,支持 'dashed', 'dotted'。\n",
+ " border_type: str = \"solid\",\n",
+ "\n",
+ " # 圆角半径,单位px,支持传入数组分别指定 4 个圆角半径。 如:\n",
+ " # barBorderRadius: 5, // 统一设置四个角的圆角大小\n",
+ " # barBorderRadius: [5, 5, 0, 0] //(顺时针左上,右上,右下,左下)\n",
+ " bar_border_radius: Union[Numeric, Sequence] = 0,\n",
+ "\n",
+ " # 图形阴影的模糊大小。\n",
+ " # 该属性配合 shadowColor,shadowOffsetX, shadowOffsetY 一起设置图形的阴影效果。\n",
+ " shadow_blur: Optional[Numeric] = None,\n",
+ "\n",
+ " # 阴影颜色。支持的格式同color。\n",
+ " shadow_color: Optional[str] = None,\n",
+ "\n",
+ " # 阴影水平方向��的偏移距离。\n",
+ " shadow_offset_x: Numeric = 0,\n",
+ "\n",
+ " # 阴影垂直方向上的偏移距离。\n",
+ " shadow_offset_y: Numeric = 0,\n",
+ "\n",
+ " # 图形透明度。支持从 0 到 1 的数字,为 0 时不绘制该图形。\n",
+ " opacity: Optional[Numeric] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fe13a1be",
+ "metadata": {},
+ "source": [
+ "#### 案例\n",
+ "##### Bar 基础条图"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "aacf1d1b",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-02-04T10:08:24.086806Z",
+ "start_time": "2024-02-04T10:08:13.999135Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import Bar\n",
+ "\n",
+ "# 随机生成数据\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# 导入输出图片工具\n",
+ "from pyecharts.render import make_snapshot\n",
+ "# 使用snapshot-selenium 渲染图片\n",
+ "from snapshot_selenium import snapshot\n",
+ "\n",
+ "# 创建 条图\n",
+ "bar = Bar()\n",
+ "\n",
+ "# 添加数据\n",
+ "bar.add_xaxis(Faker.choose())\n",
+ "bar.add_yaxis(\"商家A\", Faker.values())\n",
+ "bar.add_yaxis(\"商家B\", Faker.values())\n",
+ "\n",
+ "# 配置标题\n",
+ "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"Bar-基本示例\", subtitle=\"我是副标题\"))\n",
+ "\n",
+ "# 展示\n",
+ "# bar.render_notebook()\n",
+ "\n",
+ "make_snapshot(snapshot, bar.render(), './测试.png')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a03b5c44",
+ "metadata": {},
+ "source": [
+ "##### Bar-MarkLine"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "df969e84",
+ "metadata": {},
+ "source": [
+ "**单条标记线**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "681f8d76",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-25T17:03:37.832588Z",
+ "start_time": "2023-11-25T17:03:37.819864Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import Bar\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# 创建 条图\n",
+ "bar = Bar()\n",
+ "\n",
+ "# 添加数据\n",
+ "bar.add_xaxis(Faker.choose())\n",
+ "bar.add_yaxis(\"商家A\", Faker.values())\n",
+ "bar.add_yaxis(\"商家B\", Faker.values())\n",
+ "\n",
+ "# 配置标题\n",
+ "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"Bar-MarkLine(自定义)\"))\n",
+ "\n",
+ "# 配置标记线\n",
+ "bar.set_series_opts(\n",
+ " markline_opts=opts.MarkLineOpts(\n",
+ " data=[opts.MarkLineItem(y=80, name=\"合格指标\")]\n",
+ " ),\n",
+ ")\n",
+ "\n",
+ "# 展示\n",
+ "bar.render_notebook()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5b052c1a",
+ "metadata": {},
+ "source": [
+ "**多条标记线**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "e68ead39",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-25T17:08:12.043976Z",
+ "start_time": "2023-11-25T17:08:12.023739Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import Bar\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# 创建 条图\n",
+ "bar = Bar()\n",
+ "\n",
+ "# 添加数据\n",
+ "bar.add_xaxis(Faker.choose())\n",
+ "bar.add_yaxis(\"商家A\", Faker.values())\n",
+ "bar.add_yaxis(\"商家B\", Faker.values())\n",
+ "\n",
+ "# 配置标题\n",
+ "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"Bar-MarkLine(指定类型)\"))\n",
+ "\n",
+ "# 系列配置项\n",
+ "bar.set_series_opts(\n",
+ "\n",
+ " # 不显示标签\n",
+ " label_opts=opts.LabelOpts(is_show=False),\n",
+ "\n",
+ " # 配置多条标记线\n",
+ " markline_opts=opts.MarkLineOpts(\n",
+ " data=[\n",
+ " opts.MarkLineItem(type_=\"min\", name=\"最小值\"),\n",
+ " opts.MarkLineItem(type_=\"max\", name=\"最大值\"),\n",
+ " opts.MarkLineItem(type_=\"average\", name=\"平均值\"),\n",
+ " ]\n",
+ " ),\n",
+ ")\n",
+ "\n",
+ "# 展示\n",
+ "bar.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "70490873",
+ "metadata": {},
+ "source": [
+ "##### 直方图"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "dc2ecb6d",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-25T17:10:25.606016Z",
+ "start_time": "2023-11-25T17:10:25.574644Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import Bar\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# 创建 条图\n",
+ "bar = Bar()\n",
+ "\n",
+ "# 添加数据\n",
+ "bar.add_xaxis(Faker.choose())\n",
+ "bar.add_yaxis(\"商家A\", Faker.values(), \n",
+ " category_gap=0, # 设置 各数据之间,间隙为0\n",
+ " color=Faker.rand_color()) # 随机生成颜色\n",
+ "\n",
+ "# 配置标题\n",
+ "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"Bar-直方图\"))\n",
+ "\n",
+ "# 展示\n",
+ "bar.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "393e6dd8",
+ "metadata": {},
+ "source": [
+ "##### 翻转XY轴"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "8192e37e",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-25T17:12:11.610024Z",
+ "start_time": "2023-11-25T17:12:11.585937Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import Bar\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# 创建 条图\n",
+ "bar = Bar()\n",
+ "\n",
+ "# 添加数据\n",
+ "bar.add_xaxis(Faker.choose())\n",
+ "bar.add_yaxis(\"商家A\", Faker.values())\n",
+ "bar.add_yaxis(\"商家B\", Faker.values())\n",
+ "\n",
+ "# 翻转 XY轴\n",
+ "bar.reversal_axis()\n",
+ "\n",
+ "# 配置标签位置\n",
+ "bar.set_series_opts(label_opts=opts.LabelOpts(position=\"right\"))\n",
+ "\n",
+ "# 配置标题\n",
+ "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"Bar-翻转XY轴\"))\n",
+ "\n",
+ "# 展示\n",
+ "bar.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9c51c8a8",
+ "metadata": {},
+ "source": [
+ "##### 堆叠图"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "7550a703",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-25T17:12:57.633540Z",
+ "start_time": "2023-11-25T17:12:57.614588Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import Bar\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# 创建 条图\n",
+ "bar = Bar()\n",
+ "\n",
+ "# 添加数据\n",
+ "bar.add_xaxis(Faker.choose())\n",
+ "bar.add_yaxis(\"商家A\", Faker.values(), stack=\"stack1\")\n",
+ "bar.add_yaxis(\"商家B\", Faker.values(), stack=\"stack1\")\n",
+ "\n",
+ "# 不显示标签\n",
+ "bar.set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n",
+ "\n",
+ "# 设置标题\n",
+ "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"Bar-堆叠数据(全部)\"))\n",
+ "\n",
+ "# 展示\n",
+ "bar.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "baf19a0d",
+ "metadata": {},
+ "source": [
+ "### 折线图 Line"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f6f0f30b",
+ "metadata": {},
+ "source": [
+ "> 源码 class pyecharts.charts.Line(RectChart) 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e63241db",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Line(\n",
+ " # 初始化配置项,参考 `global_options.InitOpts`\n",
+ " init_opts: opts.InitOpts = opts.InitOpts()\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0561480d",
+ "metadata": {},
+ "source": [
+ "> 源码 func pyecharts.charts.Line.add_yaxis 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "03453c38",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def add_yaxis(\n",
+ " # 系列名称,用于 tooltip 的显示,legend 的图例筛选。\n",
+ " series_name: str,\n",
+ "\n",
+ " # 系列数据\n",
+ " y_axis: types.Sequence[types.Union[opts.LineItem, dict]],\n",
+ "\n",
+ " # 是否选中图例\n",
+ " is_selected: bool = True,\n",
+ "\n",
+ " # 是否连接空数据,空数据使用 `None` 填充\n",
+ " is_connect_nones: bool = False,\n",
+ "\n",
+ " # 使用的 x 轴的 index,在单个图表实例中存在多个 x 轴的时候有用。\n",
+ " xaxis_index: Optional[Numeric] = None,\n",
+ "\n",
+ " # 使用的 y 轴的 index,在单个图表实例中存在多个 y 轴的时候有用。\n",
+ " yaxis_index: Optional[Numeric] = None,\n",
+ "\n",
+ " # 系列 label 颜色\n",
+ " color: Optional[str] = None,\n",
+ "\n",
+ " # 是否显示 symbol, 如果 false 则只有在 tooltip hover 的时候显示。\n",
+ " is_symbol_show: bool = True,\n",
+ "\n",
+ " # 标记的图形。\n",
+ " # ECharts 提供的标记类型包括 'circle', 'rect', 'roundRect', 'triangle', \n",
+ " # 'diamond', 'pin', 'arrow', 'none'\n",
+ " # 可以通过 'image://url' 设置为图片,其中 URL 为图片的链接,或者 dataURI。\n",
+ " symbol: Optional[str] = None,\n",
+ "\n",
+ " # 标记的大小,可以设置成诸如 10 这样单一的数字,也可以用数组分开表示宽和高,\n",
+ " # 例如 [20, 10] 表示标记宽为 20,高为 10。\n",
+ " symbol_size: Union[Numeric, Sequence] = 4,\n",
+ "\n",
+ " # 数据堆叠,同个类目轴上系列配置相同的 stack 值可以堆叠放置。\n",
+ " stack: Optional[str] = None,\n",
+ "\n",
+ " # 是否平滑曲线\n",
+ " is_smooth: bool = False,\n",
+ "\n",
+ " # 是否裁剪超出坐标系部分的图形。折线图:裁掉所有超出坐标系的折线部分,拐点图形的逻辑按照散点图处理\n",
+ " is_clip: bool = True,\n",
+ "\n",
+ " # 是否显示成阶梯图\n",
+ " is_step: bool = False,\n",
+ "\n",
+ " # 是否开启 hover 在拐点标志上的提示动画效果。\n",
+ " is_hover_animation: bool = True,\n",
+ "\n",
+ " # 折线图所有图形的 zlevel 值。\n",
+ " # zlevel用于 Canvas 分层,不同zlevel值的图形会放置在不同的 Canvas 中,Canvas 分层是一种常见的优化手段。\n",
+ " # zlevel 大的 Canvas 会放在 zlevel 小的 Canvas 的上面。\n",
+ " z_level: types.Numeric = 0,\n",
+ "\n",
+ " # 折线图组件的所有图形的z值。控制图形的前后顺序。z值小的图形会被z值大的图形覆盖。\n",
+ " # z 相比 zlevel 优先级更低,而且不会创建新的 Canvas。\n",
+ " z: types.Numeric = 0,\n",
+ "\n",
+ " # 标记点配置项,参考 `series_options.MarkPointOpts`\n",
+ " markpoint_opts: Union[opts.MarkPointOpts, dict, None] = None,\n",
+ "\n",
+ " # 标记线配置项,参考 `series_options.MarkLineOpts`\n",
+ " markline_opts: Union[opts.MarkLineOpts, dict, None] = None,\n",
+ "\n",
+ " # 提示框组件配置项,参考 `series_options.TooltipOpts`\n",
+ " tooltip_opts: Union[opts.TooltipOpts, dict, None] = None,\n",
+ "\n",
+ " # 标签配置项,参考 `series_options.LabelOpts`\n",
+ " label_opts: Union[opts.LabelOpts, dict] = opts.LabelOpts(),\n",
+ "\n",
+ " # 线样式配置项,参考 `series_options.LineStyleOpts`\n",
+ " linestyle_opts: Union[opts.LineStyleOpts, dict] = opts.LineStyleOpts(),\n",
+ "\n",
+ " # 填充区域配置项,参考 `series_options.AreaStyleOpts`\n",
+ " areastyle_opts: Union[opts.AreaStyleOpts, dict] = opts.AreaStyleOpts(),\n",
+ "\n",
+ " # 图元样式配置项,参考 `series_options.ItemStyleOpts`\n",
+ " itemstyle_opts: Union[opts.ItemStyleOpts, dict, None] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f522993f",
+ "metadata": {},
+ "source": [
+ "#### LineItem:折线图数据项"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91713ffa",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class LineItem(\n",
+ " # 数据项名称。\n",
+ " name: Union[str, Numeric] = None,\n",
+ "\n",
+ " # 数据项的值\n",
+ " value: Union[str, Numeric] = None,\n",
+ "\n",
+ " # 单个数据标记的图形。\n",
+ " symbol: Optional[str] = None,\n",
+ "\n",
+ " # 单个数据标记的大小\n",
+ " symbol_size: Union[Sequence[Numeric], Numeric] = None,\n",
+ "\n",
+ " # 单个数据标记的旋转角度(而非弧度)。\n",
+ " symbol_rotate: Optional[Numeric] = None,\n",
+ "\n",
+ " # 如果 symbol 是 path:// 的形式,是否在缩放时保持该图形的长宽比。\n",
+ " symbol_keep_aspect: bool = False,\n",
+ "\n",
+ " # 单个数据标记相对于原本位置的偏移。\n",
+ " symbol_offset: Optional[Sequence] = None,\n",
+ "\n",
+ " # 标签配置项,参考 `series_options.LabelOpts`\n",
+ " label_opts: Union[LabelOpts, dict, None] = None,\n",
+ "\n",
+ " # 图元样式配置项,参考 `series_options.ItemStyleOpts`\n",
+ " itemstyle_opts: Union[ItemStyleOpts, dict, None] = None,\n",
+ "\n",
+ " # 提示框组件配置项,参考 `series_options.TooltipOpts`\n",
+ " tooltip_opts: Union[TooltipOpts, dict, None] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e6db678a",
+ "metadata": {},
+ "source": [
+ "#### 案例\n",
+ "##### 基本折线"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "230d51a6",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-26T08:50:08.707728Z",
+ "start_time": "2023-11-26T08:50:08.695694Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import pyecharts.options as opts\n",
+ "from pyecharts.charts import Line\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# 创建 折线对象\n",
+ "line = Line()\n",
+ "\n",
+ "# 不显示 y轴分割线\n",
+ "line.set_global_opts(\n",
+ " yaxis_opts=opts.AxisOpts(\n",
+ " splitline_opts=opts.SplitLineOpts(is_show=False),\n",
+ " ),\n",
+ ")\n",
+ "\n",
+ "# 添加数据\n",
+ "line.add_xaxis(xaxis_data=Faker.choose())\n",
+ "line.add_yaxis(series_name=\"商家A\",y_axis=Faker.values())\n",
+ "line.add_yaxis(series_name=\"商家B\",y_axis=Faker.values())\n",
+ "\n",
+ "# 展示\n",
+ "line.render_notebook()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cb0d203d",
+ "metadata": {},
+ "source": [
+ "##### 圆滑线条"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "6c9f9cac",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-02-04T13:15:19.364185Z",
+ "start_time": "2024-02-04T13:15:19.346844Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import pyecharts.options as opts\n",
+ "from pyecharts.charts import Line\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# 创建 折线图对象\n",
+ "line = Line()\n",
+ "\n",
+ "# 添加 数据\n",
+ "line.add_xaxis(Faker.choose())\n",
+ "line.add_yaxis(\"商家A\", Faker.values(), is_smooth=True)\n",
+ "line.add_yaxis(\"商家B\", Faker.values(), is_smooth=True)\n",
+ "\n",
+ "# 添加主题\n",
+ "line.set_global_opts(title_opts=opts.TitleOpts(title=\"Line-smooth\"))\n",
+ "\n",
+ "# 展示\n",
+ "line.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "555ffbca",
+ "metadata": {},
+ "source": [
+ "##### Line-MarkPoint"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "f330bf11",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-26T09:01:30.787210Z",
+ "start_time": "2023-11-26T09:01:30.772857Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import pyecharts.options as opts\n",
+ "from pyecharts.charts import Line\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# 随机生成数据\n",
+ "x, y = Faker.choose(), Faker.values()\n",
+ "\n",
+ "# 创建 折线对象\n",
+ "line= Line()\n",
+ "\n",
+ "# 添加 数据\n",
+ "line.add_xaxis(x)\n",
+ "line.add_yaxis(\"商家A\", y,\n",
+ " \n",
+ " # 配置 标记点\n",
+ " markpoint_opts=opts.MarkPointOpts(\n",
+ " data=[opts.MarkPointItem(name=\"自定义标记点\", coord=[x[2], y[2]], value=y[2])]\n",
+ " ),\n",
+ ")\n",
+ "\n",
+ "# 添加标题\n",
+ "line.set_global_opts(title_opts=opts.TitleOpts(title=\"Line-MarkPoint(自定义)\"))\n",
+ "\n",
+ "# 展示\n",
+ "line.render_notebook()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d4e95dba",
+ "metadata": {},
+ "source": [
+ "##### 折线图堆叠"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "e84f6d47",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-26T09:13:07.818652Z",
+ "start_time": "2023-11-26T09:13:07.793167Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import pyecharts.options as opts\n",
+ "from pyecharts.charts import Line\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# x轴 数据\n",
+ "x_data = [\"周一\", \"周二\", \"周三\", \"周四\", \"周五\", \"周六\", \"周日\"]\n",
+ "data_type = Faker.choose()\n",
+ "\n",
+ "# 创建 折线图对象\n",
+ "line = Line()\n",
+ "\n",
+ "# 添加数据\n",
+ "line.add_xaxis(xaxis_data=x_data)\n",
+ "line.add_yaxis(\n",
+ " series_name=data_type[0], # 名称\n",
+ " stack=\"总量\", # 堆叠效果\n",
+ " y_axis=Faker.values(), # 生成数据\n",
+ " label_opts=opts.LabelOpts(is_show=False), # 不显示分割线\n",
+ ")\n",
+ "line.add_yaxis(\n",
+ " series_name=data_type[1],\n",
+ " stack=\"总量\",\n",
+ " y_axis=Faker.values(),\n",
+ " label_opts=opts.LabelOpts(is_show=False),\n",
+ ")\n",
+ "line.add_yaxis(\n",
+ " series_name=data_type[2],\n",
+ " stack=\"总量\",\n",
+ " y_axis=Faker.values(),\n",
+ " label_opts=opts.LabelOpts(is_show=False),\n",
+ ")\n",
+ "line.add_yaxis(\n",
+ " series_name=data_type[3],\n",
+ " stack=\"总量\",\n",
+ " y_axis=Faker.values(),\n",
+ " label_opts=opts.LabelOpts(is_show=False),\n",
+ ")\n",
+ "line.add_yaxis(\n",
+ " series_name=data_type[4],\n",
+ " stack=\"总量\",\n",
+ " y_axis=Faker.values(),\n",
+ " label_opts=opts.LabelOpts(is_show=False),\n",
+ ")\n",
+ "\n",
+ "\n",
+ "line.set_global_opts(\n",
+ " \n",
+ " # 添加 标题\n",
+ " title_opts=opts.TitleOpts(title=\"折线图堆叠\"),\n",
+ " \n",
+ " # 设置 提示框 -- 碰到 坐标轴线 触发\n",
+ " tooltip_opts=opts.TooltipOpts(trigger=\"axis\"),\n",
+ " \n",
+ " # 配置 y轴\n",
+ " yaxis_opts=opts.AxisOpts(\n",
+ " type_=\"value\", # 类型:数值\n",
+ " axistick_opts=opts.AxisTickOpts(is_show=False), # 不显示刻度\n",
+ " splitline_opts=opts.SplitLineOpts(is_show=True), # 显示 分割线\n",
+ " ),\n",
+ " \n",
+ " \n",
+ " # 配置 x轴,类型:类别\n",
+ " xaxis_opts=opts.AxisOpts(type_=\"category\"),\n",
+ ")\n",
+ "\n",
+ "# 展示\n",
+ "line.render_notebook()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4365560e",
+ "metadata": {},
+ "source": [
+ "##### 面积图"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "32fb2ede",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-26T09:23:44.929861Z",
+ "start_time": "2023-11-26T09:23:44.909618Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import pyecharts.options as opts\n",
+ "from pyecharts.charts import Line\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# x轴数据\n",
+ "x_data = [\"周一\", \"周二\", \"周三\", \"周四\", \"周五\", \"周六\", \"周日\"]\n",
+ "\n",
+ "# 生成类别\n",
+ "data_type = Faker.choose()\n",
+ "\n",
+ "# 创建 折线图对象\n",
+ "line = Line()\n",
+ "\n",
+ "# 添加数据\n",
+ "line.add_xaxis(xaxis_data=x_data)\n",
+ "line.add_yaxis(\n",
+ " series_name=data_type[0], # 类别名称\n",
+ " stack=\"总量\", # 堆叠\n",
+ " y_axis=Faker.values(), # 数据\n",
+ " label_opts=opts.LabelOpts(is_show=False), # 不显示���签\n",
+ " areastyle_opts=opts.AreaStyleOpts(opacity=0.5), # 配置 区域样式\n",
+ ")\n",
+ "line.add_yaxis(\n",
+ " series_name=data_type[1],\n",
+ " stack=\"总量\",\n",
+ " y_axis=Faker.values(),\n",
+ " label_opts=opts.LabelOpts(is_show=False),\n",
+ " areastyle_opts=opts.AreaStyleOpts(opacity=0.5),\n",
+ ")\n",
+ "line.add_yaxis(\n",
+ " series_name=data_type[2],\n",
+ " stack=\"总量\",\n",
+ " y_axis=Faker.values(),\n",
+ " label_opts=opts.LabelOpts(is_show=False),\n",
+ " areastyle_opts=opts.AreaStyleOpts(opacity=0.5),\n",
+ ")\n",
+ "line.add_yaxis(\n",
+ " series_name=data_type[3],\n",
+ " stack=\"总量\",\n",
+ " y_axis=Faker.values(),\n",
+ " label_opts=opts.LabelOpts(is_show=False),\n",
+ " areastyle_opts=opts.AreaStyleOpts(opacity=0.5),\n",
+ ")\n",
+ "line.add_yaxis(\n",
+ " series_name=data_type[4],\n",
+ " stack=\"总量\",\n",
+ " y_axis=Faker.values(),\n",
+ " label_opts=opts.LabelOpts(is_show=False,position=\"top\"),\n",
+ " areastyle_opts=opts.AreaStyleOpts(opacity=0.5),\n",
+ ")\n",
+ "\n",
+ "\n",
+ "line.set_global_opts(\n",
+ " \n",
+ " # 添加标题\n",
+ " title_opts=opts.TitleOpts(title=\"折线图堆叠\"),\n",
+ " \n",
+ " # 碰到轴线 显示提示框 \n",
+ " tooltip_opts=opts.TooltipOpts(trigger=\"axis\"),\n",
+ " \n",
+ " # 配置 y轴\n",
+ " yaxis_opts=opts.AxisOpts(\n",
+ " type_=\"value\", # 类型为 数值\n",
+ " axistick_opts=opts.AxisTickOpts(is_show=False), # 不显示刻度\n",
+ " splitline_opts=opts.SplitLineOpts(is_show=True), # 显示 分割线\n",
+ " ),\n",
+ " \n",
+ " # 配置 x轴\n",
+ " xaxis_opts=opts.AxisOpts(type_=\"category\", # 类型为 种类\n",
+ " boundary_gap=False), # 填充边界\n",
+ ")\n",
+ "line.render_notebook()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a03e2129",
+ "metadata": {},
+ "source": [
+ "### 饼图 Pie\n",
+ "> 源码 class pyecharts.charts.Pie 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fd238d76",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Pie(\n",
+ " # 初始化配置项,参考 `global_options.InitOpts`\n",
+ " init_opts: opts.InitOpts = opts.InitOpts()\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c4a0a81d",
+ "metadata": {},
+ "source": [
+ "> 源码 func pyecharts.charts.Pie.add 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3f598840",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def add(\n",
+ " # 系列名称,用于 tooltip 的显示,legend 的图例筛选。\n",
+ " series_name: str,\n",
+ "\n",
+ " # 系列数据项,格式为 [(key1, value1), (key2, value2)]\n",
+ " data_pair: types.Sequence[types.Union[types.Sequence, opts.PieItem, dict]],\n",
+ "\n",
+ " # 系列 label 颜色\n",
+ " color: Optional[str] = None,\n",
+ "\n",
+ " # 饼图的半径,数组的第一项是内半径,第二项是外半径\n",
+ " # 默认设置成百分比,相对于容器高宽中较小的一项的一半\n",
+ " radius: Optional[Sequence] = None,\n",
+ "\n",
+ " # 饼图的中心(圆心)坐标,数组的第一项是横坐标,第二项是纵坐标\n",
+ " # 默认设置成百分比,设置成百分比时第一项是相对于容器宽度,第二项是相对于容器高度\n",
+ " center: Optional[Sequence] = None,\n",
+ "\n",
+ " # 是否展示成南丁格尔图,通过半径区分数据大小,有'radius'和'area'两种模式。\n",
+ " # radius:扇区圆心角展现数据的百分比,半径展现数据的大小\n",
+ " # area:所有扇区圆心角相同,仅通过半径展现数据大小\n",
+ " rosetype: Optional[str] = None,\n",
+ "\n",
+ " # 饼图的扇区是否是顺时针排布。\n",
+ " is_clockwise: bool = True,\n",
+ "\n",
+ " # 标签配置项,参考 `series_options.LabelOpts`\n",
+ " label_opts: Union[opts.LabelOpts, dict] = opts.LabelOpts(),\n",
+ "\n",
+ " # 提示框组件配置项,参考 `series_options.TooltipOpts`\n",
+ " tooltip_opts: Union[opts.TooltipOpts, dict, None] = None,\n",
+ "\n",
+ " # 图元样式配置项,参考 `series_options.ItemStyleOpts`\n",
+ " itemstyle_opts: Union[opts.ItemStyleOpts, dict, None] = None,\n",
+ "\n",
+ " # 可以定义 data 的哪个维度被编码成什么。\n",
+ " encode: types.Union[types.JSFunc, dict, None] = None,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fe848632",
+ "metadata": {},
+ "source": [
+ "#### PieItem:饼图数据项"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "194690e0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class PieItem(\n",
+ " # 数据项名称。\n",
+ " name: Optional[str] = None,\n",
+ "\n",
+ " # 数据值。\n",
+ " value: Optional[Numeric] = None,\n",
+ "\n",
+ " # 该数据项是否被选中。\n",
+ " is_selected: bool = False,\n",
+ "\n",
+ " # 标签配置项,参考 `series_options.LabelOpts`\n",
+ " label_opts: Union[LabelOpts, dict, None] = None,\n",
+ "\n",
+ " # 图元样式配置项,参考 `series_options.ItemStyleOpts`\n",
+ " itemstyle_opts: Union[ItemStyleOpts, dict, None] = None,\n",
+ "\n",
+ " # 提示框组件配置项,参考 `series_options.TooltipOpts`\n",
+ " tooltip_opts: Union[TooltipOpts, dict, None] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "19ebdc94",
+ "metadata": {},
+ "source": [
+ "#### PieLabelLineOpts: 饼图标签的视觉引导线样式"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "357fb134",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class PieLabelLineOpts(\n",
+ " # 是否显示视觉引导线。\n",
+ " is_show: bool = True,\n",
+ "\n",
+ " # 视觉引导线第一段的长度。\n",
+ " length: Numeric = None,\n",
+ "\n",
+ " # 视觉引导项第二段的长度。\n",
+ " length_2: Numeric = None,\n",
+ "\n",
+ " # 是否平滑视觉引导线,默认不平滑,可以设置成 true 平滑显示。\n",
+ " # 也可以设置为 0 到 1 的值,表示平滑程度。\n",
+ " smooth: Union[bool, Numeric] = False,\n",
+ "\n",
+ " # 线条样式,参考 `LineStyleOpts`\n",
+ " linestyle_opts: Union[LineStyleOpts, dict, None] = None,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5c5322d3",
+ "metadata": {},
+ "source": [
+ "#### 案例\n",
+ "##### 基础饼图"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "ea7af312",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-26T09:28:49.412301Z",
+ "start_time": "2023-11-26T09:28:49.399026Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import Pie\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# 创建 饼图\n",
+ "pie = Pie()\n",
+ "\n",
+ "# 添加数据\n",
+ "pie.add(\"\", [list(z) for z in zip(Faker.choose(), Faker.values())])\n",
+ "\n",
+ "# 添加标题\n",
+ "pie.set_global_opts(title_opts=opts.TitleOpts(title=\"Pie-基本示例\"))\n",
+ "\n",
+ "# 添加标签\n",
+ "pie.set_series_opts(label_opts=opts.LabelOpts(formatter=\"{b}: {c}\"))\n",
+ "\n",
+ "# 展示\n",
+ "pie.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a0fac9bf",
+ "metadata": {},
+ "source": [
+ "##### 改变颜色"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "b26b26e6",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-26T09:31:59.206372Z",
+ "start_time": "2023-11-26T09:31:59.192895Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import Pie\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# 创建 饼图对象\n",
+ "pie = Pie()\n",
+ "\n",
+ "# 添加数据\n",
+ "pie.add(\"\", [list(z) for z in zip(Faker.choose(), Faker.values())])\n",
+ "\n",
+ "# 设置颜色\n",
+ "pie.set_colors([\"blue\", \"green\", \"yellow\", \"red\", \"pink\", \"orange\", \"purple\"])\n",
+ "\n",
+ "# 配置标题\n",
+ "pie.set_global_opts(title_opts=opts.TitleOpts(title=\"Pie-设置颜色\"))\n",
+ "\n",
+ "# 配置标签\n",
+ "pie.set_series_opts(label_opts=opts.LabelOpts(formatter=\"{b}: {c}\"))\n",
+ "\n",
+ "# 展示\n",
+ "pie.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "866e5935",
+ "metadata": {},
+ "source": [
+ "##### 空心饼图"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "95193ee1",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-26T09:33:59.803685Z",
+ "start_time": "2023-11-26T09:33:59.790808Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import Pie\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# 创建 饼图\n",
+ "pie = Pie()\n",
+ "\n",
+ "# 添加数据\n",
+ "pie.add(\"\", \n",
+ " [list(z) for z in zip(Faker.choose(), Faker.values())], # 数据\n",
+ " radius=['50%', '70%']) # 设置半径\n",
+ "\n",
+ "# 配置标题\n",
+ "pie.set_global_opts(title_opts=opts.TitleOpts(title=\"Pie-基本示例\"))\n",
+ "\n",
+ "# 配置标签\n",
+ "pie.set_series_opts(label_opts=opts.LabelOpts(formatter=\"{b}: {c}\"))\n",
+ "\n",
+ "# 展示\n",
+ "pie.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "865cfc80",
+ "metadata": {},
+ "source": [
+ "##### 玫瑰形饼图"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "c91813f6",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-02-04T16:33:59.992808Z",
+ "start_time": "2024-02-04T16:33:59.977998Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import Pie\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# 创建 饼图对象\n",
+ "pie = Pie()\n",
+ "\n",
+ "# 添加数据\n",
+ "pie.add(\"\", \n",
+ " [list(z) for z in zip(Faker.choose(), Faker.values())], # 数据\n",
+ " rosetype=\"radius\") # 玫瑰型\n",
+ "\n",
+ "# 配置标题\n",
+ "pie.set_global_opts(title_opts=opts.TitleOpts(title=\"Pie-基本示例\"))\n",
+ "\n",
+ "# 配置标签\n",
+ "pie.set_series_opts(label_opts=opts.LabelOpts(formatter=\"{b}: {c}\"))\n",
+ "\n",
+ "# 展示\n",
+ "pie.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3731211b",
+ "metadata": {},
+ "source": [
+ "### 散点图 Scatter"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e2cc0a51",
+ "metadata": {},
+ "source": [
+ "> 源码 class pyecharts.charts.Scatter(RectChart) 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d135aaa7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Scatter(\n",
+ " # 初始化配置项,参考 `global_options.InitOpts`\n",
+ " init_opts: opts.InitOpts = opts.InitOpts()\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dc3dc36e",
+ "metadata": {},
+ "source": [
+ "> 源码 func pyecharts.charts.Scatter.add_yaxis 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "faf382c9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def add_yaxis(\n",
+ " # 系列名称,用于 tooltip 的显示,legend 的图例筛选。\n",
+ " series_name: str,\n",
+ "\n",
+ " # 系列数据\n",
+ " y_axis: Sequence,\n",
+ "\n",
+ " # 是否选中图例\n",
+ " is_selected: bool = True,\n",
+ "\n",
+ " # 使用的 x 轴的 index,在单个图表实例中存在多个 x 轴的时候有用。\n",
+ " xaxis_index: Optional[Numeric] = None,\n",
+ "\n",
+ " # 使用的 y 轴的 index,在单个图表实例中存在多个 y 轴的时候有用。\n",
+ " yaxis_index: Optional[Numeric] = None,\n",
+ "\n",
+ " # 系列 label 颜色\n",
+ " color: Optional[str] = None,\n",
+ "\n",
+ " # 标记的图形。\n",
+ " # ECharts 提供的标记类型包括 'circle', 'rect', 'roundRect', 'triangle', \n",
+ " # 'diamond', 'pin', 'arrow', 'none'\n",
+ " # 可以通过 'image://url' 设置为图片,其中 URL 为图片的链接,或者 dataURI。\n",
+ " symbol: Optional[str] = None,\n",
+ "\n",
+ " # 标记的大小,可以设置成诸如 10 这样单一的数字,也可以用数组分开表示宽和高,\n",
+ " # 例如 [20, 10] 表示标记宽为 20,高为 10。\n",
+ " symbol_size: Numeric = 10,\n",
+ "\n",
+ " # 标记的旋转角度。注意在 markLine 中当 symbol 为 'arrow' 时会忽略 symbolRotate 强制设置为切线的角度。\n",
+ " symbol_rotate: types.Optional[types.Numeric] = None,\n",
+ "\n",
+ " # 标签配置项,参考 `series_options.LabelOpts`\n",
+ " label_opts: Union[opts.LabelOpts, dict] = opts.LabelOpts(position=\"right\"),\n",
+ "\n",
+ " # 标记点配置项,参考 `series_options.MarkPointOpts`\n",
+ " markpoint_opts: Union[opts.MarkPointOpts, dict, None] = None,\n",
+ "\n",
+ " # 标记线配置项,参考 `series_options.MarkLineOpts`\n",
+ " markline_opts: Union[opts.MarkLineOpts, dict, None] = None,\n",
+ "\n",
+ " # 图表标域,常用于标记图表中某个范围的数据,参考 `series_options.MarkAreaOpts`\n",
+ " markarea_opts: types.MarkArea = None,\n",
+ "\n",
+ " # 提示框组件配置项,参考 `series_options.TooltipOpts`\n",
+ " tooltip_opts: Union[opts.TooltipOpts, dict, None] = None,\n",
+ "\n",
+ " # 图元样式配置项,参考 `series_options.ItemStyleOpts`\n",
+ " itemstyle_opts: Union[opts.ItemStyleOpts, dict, None] = None,\n",
+ "\n",
+ " # 可以定义 data 的哪个维度被编码成什么。\n",
+ " encode: types.Union[types.JSFunc, dict, None] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1612b05a",
+ "metadata": {},
+ "source": [
+ "#### ScatterItem:散点图数据项"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "36e25171",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class ScatterItem(\n",
+ " # 数据项名称。\n",
+ " name: Union[str, Numeric] = None,\n",
+ "\n",
+ " # 数据项值。\n",
+ " value: Union[str, Numeric] = None,\n",
+ "\n",
+ " # 单个数据标记的图形。\n",
+ " # ECharts 提供的标记类型包括 \n",
+ " # 'circle', 'rect', 'roundRect', 'triangle', 'diamond', 'pin', 'arrow', 'none'\n",
+ " # 可以通过 'image://url' 设置为图片,其中 URL 为图片的链接,或者 dataURI。\n",
+ " # 可以通过 'path://' 将图标设置为任意的矢量路径。\n",
+ " symbol: Optional[str] = None,\n",
+ "\n",
+ " # 单个数据标记的大小,可以设置成诸如 10 这样单一的数字\n",
+ " # 也可以用数组分开表示宽和高,例如 [20, 10] 表示标记宽为20,高为10。\n",
+ " symbol_size: Union[Sequence[Numeric], Numeric] = None,\n",
+ "\n",
+ " # 单个数据标记的旋转角度(而非弧度)。正\n",
+ " symbol_rotate: Optional[Numeric] = None,\n",
+ "\n",
+ " # 如果 symbol 是 path:// 的形式,是否在缩放时保持该图形的长宽比。\n",
+ " symbol_keep_aspect: bool = False,\n",
+ "\n",
+ " # 单个数据标记相对于原本位置的偏移。\n",
+ " symbol_offset: Optional[Sequence] = None,\n",
+ "\n",
+ " # 标签配置项,参考 `series_options.LabelOpts`\n",
+ " label_opts: Union[LabelOpts, dict, None] = None,\n",
+ "\n",
+ " # 图元样式配置项,参考 `series_options.ItemStyleOpts`\n",
+ " itemstyle_opts: Union[ItemStyleOpts, dict, None] = None,\n",
+ "\n",
+ " # 提示框组件配置项,参考 `series_options.TooltipOpts`\n",
+ " tooltip_opts: Union[TooltipOpts, dict, None] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "04006a14",
+ "metadata": {},
+ "source": [
+ "#### 案例\n",
+ "##### 基本散点图"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "id": "b2386ea7",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-26T10:48:03.795613Z",
+ "start_time": "2023-11-26T10:48:03.782634Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts.charts import Scatter\n",
+ "import pyecharts.options as opts\n",
+ "from random import randint,random\n",
+ "\n",
+ "x = [random()*100 for i in range(100)]\n",
+ "y = [random()*100 for i in range(100)]\n",
+ "\n",
+ "# 创建 散点图对象,并设置宽高\n",
+ "sca = Scatter(init_opts=opts.InitOpts(width=\"500px\", height=\"300px\"))\n",
+ "\n",
+ "# 添加数据\n",
+ "sca.add_xaxis(xaxis_data=x)\n",
+ "sca.add_yaxis(series_name=\"\",y_axis=y, \n",
+ " label_opts=opts.LabelOpts(is_show=False), # 不显示标签\n",
+ " symbol_size = 5 # 散点大小\n",
+ ")\n",
+ "\n",
+ "# x轴类型 -- 数值\n",
+ "sca.set_global_opts(xaxis_opts=opts.AxisOpts(type_=\"value\"),)\n",
+ "\n",
+ "# 展示\n",
+ "sca.render_notebook()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a92fb8b3",
+ "metadata": {},
+ "source": [
+ "##### 视觉映射配置项"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "a17643bc",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-26T10:49:37.097634Z",
+ "start_time": "2023-11-26T10:49:37.080768Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts.charts import Scatter\n",
+ "import pyecharts.options as opts\n",
+ "from random import randint, random\n",
+ "\n",
+ "# 准备数据\n",
+ "x = [randint(1, 100) for i in range(10)]\n",
+ "y = [randint(1, 100) for i in range(10)]\n",
+ "\n",
+ "# 创建 散点图,设置宽高\n",
+ "sca = Scatter(init_opts=opts.InitOpts(width=\"500px\", height=\"300px\"))\n",
+ "\n",
+ "# 添加数据\n",
+ "sca.add_xaxis(xaxis_data=x)\n",
+ "sca.add_yaxis(series_name=\"\", y_axis=y)\n",
+ "\n",
+ "\n",
+ "sca.set_global_opts(\n",
+ " \n",
+ " # x轴 -- 数值类型\n",
+ " xaxis_opts=opts.AxisOpts(type_=\"value\"),\n",
+ " \n",
+ " # 视觉映射配置项\n",
+ " visualmap_opts=opts.VisualMapOpts(type_=\"size\"),\n",
+ ")\n",
+ "\n",
+ "# 展示\n",
+ "sca.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "122d928c",
+ "metadata": {},
+ "source": [
+ "### 箱形图 Boxplot"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b76bffe7",
+ "metadata": {},
+ "source": [
+ "> 源码 class pyecharts.charts.Boxplot(RectChart) 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3f58a727",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Boxplot(\n",
+ " # 初始化配置项,参考 `global_options.InitOpts`\n",
+ " init_opts: opts.InitOpts = opts.InitOpts()\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aacf7367",
+ "metadata": {},
+ "source": [
+ "> 源码 func pyecharts.charts.Boxplot.add_yaxis 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c86018ae",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def add_yaxis(\n",
+ " # 系列名称,用于 tooltip 的显示,legend 的图例筛选。\n",
+ " series_name: str,\n",
+ "\n",
+ " # 系列数据\n",
+ " y_axis: types.Sequence[types.Union[opts.BoxplotItem, dict]],\n",
+ "\n",
+ " # 是否选中图例\n",
+ " is_selected: bool = True,\n",
+ "\n",
+ " # 使用的 x 轴的 index,在单个图表实例中存在多个 x 轴的时候有用。\n",
+ " xaxis_index: Optional[Numeric] = None,\n",
+ "\n",
+ " # 使用的 y 轴的 index,在单个图表实例中存在多个 y 轴的时候有用。\n",
+ " yaxis_index: Optional[Numeric] = None,\n",
+ "\n",
+ " # 标签配置项,参考 `series_options.LabelOpts`\n",
+ " label_opts: Union[opts.LabelOpts, dict] = opts.LabelOpts(),\n",
+ "\n",
+ " # 标记点配置项,参考 `series_options.MarkPointOpts`\n",
+ " markpoint_opts: Union[opts.MarkPointOpts, dict] = opts.MarkPointOpts(),\n",
+ "\n",
+ " # 标记线配置项,参考 `series_options.MarkLineOpts`\n",
+ " markline_opts: Union[opts.MarkLineOpts, dict] = opts.MarkLineOpts(),\n",
+ "\n",
+ " # 提示框组件配置项,参考 `series_options.TooltipOpts`\n",
+ " tooltip_opts: Union[opts.TooltipOpts, dict, None] = None,\n",
+ "\n",
+ " # 图元样式配置项,参考 `series_options.ItemStyleOpts`\n",
+ " itemstyle_opts: Union[opts.ItemStyleOpts, dict, None] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5ee415fc",
+ "metadata": {},
+ "source": [
+ "#### BoxplotItem:箱形图数据项"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1cf046a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class BoxplotItem(\n",
+ " # 数据项名称。\n",
+ " name: Optional[str] = None,\n",
+ "\n",
+ " # 单个数据项的数值。\n",
+ " value: Optional[Numeric] = None,\n",
+ "\n",
+ " # 文本的样式设置,参考 `series_options.LabelOpts`。\n",
+ " label_opts: Union[LabelOpts, dict, None] = None,\n",
+ "\n",
+ " # 图元样式配置项,参考 `series_options.ItemStyleOpts`\n",
+ " itemstyle_opts: Union[ItemStyleOpts, dict, None] = None,\n",
+ "\n",
+ " # 提示框组件配置项,参考 `series_options.TooltipOpts`\n",
+ " tooltip_opts: Union[TooltipOpts, dict, None] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9858af65",
+ "metadata": {},
+ "source": [
+ "#### 案例\n",
+ "##### 单组箱型图"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "f1b71a10",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-02-04T12:28:46.527274Z",
+ "start_time": "2024-02-04T12:28:46.512866Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import Boxplot\n",
+ "\n",
+ "# 准备数据\n",
+ "v1 = [\n",
+ " [890, 810, 810, 820, 800, 770, 760, 740, 750, 760, 910, 920],\n",
+ " [890, 840, 780, 810, 760, 810, 790, 810, 820, 850, 870, 870],\n",
+ " [850, 740, 900, 1070, 930, 850, 950, 980, 980, 880, 1000, 980],\n",
+ " [960, 940, 960, 940, 880, 800, 850, 880, 900, 840, 830, 790],\n",
+ "]\n",
+ "\n",
+ "# 创建 箱型图 对象\n",
+ "c = Boxplot()\n",
+ "\n",
+ "# 添加数据\n",
+ "c.add_xaxis([\"expr1\", \"expr2\", \"expr3\", \"expr4\"])\n",
+ "c.add_yaxis(\"A\", c.prepare_data(v1))\n",
+ "\n",
+ "# 配置标题\n",
+ "c.set_global_opts(title_opts=opts.TitleOpts(title=\"BoxPlot-基本示例\"))\n",
+ "\n",
+ "# 展示\n",
+ "c.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "202675c5",
+ "metadata": {},
+ "source": [
+ "##### 多组箱型图"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "c5ba0463",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-02-04T12:28:34.957949Z",
+ "start_time": "2024-02-04T12:28:34.939123Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import Boxplot\n",
+ "\n",
+ "# 准备数据\n",
+ "v1 = [\n",
+ " [890, 810, 810, 820, 800, 770, 760, 740, 750, 760, 910, 920],\n",
+ " [890, 840, 780, 810, 760, 810, 790, 810, 820, 850, 870, 870],\n",
+ "]\n",
+ "v2 = [\n",
+ " [850, 740, 900, 1070, 930, 850, 950, 980, 980, 880, 1000, 980],\n",
+ " [960, 940, 960, 940, 880, 800, 850, 880, 900, 840, 830, 790],\n",
+ "]\n",
+ "\n",
+ "# 创建 箱型图对象\n",
+ "c = Boxplot()\n",
+ "\n",
+ "# 添加数据\n",
+ "c.add_xaxis([\"expr1\", \"expr2\"])\n",
+ "c.add_yaxis(\"A\", c.prepare_data(v1))\n",
+ "c.add_yaxis(\"B\", c.prepare_data(v2))\n",
+ "\n",
+ "# 设置标题\n",
+ "c.set_global_opts(title_opts=opts.TitleOpts(title=\"BoxPlot-基本示例\"))\n",
+ "\n",
+ "# 展示\n",
+ "c.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5697db1a",
+ "metadata": {},
+ "source": [
+ "### 热力图 HeatMap"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b9d83603",
+ "metadata": {},
+ "source": [
+ "> 源代码 class pyecharts.charts.HeatMap(RectChart) 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6bae254d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class HeatMap(\n",
+ " \n",
+ " # 初始化配置项,参考 `global_options.InitOpts`\n",
+ " init_opts: opts.InitOpts = opts.InitOpts()\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2686a924",
+ "metadata": {},
+ "source": [
+ "> 源代码 func pyecharts.charts.HeatMap.add_yaxis 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8153bf25",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def add_yaxis(\n",
+ " # 系列名称,用于 tooltip 的显示,legend 的图例筛选。\n",
+ " series_name: str,\n",
+ "\n",
+ " # Y 坐标轴数据\n",
+ " yaxis_data: types.Sequence[types.Union[opts.HeatMapItem, dict]],\n",
+ "\n",
+ " # 系列数据项\n",
+ " value: types.Sequence[types.Union[opts.HeatMapItem, dict]],\n",
+ "\n",
+ " # 是否选中图例\n",
+ " is_selected: bool = True,\n",
+ "\n",
+ " # 使用的 x 轴的 index,在单个图表实例中存在多个 x 轴的时候有用。\n",
+ " xaxis_index: Optional[Numeric] = None,\n",
+ "\n",
+ " # 使用的 y 轴的 index,在单个图表实例中存在多个 y 轴的时候有用。\n",
+ " yaxis_index: Optional[Numeric] = None,\n",
+ "\n",
+ " # 标签配置项,参考 `series_options.LabelOpts`\n",
+ " label_opts: Union[opts.LabelOpts, dict] = opts.LabelOpts(),\n",
+ "\n",
+ " # 标记点配置项,参考 `series_options.MarkPointOpts`\n",
+ " markpoint_opts: Union[opts.MarkPointOpts, dict, None] = None,\n",
+ "\n",
+ " # 标记线配置项,参考 `series_options.MarkLineOpts`\n",
+ " markline_opts: Union[opts.MarkLineOpts, dict, None] = None,\n",
+ "\n",
+ " # 提示框组件配置项,参考 `series_options.TooltipOpts`\n",
+ " tooltip_opts: Union[opts.TooltipOpts, dict, None] = None,\n",
+ "\n",
+ " # 图元样式配置项,参考 `series_options.ItemStyleOpts`\n",
+ " itemstyle_opts: Union[opts.ItemStyleOpts, dict, None] = None,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c4ce795c",
+ "metadata": {},
+ "source": [
+ "#### HeatMapItem:热力图数据项"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "627f5539",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class HeatMapItem(\n",
+ " # 数据项名称。\n",
+ " name: Optional[str] = None,\n",
+ "\n",
+ " # 数据项的值。\n",
+ " value: Optional[Sequence] = None,\n",
+ "\n",
+ " # 图元样式配置项,参考 `series_options.ItemStyleOpts`\n",
+ " itemstyle_opts: Union[ItemStyleOpts, dict, None] = None,\n",
+ "\n",
+ " # 提示框组件配置项,参考 `series_options.TooltipOpts`\n",
+ " tooltip_opts: Union[TooltipOpts, dict, None] = None,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c9022544",
+ "metadata": {},
+ "source": [
+ "#### 案例\n",
+ "##### 基础热力图"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "dfd2c7fe",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-02-04T14:10:20.468775Z",
+ "start_time": "2024-02-04T14:10:20.446496Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import random\n",
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import HeatMap\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# 生成数据\n",
+ "value = [[i, j, random.randint(0, 50)] for i in range(24) for j in range(7)]\n",
+ "\n",
+ "# 创建 热力图\n",
+ "hm = HeatMap()\n",
+ "\n",
+ "# 添加数据\n",
+ "hm.add_xaxis(Faker.clock)\n",
+ "hm.add_yaxis(\"series0\", Faker.week, value,\n",
+ " label_opts=opts.LabelOpts(is_show=False))\n",
+ "\n",
+ "hm.set_global_opts(\n",
+ " \n",
+ " # 添加标题\n",
+ " title_opts=opts.TitleOpts(title=\"HeatMap-基本示例\"),\n",
+ " \n",
+ " # 添加视觉映射配置项\n",
+ " visualmap_opts=opts.VisualMapOpts(),\n",
+ ")\n",
+ "\n",
+ "# 展示\n",
+ "hm.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "da7982d0",
+ "metadata": {},
+ "source": [
+ "##### 带值热力图"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "a225ee2f",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-02-04T14:10:23.749894Z",
+ "start_time": "2024-02-04T14:10:23.730681Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import HeatMap\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# 数据\n",
+ "value = [[i, j, random.randint(0, 50)] for i in range(24) for j in range(7)]\n",
+ "\n",
+ "# 创建 热力图对象\n",
+ "hm = HeatMap()\n",
+ "\n",
+ "# 添加数据\n",
+ "hm.add_xaxis(Faker.clock)\n",
+ "hm.add_yaxis(\"series0\", Faker.week, value,\n",
+ " label_opts=opts.LabelOpts(is_show=True, position=\"inside\")) # 显示标签\n",
+ "\n",
+ "hm.set_global_opts(\n",
+ " \n",
+ " # 添加标题\n",
+ " title_opts=opts.TitleOpts(title=\"HeatMap-Label 显示\"),\n",
+ " \n",
+ " # 视觉映射配置项\n",
+ " visualmap_opts=opts.VisualMapOpts(),\n",
+ ")\n",
+ "\n",
+ "# 展示\n",
+ "hm.render_notebook()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b357bc9f",
+ "metadata": {},
+ "source": [
+ "### 涟漪特效散点图 EffectScatter"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f9d56672",
+ "metadata": {},
+ "source": [
+ "> 源码 class pyecharts.charts.EffectScatter(RectChart) 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0077c503",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class EffectScatter(\n",
+ " # 初始化配置项,参考 `global_options.InitOpts`\n",
+ " init_opts: opts.InitOpts = opts.InitOpts()\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "28f2efe4",
+ "metadata": {},
+ "source": [
+ "> 源码 func pyecharts.charts.EffectScatter.add_yaxis 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ba8dc559",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ " def add_yaxis(\n",
+ " # 系列名称,用于 tooltip 的显示,legend 的图例筛选。\n",
+ " series_name: str,\n",
+ "\n",
+ " # 系列数据\n",
+ " y_axis: types.Sequence[types.Union[opts.BoxplotItem, dict]],\n",
+ "\n",
+ " # 是否选中图例\n",
+ " is_selected: bool = True,\n",
+ "\n",
+ " # 使用的 x 轴的 index,在单个图表实例中存在多个 x 轴的时候有用。\n",
+ " xaxis_index: Optional[Numeric] = None,\n",
+ "\n",
+ " # 使用的 y 轴的 index,在单个图表实例中存在多个 y 轴的时候有用。\n",
+ " yaxis_index: Optional[Numeric] = None,\n",
+ "\n",
+ " # 系列 label 颜色\n",
+ " color: Optional[str] = None,\n",
+ "\n",
+ " # 标记图形形状\n",
+ " symbol: Optional[str] = None,\n",
+ "\n",
+ " # 标记的大小\n",
+ " symbol_size: Numeric = 10,\n",
+ "\n",
+ " # 标记的旋转角度。注意在 markLine 中当 symbol 为 'arrow' 时会忽略 symbolRotate 强制设置为切线的角度。\n",
+ " symbol_rotate: types.Optional[types.Numeric] = None,\n",
+ "\n",
+ " # 标签配置项,参考 `series_options.LabelOpts`\n",
+ " label_opts: Union[opts.LabelOpts, dict] = opts.LabelOpts(),\n",
+ "\n",
+ " # 涟漪特效配置项,参考 `series_options.EffectOpts`\n",
+ " effect_opts: Union[opts.EffectOpts, dict] = opts.EffectOpts(),\n",
+ "\n",
+ " # 提示框组件配置项,参考 `series_options.TooltipOpts`\n",
+ " tooltip_opts: Union[opts.TooltipOpts, dict, None] = None,\n",
+ "\n",
+ " # 图元样式配置项,参考 `series_options.ItemStyleOpts`\n",
+ " itemstyle_opts: Union[opts.ItemStyleOpts, dict, None] = None,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5a2a1a88",
+ "metadata": {},
+ "source": [
+ "#### EffectScatterItem:涟漪特效散点图数据项"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6ca3988f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class EffectScatterItem(\n",
+ " # 数据项名称。\n",
+ " name: Union[str, Numeric] = None,\n",
+ "\n",
+ " # 数据项的值\n",
+ " value: Union[str, Numeric] = None,\n",
+ "\n",
+ " # 单个数据标记的图形。\n",
+ " symbol: Optional[str] = None,\n",
+ "\n",
+ " # 单个数据标记的大小\n",
+ " symbol_size: Union[Sequence[Numeric], Numeric] = None,\n",
+ "\n",
+ " # 单个数据标记的旋转角度(而非弧度)。\n",
+ " symbol_rotate: Optional[Numeric] = None,\n",
+ "\n",
+ " # 如果 symbol 是 path:// 的形式,是否在缩放时保持该图形的长宽比。\n",
+ " symbol_keep_aspect: bool = False,\n",
+ "\n",
+ " # 单个数据标记相对于原本位置的偏移。\n",
+ " symbol_offset: Optional[Sequence] = None,\n",
+ "\n",
+ " # 标签配置项,参考 `series_options.LabelOpts`\n",
+ " label_opts: Union[LabelOpts, dict, None] = None,\n",
+ "\n",
+ " # 图元样式配置项,参考 `series_options.ItemStyleOpts`\n",
+ " itemstyle_opts: Union[ItemStyleOpts, dict, None] = None,\n",
+ "\n",
+ " # 提示框组件配置项,参考 `series_options.TooltipOpts`\n",
+ " tooltip_opts: Union[TooltipOpts, dict, None] = None,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "68332e8b",
+ "metadata": {},
+ "source": [
+ "#### 案例"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0900a8ba",
+ "metadata": {},
+ "source": [
+ "##### 圆形涟漪"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "id": "28401062",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-26T11:08:03.022771Z",
+ "start_time": "2023-11-26T11:08:03.006502Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import EffectScatter\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# 创建 涟漪散点图\n",
+ "esca = EffectScatter()\n",
+ "\n",
+ "# 添加数据\n",
+ "esca.add_xaxis(Faker.choose())\n",
+ "esca.add_yaxis(\"\", Faker.values())\n",
+ "\n",
+ "# 添加标题\n",
+ "esca.set_global_opts(title_opts=opts.TitleOpts(title=\"EffectScatter-基本示例\"))\n",
+ "\n",
+ "# 展示\n",
+ "esca.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0188eccf",
+ "metadata": {},
+ "source": [
+ "##### 矩形涟漪"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "id": "f58511dc",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-26T11:09:55.050326Z",
+ "start_time": "2023-11-26T11:09:55.039084Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 52,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import EffectScatter\n",
+ "from pyecharts.faker import Faker\n",
+ "from pyecharts.globals import SymbolType\n",
+ "\n",
+ "# 创建 涟漪散点图对象\n",
+ "esca = EffectScatter()\n",
+ "\n",
+ "# 添加数据\n",
+ "esca.add_xaxis(Faker.choose())\n",
+ "esca.add_yaxis(\"\", Faker.values(), \n",
+ " symbol=SymbolType.RECT) # 矩形\n",
+ "\n",
+ "# 添加标题\n",
+ "esca.set_global_opts(title_opts=opts.TitleOpts(title=\"EffectScatter-基本示例\"))\n",
+ "\n",
+ "# 展示\n",
+ "esca.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2f9e1475",
+ "metadata": {},
+ "source": [
+ "### K线图 Kline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bfcecd2a",
+ "metadata": {},
+ "source": [
+ "> 源码 class pyecharts.charts.Kline(RectChart) 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "649e5e1d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Kline(\n",
+ " # 初始化配置项,参考 `global_options.InitOpts`\n",
+ " init_opts: opts.InitOpts = opts.InitOpts()\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "684558fe",
+ "metadata": {},
+ "source": [
+ "> 源码 func pyecharts.charts.Kline.add_yaxis 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fcb6440f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def add_yaxis(\n",
+ " # 系列名称,用于 tooltip 的显示,legend 的图例筛选。\n",
+ " series_name: str,\n",
+ "\n",
+ " # 系列数据\n",
+ " y_axis: types.Sequence[types.Union[opts.CandleStickItem, dict]],\n",
+ "\n",
+ " # 是否选中图例\n",
+ " is_selected: bool = True,\n",
+ "\n",
+ " # 使用的 x 轴的 index,在单个图表实例中存在多个 x 轴的时候有用。\n",
+ " xaxis_index: Optional[Numeric] = None,\n",
+ "\n",
+ " # 使用的 y 轴的 index,在单个图表实例中存在多个 y 轴的时候有用。\n",
+ " yaxis_index: Optional[Numeric] = None,\n",
+ "\n",
+ " # 标记线配置项,参考 `series_options.MarkLineOpts`\n",
+ " markline_opts: Union[opts.MarkLineOpts, dict, None] = None,\n",
+ "\n",
+ " # 标记点配置项,参考 `series_options.MarkPointOpts`\n",
+ " markpoint_opts: Union[opts.MarkPointOpts, dict, None] = None,\n",
+ "\n",
+ " # 提示框组件配置项,参考 `series_options.TooltipOpts`\n",
+ " tooltip_opts: Union[opts.TooltipOpts, dict, None] = None,\n",
+ "\n",
+ " # 图元样式配置项,参考 `series_options.ItemStyleOpts`\n",
+ " itemstyle_opts: Union[opts.ItemStyleOpts, dict, None] = None,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "57756b53",
+ "metadata": {},
+ "source": [
+ "#### CandleStickItem:K 线图数据项"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "de294713",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class CandleStickItem(\n",
+ " # 数据项名称。\n",
+ " name: Optional[str] = None,\n",
+ "\n",
+ " # 数据项的值。\n",
+ " value: Optional[Sequence] = None,\n",
+ "\n",
+ " # 图元样式配置项,参考 `series_options.ItemStyleOpts`\n",
+ " itemstyle_opts: Union[ItemStyleOpts, dict, None] = None,\n",
+ "\n",
+ " # 提示框组件配置项,参考 `series_options.TooltipOpts`\n",
+ " tooltip_opts: Union[TooltipOpts, dict, None] = None,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a9263d12",
+ "metadata": {},
+ "source": [
+ "#### 案例"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "id": "b9f2c4f4",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-26T11:13:44.440430Z",
+ "start_time": "2023-11-26T11:13:44.414520Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 56,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import Kline\n",
+ "\n",
+ "# 数据\n",
+ "data = [\n",
+ " [2320.26, 2320.26, 2287.3, 2362.94],\n",
+ " [2300, 2291.3, 2288.26, 2308.38],\n",
+ " [2295.35, 2346.5, 2295.35, 2345.92],\n",
+ " [2347.22, 2358.98, 2337.35, 2363.8],\n",
+ " [2360.75, 2382.48, 2347.89, 2383.76],\n",
+ " [2383.43, 2385.42, 2371.23, 2391.82],\n",
+ " [2377.41, 2419.02, 2369.57, 2421.15],\n",
+ " [2425.92, 2428.15, 2417.58, 2440.38],\n",
+ " [2411, 2433.13, 2403.3, 2437.42],\n",
+ " [2432.68, 2334.48, 2427.7, 2441.73],\n",
+ " [2430.69, 2418.53, 2394.22, 2433.89],\n",
+ " [2416.62, 2432.4, 2414.4, 2443.03],\n",
+ " [2441.91, 2421.56, 2418.43, 2444.8],\n",
+ " [2420.26, 2382.91, 2373.53, 2427.07],\n",
+ " [2383.49, 2397.18, 2370.61, 2397.94],\n",
+ " [2378.82, 2325.95, 2309.17, 2378.82],\n",
+ " [2322.94, 2314.16, 2308.76, 2330.88],\n",
+ " [2320.62, 2325.82, 2315.01, 2338.78],\n",
+ " [2313.74, 2293.34, 2289.89, 2340.71],\n",
+ " [2297.77, 2313.22, 2292.03, 2324.63],\n",
+ " [2322.32, 2365.59, 2308.92, 2366.16],\n",
+ " [2364.54, 2359.51, 2330.86, 2369.65],\n",
+ " [2332.08, 2273.4, 2259.25, 2333.54],\n",
+ " [2274.81, 2326.31, 2270.1, 2328.14],\n",
+ " [2333.61, 2347.18, 2321.6, 2351.44],\n",
+ " [2340.44, 2324.29, 2304.27, 2352.02],\n",
+ " [2326.42, 2318.61, 2314.59, 2333.67],\n",
+ " [2314.68, 2310.59, 2296.58, 2320.96],\n",
+ " [2309.16, 2286.6, 2264.83, 2333.29],\n",
+ " [2282.17, 2263.97, 2253.25, 2286.33],\n",
+ " [2255.77, 2270.28, 2253.31, 2276.22],\n",
+ "]\n",
+ "\n",
+ "# 创建 K线图 对象\n",
+ "kline= Kline()\n",
+ "\n",
+ "# 添加数据\n",
+ "kline.add_xaxis([\"2020/7/{}\".format(i + 1) for i in range(31)])\n",
+ "kline.add_yaxis(\"kline\", data)\n",
+ "\n",
+ "\n",
+ "kline.set_global_opts(\n",
+ " \n",
+ " # 限定坐标轴范围\n",
+ " yaxis_opts=opts.AxisOpts(is_scale=True),\n",
+ " xaxis_opts=opts.AxisOpts(is_scale=True),\n",
+ " \n",
+ " # 添加标题\n",
+ " title_opts=opts.TitleOpts(title=\"Kline-基本示例\"),\n",
+ ")\n",
+ "\n",
+ "# 展示\n",
+ "kline.render_notebook()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6a2e9ef2",
+ "metadata": {},
+ "source": [
+ "### 漏斗图 Funnel"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bdc4554c",
+ "metadata": {},
+ "source": [
+ "> 源码 class pyecharts.charts.Funnel 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5ad13842",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Funnel(\n",
+ " # 初始化配置项,参考 `global_options.InitOpts`\n",
+ " init_opts: opts.InitOpts = opts.InitOpts()\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cf1e4e62",
+ "metadata": {},
+ "source": [
+ "> 源码 func pyecharts.charts.Funnel.add 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c5ccf21",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def add(\n",
+ " # 系列名称,用于 tooltip 的显示,legend 的图例筛选。\n",
+ " series_name: str,\n",
+ "\n",
+ " # 系列数据项,格式为 [(key1, value1), (key2, value2)]\n",
+ " data_pair: Sequence,\n",
+ "\n",
+ " # 是否选中图例\n",
+ " is_selected: bool = True,\n",
+ "\n",
+ " # 系列 label 颜色\n",
+ " color: Optional[str] = None,\n",
+ "\n",
+ " # 数据排序, 可以取 'ascending','descending','none'(表示按 data 顺序)\n",
+ " sort_: str = \"descending\",\n",
+ "\n",
+ " # 数据图形间距\n",
+ " gap: Numeric = 0,\n",
+ "\n",
+ " # 标签配置项,参考 `series_options.LabelOpts`\n",
+ " label_opts: Union[opts.LabelOpts, dict] = opts.LabelOpts(),\n",
+ "\n",
+ " # 提示框组件配置项,参考 `series_options.TooltipOpts`\n",
+ " tooltip_opts: Union[opts.TooltipOpts, dict, None] = None,\n",
+ "\n",
+ " # 图元样式配置项,参考 `series_options.ItemStyleOpts`\n",
+ " itemstyle_opts: Union[opts.ItemStyleOpts, dict, None] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a7f66239",
+ "metadata": {},
+ "source": [
+ "#### 案例"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "61872ad3",
+ "metadata": {},
+ "source": [
+ "##### 升序"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "id": "e93ae4fc",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-26T11:17:34.456229Z",
+ "start_time": "2023-11-26T11:17:34.444588Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 57,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import Funnel\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# 创建 漏斗图对象\n",
+ "funnel = Funnel()\n",
+ "\n",
+ "# 添加数据\n",
+ "funnel.add(\"商品\", [list(z) for z in zip(Faker.choose(), Faker.values())])\n",
+ "\n",
+ "# 配置标题\n",
+ "funnel.set_global_opts(title_opts=opts.TitleOpts(title=\"Funnel-基本示例\"))\n",
+ "\n",
+ "# 展示\n",
+ "funnel.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6e0c8f6b",
+ "metadata": {},
+ "source": [
+ "##### 降序"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "id": "d51fb1f2",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-26T11:18:08.241622Z",
+ "start_time": "2023-11-26T11:18:08.229275Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 58,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import Funnel\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# 创建 漏斗图对象\n",
+ "funnel = Funnel()\n",
+ "\n",
+ "# 添加数据\n",
+ "funnel.add(\"商品\",\n",
+ " [list(z) for z in zip(Faker.choose(), Faker.values())],\n",
+ " sort_=\"ascending\", # 降序\n",
+ " label_opts=opts.LabelOpts(position=\"inside\"), # 标签位置\n",
+ " )\n",
+ "\n",
+ "# 添加标题\n",
+ "funnel.set_global_opts(title_opts=opts.TitleOpts(title=\"Funnel-基本示例\"))\n",
+ "\n",
+ "# 展示\n",
+ "funnel.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "14b06d4f",
+ "metadata": {},
+ "source": [
+ "### 词云图 WordCloud"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8ef86027",
+ "metadata": {},
+ "source": [
+ "> 源码 class pyecharts.charts.WordCloud 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e477ec2e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class WordCloud(\n",
+ " # 初始化配置项,参考 `global_options.InitOpts`\n",
+ " init_opts: opts.InitOpts = opts.InitOpts()\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c88eda77",
+ "metadata": {},
+ "source": [
+ "> 源码 func pyecharts.charts.WordCloud.add 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f2dbe9e9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def add(\n",
+ " # 系列名称,用于 tooltip 的显示,legend 的图例筛选。\n",
+ " series_name: str,\n",
+ "\n",
+ " # 系列数据项,[(word1, count1), (word2, count2)]\n",
+ " data_pair: Sequence,\n",
+ "\n",
+ " # 词云图轮廓,有 'circle', 'cardioid', 'diamond', 'triangle-forward', 'triangle', 'pentagon', 'star' 可选\n",
+ " shape: str = \"circle\",\n",
+ "\n",
+ " # 自定义的图片(目前支持 jpg, jpeg, png, ico 的格式,其他的图片格式待测试)\n",
+ " # 该参数支持:\n",
+ " # 1、 base64 (需要补充 data 头);\n",
+ " # 2、本地文件路径(相对或者绝对路径都可以)\n",
+ " # 注:如果使用了 mask_image 之后第一次渲染会出现空白的情况,再刷新一次就可以了(Echarts 的问题)\n",
+ " # Echarts Issue: https://github.com/ecomfe/echarts-wordcloud/issues/74\n",
+ " mask_image: types.Optional[str] = None,\n",
+ "\n",
+ " # 单词间隔\n",
+ " word_gap: Numeric = 20,\n",
+ "\n",
+ " # 单词字体大小范围\n",
+ " word_size_range=None,\n",
+ "\n",
+ " # 旋转单词角度\n",
+ " rotate_step: Numeric = 45,\n",
+ "\n",
+ " # 距离左侧的距离\n",
+ " pos_left: types.Optional[str] = None,\n",
+ "\n",
+ " # 距离顶部的距离\n",
+ " pos_top: types.Optional[str] = None,\n",
+ "\n",
+ " # 距离右侧的距离\n",
+ " pos_right: types.Optional[str] = None,\n",
+ "\n",
+ " # 距离底部的距���\n",
+ " pos_bottom: types.Optional[str] = None,\n",
+ "\n",
+ " # 词云图的宽度\n",
+ " width: types.Optional[str] = None,\n",
+ "\n",
+ " # 词云图的高度\n",
+ " height: types.Optional[str] = None,\n",
+ "\n",
+ " # 允许词云图的数据展示在画布范围之外\n",
+ " is_draw_out_of_bound: bool = False,\n",
+ "\n",
+ " # 提示框组件配置项,参考 `series_options.TooltipOpts`\n",
+ " tooltip_opts: Union[opts.TooltipOpts, dict, None] = None,\n",
+ "\n",
+ " # 词云图文字的配置\n",
+ " textstyle_opts: types.TextStyle = None,\n",
+ "\n",
+ " # 词云图文字阴影的范围\n",
+ " emphasis_shadow_blur: types.Optional[types.Numeric] = None,\n",
+ "\n",
+ " # 词云图文字阴影的颜色\n",
+ " emphasis_shadow_color: types.Optional[str] = None,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9eaaba86",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "id": "89ff5571",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-26T11:23:57.818779Z",
+ "start_time": "2023-11-26T11:23:57.795556Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 61,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import pyecharts.options as opts\n",
+ "from pyecharts.charts import WordCloud\n",
+ "from pyecharts.globals import SymbolType\n",
+ "\n",
+ "# 准备数据\n",
+ "data = [\n",
+ "['ThinkPad','15.7'],\n",
+ "['联想','14.5'],\n",
+ "['惠普','14.4'],\n",
+ "['华为','11.7'],\n",
+ "['华硕','8.2'],\n",
+ "['戴尔','8.1'],\n",
+ "['Acer宏碁','4.5'],\n",
+ "['苹果','3.5'],\n",
+ "['神舟','3.2'],\n",
+ "['ROG','3.1'],\n",
+ "['机械革命','2.4'],\n",
+ "['msi微星','1.8'],\n",
+ "['外星人','1.5'],\n",
+ "['微软','1.4'],\n",
+ "['荣耀','1.2'],\n",
+ "['雷神','1'],\n",
+ "['三星','0.7'],\n",
+ "['红米','0.6'],\n",
+ "['机械师','0.5'],\n",
+ "['小米','0.5'],\n",
+ "['炫龙','0.4'],\n",
+ "['雷蛇','0.2'],\n",
+ "['壹号本','0.1'],\n",
+ "['a豆','0.1'],\n",
+ "['未来人类','0.1'],\n",
+ "['技嘉','0.1'],\n",
+ "['中柏','0.1'],\n",
+ "['VAIO','0.1'],\n",
+ "['火影','0.1'],\n",
+ "['LG','0.1'],\n",
+ "['松下','0'],\n",
+ "['麦本本','0'],\n",
+ "['吾空','0'],\n",
+ "['长城','0'],\n",
+ "['GPD','0'],\n",
+ "['清华同方','0'],\n",
+ "['神基','0'],\n",
+ "['爱尔轩','0'],\n",
+ "['酷比魔方','0'],\n",
+ "['海尔','0'],\n",
+ "['谷歌','0'],\n",
+ "['台电','0'],\n",
+ "['iru','0'],\n",
+ "['攀升IPASON','0'],\n",
+ "['NEC','0'],\n",
+ "['夏普','0'],\n",
+ "['京东京造','0'],\n",
+ "['锡恩帝','0'],\n",
+ "['皓勤','0'],\n",
+ "['Intel','0'],\n",
+ "]\n",
+ "\n",
+ "# 生成 词云对象\n",
+ "wc = WordCloud()\n",
+ "\n",
+ "# 添加数据\n",
+ "wc.add(series_name=\"热点分析\", data_pair=data)\n",
+ "\n",
+ "wc.set_global_opts(\n",
+ " \n",
+ " # 配置标题\n",
+ " title_opts=opts.TitleOpts(\n",
+ " title=\"热点分析\", \n",
+ " title_textstyle_opts=opts.TextStyleOpts(font_size=23) # ��题大小\n",
+ " ),\n",
+ " \n",
+ " # 显示提示框\n",
+ " tooltip_opts=opts.TooltipOpts(is_show=True),\n",
+ ")\n",
+ "\n",
+ "\n",
+ "wc.render_notebook()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dec3bf85",
+ "metadata": {},
+ "source": [
+ "### 雷达图 Radar"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "695460c9",
+ "metadata": {},
+ "source": [
+ "> 源码 class pyecharts.charts.Radar 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0b454841",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Radar(\n",
+ " # 初始化配置项,参考 `global_options.InitOpts`\n",
+ " init_opts: opts.InitOpts = opts.InitOpts()\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "40d710b5",
+ "metadata": {},
+ "source": [
+ "> 源码 func pyecharts.charts.Radar.add_schema 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e3707f7d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def add_schema(\n",
+ " # 雷达指示器配置项列表,参考 `RadarIndicatorItem`\n",
+ " schema: Sequence[Union[opts.RadarIndicatorItem, dict]],\n",
+ "\n",
+ " # 雷达图绘制类型,可选 'polygon' 和 'circle'\n",
+ " shape: Optional[str] = None,\n",
+ "\n",
+ " # 雷达的中心(圆心)坐标,数组的第一项是横坐标,第二项是纵坐标。\n",
+ " # 支持设置成百分比,设置成百分比时第一项是相对于容器宽度,第二项是相对于容器高度。\n",
+ " center: Optional[types.Sequence] = None,\n",
+ "\n",
+ " # 文字样式配置项,参考 `series_options.TextStyleOpts`\n",
+ " textstyle_opts: Union[opts.TextStyleOpts, dict] = opts.TextStyleOpts(),\n",
+ "\n",
+ " # 分割线配置项,参考 `series_options.SplitLineOpts`\n",
+ " splitline_opt: Union[opts.SplitLineOpts, dict] = opts.SplitLineOpts(is_show=True),\n",
+ "\n",
+ " # 分隔区域配置项,参考 `series_options.SplitAreaOpts`\n",
+ " splitarea_opt: Union[opts.SplitAreaOpts, dict] = opts.SplitAreaOpts(),\n",
+ "\n",
+ " # 坐标轴轴线配置项,参考 `global_options.AxisLineOpts`\n",
+ " axisline_opt: Union[opts.AxisLineOpts, dict] = opts.AxisLineOpts(),\n",
+ "\n",
+ " # 极坐标系的径向轴。参考 `basic_charts.RadiusAxisOpts`\n",
+ " radiusaxis_opts: types.RadiusAxis = None,\n",
+ "\n",
+ " # 极坐标系的角度轴。参考 `basic_charts.AngleAxisOpts`\n",
+ " angleaxis_opts: types.AngleAxis = None,\n",
+ "\n",
+ " # 极坐标系配置,参考 `global_options.PolorOpts`\n",
+ " polar_opts: types.Polar = None,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9528ae8f",
+ "metadata": {},
+ "source": [
+ "> 源码 func pyecharts.charts.Radar.add 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6c4d95c8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def add(\n",
+ " # 系列名称,用于 tooltip 的显示,legend 的图例筛选。\n",
+ " series_name: str,\n",
+ "\n",
+ " # 系列数据项\n",
+ " data: types.Sequence[types.Union[opts.RadarItem, dict]],\n",
+ "\n",
+ " # 是否选中图例\n",
+ " is_selected: bool = True,\n",
+ "\n",
+ " # ECharts 提供的标记类型包括 'circle', 'rect', 'roundRect', 'triangle', \n",
+ " # 'diamond', 'pin', 'arrow', 'none'\n",
+ " # 可以通过 'image://url' 设置为图片,其中 URL 为图片的链接,或者 dataURI。\n",
+ " symbol: Optional[str] = None,\n",
+ "\n",
+ " # 系列 label 颜色\n",
+ " color: Optional[str] = None,\n",
+ "\n",
+ " # 标签配置项,参考 `series_options.LabelOpts`\n",
+ " label_opts: opts.LabelOpts = opts.LabelOpts(),\n",
+ "\n",
+ " # 线样式配置项,参考 `series_options.LineStyleOpts`\n",
+ " linestyle_opts: opts.LineStyleOpts = opts.LineStyleOpts(),\n",
+ "\n",
+ " # 区域填充样式配置项,参考 `series_options.AreaStyleOpts`\n",
+ " areastyle_opts: opts.AreaStyleOpts = opts.AreaStyleOpts(),\n",
+ "\n",
+ " # 提示框组件配置项,参考 `series_options.TooltipOpts`\n",
+ " tooltip_opts: Union[opts.TooltipOpts, dict, None] = None,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "092ab714",
+ "metadata": {},
+ "source": [
+ "#### RadarIndicatorItem\n",
+ "> 源码 class pyecharts.options.RadarIndicatorItem 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c87af027",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class RadarIndicatorItem(\n",
+ " # 指示器名称。\n",
+ " name: Optional[str] = None,\n",
+ "\n",
+ " # 指示器的最大值,可选,建议设置\n",
+ " min_: Optional[Numeric] = None,\n",
+ "\n",
+ " # 指示器的最小值,可选,默认为 0。\n",
+ " max_: Optional[Numeric] = None,\n",
+ "\n",
+ " # 标签特定的颜色。\n",
+ " color: Optional[str] = None,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "72212e41",
+ "metadata": {},
+ "source": [
+ "#### RadarItem:雷达图数据项"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0801bf15",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class RadarItem(\n",
+ " # 数据项名称\n",
+ " name: Optional[str] = None,\n",
+ "\n",
+ " # 单个数据项的数值。\n",
+ " value: Optional[Numeric] = None,\n",
+ "\n",
+ " # 单个数据标记的图形。\n",
+ " symbol: Optional[str] = None,\n",
+ "\n",
+ " # 单个数据标记的大小\n",
+ " symbol_size: Union[Sequence[Numeric], Numeric] = None,\n",
+ "\n",
+ " # 单个数据标记的旋转角度(而非弧度)。\n",
+ " symbol_rotate: Optional[Numeric] = None,\n",
+ "\n",
+ " # 如果 symbol 是 path:// 的形式,是否在缩放时保持该图形的长宽比。\n",
+ " symbol_keep_aspect: bool = False,\n",
+ "\n",
+ " # 单个数据标记相对于原本位置的偏移。\n",
+ " symbol_offset: Optional[Sequence] = None,\n",
+ "\n",
+ " # 标签配置项,参考 `series_options.LabelOpts`\n",
+ " label_opts: Union[LabelOpts, dict, None] = None,\n",
+ "\n",
+ " # 图元样式配置项,参考 `series_options.ItemStyleOpts`\n",
+ " itemstyle_opts: Union[ItemStyleOpts, dict, None] = None,\n",
+ "\n",
+ " # 提示框组件配置项,参考 `series_options.TooltipOpts`\n",
+ " tooltip_opts: Union[TooltipOpts, dict, None] = None,\n",
+ "\n",
+ " # 线样式配置项,参考 `series_options.LineStyleOpts`\n",
+ " linestyle_opts: Union[LineStyleOpts, dict, None] = None,\n",
+ "\n",
+ " # 区域填充样式配置项,参考 `series_options.AreaStyleOpts`\n",
+ " areastyle_opts: Union[AreaStyleOpts, dict, None] = None,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c389dbbf",
+ "metadata": {},
+ "source": [
+ "#### 案例"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "id": "71db801c",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-26T11:29:11.295265Z",
+ "start_time": "2023-11-26T11:29:11.277770Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 63,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import Radar\n",
+ "\n",
+ "# 生成数据\n",
+ "v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]\n",
+ "v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]\n",
+ "\n",
+ "# 创建 雷达图对象\n",
+ "radar = Radar()\n",
+ "\n",
+ "# 添加数据\n",
+ "radar.add_schema(\n",
+ " schema=[\n",
+ " opts.RadarIndicatorItem(name=\"销售\", max_=6500),\n",
+ " opts.RadarIndicatorItem(name=\"管理\", max_=16000),\n",
+ " opts.RadarIndicatorItem(name=\"信息技术\", max_=30000),\n",
+ " opts.RadarIndicatorItem(name=\"客服\", max_=38000),\n",
+ " opts.RadarIndicatorItem(name=\"研发\", max_=52000),\n",
+ " opts.RadarIndicatorItem(name=\"市场\", max_=25000),\n",
+ " ]\n",
+ ")\n",
+ "radar.add(\"预算分配\", v1)\n",
+ "radar.add(\"实际开销\", v2)\n",
+ "\n",
+ "# 显示标签\n",
+ "radar.set_series_opts(label_opts=opts.LabelOpts(is_show=True))\n",
+ "\n",
+ "\n",
+ "radar.set_global_opts(\n",
+ " \n",
+ " # 图例\n",
+ " legend_opts=opts.LegendOpts(selected_mode=\"single\"),\n",
+ " \n",
+ " # 添加标题\n",
+ " title_opts=opts.TitleOpts(title=\"Radar-单例模式\"),\n",
+ ")\n",
+ "radar.render_notebook()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dd3e8c82",
+ "metadata": {},
+ "source": [
+ "### 地图 Map\n",
+ "> 源码 class pyecharts.charts.Map 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "54d8cbdb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Map(\n",
+ " # 初始化配置项,参考 `global_options.InitOpts`\n",
+ " init_opts: opts.InitOpts = opts.InitOpts()\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7522d674",
+ "metadata": {},
+ "source": [
+ "> 源码 func pyecharts.charts.Map.add 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0077fdcc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def add(\n",
+ " # 系列名称,用于 tooltip 的显示,legend 的图例筛选。\n",
+ " series_name: str,\n",
+ "\n",
+ " # 数据项 (坐标点名称,坐标点值)\n",
+ " data_pair: types.Sequence[types.Union[types.Sequence, opts.MapItem, dict]],\n",
+ "\n",
+ " # 地图类型,具体参考 pyecharts.datasets.map_filenames.json 文件\n",
+ " maptype: str = \"china\",\n",
+ "\n",
+ " # 是否选中图例\n",
+ " is_selected: bool = True,\n",
+ "\n",
+ " # 是否开启鼠标缩放和平移漫游。\n",
+ " is_roam: bool = True,\n",
+ "\n",
+ " # 当前视角的中心点,用经纬度表示\n",
+ " center: Optional[Sequence] = None,\n",
+ "\n",
+ " # 参数用于 scale 地图的长宽比。\n",
+ " aspect_scale: types.Numeric = 0.75,\n",
+ "\n",
+ " # 二维数组,定义定位的左上角以及右下角分别所对应的经纬度。\n",
+ " bounding_coords: types.Optional[types.Sequence[types.Numeric]] = None,\n",
+ "\n",
+ " # 最小的缩放值。\n",
+ " min_scale_limit: types.Optional[types.Numeric] = None,\n",
+ "\n",
+ " # 最大的缩放值。\n",
+ " max_scale_limit: types.Optional[types.Numeric] = None,\n",
+ "\n",
+ " # 默认是 'name',针对 GeoJSON 要素的自定义属性名称,作为主键用于关联数据点和 GeoJSON 地理要素。\n",
+ " name_property: str = \"name\",\n",
+ "\n",
+ " # 选中模式,表示是否支持多个选中,默认关闭,支持布尔值和字符串。\n",
+ " # 字符串取值可选'single'表示单选,或者'multiple'表示多选。\n",
+ " selected_mode: types.Union[bool, str] = False,\n",
+ "\n",
+ " # 当前视角的缩放比例。\n",
+ " zoom: Optional[Numeric] = 1,\n",
+ "\n",
+ " # 自定义地区的名称映射\n",
+ " name_map: Optional[dict] = None,\n",
+ "\n",
+ " # 标记图形形状\n",
+ " symbol: Optional[str] = None,\n",
+ "\n",
+ " # 多个拥有相同地图类型的系列会使用同一个地图展现。\n",
+ " # 如果多个系列都在同一个区域有值,ECharts 会对这些值统计得到一个数据。\n",
+ " # 这个配置项就是用于配置统计的方式,目前有:\n",
+ " # 'sum' 取和。\n",
+ " # 'average' 取平均值。\n",
+ " # 'max' 取最大值。\n",
+ " # 'min' 取最小值。\n",
+ " map_value_calculation: str = \"sum\",\n",
+ "\n",
+ " # 是否显示标记图形\n",
+ " is_map_symbol_show: bool = True,\n",
+ "\n",
+ " # pyecharts 暂时没有提供 left/top/right/bottom 的配置\n",
+ " # layoutCenter 和 layoutSize 提供了除了 left/right/top/bottom/width/height 之外的布局手段。\n",
+ " # 在使用 left/right/top/bottom/width/height 的时候\n",
+ " # 可能很难在保持地图高宽比的情况下把地图放在某个盒形区域的正中间,并且保证不超出盒形的范围。\n",
+ " # 此时可以通过 layoutCenter 属性定义地图中心在屏幕中的位置,layoutSize 定义地图的大小。\n",
+ " # 如下示例\n",
+ " # layoutCenter: ['30%', '30%'],\n",
+ " # // 如果宽高比大于 1 则宽度为 100,如果小于 1 则高度为 100,保证了不超过 100x100 的区域\n",
+ " # layoutSize: 100\n",
+ " layout_center: types.Optional[types.Sequence[str]] = None,\n",
+ "\n",
+ " # 地图的大小,见 layoutCenter。支持相对于屏幕宽高的百分比或者绝对的像素大小。\n",
+ " layout_size: types.Union[str, types.Numeric] = None,\n",
+ "\n",
+ " # 标签配置项,参考 `series_options.LabelOpts`\n",
+ " label_opts: Union[opts.LabelOpts, dict] = opts.LabelOpts(),\n",
+ "\n",
+ " # 提示框组件配置项,参考 `series_options.TooltipOpts`\n",
+ " tooltip_opts: Union[opts.TooltipOpts, dict, None] = None,\n",
+ "\n",
+ " # 图元样式配置项,参考 `series_options.ItemStyleOpts`\n",
+ " itemstyle_opts: Union[opts.ItemStyleOpts, dict, None] = None,\n",
+ "\n",
+ " # 高亮标签配置项,参考 `series_options.LabelOpts`\n",
+ " emphasis_label_opts: Union[opts.LabelOpts, dict, None] = None,\n",
+ "\n",
+ " # 高亮图元样式配置项,参考 `series_options.ItemStyleOpts`\n",
+ " emphasis_itemstyle_opts: Union[opts.ItemStyleOpts, dict, None] = None,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f3cca219",
+ "metadata": {},
+ "source": [
+ "#### MapItem:地图数据项"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cae1f51e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class MapItem(\n",
+ " # 数据所对应的地图区域的名称,例如 '广东','浙江'。\n",
+ " name: Optional[str] = None,\n",
+ "\n",
+ " # 该区域的数据值。\n",
+ " value: Optional[Numeric] = None,\n",
+ "\n",
+ " # 该区域是否选中。\n",
+ " is_selected: bool = False,\n",
+ "\n",
+ " # 标签配置项,参考 `series_options.LabelOpts`\n",
+ " label_opts: Union[LabelOpts, dict, None] = None,\n",
+ "\n",
+ " # 图元样式配置项,参考 `series_options.ItemStyleOpts`\n",
+ " itemstyle_opts: Union[ItemStyleOpts, dict, None] = None,\n",
+ "\n",
+ " # 提示��组件配置项,参考 `series_options.TooltipOpts`\n",
+ " tooltip_opts: Union[TooltipOpts, dict, None] = None,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a646756e",
+ "metadata": {},
+ "source": [
+ "#### 案例"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "bfdb2d53",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2024-01-16T04:36:08.428499Z",
+ "start_time": "2024-01-16T04:36:08.415195Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import Map\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# 创建 地图对象\n",
+ "map = Map()\n",
+ "\n",
+ "# 添加数据\n",
+ "map.add(\n",
+ " \"商家A\",\n",
+ " [['和田',100],['阿里',50]],\n",
+ " \"china-cities\", # 地图:中国城市\n",
+ " \n",
+ " label_opts=opts.LabelOpts(is_show=False), # 不显示 标签\n",
+ " is_map_symbol_show=True,\n",
+ ")\n",
+ "\n",
+ "map.set_global_opts(\n",
+ " \n",
+ " # 配置标题\n",
+ " title_opts=opts.TitleOpts(title=\"Map-中国地图(带城市)\"),\n",
+ " \n",
+ " # 添加视觉映射\n",
+ " visualmap_opts=opts.VisualMapOpts(),\n",
+ ")\n",
+ "\n",
+ "# 展示\n",
+ "map.render_notebook()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a18d4121",
+ "metadata": {},
+ "source": [
+ "### 地理图表 Geo"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0a0924d4",
+ "metadata": {},
+ "source": [
+ "> 源码 class pyecharts.charts.Geo 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41255bee",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Geo(\n",
+ " # 初始化配置项,参考 `global_options.InitOpts`\n",
+ " init_opts: opts.InitOpts = opts.InitOpts()\n",
+ "\n",
+ " # 是否忽略不存在的坐标,默认值为 False,即不忽略\n",
+ " is_ignore_nonexistent_coord: bool = False\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d8ed6beb",
+ "metadata": {},
+ "source": [
+ "> 源码 func pyecharts.charts.Geo.add_schema 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4bdc3052",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def add_schema(\n",
+ " # 地图类型,具体参考 pyecharts.datasets.map_filenames.json 文件\n",
+ " maptype: str = \"china\",\n",
+ "\n",
+ " # 是否开启鼠标缩放和平移漫游。\n",
+ " is_roam: bool = True,\n",
+ "\n",
+ " # 当前视角的缩放比例。默认为 1\n",
+ " zoom: Optional[Numeric] = None,\n",
+ "\n",
+ " # 当前视角的中心点,用经纬度表示。例如:center: [115.97, 29.71]\n",
+ " center: Optional[Sequence] = None,\n",
+ "\n",
+ " # 参数用于 scale 地图的长宽比。\n",
+ " aspect_scale: types.Numeric = 0.75,\n",
+ "\n",
+ " # 二维数组,定义定位的左上角以及右下角分别所对应的经纬度。\n",
+ " bounding_coords: types.Optional[types.Sequence[types.Numeric]] = None,\n",
+ "\n",
+ " # 最小的缩放值。\n",
+ " min_scale_limit: types.Optional[types.Numeric] = None,\n",
+ "\n",
+ " # 最大的缩放值。\n",
+ " max_scale_limit: types.Optional[types.Numeric] = None,\n",
+ "\n",
+ " # 默认是 'name',针对 GeoJSON 要素的自定义属性名称,作为主键用于关联数据点和 GeoJSON 地理要素。\n",
+ " name_property: str = \"name\",\n",
+ "\n",
+ " # 选中模式,表示是否支持多个选中,默认关闭,支持布尔值和字符串。\n",
+ " # 字符串取值可选'single'表示单选,或者'multiple'表示多选。\n",
+ " selected_mode: types.Union[bool, str] = False,\n",
+ "\n",
+ " # pyecharts 暂时没有提供 left/top/right/bottom 的配置\n",
+ " # layoutCenter 和 layoutSize 提供了除了 left/right/top/bottom/width/height 之外的布局手段。\n",
+ " # 在使用 left/right/top/bottom/width/height 的时候\n",
+ " # 可能很难在保持地图高宽比的情况下把地图放在某个盒形区域的正中间,并且保证不超出盒形的范围。\n",
+ " # 此时可以通过 layoutCenter 属性定义地图中心在屏幕中的位置,layoutSize 定义地图的大小。\n",
+ " # 如下示例\n",
+ " # layoutCenter: ['30%', '30%'],\n",
+ " # // 如果宽高比大于 1 则宽度为 100,如果小于 1 则高度为 100,保证了不超过 100x100 的区域\n",
+ " # layoutSize: 100\n",
+ " layout_center: types.Optional[types.Sequence[str]] = None,\n",
+ "\n",
+ " # 地图的大小,见 layoutCenter。支持相对于屏幕宽高的百分比或者绝对的像素大小。\n",
+ " layout_size: types.Union[str, types.Numeric] = None,\n",
+ "\n",
+ " # # 标签配置项,参考 `series_options.LabelOpts`\n",
+ " label_opts: Union[opts.LabelOpts, dict, None] = None,\n",
+ "\n",
+ " # 地图区域的多边形 图形样式。\n",
+ " itemstyle_opts: Union[opts.ItemStyleOpts, dict, None] =None,\n",
+ "\n",
+ " # 高亮状态下的多边形样式\n",
+ " emphasis_itemstyle_opts: Union[opts.ItemStyleOpts, dict,None] = None,\n",
+ "\n",
+ " # 高亮状态下的标签样式。\n",
+ " emphasis_label_opts: Union[opts.LabelOpts, dict, None] =None,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a19daeaa",
+ "metadata": {},
+ "source": [
+ "> 源码 func pyecharts.charts.Geo.add 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2865997b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def add(\n",
+ " # 系列名称,用于 tooltip 的显示,legend 的图例筛选。\n",
+ " series_name: str,\n",
+ "\n",
+ " # 数据项 (坐标点名称,坐标点值)\n",
+ " data_pair: Sequence,\n",
+ "\n",
+ " # Geo 图类型,有 scatter, effectScatter, heatmap, lines 4 种,建议使用\n",
+ " # from pyecharts.globals import GeoType\n",
+ " # GeoType.GeoType.EFFECT_SCATTER,GeoType.HEATMAP,GeoType.LINES\n",
+ " type_: str = \"scatter\",\n",
+ "\n",
+ " # 是否选中图例\n",
+ " is_selected: bool = True,\n",
+ "\n",
+ " # 标记图形形状\n",
+ " symbol: Optional[str] = None,\n",
+ "\n",
+ " # 标记的大小\n",
+ " symbol_size: Numeric = 12,\n",
+ "\n",
+ " # 每个点的大小,在地理坐标系(coordinateSystem: 'geo')上有效。\n",
+ " blur_size: types.Numeric = 20,\n",
+ "\n",
+ " # 每个点模糊的大小,在地理坐标系(coordinateSystem: 'geo')上有效。\n",
+ " point_size: types.Numeric = 20,\n",
+ "\n",
+ " # 系列 label 颜色\n",
+ " color: Optional[str] = None,\n",
+ "\n",
+ " # 是否是多段线,在画 lines 图情况下\n",
+ " is_polyline: bool = False,\n",
+ "\n",
+ " # 是否启用大规模线图的优化,在数据图形特别多的时候(>=5k)可以开启\n",
+ " is_large: bool = False,\n",
+ "\n",
+ " # 特效尾迹的长度。取从 0 到 1 的值,数值越大尾迹越长。默认值 0.2\n",
+ " trail_length: Numeric = 0.2,\n",
+ "\n",
+ " # 开启绘制优化的阈值。\n",
+ " large_threshold: Numeric = 2000,\n",
+ "\n",
+ " # 配置该系列每一帧渲染的图形数\n",
+ " progressive: types.Numeric = 400,\n",
+ "\n",
+ " # 启用渐进式渲染的图形数量阈值,在单个系列的图形数量超过该阈值时启用渐进式渲染。\n",
+ " progressive_threshold: types.Numeric = 3000,\n",
+ "\n",
+ " # 标签配置项,参考 `series_options.LabelOpts`\n",
+ " label_opts: Union[opts.LabelOpts, dict] = opts.LabelOpts(),\n",
+ "\n",
+ " # 涟漪特效配置项,��考 `series_options.EffectOpts`\n",
+ " effect_opts: Union[opts.EffectOpts, dict] = opts.EffectOpts(),\n",
+ "\n",
+ " # 线样式配置项,参考 `series_options.LineStyleOpts`\n",
+ " linestyle_opts: Union[opts.LineStyleOpts, dict] = opts.LineStyleOpts(),\n",
+ "\n",
+ " # 提示框组件配置项,参考 `series_options.TooltipOpts`\n",
+ " tooltip_opts: Union[opts.TooltipOpts, dict, None] = None,\n",
+ "\n",
+ " # 图元样式配置项,参考 `series_options.ItemStyleOpts`\n",
+ " itemstyle_opts: Union[opts.ItemStyleOpts, dict, None] = None,\n",
+ "\n",
+ " # 这个配置相对非常复杂(参照地址: https://www.echartsjs.com/zh/option.html#series-custom.renderItem)\n",
+ " render_item: types.JsCode = None,\n",
+ "\n",
+ " # 这个配置相对非常复杂(参照地址: https://www.echartsjs.com/zh/option.html#series-custom.encode)\n",
+ " encode: types.Union[types.JsCode, dict] = None,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fc032601",
+ "metadata": {},
+ "source": [
+ "> 源码 func pyecharts.charts.Geo.add_coordinate 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "920270ba",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def add_coordinate(\n",
+ " \"\"\"新增一个坐标点\"\"\"\n",
+ " # 坐标地点名称\n",
+ " name: str,\n",
+ "\n",
+ " # 经度\n",
+ " longitude: Numeric,\n",
+ "\n",
+ " # 纬度\n",
+ " latitude: Numeric,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f44253f3",
+ "metadata": {},
+ "source": [
+ "> 源码 func pyecharts.charts.Geo.add_coordinate_json 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "289c9c3a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def add_coordinate_json(\n",
+ " \"\"\"以 JOSN 文件格式新增多个坐标点\"\"\"\n",
+ " # json 文件格式的坐标数据\n",
+ " # 格式如下\n",
+ " # {\n",
+ " # \"阿城\": [126.58, 45.32],\n",
+ " # \"阿克苏\": [80.19, 41.09]\n",
+ " # }\n",
+ " json_file: str\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e9ae6f26",
+ "metadata": {},
+ "source": [
+ "> 源码 func pyecharts.charts.Geo.get_coordinate"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a3ddd478",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_coordinate(\n",
+ " \"\"\"查询指定地点的坐标 \"\"\"\n",
+ " \n",
+ " # 地点名称\n",
+ " name: str\n",
+ ") -> Sequence\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "574a0730",
+ "metadata": {},
+ "source": [
+ "Geo 图的坐标引用自 pyecharts.datasets.COORDINATES,COORDINATES 是一个支持模糊匹配的字典类。可设置匹配的阈值"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bcd845df",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pyecharts.datasets import COORDINATES\n",
+ "# cutoff 为匹配阈值,阈值越高相似性越高,1 为完全相同。默认为 0.6\n",
+ "COORDINATES.cutoff = 0.75\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9ca779fa",
+ "metadata": {},
+ "source": [
+ "#### 案例\n",
+ "##### 基础地理图表"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "id": "ba15b799",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-26T11:45:33.973506Z",
+ "start_time": "2023-11-26T11:45:33.805806Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 69,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import Geo\n",
+ "from pyecharts.faker import Faker\n",
+ "from pyecharts.globals import ChartType\n",
+ "\n",
+ "# 创建 地理图表\n",
+ "geo = Geo()\n",
+ "\n",
+ "# 中国地图\n",
+ "geo.add_schema(maptype=\"china\")\n",
+ "\n",
+ "# 添加数据\n",
+ "geo.add(\"geo\",\n",
+ " [list(z) for z in zip(Faker.provinces, Faker.values())],\n",
+ " type_=ChartType.EFFECT_SCATTER, # 涟漪散点\n",
+ " )\n",
+ "\n",
+ "# 隐藏 标签\n",
+ "geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n",
+ "\n",
+ "geo.set_global_opts(\n",
+ " \n",
+ " # 视觉映射配置项\n",
+ " visualmap_opts=opts.VisualMapOpts(), \n",
+ " \n",
+ " # 设置标题\n",
+ " title_opts=opts.TitleOpts(title=\"Geo-基本示例\")\n",
+ ")\n",
+ "\n",
+ "# 展示\n",
+ "geo.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ee21352d",
+ "metadata": {},
+ "source": [
+ "##### 行为轨迹"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "id": "cd21393b",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-26T11:50:51.666718Z",
+ "start_time": "2023-11-26T11:50:51.652067Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 70,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import Geo\n",
+ "from pyecharts.globals import ChartType, SymbolType\n",
+ "\n",
+ "# 创建 地理图表\n",
+ "geo = Geo()\n",
+ "\n",
+ "# 中国地图\n",
+ "geo.add_schema(maptype=\"china\")\n",
+ "\n",
+ "# 添加数据\n",
+ "geo.add(\n",
+ " \"geo\",\n",
+ " [(\"广州\", \"上海\"), (\"广州\", \"北京\"), (\"广州\", \"杭州\"), (\"广州\", \"重庆\")],\n",
+ " \n",
+ " # 线段\n",
+ " type_=ChartType.LINES,\n",
+ " \n",
+ " \n",
+ " effect_opts=opts.EffectOpts(\n",
+ " \n",
+ " # 添加箭头\n",
+ " symbol=SymbolType.ARROW, \n",
+ " \n",
+ " # 线段粗细\n",
+ " symbol_size=6, \n",
+ " \n",
+ " # 线段颜色\n",
+ " color=\"blue\"\n",
+ " ),\n",
+ " \n",
+ " # 线段的曲率\n",
+ " linestyle_opts=opts.LineStyleOpts(curve=0.2),\n",
+ ")\n",
+ "\n",
+ "# 不显示标签\n",
+ "geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n",
+ "\n",
+ "# 添加标题\n",
+ "geo.set_global_opts(title_opts=opts.TitleOpts(title=\"Geo-Lines\"))\n",
+ "\n",
+ "# 展示\n",
+ "geo.render_notebook()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "014fde86",
+ "metadata": {},
+ "source": [
+ "### 组合图"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "id": "7aba208e",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-26T11:52:42.021813Z",
+ "start_time": "2023-11-26T11:52:42.000246Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 71,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import pyecharts.options as opts\n",
+ "from pyecharts.charts import Bar, Line\n",
+ "\n",
+ "x_data = [\"1月\", \"2月\", \"3月\", \"4月\", \"5月\", \"6月\", \"7月\", \"8月\", \"9月\", \"10月\", \"11月\", \"12月\"]\n",
+ "\n",
+ "\n",
+ "# 创建 条图对象\n",
+ "bar = Bar()\n",
+ "\n",
+ "# 添加数据\n",
+ "bar.add_xaxis(xaxis_data=x_data)\n",
+ "bar.add_yaxis(\"蒸发量\",\n",
+ " [2.0,4.9,7.0,23.2,25.6,76.7,135.6,162.2,32.6,20.0,6.4,3.3],\n",
+ " label_opts=opts.LabelOpts(is_show=False), # 不显示标签\n",
+ ")\n",
+ "bar.add_yaxis(\"降水量\",\n",
+ " [2.6,5.9,9.0,26.4,28.7,70.7,175.6,182.2,48.7,18.8,6.0,2.3],\n",
+ " label_opts=opts.LabelOpts(is_show=False),\n",
+ ")\n",
+ "\n",
+ "# 添加 y轴\n",
+ "bar.extend_axis(\n",
+ " yaxis=opts.AxisOpts(\n",
+ " name=\"温度\",\n",
+ " type_=\"value\",\n",
+ " min_=0,\n",
+ " max_=25,\n",
+ " interval=5,\n",
+ " axislabel_opts=opts.LabelOpts(formatter=\"{value} °C\"), # 标签\n",
+ " )\n",
+ ")\n",
+ "\n",
+ "\n",
+ "bar.set_global_opts(\n",
+ " \n",
+ " # 设置提示框\n",
+ " tooltip_opts=opts.TooltipOpts(\n",
+ " is_show=True, trigger=\"axis\", axis_pointer_type=\"cross\"\n",
+ " ),\n",
+ " xaxis_opts=opts.AxisOpts(\n",
+ " type_=\"category\",\n",
+ " \n",
+ " \n",
+ " axispointer_opts=opts.AxisPointerOpts(is_show=True, type_=\"shadow\"),\n",
+ " ),\n",
+ " yaxis_opts=opts.AxisOpts(\n",
+ " name=\"水量\",\n",
+ " type_=\"value\",\n",
+ " min_=0,\n",
+ " max_=250,\n",
+ " interval=50,\n",
+ " axislabel_opts=opts.LabelOpts(formatter=\"{value} ml\"),\n",
+ " axistick_opts=opts.AxisTickOpts(is_show=True),\n",
+ " splitline_opts=opts.SplitLineOpts(is_show=True),\n",
+ " ),\n",
+ ")\n",
+ "\n",
+ "\n",
+ "line = (\n",
+ " Line()\n",
+ " .add_xaxis(xaxis_data=x_data)\n",
+ " .add_yaxis(\n",
+ " series_name=\"平均温度\",\n",
+ " yaxis_index=1,\n",
+ " y_axis=[2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2],\n",
+ " label_opts=opts.LabelOpts(is_show=False),\n",
+ " )\n",
+ ")\n",
+ "\n",
+ "bar.overlap(line)\n",
+ "bar.render_notebook()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "07e3547c",
+ "metadata": {},
+ "source": [
+ "### 时间线轮播多图 Timeline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "74b0ad6f",
+ "metadata": {},
+ "source": [
+ "> 源码 class pyecharts.charts.Timeline 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e62a16c5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Timeline(\n",
+ " # 初始化配置项,参考 `global_options.InitOpts`\n",
+ " init_opts: opts.InitOpts = opts.InitOpts()\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "df945299",
+ "metadata": {},
+ "source": [
+ "> 源码 func pyecharts.charts.Timeline.add_schema 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4db0513b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def add_schema(\n",
+ " # 坐标轴类型。可选:\n",
+ " # '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",
+ " axis_type: str = \"category\",\n",
+ "\n",
+ " # 时间轴的类型。可选:\n",
+ " # 'horizontal': 水平\n",
+ " # 'vertical': 垂直\n",
+ " orient: str = \"horizontal\",\n",
+ "\n",
+ " # timeline 标记的图形。\n",
+ " # ECharts 提供的标记类型包括 'circle', 'rect', 'roundRect', 'triangle', 'diamond', \n",
+ " # 'pin', 'arrow', 'none'\n",
+ " # 可以通过 'image://url' 设置为图片,其中 URL 为图片的链接,或者 dataURI。\n",
+ " symbol: Optional[str] = None,\n",
+ "\n",
+ " # timeline 标记的大小,可以设置成诸如 10 这样单一的数字,也可以用数组分开表示宽和高,\n",
+ " # 例如 [20, 10] 表示标记宽为 20,高为 10。\n",
+ " symbol_size: Optional[Numeric] = None,\n",
+ "\n",
+ " # 表示播放的速度(跳动的间隔),单位毫秒(ms)。\n",
+ " play_interval: Optional[Numeric] = None,\n",
+ "\n",
+ " # 表示播放按钮的位置。可选值:'left'、'right'。\n",
+ " control_position: str = \"left\",\n",
+ "\n",
+ " # 是否自动播放。\n",
+ " is_auto_play: bool = False,\n",
+ "\n",
+ " # 是否循环播放。\n",
+ " is_loop_play: bool = True,\n",
+ "\n",
+ " # 是否反向播放。\n",
+ " is_rewind_play: bool = False,\n",
+ "\n",
+ " # 是否显示 timeline 组件。如果设置为 false,不会显示,但是功能还存在。\n",
+ " is_timeline_show: bool = True,\n",
+ "\n",
+ " # 是否反向放置 timeline,反向则首位颠倒过来\n",
+ " is_inverse: bool = False,\n",
+ "\n",
+ " # Timeline 组件离容器左侧的距离。\n",
+ " # left 的值可以是像 20 这样的具体像素值,可以是像 '20%' 这样相对于容器高宽的百分比,\n",
+ " # 也可以是 'left', 'center', 'right'。\n",
+ " # 如果 left 的值为'left', 'center', 'right',组件会根据相应的位置自动对齐\n",
+ " pos_left: Optional[str] = None,\n",
+ "\n",
+ " # timeline 组件离容器右侧的距离。\n",
+ " # right 的值可以是像 20 这样的具体像素值,可以是像 '20%' 这样相对于容器高宽的百分比。\n",
+ " pos_right: Optional[str] = None,\n",
+ "\n",
+ " # Timeline 组件离容器上侧的距离。\n",
+ " # left 的值可以是像 20 这样的具体像素值,可以是像 '20%' 这样相对于容器高宽的百分比,\n",
+ " # 也可以是 'top', 'middle', 'bottom'。\n",
+ " # 如果 left 的值为 'top', 'middle', 'bottom',组件会根据相应的位置自动对齐\n",
+ " pos_top: Optional[str] = None,\n",
+ "\n",
+ " # timeline 组件离容器下侧的距离。\n",
+ " # bottom 的值可以是像 20 这样的具体像素值,可以是像 '20%' 这样相对于容器高宽的百分比。\n",
+ " pos_bottom: Optional[str] = \"-5px\",\n",
+ "\n",
+ " # 时间轴区域的宽度, 影响垂直的时候时间轴的轴标签和轴之间的距离\n",
+ " width: Optional[str] = None,\n",
+ "\n",
+ " # 时间轴区域的高度\n",
+ " height: Optional[str] = None,\n",
+ "\n",
+ " # 时间轴的坐标轴线配置,参考 `series_options.LineStyleOpts`\n",
+ " linestyle_opts: Union[opts.LineStyleOpts, dict, None] = None,\n",
+ "\n",
+ " # 时间轴的轴标签配置,参考 `series_options.LabelOpts`\n",
+ " label_opts: Optional[opts.LabelOpts] = None,\n",
+ "\n",
+ " # 时间轴的图形样式,参考 `series_options.ItemStyleOpts`\n",
+ " itemstyle_opts: Union[opts.ItemStyleOpts, dict, None] = None,\n",
+ "\n",
+ " # Graphic 样式\n",
+ " graphic_opts: types.Graphic = None,\n",
+ "\n",
+ " # 『当前项』(checkpoint)的图形样式。\n",
+ " checkpointstyle_opts: types.TimeLinkCheckPoint = None,\n",
+ "\n",
+ " # 控制按钮』的样式。『控制按钮』包括:『播放按钮』、『前进按钮』、『后退按钮』。\n",
+ " controlstyle_opts: types.TimeLineControl = None,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2d6b5c02",
+ "metadata": {},
+ "source": [
+ "> 源码 func pyecharts.charts.Timeline.add 内容如下:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "49b305f3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def add(\n",
+ " # 图表实例\n",
+ " chart: Base, \n",
+ "\n",
+ " # 时间点\n",
+ " time_point: str\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "24a9a895",
+ "metadata": {},
+ "source": [
+ "#### TimeLinkCheckPoint: 时间轴 checkpoint 样式配置"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "29053524",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class TimelineCheckPointerStyle(\n",
+ " # ECharts 提供的标记类型包括 'circle', 'rect', 'roundRect', 'triangle', 'diamond', 'pin', 'arrow', 'none'\n",
+ " # 可以通过 'image://url' 设置为图片,其中 URL 为图片的链接,或者 dataURI。\n",
+ " # 可以通过 'path://' 将图标设置为任意的矢量路径。\n",
+ " # 这种方式相比于使用图片的方式,不用担心因为缩放而产生锯齿或模糊,而且可以设置为任意颜色。\n",
+ " # 路径图形会自适应调整为合适的大小。路径的格式参见 SVG PathData。\n",
+ " # 可以从 Adobe Illustrator 等工具编辑导出。\n",
+ " symbol: str = \"circle\",\n",
+ "\n",
+ " # 标记的大小。\n",
+ " symbol_size: Union[Numeric, Sequence[Numeric]] = 13,\n",
+ "\n",
+ " # 标记的旋转角度(而非弧度)。正值表示逆时针旋转。\n",
+ " symbol_rotate: Optional[Numeric] = None,\n",
+ "\n",
+ " # 如果 symbol 是 path:// 的形式,是否在缩放时保持该图形的长宽比。\n",
+ " symbol_keep_aspect: bool = False,\n",
+ "\n",
+ " # 标记相对于原本位置的偏移。\n",
+ " symbol_offset: Optional[Sequence[Union[str, Numeric]]] = None,\n",
+ "\n",
+ " # 『当前项』(checkpoint)的颜色。\n",
+ " color: str = \"#c23531\",\n",
+ "\n",
+ " # 『当前项』(checkpoint)的边框宽度。\n",
+ " border_width: Numeric = 5,\n",
+ "\n",
+ " # 『当前项』(checkpoint)的边框颜色。\n",
+ " border_color: str = \"rgba(194,53,49,0.5)\",\n",
+ "\n",
+ " # 『当前项』(checkpoint)在 timeline 播放切换中的移动,是否有动画。\n",
+ " is_animation: bool = True,\n",
+ "\n",
+ " # 『当前项』(checkpoint)的动画时长。\n",
+ " animation_duration: Numeric = 300,\n",
+ "\n",
+ " # 『当前项』(checkpoint)的动画的缓动效果。\n",
+ " animation_easing: str = \"quinticInOut\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ec0cad7e",
+ "metadata": {},
+ "source": [
+ "#### TimelineControlStyle: 时间轴控制按钮样式"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "daf5cbc2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class TimelineControlStyle(\n",
+ " # 是否显示『控制按钮』。设置为 false 则全不显示。\n",
+ " is_show: bool = True,\n",
+ "\n",
+ " # 是否显示『播放按钮』。\n",
+ " is_show_play_button: bool = True,\n",
+ "\n",
+ " # 是否显示『后退按钮』。\n",
+ " is_show_prev_button: bool = True,\n",
+ "\n",
+ " # 是否显示『前进按钮』。\n",
+ " is_show_next_button: bool = True,\n",
+ "\n",
+ " # 『控制按钮』的尺寸,单位为像素(px)。\n",
+ " item_size: Numeric = 22,\n",
+ "\n",
+ " # 『控制按钮』的间隔,单位为像素(px)。\n",
+ " item_gap: Numeric = 12,\n",
+ "\n",
+ " # 『控制按钮』的位置。\n",
+ " # 当 timeline.orient 为 'horizontal'时,'left'、'right'有效。\n",
+ " # 当 timeline.orient 为 'vertical'时,'top'、'bottom'有效。\n",
+ " position: str = \"left\",\n",
+ "\n",
+ " # 『播放按钮』的『可播放状态』的图形。\n",
+ " # 可以通过 'image://url' 设置为图片,其中 URL 为图片的链接,或者 dataURI。\n",
+ " # 可以通过 'path://' 将图标设置为任意的矢量路径。\n",
+ " # 这种方式相比于使用图片的方式,不用担心因为缩放而产生锯齿或模糊,而且可以设置为任意颜色。\n",
+ " # 路径图形会自适应调整为合适的大小。路径的格式参见 SVG PathData。\n",
+ " # 可以从 Adobe Illustrator 等工具编辑导出。\n",
+ " play_icon: Optional[str] = None,\n",
+ "\n",
+ " # 同上\n",
+ " stop_icon: Optional[str] = None,\n",
+ "\n",
+ " # 同上\n",
+ " prev_icon: Optional[str] = None,\n",
+ "\n",
+ " # 同上\n",
+ " next_icon: Optional[str] = None,\n",
+ "\n",
+ " # 按钮颜色。\n",
+ " color: str = \"#304654\",\n",
+ "\n",
+ " # 按钮边框颜色。\n",
+ " border_color: str = \"#304654\",\n",
+ "\n",
+ " # 按钮边框线宽。\n",
+ " border_width: Numeric = 1,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ce534ffe",
+ "metadata": {},
+ "source": [
+ "#### 案例"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "id": "86de243f",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-11-26T11:56:59.945245Z",
+ "start_time": "2023-11-26T11:56:59.925733Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 72,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import Bar, Timeline\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# 随机生成数据\n",
+ "x = Faker.choose()\n",
+ "\n",
+ "# 时间线轮播图\n",
+ "tl = Timeline()\n",
+ "\n",
+ "for i in range(2015, 2020):\n",
+ " \n",
+ " # 条图\n",
+ " bar = (\n",
+ " Bar()\n",
+ " .add_xaxis(x)\n",
+ " .add_yaxis(\"商家A\", Faker.values())\n",
+ " .add_yaxis(\"商家B\", Faker.values())\n",
+ " .set_global_opts(title_opts=opts.TitleOpts(\"某商店{}年营业额\".format(i)))\n",
+ " )\n",
+ " \n",
+ " # 添加进轮播图\n",
+ " tl.add(bar, \"{}年\".format(i))\n",
+ " \n",
+ "# 展示\n",
+ "tl.render_notebook()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "53ba03cf",
+ "metadata": {},
+ "source": [
+ "## 整合 Flask 框架"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "53a115e7",
+ "metadata": {},
+ "source": [
+ "### 通过pyechart返回"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7cc8deb4",
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-26T12:13:44.547Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * Serving Flask app \"__main__\" (lazy loading)\n",
+ " * Environment: production\n",
+ "\u001b[31m WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n",
+ "\u001b[2m Use a production WSGI server instead.\u001b[0m\n",
+ " * Debug mode: off\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\n",
+ "127.0.0.1 - - [26/Nov/2023 20:13:45] \"GET / HTTP/1.1\" 200 -\n"
+ ]
+ }
+ ],
+ "source": [
+ "from flask import Flask, Markup\n",
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import Bar, Timeline\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# 创建 Flask对象\n",
+ "app = Flask(__name__)\n",
+ "\n",
+ "def create_table():\n",
+ " \n",
+ " # 创建 条图对象\n",
+ " bar = Bar()\n",
+ " \n",
+ " # 添加数据\n",
+ " bar.add_xaxis(Faker.choose())\n",
+ " bar.add_yaxis('北京',Faker.values())\n",
+ " \n",
+ " # 标签为 蓝色\n",
+ " bar.set_series_opts(label_opts=opts.LabelOpts(color='blue'))\n",
+ " return bar\n",
+ "\n",
+ "@app.route('/')\n",
+ "def show_bar01():\n",
+ " return Markup(create_table().render_embed())\n",
+ "\n",
+ "if __name__ ==\"__main__\":\n",
+ " app.run()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0285602f",
+ "metadata": {},
+ "source": [
+ "### 返回模版文件"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "44e6af26",
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-26T12:11:55.890Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * Serving Flask app \"__main__\" (lazy loading)\n",
+ " * Environment: production\n",
+ "\u001b[31m WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n",
+ "\u001b[2m Use a production WSGI server instead.\u001b[0m\n",
+ " * Debug mode: off\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\n",
+ "127.0.0.1 - - [26/Nov/2023 20:11:57] \"GET / HTTP/1.1\" 200 -\n"
+ ]
+ }
+ ],
+ "source": [
+ "from flask import Flask\n",
+ "from flask import render_template\n",
+ "\n",
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import Bar, Timeline\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# 创建 Flask对象\n",
+ "app = Flask(__name__)\n",
+ "\n",
+ "\n",
+ "def create_table2():\n",
+ "\n",
+ " # 创建 条图\n",
+ " bar = Bar()\n",
+ "\n",
+ " # 添加数据\n",
+ " bar.add_xaxis(Faker.choose())\n",
+ " bar.add_yaxis('北京', Faker.values())\n",
+ "\n",
+ " # 设置 x轴\n",
+ " bar.set_global_opts(xaxis_opts=opts.AxisOpts(is_show=True, min_=1, max_=5))\n",
+ "\n",
+ " # 设置 y轴\n",
+ " bar.set_global_opts(yaxis_opts=opts.AxisOpts(max_=140))\n",
+ "\n",
+ " # 生成 html\n",
+ " bar.render('./templates/bar.html')\n",
+ "\n",
+ "\n",
+ "@app.route('/')\n",
+ "def show_bar02():\n",
+ " create_table2()\n",
+ " return render_template('./bar.html')\n",
+ "\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ " app.run()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b35d790d",
+ "metadata": {},
+ "source": [
+ "### 前后分离"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ccd3ac6f",
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-26T12:17:57.220Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * Serving Flask app \"__main__\" (lazy loading)\n",
+ " * Environment: production\n",
+ "\u001b[31m WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n",
+ "\u001b[2m Use a production WSGI server instead.\u001b[0m\n",
+ " * Debug mode: off\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\n",
+ "127.0.0.1 - - [26/Nov/2023 20:18:02] \"GET / HTTP/1.1\" 404 -\n",
+ "127.0.0.1 - - [26/Nov/2023 20:18:05] \"GET /get_data HTTP/1.1\" 200 -\n",
+ "127.0.0.1 - - [26/Nov/2023 20:18:23] \"GET /show_bar HTTP/1.1\" 200 -\n"
+ ]
+ }
+ ],
+ "source": [
+ "from flask import Flask\n",
+ "from flask import render_template\n",
+ "\n",
+ "from pyecharts import options as opts\n",
+ "from pyecharts.charts import Bar, Timeline\n",
+ "from pyecharts.faker import Faker\n",
+ "\n",
+ "# 创建 Flask对象\n",
+ "app = Flask(__name__)\n",
+ "\n",
+ "\n",
+ "def create_table3():\n",
+ "\n",
+ " # 创建 条图\n",
+ " bar = Bar()\n",
+ "\n",
+ " # 添加数据\n",
+ " bar.add_xaxis(Faker.choose())\n",
+ " bar.add_yaxis('北京', Faker.values())\n",
+ "\n",
+ " # 设置 x轴\n",
+ " bar.set_global_opts(xaxis_opts=opts.AxisOpts(is_show=True, min_=1, max_=5))\n",
+ "\n",
+ " # 设置 y轴\n",
+ " bar.set_global_opts(yaxis_opts=opts.AxisOpts(max_=140))\n",
+ "\n",
+ " # 生成 html\n",
+ " bar.render('./templates/index.html')\n",
+ " \n",
+ " return bar\n",
+ "\n",
+ "\n",
+ "@app.route('/get_data')\n",
+ "def get_data():\n",
+ " return create_table3().dump_options_with_quotes()\n",
+ "\n",
+ "\n",
+ "@app.route('/show_bar')\n",
+ "def show_bar03():\n",
+ " return render_template('index.html')\n",
+ "\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ " app.run()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2309cb7c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ff0b0e6d",
+ "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": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {
+ "height": "calc(100% - 180px)",
+ "left": "10px",
+ "top": "150px",
+ "width": "362.797px"
+ },
+ "toc_section_display": true,
+ "toc_window_display": true
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}