{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pyecharts 可视化教程"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Echarts 是一个由百度开源的数据可视化javascript库，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。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**注意，我们安装的是pyecharts的0.5.11的版本，不是最新版本！**"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting pyecharts==0.5.11\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/52/b8/e46a41c44176cd247cb1fd437e38e179ea604de28c91755c30be1046772c/pyecharts-0.5.11-py2.py3-none-any.whl (122kB)\n",
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" Downloading https://files.pythonhosted.org/packages/8a/6f/0eb22694df07072ea467effdbb8e313222a21788ea7914773f59fd2bb857/lml-0.0.2-py2.py3-none-any.whl\n",
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"Collecting jupyter-echarts-pypkg==0.1.2 (from pyecharts==0.5.11)\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/46/71/839e9397d924d577aa44e78ac06a813c761b39a6eab8cd3ba3821b4c894b/jupyter-echarts-pypkg-0.1.2.tar.gz (520kB)\n",
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" Downloading https://files.pythonhosted.org/packages/6f/68/23644a394341fef4cbcb48a3fa4b2816408a13a1fa3281cf9030b2ac2d67/pyecharts_javascripthon-0.0.6-py2.py3-none-any.whl\n",
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"Collecting javascripthon>=0.10; python_version > \"3.4\" (from pyecharts-javascripthon==0.0.6->pyecharts==0.5.11)\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/ea/ef/b46401e181768b3738660f5dc1f7ea0b334ed17fff685a61503d2989e77e/javascripthon-0.10.tar.gz (538kB)\n",
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"Collecting dukpy (from javascripthon>=0.10; python_version > \"3.4\"->pyecharts-javascripthon==0.0.6->pyecharts==0.5.11)\n",
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"\u001b[?25hCollecting macropy3==1.1.0b2 (from javascripthon>=0.10; python_version > \"3.4\"->pyecharts-javascripthon==0.0.6->pyecharts==0.5.11)\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/8c/b7/a61f9d9bd7de80ca51ef362db5bb6434ea21484c907d2a5f396d97b0274c/macropy3-1.1.0b2.tar.gz (44kB)\n",
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"\u001b[?25hBuilding wheels for collected packages: jupyter-echarts-pypkg, javascripthon, dukpy, macropy3\n",
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"Successfully installed dukpy-0.2.2 javascripthon-0.10 jupyter-echarts-pypkg-0.1.2 lml-0.0.2 macropy3-1.1.0b2 pyecharts-0.5.11 pyecharts-javascripthon-0.0.6\n",
"Collecting pyecharts_snapshot\n",
" Downloading https://files.pythonhosted.org/packages/85/e8/f0a14cc92d89d43e52efb48bca5053c24b53e06e93b43579071476ed0cc6/pyecharts_snapshot-0.2.0-py2.py3-none-any.whl\n",
"Collecting pyppeteer>=0.0.25 (from pyecharts_snapshot)\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/b0/16/a5e8d617994cac605f972523bb25f12e3ff9c30baee29b4a9c50467229d9/pyppeteer-0.0.25.tar.gz (1.2MB)\n",
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" Downloading https://files.pythonhosted.org/packages/ad/d8/5608d571ffad3d7de0192b0b3099fe3f38d87c0817ebff3cee19264f0bc2/pyee-6.0.0-py2.py3-none-any.whl\n",
"Collecting websockets (from pyppeteer>=0.0.25->pyecharts_snapshot)\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/8b/6b/dcbafe10a1b889f3d31bef7048dbfb23973d4b56e8fb47c9158c47fa5643/websockets-8.0.2-cp37-cp37m-macosx_10_6_intel.whl (65kB)\n",
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"\u001b[?25hCollecting appdirs (from pyppeteer>=0.0.25->pyecharts_snapshot)\n",
" Downloading https://files.pythonhosted.org/packages/56/eb/810e700ed1349edde4cbdc1b2a21e28cdf115f9faf263f6bbf8447c1abf3/appdirs-1.4.3-py2.py3-none-any.whl\n",
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" Building wheel for pyppeteer (setup.py) ... \u001b[?25ldone\n",
"\u001b[?25h Stored in directory: /Users/liangyun/Library/Caches/pip/wheels/34/e0/5d/070e22eceecf7ecd5fa4b86bbc18c1c7d0b90e02e9b57f35eb\n",
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]
}
],
"source": [
"# 安装pyecharts\n",
"!pip install pyecharts==0.5.11\n",
"\n",
"# pyecharts_snapshot 提供图片导出功能\n",
"!pip install pyecharts_snapshot "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 一，基本图表"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"pyecharts支持30多种不同的图表。我们只对其中最常用的几种类型进行基本范例的介绍。\n",
"\n",
"下面是pyecharts主要类的继承关系。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**1，柱形图示范**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"柱形图适合表现几组数据之间的对比关系，柱形图的数据的数量一般不宜太多。"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pyecharts import Bar\n",
"\n",
"x = [\"衬衫\", \"羊毛衫\", \"雪纺衫\", \"裤子\", \"高跟鞋\"]\n",
"y1 = [5, 20, 36, 10, 75]\n",
"y2 = [10, 25, 8, 60, 20]\n",
"\n",
"bar = Bar(title = \"产品月销量\",width = 600,height = 420)\n",
"bar.add(name = \"商家A\", x_axis = x, y_axis = y1)\n",
"bar.add(name = \"商家B\", x_axis = x, y_axis = y2,is_xaxis_boundarygap =True)\n",
"\n",
"bar.render('data/柱形图示范.html')\n",
"bar"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**2，折线图示范**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"折线图适合描述两个变量之间的函数关系，例如常用它来描述一个变量随时间的变化趋势。"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pyecharts import Line\n",
"\n",
"x = ['2018-{:0>2d}'.format(s) for s in range(1,13)]\n",
"y1 = [5,10,26,30,35,30,20,26,40,46,40,50]\n",
"y2 = [8,20,24,36,40,36,40,45,50,53,48,58]\n",
"\n",
"line = Line(title = \"月销售总额\",width = 600,height = 420)\n",
"\n",
"line.add(name = \"商家A\", x_axis = x, y_axis = y1,\n",
" line_width = 3,line_color = 'red')\n",
"line.add(name = \"商家B\", x_axis = x, y_axis = y2,\n",
" yaxis_min = 0,yaxis_max = 100,is_xaxis_boundarygap = False,\n",
" is_datazoom_show =True,line_width = 2,line_color = 'cyan')\n",
"\n",
"line.render('data/折线图示范.html')\n",
"line"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3，散点图示范**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"散点图适合表现大量样本的多个属性的分布规律。散点图的每个点表示一个样本，每个坐标维度表示一个属性。\n",
"\n",
"当样本属性维度多于2个时，可以使用点的颜色或大小等方式来表达更多属性维度。"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pyecharts import Scatter\n",
"import pandas as pd \n",
"\n",
"dfboy = pd.DataFrame()\n",
"dfboy['weight'] = [56,67,65,70,57,60,80,85,76,64]\n",
"dfboy['height'] = [162,170,168,172,168,172,180,176,178,170]\n",
"\n",
"dfgirl = pd.DataFrame()\n",
"dfgirl['weight'] = [50,62,60,70,57,45,62,65,70,56]\n",
"dfgirl['height'] = [155,162,165,170,166,158,160,170,172,165]\n",
"\n",
"scatter = Scatter(title = \"体格数据\",width = 600,height = 420)\n",
"scatter.add(name = \"boy\", x_axis = dfboy['weight'], y_axis = dfboy['height'])\n",
"scatter.add(name = \"girl\", x_axis = dfgirl['weight'], y_axis = dfgirl['height'],\n",
" yaxis_min = 130,yaxis_max = 200,xaxis_min = 30,xaxis_max = 100)\n",
"\n",
"scatter.render(\"data/散点图示范.html\")\n",
"\n",
"scatter"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"以下示范3个维度数据的散点图展示"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pyecharts import Scatter\n",
"import pandas as pd \n",
"\n",
"def custom_formatter(params):\n",
" return (params.value[3] + ':' +\n",
" str(params.value[0]) +','\n",
" +str(params.value[1]) + ','\n",
" +str(params.value[2]))\n",
"\n",
"df = pd.DataFrame()\n",
"df['country'] = [\"中国\",'美国','德国','法国','英国','日本','俄罗斯','印度','澳大利亚','加拿大']\n",
"df['life-expectancy'] = [76.9,79.1,81.1,81.9,81.4,83.5,73.13,66.8,81.8,81.7]\n",
"df['capita-gdp'] = [13334,53354,44053,37599,38225,36162,23038,5903,44056,43294]\n",
"df['population'] = [1376048943,321773631,80688545,64395345,64715810,126573481,143456918,\n",
" 1311050527,23968973,35939927]\n",
"\n",
"scatter = Scatter(title = \"各国发展水平\",width = 600,height = 420)\n",
"scatter.add(name = '',\n",
" x_axis = df['capita-gdp'], # params.values[0]\n",
" y_axis = df['life-expectancy'], # params.values[1]\n",
" extra_data = df['population'].values.tolist(), # params.values[2]\n",
" extra_name = df['country'].values.tolist(), # params.values[3]\n",
" tooltip_formatter=custom_formatter, #自定义提示框格式内容\n",
" is_visualmap=True, # 显示\n",
" visual_orient=\"horizontal\",\n",
" visual_type = 'size', #可以是size或者color\n",
" visual_dimension=2,\n",
" visual_range=[20000000, 1500000000],\n",
" )\n",
"scatter"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**4，箱型图示范**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"箱型图适合表现一组数据的统计分布规律，它能显示出一组数据的最大值、最小值、中位数、及上下四分位数。\n",
"\n",
"箱型图的进阶版本是小提琴图，可以展示数据的密度估计曲线，可以用seaborn画出。"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pyecharts import Boxplot\n",
"\n",
"x =['1班','2班','3班','4班']\n",
"y1=[78, 98, 56, 78, 90.0, 45, 78, 20, 87, 86, 74, 89, 94]\n",
"y2=[89, 82, 45, 67, 68, 78.0, 79, 98, 71, 56, 78, 81, 80]\n",
"y3=[90, 80, 60, 89, 76, 73.0, 72, 92, 89, 87, 65, 66, 76]\n",
"y4=[82, 72, 55, 100, 90.0, 78, 69, 67, 87, 66, 78, 71, 82]\n",
"\n",
"box = Boxplot(title = '考试成绩箱型图',width = 600,height = 420)\n",
"\n",
"# 预处理数据计算最大值，最小值，中位数以及上下四分位数\n",
"y_prepared = box.prepare_data([y1,y2,y3,y4]) \n",
"box.add(name = '',x_axis = x,y_axis = y_prepared)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"附：用seaborn 进行小提琴图的绘制"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'svg'"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"#设置风格\n",
"sns.set(style=\"white\", context=\"notebook\")\n",
"#处理中文问题\n",
"sns.set_style({'font.sans-serif':['simhei', 'Arial']}) "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"dfdata = pd.DataFrame()\n",
"dfdata['score'] = y1 + y2 + y3 + y4\n",
"dfdata['class'] = ['class1']*len(y1)+['class2']*len(y2)+['class3']*len(y3)+['class4']*len(y4)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = sns.violinplot(x= 'class', y = 'score',data = dfdata,\n",
" palette = 'hls', # 设置调色板\n",
" inner = 'box', # 设置内部显示类型 → “box”, “quartile”, “point”, “stick”, None\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**5，词云图示范**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"词云图适合表现不同关键词的出现频率或重要性程度。"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
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],
"source": [
"from pyecharts import WordCloud\n",
"\n",
"words = ['python','jupyter','numpy','pandas','matplotlib','sklearn',\n",
" 'xgboost','lightGBM','simpy','keras','tensorflow',\n",
" 'hive','hadoop','spark']\n",
"counts = [100,90,65,95,50,60,70,70,20,70,80,80,60,60]\n",
"\n",
"cloud = WordCloud(title = '数据算法常用工具',width = 600,height = 420)\n",
"cloud.add(name = 'utils',attr = words,value = counts,\n",
" shape = \"circle\",word_size_range = (10,70))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 二，图表配置"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"如果要对绘制的图表进行诸如图像尺寸，坐标轴刻度，线的颜色，以及网格线等各个细节方面的修改，我们则需要手动对图表进行配置，而不是依赖于默认配置。\n",
"\n",
"图表配置有3种主要方式：\n",
"\n",
"1，修改图表主题风格：利用configure或use_theme指定图表主题风格，对图表整体颜色风格产生影响。 \n",
"\n",
"2，初始化图表通用属性：创建图表时指定图表height,title等属性，对图像尺寸和标题等通用属性产生影响。\n",
"\n",
"3，配置特定元素属性：使用add给图表配置xyAxis,datazoom,lineStyle等特定元素属性。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**1，修改图表主题风格**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"可以使用use_theme修改单个图表主题，也可以用configure修改全局图表风格。"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 默认light主题效果\n",
"import random\n",
"from pyecharts import Bar\n",
"\n",
"X_AXIS = [\"衬衫\", \"羊毛衫\", \"雪纺衫\", \"裤子\", \"高跟鞋\", \"袜子\"]\n",
"bar = Bar(\"light主题效果\", \"这里是副标题\")\n",
"bar.add(\"商家A\", X_AXIS, [random.randint(10, 100) for _ in range(6)])\n",
"bar.add(\"商家B\", X_AXIS, [random.randint(10, 100) for _ in range(6)])\n",
"bar.add(\"商家C\", X_AXIS, [random.randint(10, 100) for _ in range(6)])\n",
"bar.add(\"商家D\", X_AXIS, [random.randint(10, 100) for _ in range(6)])\n",
"bar"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 使用dark主题\n",
"import random\n",
"from pyecharts import Bar\n",
"\n",
"\n",
"X_AXIS = [\"衬衫\", \"羊毛衫\", \"雪纺衫\", \"裤子\", \"高跟鞋\", \"袜子\"]\n",
"bar = Bar(\"dark主题展示\", \"这里是副标题\")\n",
"bar.use_theme(\"dark\")\n",
"bar.add(\"商家A\", X_AXIS, [random.randint(10, 100) for _ in range(6)])\n",
"bar.add(\"商家B\", X_AXIS, [random.randint(10, 100) for _ in range(6)])\n",
"bar.add(\"商家C\", X_AXIS, [random.randint(10, 100) for _ in range(6)])\n",
"bar.add(\"商家D\", X_AXIS, [random.randint(10, 100) for _ in range(6)])\n",
"bar"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"如果要设置某个主题风格应用到所有图表，可以在绘图开始前用configure进行设置。"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"from pyecharts import configure\n",
"# 将这行代码置于首部\n",
"configure(global_theme='light')"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pyecharts import Pie\n",
"attr = [\"衬衫\", \"羊毛衫\", \"雪纺衫\", \"裤子\", \"高跟鞋\", \"袜子\"]\n",
"value = [11, 12, 13, 10, 10, 10]\n",
"pie = Pie(\"销售额占比\",width = 600,height = 420)\n",
"pie.add(\"\", attr, value, is_label_show=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**2，初始化图表通用属性**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"图表通用属性指的是图表的title,subtitle,height,width,title_pos,title_color,title_text_size,background_color等属性，这些属性对所有类型的图表都适用。\n",
"\n",
"所有通用属性设置方式详细参见：https://github.com/lyhue1991/pyecharts/blob/master/docs/zh-cn/charts_configure.md"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pyecharts import Pie\n",
"attr = [\"衬衫\", \"羊毛衫\", \"雪纺衫\", \"裤子\", \"高跟鞋\", \"袜子\"]\n",
"value = [11, 12, 13, 10, 10, 10]\n",
"\n",
"# 初始化图表通用属性\n",
"pie = Pie(title = \"销售额占比\",\n",
" title_pos = 'center', # 标题居中\n",
" title_top = 'bottom', # 标题在底部\n",
" title_color = '#0000ff', # 标题颜色设置为蓝色，256位rgb格式\n",
" background_color = \"#aee\", # 设置背景颜色，16位rgb格式\n",
" width = 600,height = 420)\n",
"\n",
"pie.add(\"\", attr, value, is_label_show=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3，配置特定元素属性**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"特定元素属性包括xyAxis,dataZoom,lineStyle,markLine-markPoint,visualMap等元素属性，这些元素只在某些种类的图表中适用。例如xyAxis只在Line、Bar、Scatter、EffectScatter、Kline这几类图表中适用。\n",
"\n",
"所有特定元素属性设置方式详细参见：https://github.com/lyhue1991/pyecharts/blob/master/docs/zh-cn/charts_configure.md"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 设置xyAxis示范\n",
"\n",
"from pyecharts import Line\n",
"\n",
"x = ['2018-{:0>2d}'.format(s) for s in range(1,13)]\n",
"y1 = [5,10,26,30,35,30,20,26,40,46,40,50]\n",
"y2 = [8,20,24,36,40,36,40,45,50,53,48,58]\n",
"\n",
"line = Line(title = \"月销售总额\",width = 600,height = 420)\n",
"\n",
"line.add(name = \"商家A\", x_axis = x, y_axis = y1)\n",
"line.add(name = \"商家B\", x_axis = x, y_axis = y2,\n",
" #\n",
" #=====设置xyAxis=====\n",
" yaxis_min = 0,yaxis_max = 100, # 设置y坐标轴刻度范围\n",
" xaxis_name = '月份', yaxis_name = '销售额', #x轴名称，y轴名称\n",
" xaxis_name_gap = 40, # x轴名称与轴距离\n",
" xaxis_rotate = 30, # x轴刻度旋转角度\n",
" is_splitline_show = True, # 显示y轴网格线\n",
" is_xaxislabel_align = True # x轴刻度和标签是否对齐\n",
" )\n",
"line"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 设置dataZoom示范\n",
"\n",
"from pyecharts import Line\n",
"\n",
"x = ['2018-{:0>2d}'.format(s) for s in range(1,13)]\n",
"y1 = [5,10,26,30,35,30,20,26,40,46,40,50]\n",
"y2 = [8,20,24,36,40,36,40,45,50,53,48,58]\n",
"\n",
"line = Line(title = \"月销售总额\",width = 600,height = 420)\n",
"\n",
"line.add(name = \"商家A\", x_axis = x, y_axis = y1)\n",
"line.add(name = \"商家B\", x_axis = x, y_axis = y2,\n",
" #\n",
" #=====设置xyAxis=====\n",
" is_xaxis_boundarygap = False, # x坐标刻度对准数据，而不是作为分类边界\n",
" #\n",
" #=====设置dataZoom=====\n",
" is_datazoom_show = True, #显示 dataZoom控制条\n",
" datazoom_type = 'both' # 可以是slider,inside或both\n",
" #\n",
" #\n",
" )\n",
"line"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 设置lineStyle示范\n",
"\n",
"from pyecharts import Line\n",
"\n",
"x = ['2018-{:0>2d}'.format(s) for s in range(1,13)]\n",
"y1 = [5,10,26,30,35,30,20,26,40,46,40,50]\n",
"y2 = [8,20,24,36,40,36,40,45,50,53,48,58]\n",
"\n",
"line = Line(title = \"月销售总额\",width = 600,height = 420)\n",
"\n",
"line.add(name = \"商家A\", x_axis = x, y_axis = y1,\n",
" #\n",
" #=====设置lineStyle=====\n",
" line_width = 2,\n",
" line_opacity = 0.5, # 透明度\n",
" line_color = 'red'\n",
" )\n",
"line.add(name = \"商家B\", x_axis = x, y_axis = y2,\n",
" #\n",
" #=====设置xyAxis=====\n",
" is_xaxis_boundarygap = False, # x坐标刻度对准数据，而不是作为分类边界\n",
" #\n",
" #=====设置dataZoom=====\n",
" is_datazoom_show = True, #显示 dataZoom控制条\n",
" #\n",
" #=====设置lineStyle=====\n",
" line_width = 3,\n",
" line_color = '#11ffbb',\n",
" line_type = 'dashed', # 线型，可以是solid,dashed,或者dotted\n",
" )\n",
"line"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 设置markPoint和markLine示范\n",
"\n",
"from pyecharts import Line\n",
"\n",
"x = ['2018-{:0>2d}'.format(s) for s in range(1,13)]\n",
"y1 = [5,10,26,30,35,30,20,26,40,46,40,50]\n",
"y2 = [8,20,24,36,40,36,40,45,50,53,48,58]\n",
"\n",
"line = Line(title = \"月销售总额\",width = 600,height = 420)\n",
"\n",
"line.add(name = \"商家A\", x_axis = x, y_axis = y1,\n",
" #\n",
" #=====设置lineStyle=====\n",
" line_width = 2,\n",
" line_opacity = 0.5, # 透明度\n",
" line_color = 'red',\n",
" #\n",
" #=====设置markPoint&markLine=====\n",
" mark_point = ['min','max'], #标记点\n",
" mark_line = ['average'] #标记线\n",
" )\n",
"line.add(name = \"商家B\", x_axis = x, y_axis = y2,\n",
" #\n",
" #=====设置xyAxis=====\n",
" is_xaxis_boundarygap = False, # x坐标刻度对准数据，而不是作为分类边界\n",
" yaxis_min = 0,yaxis_max = 100, # 设置y坐标轴刻度范围\n",
" #\n",
" #=====设置dataZoom=====\n",
" is_datazoom_show = True, #显示 dataZoom控制条\n",
" #\n",
" #=====设置lineStyle=====\n",
" line_width = 3,\n",
" line_type = 'dashed', # 线型，可以是solid,dashed,或者dotted\n",
" \n",
" #=====设置markPoint&markLine=====\n",
" mark_point = [{\"coord\": ['2018-09', 60], \"name\": \"2018/09销售目标\"}, \n",
" {\"coord\": ['2018-11', 80], \"name\": \"2018/10销售目标\"}] # 自定义标记点\n",
" )\n",
"line"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"如果需要反复使用相同的配置，可以使用Style类简化这个过程。"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pyecharts import Pie,Style\n",
"\n",
"pie = Pie('各类电影中\"好片\"所占的比例', \"数据来自豆瓣\", title_pos='center')\n",
"style = Style()\n",
"pie_style = style.add(\n",
" label_pos=\"center\",\n",
" is_label_show=True,\n",
" label_text_color=None\n",
")\n",
"\n",
"pie.add(\n",
" \"\", [\"剧情\", \"\"], [25, 75], center=[10, 30], radius=[18, 24], **pie_style\n",
")\n",
"pie.add(\n",
" \"\", [\"奇幻\", \"\"], [24, 76], center=[30, 30], radius=[18, 24], **pie_style\n",
")\n",
"pie.add(\n",
" \"\", [\"爱情\", \"\"], [14, 86], center=[50, 30], radius=[18, 24], **pie_style\n",
")\n",
"pie.add(\n",
" \"\", [\"惊悚\", \"\"], [11, 89], center=[70, 30], radius=[18, 24], **pie_style\n",
")\n",
"pie.add(\n",
" \"\", [\"冒险\", \"\"], [27, 73], center=[90, 30], radius=[18, 24], **pie_style\n",
")\n",
"pie.add(\n",
" \"\", [\"动作\", \"\"], [15, 85], center=[10, 70], radius=[18, 24], **pie_style\n",
")\n",
"pie.add(\n",
" \"\", [\"喜剧\", \"\"], [54, 46], center=[30, 70], radius=[18, 24], **pie_style\n",
")\n",
"pie.add(\n",
" \"\", [\"科幻\", \"\"], [26, 74], center=[50, 70], radius=[18, 24], **pie_style\n",
")\n",
"pie.add(\n",
" \"\", [\"悬疑\", \"\"], [25, 75], center=[70, 70], radius=[18, 24], **pie_style\n",
")\n",
"pie.add(\n",
" \"\", [\"犯罪\", \"\"],[28, 72],center=[90, 70], radius=[18, 24], legend_top=\"bottom\", \n",
" **pie_style\n",
")\n",
"pie"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 三，图表组合"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"如果要把多张图作为子图绘制在一张图中，或者要把柱状图和折线图这样不同类型的图表叠加在一起的时候，我们需要使用对基本图表进行一些组合来实现更加丰富的呈现效果。\n",
"\n",
"在pyecharts 中 对图表进行组合的方式主要有：Grid, Overlap, Page, Timeline这四种方式。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**1，Grid图表组合**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Grid图表组合可以并行显示多张图，类似子图的作用。Grid中的子图可以是Overlap。"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pyecharts import Bar, Line, Grid\n",
"\n",
"x = [\"衬衫\", \"羊毛衫\", \"雪纺衫\", \"裤子\", \"高跟鞋\", \"袜子\"]\n",
"y1 = [5, 20, 36, 10, 75, 90]\n",
"y2 = [10, 25, 8, 60, 20, 80]\n",
"bar = Bar(\"柱状图示例\", height=720)\n",
"bar.add(\"商家A\", x, y1, is_stack=True)\n",
"bar.add(\"商家B\", x, y2, is_stack=True)\n",
"\n",
"\n",
"line = Line(\"折线图示例\", title_top=\"50%\")\n",
"x = [\"周一\", \"周二\", \"周三\", \"周四\", \"周五\", \"周六\", \"周日\"]\n",
"line.add(\n",
" \"最高气温\",\n",
" x,\n",
" [11, 11, 15, 13, 12, 13, 10],\n",
" mark_point=[\"max\", \"min\"],\n",
" mark_line=[\"average\"],\n",
")\n",
"line.add(\n",
" \"最低气温\",\n",
" x,\n",
" [1, -2, 2, 5, 3, 2, 0],\n",
" mark_point=[\"max\", \"min\"],\n",
" mark_line=[\"average\"],\n",
" legend_top=\"50%\",\n",
")\n",
"\n",
"grid = Grid()\n",
"#利用grid_bottom,grid_top,grid_left,grid_right四个参数控制子图的相对位置\n",
"grid.add(bar, grid_bottom=\"60%\") \n",
"grid.add(line, grid_top=\"60%\")\n",
"grid"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"利用Grid解决dataZoom与X轴标签重叠的问题"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pyecharts import Bar, Grid\n",
"\n",
"x = [\n",
" \"名字很长的x轴1\",\n",
" \"名字很长的x轴2\",\n",
" \"名字很长的x轴3\",\n",
" \"名字很长的x轴4\",\n",
" \"名字很长的x轴5\",\n",
" \"名字很长的x轴6\",\n",
" \"名字很长的x轴7\",\n",
" \"名字很长的x轴8\",\n",
" \"名字很长的x轴9\",\n",
"]\n",
"y = [10, 20, 30, 40, 50, 60, 70, 80, 90]\n",
"\n",
"grid = Grid()\n",
"bar = Bar(\"利用 Grid 解决 dataZoom 与 X 轴标签重叠问题\")\n",
"bar.add(\"\", x, y, is_datazoom_show=True, xaxis_interval=0, xaxis_rotate=30)\n",
"\n",
"# 把 bar 加入到 grid 中，并适当调整 grid_bottom 参数，使 bar 图整体上移\n",
"grid.add(bar, grid_bottom=\"25%\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**2，Overlap图表组合**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Overlap图表组合可以将不同类型的图表画在同一张图上。"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pyecharts import Bar, Line, Overlap\n",
"\n",
"attr = ['A', 'B', 'C', 'D', 'E', 'F']\n",
"v1 = [10, 20, 30, 40, 50, 60]\n",
"v2 = [38, 28, 58, 48, 78, 68]\n",
"bar = Bar(\"Line - Bar 示例\")\n",
"bar.add(\"bar\", attr, v1)\n",
"line = Line()\n",
"line.add(\"line\", attr, v2)\n",
"\n",
"overlap = Overlap()\n",
"overlap.add(bar)\n",
"overlap.add(line)\n",
"overlap"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Overlap显示双坐标轴"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pyecharts import Line, Bar, Overlap\n",
"\n",
"attr = [\"{}月\".format(i) for i in range(1, 13)]\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",
"bar = Bar()\n",
"bar.add(\"蒸发量\", attr, v1)\n",
"bar.add(\"降水量\", attr, v2, yaxis_formatter=\" ml\",\n",
" yaxis_interval=50, yaxis_max=250)\n",
"\n",
"line = Line()\n",
"line.add(\"平均温度\", attr, v3, yaxis_formatter=\" °C\", yaxis_interval=5)\n",
"\n",
"overlap = Overlap(width=800, height=500)\n",
"# 默认不新增 x y 轴，并且 x y 轴的索引都为 0\n",
"overlap.add(bar)\n",
"# 新增一个 y 轴，此时 y 轴的数量为 2，第二个 y 轴的索引为 1（索引从 0 开始），所以设置 yaxis_index = 1\n",
"# 由于使用的是同一个 x 轴，所以 x 轴部分不用做出改变\n",
"overlap.add(line, yaxis_index=1, is_add_yaxis=True)\n",
"overlap"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3，Pages图表组合**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pages可以将多张图表按顺序展示在一张网页中，适合制作图形化报表。Pages中的图表可以是Grid,Overlap或Timeline."
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#coding=utf-8\n",
"from __future__ import unicode_literals\n",
"from pyecharts import Bar, Scatter3D\n",
"from pyecharts import Page\n",
"\n",
"page = Page() # step 1\n",
"\n",
"# bar\n",
"attr = [\"衬衫\", \"羊毛衫\", \"雪纺衫\", \"裤子\", \"高跟鞋\", \"袜子\"]\n",
"v1 = [5, 20, 36, 10, 75, 90]\n",
"v2 = [10, 25, 8, 60, 20, 80]\n",
"bar = Bar(\"柱状图数据堆叠示例\")\n",
"bar.add(\"商家A\", attr, v1, is_stack=True)\n",
"bar.add(\"商家B\", attr, v2, is_stack=True)\n",
"page.add(bar) # step 2\n",
"\n",
"# scatter3D\n",
"import random\n",
"data = [\n",
" [random.randint(0, 100),\n",
" random.randint(0, 100),\n",
" random.randint(0, 100)] for _ in range(80)\n",
"]\n",
"range_color = [\n",
" '#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',\n",
" '#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026']\n",
"scatter3D = Scatter3D(\"3D 散点图示例\", width=800, height=600)\n",
"scatter3D.add(\"\", data, is_visualmap=True, visual_range_color=range_color)\n",
"page.add(scatter3D) # step 2\n",
"\n",
"page # step 3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**4，Timeline图表组合**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Timeline可以将多个图表制作成动画。"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pyecharts import Bar, Timeline\n",
"from random import randint\n",
"\n",
"attr = [\"衬衫\", \"羊毛衫\", \"雪纺衫\", \"裤子\", \"高跟鞋\", \"袜子\"]\n",
"bar_1 = Bar(\"2012 年销量\", \"数据纯属虚构\")\n",
"bar_1.add(\"春季\", attr, [randint(10, 100) for _ in range(6)])\n",
"bar_1.add(\"夏季\", attr, [randint(10, 100) for _ in range(6)])\n",
"bar_1.add(\"秋季\", attr, [randint(10, 100) for _ in range(6)])\n",
"bar_1.add(\"冬季\", attr, [randint(10, 100) for _ in range(6)])\n",
"\n",
"bar_2 = Bar(\"2013 年销量\", \"数据纯属虚构\")\n",
"bar_2.add(\"春季\", attr, [randint(10, 100) for _ in range(6)])\n",
"bar_2.add(\"夏季\", attr, [randint(10, 100) for _ in range(6)])\n",
"bar_2.add(\"秋季\", attr, [randint(10, 100) for _ in range(6)])\n",
"bar_2.add(\"冬季\", attr, [randint(10, 100) for _ in range(6)])\n",
"\n",
"bar_3 = Bar(\"2014 年销量\", \"数据纯属虚构\")\n",
"bar_3.add(\"春季\", attr, [randint(10, 100) for _ in range(6)])\n",
"bar_3.add(\"夏季\", attr, [randint(10, 100) for _ in range(6)])\n",
"bar_3.add(\"秋季\", attr, [randint(10, 100) for _ in range(6)])\n",
"bar_3.add(\"冬季\", attr, [randint(10, 100) for _ in range(6)])\n",
"\n",
"bar_4 = Bar(\"2015 年销量\", \"数据纯属虚构\")\n",
"bar_4.add(\"春季\", attr, [randint(10, 100) for _ in range(6)])\n",
"bar_4.add(\"夏季\", attr, [randint(10, 100) for _ in range(6)])\n",
"bar_4.add(\"秋季\", attr, [randint(10, 100) for _ in range(6)])\n",
"bar_4.add(\"冬季\", attr, [randint(10, 100) for _ in range(6)])\n",
"\n",
"bar_5 = Bar(\"2016 年销量\", \"数据纯属虚构\")\n",
"bar_5.add(\"春季\", attr, [randint(10, 100) for _ in range(6)])\n",
"bar_5.add(\"夏季\", attr, [randint(10, 100) for _ in range(6)])\n",
"bar_5.add(\"秋季\", attr, [randint(10, 100) for _ in range(6)])\n",
"bar_5.add(\"冬季\", attr, [randint(10, 100) for _ in range(6)], is_legend_show=True)\n",
"\n",
"timeline = Timeline(is_auto_play=True,\n",
" timeline_bottom=0,\n",
" timeline_play_interval=800 # 每800ms播放一张\n",
" )\n",
"\n",
"timeline.add(bar_1, '2012 年')\n",
"timeline.add(bar_2, '2013 年')\n",
"timeline.add(bar_3, '2014 年')\n",
"timeline.add(bar_4, '2015 年')\n",
"timeline.add(bar_5, '2016 年')\n",
"timeline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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"display_name": "Python 3",
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