{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"\n",
"# 使用PyEcharts进行可视化\n",
"\n",
"Visualization with PyEcharts\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Echarts\n",
"\n",
"https://echarts.apache.org/examples/zh/index.html\n",
"\n",
"- 第一步，选取图类型 \n",
"- 第二步，修改图\n",
"- 第三步，点击download下载html文件\n",
"- 第四步，修改下载的html文件\n",
"\n",
"案例1：散点图\n",
"https://echarts.apache.org/examples/zh/editor.html?c=bubble-gradient"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"案例2：Put echarts into a html\n",
"\n",
"Note: set the **height** of section.\n",
"\n",
"**Question**: How to add more echarts into a html?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"案例3：读取json数据\n",
"\n",
"https://echarts.apache.org/examples/zh/editor.html?c=scatter-life-expectancy-timeline\n",
"\n",
"前端的开发的html给我们的时候，由于内部有一些ajax请求的json的数据，需要在一个web server中查看，每次放到http服务器太麻烦。还是直接用python造一个最方便。最简单的，直接用\n",
"\n",
"> python3 -m http.server\n",
"\n",
"同时，读取json数据时，需要调用jquery\n",
"\n",
"```\n",
"\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## pyecharts安装\n",
" https://github.com/pyecharts/pyecharts\n",
"\n",
"> pip install pyecharts -U\n",
"\n",
" \n",
"- pip install echarts-countries-pypkg\n",
"- pip install echarts-china-provinces-pypkg\n",
"- pip install echarts-china-cities-pypkg"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## pyecharts使用简介\n",
"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。而 Python 是一门富有表达力的语言，很适合用于数据处理。当数据分析遇上数据可视化时，pyecharts 诞生了。https://pyecharts.org/#/\n",
"\n",
"- 配置项: 全局配置项 | 系列配置项\n",
"- 基本使用: 图表 API | 示例数据 | 全局变量\n",
"- 图表类型: 基本图表 | 直角坐标系图表 | 地理图表 | 3D 图表 | 组合图表 | HTML 组件\n",
"- 进阶话题: 参数传递 | 数据格式 | 定制主题 | 定制地图 | 渲染图片 | Notebook | 原生 Javascript | 资源引用\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-13T09:06:10.927230Z",
"start_time": "2020-05-13T09:06:10.910267Z"
},
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pyecharts.charts import Bar\n",
"\n",
"bar = Bar()\n",
"bar.add_xaxis([\"衬衫\", \"羊毛衫\", \"雪纺衫\", \"裤子\", \"高跟鞋\", \"袜子\"])\n",
"bar.add_yaxis(\"商家A\", [5, 20, 36, 10, 75, 90])\n",
"# render 会生成本地 HTML 文件，默认会在当前目录生成 render.html 文件\n",
"# 也可以传入路径参数，如 bar.render(\"mycharts.html\")\n",
"bar.render_notebook()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-13T09:06:17.293145Z",
"start_time": "2020-05-13T09:06:17.287112Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"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",
"\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",
"bar.render_notebook()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-13T09:06:28.574234Z",
"start_time": "2020-05-13T09:06:28.565978Z"
},
"code_folding": [
3
],
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 7,
"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",
" .set_global_opts(title_opts=opts.TitleOpts(title=\"主标题\", subtitle=\"副标题\"))\n",
" # 或者直接使用字典参数\n",
" # .set_global_opts(title_opts={\"text\": \"主标题\", \"subtext\": \"副标题\"})\n",
")\n",
"bar.render_notebook()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-13T09:06:36.172922Z",
"start_time": "2020-05-13T09:06:36.165043Z"
},
"code_folding": [
5
],
"slideshow": {
"slide_type": "subslide"
}
},
"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",
"# 内置主题类型可查看 pyecharts.globals.ThemeType\n",
"from pyecharts.globals import ThemeType\n",
"\n",
"bar = (\n",
" Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))\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",
" .add_yaxis(\"商家B\", [15, 6, 45, 20, 35, 66])\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",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## PyEcharts Gallery\n",
"\n",
"https://github.com/pyecharts/pyecharts-gallery\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Bar\n",
"\n",
"https://gallery.pyecharts.org/#/Bar/bar_base"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-11T18:07:53.901582Z",
"start_time": "2020-05-11T18:07:53.891063Z"
},
"code_folding": [],
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 146,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# vis\n",
"from pyecharts.charts import Bar\n",
"from pyecharts import options as opts\n",
"\n",
"# V1 版本开始支持链式调用\n",
"bar = (\n",
" Bar()\n",
" .add_xaxis([\"衬衫\", \"毛衣\", \"领带\", \"裤子\", \"风衣\", \"高跟鞋\", \"袜子\"])\n",
" .add_yaxis(\"商家A\", [114, 55, 27, 101, 125, 27, 105])\n",
" .add_yaxis(\"商家B\", [57, 134, 137, 129, 145, 60, 49])\n",
" .set_global_opts(title_opts=opts.TitleOpts(title=\"某商场销售情况\"))\n",
")\n",
"bar.render_notebook()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Bar3D"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-11T18:08:19.968077Z",
"start_time": "2020-05-11T18:08:19.870704Z"
},
"code_folding": [
0
],
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 147,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# vis\n",
"import random\n",
"\n",
"from pyecharts import options as opts\n",
"from pyecharts.charts import Bar3D\n",
"from pyecharts.faker import Faker\n",
"\n",
"\n",
"data = [(i, j, random.randint(0, 12)) for i in range(6) for j in range(24)]\n",
"bar3d = (\n",
" Bar3D()\n",
" .add(\n",
" \"\",\n",
" [[d[1], d[0], d[2]] for d in data],\n",
" xaxis3d_opts=opts.Axis3DOpts(Faker.clock, type_=\"category\"),\n",
" yaxis3d_opts=opts.Axis3DOpts(Faker.week_en, type_=\"category\"),\n",
" zaxis3d_opts=opts.Axis3DOpts(type_=\"value\"),\n",
" )\n",
" .set_global_opts(\n",
" visualmap_opts=opts.VisualMapOpts(max_=20),\n",
" title_opts=opts.TitleOpts(title=\"Bar3D-基本示例\"),\n",
" )\n",
" #.render(\"bar3d_base.html\")\n",
")\n",
"\n",
"bar3d.render_notebook()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## EffectScatter"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-10T09:10:15.730873Z",
"start_time": "2020-05-10T09:10:15.721001Z"
},
"code_folding": [
0
],
"scrolled": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# vis\n",
"from pyecharts import options as opts\n",
"from pyecharts.charts import EffectScatter\n",
"from pyecharts.faker import Faker\n",
"\n",
"c = (\n",
" EffectScatter()\n",
" .add_xaxis(Faker.choose())\n",
" .add_yaxis(\"\", Faker.values())\n",
" .set_global_opts(title_opts=opts.TitleOpts(title=\"EffectScatter-基本示例\"))\n",
" #.render(\"effectscatter_base.html\")\n",
")\n",
"\n",
"c.render_notebook()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Funnel"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-11T18:09:23.704202Z",
"start_time": "2020-05-11T18:09:23.677748Z"
},
"code_folding": [
4
],
"scrolled": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 148,
"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",
"c = (\n",
" Funnel()\n",
" .add(\"商品\", [list(z) for z in zip(Faker.choose(), Faker.values())])\n",
" .set_global_opts(title_opts=opts.TitleOpts(title=\"Funnel-基本示例\"))\n",
" #.render(\"funnel_base.html\")\n",
")\n",
"\n",
"c.render_notebook()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Gauge"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-11T18:09:39.840494Z",
"start_time": "2020-05-11T18:09:39.825193Z"
},
"code_folding": [
3
],
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 149,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pyecharts import options as opts\n",
"from pyecharts.charts import Gauge\n",
"\n",
"c = (\n",
" Gauge()\n",
" .add(\"\", [(\"完成率\", 55.6)])\n",
" .set_global_opts(title_opts=opts.TitleOpts(title=\"Gauge-基本示例\"))\n",
" #.render(\"gauge_base.html\")\n",
")\n",
"\n",
"c.render_notebook()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Geo"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-11T18:10:02.719815Z",
"start_time": "2020-05-11T18:10:02.668059Z"
},
"code_folding": [
2
],
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"from pyecharts.charts import Geo\n",
"\n",
"data = [\n",
" (\"海门\", 9),(\"鄂尔多斯\", 12),(\"招远\", 12),(\"舟山\", 12),(\"齐齐哈尔\", 14),(\"盐城\", 15),\n",
" (\"赤峰\", 16),(\"青岛\", 18),(\"乳山\", 18),(\"金昌\", 19),(\"泉州\", 21),(\"莱西\", 21),\n",
" (\"日照\", 21),(\"胶南\", 22),(\"南通\", 23),(\"拉萨\", 24),(\"云浮\", 24),(\"梅州\", 25),\n",
" (\"文登\", 25),(\"上海\", 25),(\"攀枝花\", 25),(\"威海\", 25),(\"承德\", 25),(\"厦门\", 26),\n",
" (\"汕尾\", 26),(\"潮州\", 26),(\"丹东\", 27),(\"太仓\", 27),(\"曲靖\", 27),(\"烟台\", 28),\n",
" (\"福州\", 29),(\"瓦房店\", 30),(\"即墨\", 30),(\"抚顺\", 31),(\"玉溪\", 31),(\"张家口\", 31),\n",
" (\"阳泉\", 31),(\"莱州\", 32),(\"湖州\", 32),(\"汕头\", 32),(\"昆山\", 33),(\"宁波\", 33),\n",
" (\"湛江\", 33),(\"揭阳\", 34),(\"荣成\", 34),(\"连云港\", 35),(\"葫芦岛\", 35),(\"常熟\", 36),\n",
" (\"东莞\", 36),(\"河源\", 36),(\"淮安\", 36),(\"泰州\", 36),(\"南宁\", 37),(\"营口\", 37),\n",
" (\"惠州\", 37),(\"江阴\", 37),(\"蓬莱\", 37),(\"韶关\", 38),(\"嘉峪关\", 38),(\"广州\", 38),\n",
" (\"延安\", 38),(\"太原\", 39),(\"清远\", 39),(\"中山\", 39),(\"昆明\", 39),(\"寿光\", 40),\n",
" (\"盘锦\", 40),(\"长治\", 41),(\"深圳\", 41),(\"珠海\", 42),(\"宿迁\", 43),(\"咸阳\", 43),\n",
" (\"铜川\", 44),(\"平度\", 44),(\"佛山\", 44),(\"海口\", 44),(\"江门\", 45),(\"章丘\", 45),\n",
" (\"肇庆\", 46),(\"大连\", 47),(\"临汾\", 47),(\"吴江\", 47),(\"石嘴山\", 49),(\"沈阳\", 50),\n",
" (\"苏州\", 50),(\"茂名\", 50),(\"嘉兴\", 51),(\"长春\", 51),(\"胶州\", 52),(\"银川\", 52),\n",
" (\"张家港\", 52),(\"三门峡\", 53),(\"锦州\", 54),(\"南昌\", 54),(\"柳州\", 54),(\"三亚\", 54),\n",
" (\"自贡\", 56),(\"吉林\", 56),(\"阳江\", 57),(\"泸州\", 57),(\"西宁\", 57),(\"宜宾\", 58),\n",
" (\"呼和浩特\", 58),(\"成都\", 58),(\"大同\", 58),(\"镇江\", 59),(\"桂林\", 59),(\"张家界\", 59),\n",
" (\"宜兴\", 59),(\"北海\", 60),(\"西安\", 61),(\"金坛\", 62),(\"东营\", 62),(\"牡丹江\", 63),\n",
" (\"遵义\", 63),(\"绍兴\", 63),(\"扬州\", 64),(\"常州\", 64),(\"潍坊\", 65),(\"重庆\", 66),\n",
" (\"台州\", 67),(\"南京\", 67),(\"滨州\", 70),(\"贵阳\", 71),(\"无锡\", 71),(\"本溪\", 71),\n",
" (\"克拉玛依\", 72),(\"渭南\", 72),(\"马鞍山\", 72),(\"宝鸡\", 72),(\"焦作\", 75),(\"句容\", 75),\n",
" (\"北京\", 79),(\"徐州\", 79),(\"衡水\", 80),(\"包头\", 80),(\"绵阳\", 80),(\"乌鲁木齐\", 84),\n",
" (\"枣庄\", 84),(\"杭州\", 84),(\"淄博\", 85),(\"鞍山\", 86),(\"溧阳\", 86),(\"库尔勒\", 86),\n",
" (\"安阳\", 90),(\"开封\", 90),(\"济南\", 92),(\"德阳\", 93),(\"温州\", 95),(\"九江\", 96),\n",
" (\"邯郸\", 98),(\"临安\", 99),(\"兰州\", 99),(\"沧州\", 100),(\"临沂\", 103),(\"南充\", 104),\n",
" (\"天津\", 105),(\"富阳\", 106),(\"泰安\", 112),(\"诸暨\", 112),(\"郑州\", 113),(\"哈尔滨\", 114),\n",
" (\"聊城\", 116),(\"芜湖\", 117),(\"唐山\", 119),(\"平顶山\", 119),(\"邢台\", 119),(\"德州\", 120),\n",
" (\"济宁\", 120),(\"荆州\", 127),(\"宜昌\", 130),(\"义乌\", 132),(\"丽水\", 133),(\"洛阳\", 134),\n",
" (\"秦皇岛\", 136),(\"株洲\", 143),(\"石家庄\", 147),(\"莱芜\", 148),(\"常德\", 152),(\"保定\", 153),\n",
" (\"湘潭\", 154),(\"金华\", 157),(\"岳阳\", 169),(\"长沙\", 175),(\"衢州\", 177),(\"廊坊\", 193),\n",
" (\"菏泽\", 194),(\"合肥\", 229),(\"武汉\", 273),(\"大庆\", 279)]"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-11T18:10:28.392941Z",
"start_time": "2020-05-11T18:10:28.338999Z"
},
"code_folding": [
0
],
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 151,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c = (\n",
" Geo()\n",
" .add_schema(maptype=\"china\")\n",
" .add(\"geo\", [list(z) for z in data])\n",
" .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n",
" .set_global_opts(\n",
" visualmap_opts=opts.VisualMapOpts(is_piecewise=True),\n",
" title_opts=opts.TitleOpts(title=\"Geo-VisualMap（分段型）\"),\n",
" )\n",
" #.render(\"geo_visualmap_piecewise.html\")\n",
")\n",
"\n",
"c.render_notebook()"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-11T18:10:46.970299Z",
"start_time": "2020-05-11T18:10:46.946593Z"
},
"code_folding": [
2
],
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 152,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pyecharts.globals import ChartType\n",
"\n",
"c = (\n",
" Geo()\n",
" .add_schema(maptype=\"china\")\n",
" .add(\n",
" \"geo\",\n",
" [list(z) for z in data],\n",
" type_=ChartType.HEATMAP,\n",
" )\n",
" .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n",
" .set_global_opts(\n",
" visualmap_opts=opts.VisualMapOpts(),\n",
" title_opts=opts.TitleOpts(title=\"Geo-HeatMap\"),\n",
" )\n",
")\n",
"\n",
"c.render_notebook()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Graph"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-11T18:11:11.165012Z",
"start_time": "2020-05-11T18:11:11.124393Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"import json\n",
"\n",
"from pyecharts import options as opts\n",
"from pyecharts.charts import Graph\n",
"\n",
"\n",
"with open(\"les-miserables.json\", \"r\", encoding=\"utf-8\") as f:\n",
" j = json.load(f)\n",
" nodes = j[\"nodes\"]\n",
" links = j[\"links\"]\n",
" categories = j[\"categories\"]\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-11T18:11:15.129285Z",
"start_time": "2020-05-11T18:11:15.114376Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"{'id': '0',\n",
" 'name': 'Myriel',\n",
" 'symbolSize': 19.12381,\n",
" 'x': -266.82776,\n",
" 'y': 299.6904,\n",
" 'value': 28.685715,\n",
" 'label': {'normal': {'show': True}},\n",
" 'category': 0}"
]
},
"execution_count": 154,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nodes[0]"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-10T09:10:41.087962Z",
"start_time": "2020-05-10T09:10:41.084318Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"{'id': '0', 'source': '1', 'target': '0'}"
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"links[0] "
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-11T18:12:07.410433Z",
"start_time": "2020-05-11T18:12:07.376597Z"
},
"code_folding": [
0
],
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 155,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c = (\n",
" Graph(init_opts=opts.InitOpts(width=\"1000px\", height=\"600px\"))\n",
" .add(\n",
" \"\",\n",
" nodes=nodes,\n",
" links=links,\n",
" categories=categories,\n",
" layout=\"circular\",\n",
" is_rotate_label=True,\n",
" linestyle_opts=opts.LineStyleOpts(color=\"source\", curve=0.5),\n",
" label_opts=opts.LabelOpts(position=\"right\"),\n",
" )\n",
" .set_global_opts(\n",
" title_opts=opts.TitleOpts(title=\"Graph-Les Miserables\"),\n",
" legend_opts=opts.LegendOpts(orient=\"vertical\", pos_left=\"2%\", pos_top=\"20%\"),\n",
" )\n",
" #.render(\"graph_les_miserables.html\")\n",
")\n",
"c.render_notebook()"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-10T09:10:45.268192Z",
"start_time": "2020-05-10T09:10:45.251254Z"
},
"code_folding": [
0
],
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c = (\n",
" Graph(init_opts=opts.InitOpts(width=\"1000px\", height=\"600px\"))\n",
" .add(\n",
" \"\",\n",
" nodes=nodes,\n",
" links=links,\n",
" categories=categories,\n",
" layout=\"none\",\n",
" is_rotate_label=True,\n",
" linestyle_opts=opts.LineStyleOpts(color=\"source\", curve=0.3),\n",
" label_opts=opts.LabelOpts(position=\"right\"),\n",
" )\n",
" .set_global_opts(\n",
" title_opts=opts.TitleOpts(title=\"Graph-Les Miserables\"),\n",
" legend_opts=opts.LegendOpts(orient=\"vertical\", pos_left=\"2%\", pos_top=\"20%\"),\n",
" )\n",
" #.render(\"graph_les_miserables.html\")\n",
")\n",
"c.render_notebook()"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-10T09:10:48.055440Z",
"start_time": "2020-05-10T09:10:48.045181Z"
},
"code_folding": [
0
],
"scrolled": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 127,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# vis\n",
"from pyecharts import options as opts\n",
"from pyecharts.charts import Graph\n",
"import networkx as nx\n",
"\n",
"ba=nx.nx.karate_club_graph()\n",
"links = []\n",
"nodes = []\n",
"for i in ba.edges:\n",
" links.append({\"source\": i[0], \"target\": i[1]})\n",
"for i in ba.nodes:\n",
" nodes.append({'name': i})\n",
" \n",
"c = (\n",
" Graph()\n",
" .add(\"\", nodes, links, repulsion=80)\n",
" .set_global_opts(title_opts=opts.TitleOpts(title=\"Graph-基本示例\"))\n",
" #.render(\"graph_base.html\")\n",
")\n",
"c.render_notebook()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## HeatMap"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-10T09:14:30.990466Z",
"start_time": "2020-05-10T09:14:30.979175Z"
},
"code_folding": [
0
],
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 139,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# vis\n",
"import random\n",
"from pyecharts import options as opts\n",
"from pyecharts.charts import HeatMap\n",
"from pyecharts.faker import Faker\n",
"\n",
"value = [[i, j, random.randint(0, 50)] for i in range(24) for j in range(7)]\n",
"c = (\n",
" HeatMap()\n",
" .add_xaxis(Faker.clock)\n",
" .add_yaxis(\n",
" \"series0\",\n",
" Faker.week,\n",
" value,\n",
" label_opts=opts.LabelOpts(is_show=True, position=\"inside\"),\n",
" )\n",
" .set_global_opts(\n",
" title_opts=opts.TitleOpts(title=\"HeatMap-Label 显示\"),\n",
" visualmap_opts=opts.VisualMapOpts(),\n",
" )\n",
" #.render(\"heatmap_with_label_show.html\")\n",
")\n",
"\n",
"c.render_notebook()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Line3D"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-10T09:11:31.005721Z",
"start_time": "2020-05-10T09:11:30.966239Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"import math\n",
"\n",
"from pyecharts import options as opts\n",
"from pyecharts.charts import Line3D\n",
"from pyecharts.faker import Faker\n",
"\n",
"data = []\n",
"for t in range(0, 25000):\n",
" _t = t / 1000\n",
" x = (1 + 0.25 * math.cos(75 * _t)) * math.cos(_t)\n",
" y = (1 + 0.25 * math.cos(75 * _t)) * math.sin(_t)\n",
" z = _t + 2.0 * math.sin(75 * _t)\n",
" data.append([x, y, z])\n"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-10T09:11:32.223007Z",
"start_time": "2020-05-10T09:11:31.765946Z"
},
"code_folding": [
0
],
"scrolled": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 131,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c = (\n",
" Line3D()\n",
" .add(\n",
" \"\",\n",
" data,\n",
" xaxis3d_opts=opts.Axis3DOpts(Faker.clock, type_=\"value\"),\n",
" yaxis3d_opts=opts.Axis3DOpts(Faker.week_en, type_=\"value\"),\n",
" grid3d_opts=opts.Grid3DOpts(\n",
" width=100, depth=100, rotate_speed=150, is_rotate=True\n",
" ),\n",
" )\n",
" .set_global_opts(\n",
" visualmap_opts=opts.VisualMapOpts(\n",
" max_=30, min_=0, range_color=Faker.visual_color\n",
" ),\n",
" title_opts=opts.TitleOpts(title=\"Line3D-旋转的弹簧\"),\n",
" )\n",
" #.render(\"line3d_autorotate.html\")\n",
")\n",
"c.render_notebook()"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-10T09:12:25.069739Z",
"start_time": "2020-05-10T09:12:25.036715Z"
},
"code_folding": [
0,
5,
134
],
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 137,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# vis\n",
"import pyecharts.options as opts\n",
"from pyecharts.charts import ThemeRiver\n",
"\n",
"x_data = [\"DQ\", \"TY\", \"SS\", \"QG\", \"SY\", \"DD\"]\n",
"y_data = [\n",
" [\"2015/11/08\", 10, \"DQ\"],\n",
" [\"2015/11/09\", 15, \"DQ\"],\n",
" [\"2015/11/10\", 35, \"DQ\"],\n",
" [\"2015/11/11\", 38, \"DQ\"],\n",
" [\"2015/11/12\", 22, \"DQ\"],\n",
" [\"2015/11/13\", 16, \"DQ\"],\n",
" [\"2015/11/14\", 7, \"DQ\"],\n",
" [\"2015/11/15\", 2, \"DQ\"],\n",
" [\"2015/11/16\", 17, \"DQ\"],\n",
" [\"2015/11/17\", 33, \"DQ\"],\n",
" [\"2015/11/18\", 40, \"DQ\"],\n",
" [\"2015/11/19\", 32, \"DQ\"],\n",
" [\"2015/11/20\", 26, \"DQ\"],\n",
" [\"2015/11/21\", 35, \"DQ\"],\n",
" [\"2015/11/22\", 40, \"DQ\"],\n",
" [\"2015/11/23\", 32, \"DQ\"],\n",
" [\"2015/11/24\", 26, \"DQ\"],\n",
" [\"2015/11/25\", 22, \"DQ\"],\n",
" [\"2015/11/26\", 16, \"DQ\"],\n",
" [\"2015/11/27\", 22, \"DQ\"],\n",
" [\"2015/11/28\", 10, \"DQ\"],\n",
" [\"2015/11/08\", 35, \"TY\"],\n",
" [\"2015/11/09\", 36, \"TY\"],\n",
" [\"2015/11/10\", 37, \"TY\"],\n",
" [\"2015/11/11\", 22, \"TY\"],\n",
" [\"2015/11/12\", 24, \"TY\"],\n",
" [\"2015/11/13\", 26, \"TY\"],\n",
" [\"2015/11/14\", 34, \"TY\"],\n",
" [\"2015/11/15\", 21, \"TY\"],\n",
" [\"2015/11/16\", 18, \"TY\"],\n",
" [\"2015/11/17\", 45, \"TY\"],\n",
" [\"2015/11/18\", 32, \"TY\"],\n",
" [\"2015/11/19\", 35, \"TY\"],\n",
" [\"2015/11/20\", 30, \"TY\"],\n",
" [\"2015/11/21\", 28, \"TY\"],\n",
" [\"2015/11/22\", 27, \"TY\"],\n",
" [\"2015/11/23\", 26, \"TY\"],\n",
" [\"2015/11/24\", 15, \"TY\"],\n",
" [\"2015/11/25\", 30, \"TY\"],\n",
" [\"2015/11/26\", 35, \"TY\"],\n",
" [\"2015/11/27\", 42, \"TY\"],\n",
" [\"2015/11/28\", 42, \"TY\"],\n",
" [\"2015/11/08\", 21, \"SS\"],\n",
" [\"2015/11/09\", 25, \"SS\"],\n",
" [\"2015/11/10\", 27, \"SS\"],\n",
" [\"2015/11/11\", 23, \"SS\"],\n",
" [\"2015/11/12\", 24, \"SS\"],\n",
" [\"2015/11/13\", 21, \"SS\"],\n",
" [\"2015/11/14\", 35, \"SS\"],\n",
" [\"2015/11/15\", 39, \"SS\"],\n",
" [\"2015/11/16\", 40, \"SS\"],\n",
" [\"2015/11/17\", 36, \"SS\"],\n",
" [\"2015/11/18\", 33, \"SS\"],\n",
" [\"2015/11/19\", 43, \"SS\"],\n",
" [\"2015/11/20\", 40, \"SS\"],\n",
" [\"2015/11/21\", 34, \"SS\"],\n",
" [\"2015/11/22\", 28, \"SS\"],\n",
" [\"2015/11/23\", 26, \"SS\"],\n",
" [\"2015/11/24\", 37, \"SS\"],\n",
" [\"2015/11/25\", 41, \"SS\"],\n",
" [\"2015/11/26\", 46, \"SS\"],\n",
" [\"2015/11/27\", 47, \"SS\"],\n",
" [\"2015/11/28\", 41, \"SS\"],\n",
" [\"2015/11/08\", 10, \"QG\"],\n",
" [\"2015/11/09\", 15, \"QG\"],\n",
" [\"2015/11/10\", 35, \"QG\"],\n",
" [\"2015/11/11\", 38, \"QG\"],\n",
" [\"2015/11/12\", 22, \"QG\"],\n",
" [\"2015/11/13\", 16, \"QG\"],\n",
" [\"2015/11/14\", 7, \"QG\"],\n",
" [\"2015/11/15\", 2, \"QG\"],\n",
" [\"2015/11/16\", 17, \"QG\"],\n",
" [\"2015/11/17\", 33, \"QG\"],\n",
" [\"2015/11/18\", 40, \"QG\"],\n",
" [\"2015/11/19\", 32, \"QG\"],\n",
" [\"2015/11/20\", 26, \"QG\"],\n",
" [\"2015/11/21\", 35, \"QG\"],\n",
" [\"2015/11/22\", 40, \"QG\"],\n",
" [\"2015/11/23\", 32, \"QG\"],\n",
" [\"2015/11/24\", 26, \"QG\"],\n",
" [\"2015/11/25\", 22, \"QG\"],\n",
" [\"2015/11/26\", 16, \"QG\"],\n",
" [\"2015/11/27\", 22, \"QG\"],\n",
" [\"2015/11/28\", 10, \"QG\"],\n",
" [\"2015/11/08\", 10, \"SY\"],\n",
" [\"2015/11/09\", 15, \"SY\"],\n",
" [\"2015/11/10\", 35, \"SY\"],\n",
" [\"2015/11/11\", 38, \"SY\"],\n",
" [\"2015/11/12\", 22, \"SY\"],\n",
" [\"2015/11/13\", 16, \"SY\"],\n",
" [\"2015/11/14\", 7, \"SY\"],\n",
" [\"2015/11/15\", 2, \"SY\"],\n",
" [\"2015/11/16\", 17, \"SY\"],\n",
" [\"2015/11/17\", 33, \"SY\"],\n",
" [\"2015/11/18\", 40, \"SY\"],\n",
" [\"2015/11/19\", 32, \"SY\"],\n",
" [\"2015/11/20\", 26, \"SY\"],\n",
" [\"2015/11/21\", 35, \"SY\"],\n",
" [\"2015/11/22\", 4, \"SY\"],\n",
" [\"2015/11/23\", 32, \"SY\"],\n",
" [\"2015/11/24\", 26, \"SY\"],\n",
" [\"2015/11/25\", 22, \"SY\"],\n",
" [\"2015/11/26\", 16, \"SY\"],\n",
" [\"2015/11/27\", 22, \"SY\"],\n",
" [\"2015/11/28\", 10, \"SY\"],\n",
" [\"2015/11/08\", 10, \"DD\"],\n",
" [\"2015/11/09\", 15, \"DD\"],\n",
" [\"2015/11/10\", 35, \"DD\"],\n",
" [\"2015/11/11\", 38, \"DD\"],\n",
" [\"2015/11/12\", 22, \"DD\"],\n",
" [\"2015/11/13\", 16, \"DD\"],\n",
" [\"2015/11/14\", 7, \"DD\"],\n",
" [\"2015/11/15\", 2, \"DD\"],\n",
" [\"2015/11/16\", 17, \"DD\"],\n",
" [\"2015/11/17\", 33, \"DD\"],\n",
" [\"2015/11/18\", 4, \"DD\"],\n",
" [\"2015/11/19\", 32, \"DD\"],\n",
" [\"2015/11/20\", 26, \"DD\"],\n",
" [\"2015/11/21\", 35, \"DD\"],\n",
" [\"2015/11/22\", 40, \"DD\"],\n",
" [\"2015/11/23\", 32, \"DD\"],\n",
" [\"2015/11/24\", 26, \"DD\"],\n",
" [\"2015/11/25\", 22, \"DD\"],\n",
" [\"2015/11/26\", 16, \"DD\"],\n",
" [\"2015/11/27\", 22, \"DD\"],\n",
" [\"2015/11/28\", 10, \"DD\"],\n",
"]\n",
"\n",
"c = (\n",
" ThemeRiver(init_opts=opts.InitOpts(width=\"1600px\", height=\"800px\"))\n",
" .add(\n",
" series_name=x_data,\n",
" data=y_data,\n",
" singleaxis_opts=opts.SingleAxisOpts(\n",
" pos_top=\"50\", pos_bottom=\"50\", type_=\"time\"\n",
" ),\n",
" )\n",
" .set_global_opts(\n",
" tooltip_opts=opts.TooltipOpts(trigger=\"axis\", axis_pointer_type=\"line\")\n",
" )\n",
" #.render(\"theme_river.html\")\n",
")\n",
"\n",
"c.render_notebook()"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-10T09:12:30.196945Z",
"start_time": "2020-05-10T09:12:30.187082Z"
},
"code_folding": [
0
],
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 138,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# vis\n",
"from pyecharts import options as opts\n",
"from pyecharts.charts import Boxplot\n",
"\n",
"v1 = [\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",
"v2 = [\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",
"c = Boxplot()\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",
"c.set_global_opts(title_opts=opts.TitleOpts(title=\"BoxPlot-基本示例\"))\n",
"c.render_notebook()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## WordCloud"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-11T18:14:15.499019Z",
"start_time": "2020-05-11T18:14:14.981549Z"
},
"code_folding": [
0
],
"scrolled": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 156,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# vis\n",
"import pyecharts.options as opts\n",
"from pyecharts.charts import WordCloud\n",
"\n",
"import jieba.analyse\n",
"import numpy as np\n",
"\n",
"with open('../data/gov_reports1954-2017.txt', 'r') as f:\n",
" reports = f.readlines()\n",
"\n",
"txt = reports[-1]\n",
"tf = jieba.analyse.extract_tags(txt, topK=100, withWeight=True)\n",
"\n",
"c = (\n",
" WordCloud()\n",
" .add(series_name=\"热点分析\", data_pair=tf, word_size_range=[6, 100])\n",
" .set_global_opts(\n",
" title_opts=opts.TitleOpts(\n",
" title=\"热点分析\", title_textstyle_opts=opts.TextStyleOpts(font_size=23)\n",
" ),\n",
" tooltip_opts=opts.TooltipOpts(is_show=True),\n",
" )\n",
" #.render(\"basic_wordcloud.html\")\n",
")\n",
"\n",
"c.render_notebook()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Timeline"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-10T09:12:21.254759Z",
"start_time": "2020-05-10T09:12:21.243527Z"
},
"code_folding": [
0
],
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 136,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# vis\n",
"from pyecharts import options as opts\n",
"from pyecharts.charts import Bar, Timeline\n",
"from pyecharts.faker import Faker\n",
"\n",
"x = Faker.choose()\n",
"tl = Timeline()\n",
"for i in range(2015, 2020):\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",
" tl.add(bar, \"{}年\".format(i))\n",
"tl.render_notebook()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Grid"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-10T09:12:10.848062Z",
"start_time": "2020-05-10T09:12:10.832269Z"
},
"code_folding": [
0,
5,
15,
26,
36
],
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# vis\n",
"from pyecharts import options as opts\n",
"from pyecharts.charts import Grid, Line, Scatter\n",
"from pyecharts.faker import Faker\n",
"\n",
"scatter = (\n",
" Scatter()\n",
" .add_xaxis(Faker.choose())\n",
" .add_yaxis(\"商家A\", Faker.values())\n",
" .add_yaxis(\"商家B\", Faker.values())\n",
" .set_global_opts(\n",
" #title_opts=opts.TitleOpts(title=\"Grid-Scatter\"),\n",
" #legend_opts=opts.LegendOpts(pos_left=\"20%\"),\n",
" )\n",
")\n",
"line = (\n",
" Line()\n",
" .add_xaxis(Faker.choose())\n",
" .add_yaxis(\"商家A\", Faker.values())\n",
" .add_yaxis(\"商家B\", Faker.values())\n",
" .set_global_opts(\n",
" #title_opts=opts.TitleOpts(title=\"Grid-Line\", pos_right=\"5%\"),\n",
" #legend_opts=opts.LegendOpts(pos_right=\"20%\"),\n",
" )\n",
")\n",
"\n",
"scatter2 = (\n",
" Scatter()\n",
" .add_xaxis(Faker.choose())\n",
" .add_yaxis(\"商家A\", Faker.values())\n",
" .add_yaxis(\"商家B\", Faker.values())\n",
" .set_global_opts(\n",
" #title_opts=opts.TitleOpts(title=\"Grid-Scatter\"),\n",
" #legend_opts=opts.LegendOpts(pos_left=\"20%\"),\n",
" )\n",
")\n",
"line2 = (\n",
" Line()\n",
" .add_xaxis(Faker.choose())\n",
" .add_yaxis(\"商家A\", Faker.values())\n",
" .add_yaxis(\"商家B\", Faker.values())\n",
" .set_global_opts(\n",
" #title_opts=opts.TitleOpts(title=\"Grid-Line\", pos_right=\"5%\"),\n",
" #legend_opts=opts.LegendOpts(pos_right=\"20%\"),\n",
" )\n",
")\n",
"grid = (\n",
" Grid()\n",
" .add(scatter, grid_opts=opts.GridOpts(pos_bottom=\"60%\",pos_left=\"60%\"))\n",
" .add(line, grid_opts=opts.GridOpts(pos_bottom=\"60%\",pos_right=\"60%\"))\n",
" .add(line2, grid_opts=opts.GridOpts(pos_top=\"60%\",pos_left=\"60%\"))\n",
" .add(scatter2, grid_opts=opts.GridOpts(pos_top=\"60%\",pos_right=\"60%\"))\n",
" #.render(\"grid_horizontal.html\")\n",
")\n",
"grid.render_notebook() "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Overlap"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-10T09:12:18.003234Z",
"start_time": "2020-05-10T09:12:17.991826Z"
},
"code_folding": [
0
],
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 135,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# vis\n",
"from pyecharts import options as opts\n",
"from pyecharts.charts import Bar, Line\n",
"from pyecharts.faker import Faker\n",
"\n",
"v1 = [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",
"v2 = [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",
"v3 = [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",
"\n",
"\n",
"bar = (\n",
" Bar()\n",
" .add_xaxis(Faker.months)\n",
" .add_yaxis(\"蒸发量\", v1)\n",
" .add_yaxis(\"降水量\", v2)\n",
" .extend_axis(\n",
" yaxis=opts.AxisOpts(\n",
" axislabel_opts=opts.LabelOpts(formatter=\"{value} °C\"), interval=5\n",
" )\n",
" )\n",
" .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n",
" .set_global_opts(\n",
" title_opts=opts.TitleOpts(title=\"Overlap-bar+line\"),\n",
" yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter=\"{value} ml\")),\n",
" )\n",
")\n",
"\n",
"line = Line().add_xaxis(Faker.months).add_yaxis(\"平均温度\", v3, yaxis_index=1)\n",
"bar.overlap(line)\n",
"bar.render_notebook()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Save html files"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-10T08:54:26.161023Z",
"start_time": "2020-05-10T08:54:26.153315Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'/Users/datalab/github/ccbook/content/grid.html'"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid.render(path = 'grid.html')\n",
"#help(grid.render)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
""
]
}
],
"metadata": {
"celltoolbar": "Slideshow",
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.6"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
"autoclose": false,
"autocomplete": true,
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 1,
"hotkeys": {
"equation": "Ctrl-E",
"itemize": "Ctrl-I"
},
"labels_anchors": false,
"latex_user_defs": false,
"report_style_numbering": false,
"user_envs_cfg": false
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": false,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": true
}
},
"nbformat": 4,
"nbformat_minor": 2
}