{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 4.1 柱状图/条形图" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Faker 数据构造器\n", "https://github.com/pyecharts/pyecharts/blob/master/pyecharts/faker.py" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pyecharts.faker import Faker" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Faker.values()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Faker.clothes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 基本柱状图" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 引入相关包\n", "from pyecharts import options as opts\n", "from pyecharts.charts import Bar" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 普通调用\n", "bar1 = Bar()\n", "bar1.add_xaxis(Faker.choose())\n", "bar1.add_yaxis('组A', Faker.values())\n", "bar1.add_yaxis('组B', Faker.values())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bar1.render_notebook()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 链式调用\n", "bar2 = (\n", " Bar()\n", " .add_xaxis(Faker.choose())\n", " .add_yaxis('组A', Faker.values())\n", " .add_yaxis('组B', Faker.values())\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bar2.render_notebook()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bar2.render(path='basic_bar.html')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 条形图" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 翻转XY轴\n", "bar3 = (\n", " Bar()\n", " .add_xaxis(Faker.choose())\n", " .add_yaxis('组A', Faker.values())\n", " .add_yaxis('组B', Faker.values())\n", " .set_series_opts(label_opts=opts.LabelOpts(position='right'))\n", " .reversal_axis() \n", ")\n", "\n", "bar3.render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4.2 其他对比图表" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 堆叠柱状图" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 全部堆叠\n", "stacked_bar1 = (\n", " Bar()\n", " .add_xaxis(Faker.choose())\n", " .add_yaxis('组A', Faker.values(), stack='stack1')\n", " .add_yaxis('组B', Faker.values(), stack='stack1')\n", " .add_yaxis('组C', Faker.values(), stack='stack1')\n", " .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n", ")\n", "\n", "stacked_bar1.render_notebook()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 部分堆叠\n", "stacked_bar2 = (\n", " Bar()\n", " .add_xaxis(Faker.choose())\n", " .add_yaxis('组A', Faker.values(), stack='stack1')\n", " .add_yaxis('组B', Faker.values(), stack='stack2')\n", " .add_yaxis('组C', Faker.values(), stack='stack2')\n", " .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n", ")\n", "\n", "stacked_bar2.render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 漏斗图" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 准备数据\n", "x_data = [\"点击\", \"访问\", \"咨询\", \"订单\", \"展现\"]\n", "y_data = [90, 50, 30, 10, 100]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for z in zip(x_data, y_data):\n", " print(list(z))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "[list(z) for z in zip(x_data, y_data)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pyecharts.charts import Funnel\n", "\n", "(\n", " Funnel()\n", " .add(\n", " series_name=\"\",\n", " data_pair=[list(z) for z in zip(x_data, y_data)],\n", " )\n", " .set_global_opts(title_opts=opts.TitleOpts(title=\"漏斗图\"))\n", ").render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4.3 常用配置项" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 标题" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 参数传递方式\n", "(\n", " Bar()\n", " .add_xaxis(Faker.choose())\n", " .add_yaxis('组A', Faker.values())\n", " .add_yaxis('组B', Faker.values())\n", " .set_global_opts(\n", " title_opts=opts.TitleOpts(title='主标题', subtitle='副标题')\n", " )\n", ").render_notebook()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 字典方式\n", "(\n", " Bar()\n", " .add_xaxis(Faker.choose())\n", " .add_yaxis('组A', Faker.values())\n", " .add_yaxis('组B', Faker.values())\n", " .set_global_opts(\n", " title_opts={'text': '主标题', 'subtext': '副标题'}\n", " )\n", ").render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 常用的全局配置项" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 动画配置项" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(\n", " Bar(\n", " init_opts=opts.InitOpts(\n", " animation_opts=opts.AnimationOpts(\n", " animation_delay=2000, animation_easing='elasticOut'\n", " )\n", " )\n", " )\n", " .add_xaxis(Faker.choose())\n", " .add_yaxis('组A', Faker.values())\n", " .add_yaxis('组B', Faker.values())\n", " .set_global_opts(\n", " title_opts=opts.TitleOpts(title='主标题', subtitle='副标题')\n", " )\n", ").render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 区域缩放配置项" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(\n", " Bar(\n", " init_opts=opts.InitOpts(\n", " animation_opts=opts.AnimationOpts(\n", " animation_delay=1000, animation_easing='elasticOut'\n", " )\n", " )\n", " )\n", " .add_xaxis(Faker.choose())\n", " .add_yaxis('组A', Faker.values())\n", " .add_yaxis('组B', Faker.values())\n", " .set_global_opts(\n", " title_opts=opts.TitleOpts(title='主标题', subtitle='副标题'),\n", "# datazoom_opts=opts.DataZoomOpts(), # 默认为slide\n", "# datazoom_opts=opts.DataZoomOpts(type_='inside'), # 使用inside\n", "# datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_='inside')] # 同时使用slide和inside\n", " datazoom_opts=opts.DataZoomOpts(orient='vertical'), # 纵向datazoom\n", " )\n", ").render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 坐标轴配置项" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(\n", " Bar(\n", " init_opts=opts.InitOpts(\n", " animation_opts=opts.AnimationOpts(\n", " animation_delay=1000, animation_easing='elasticOut'\n", " )\n", " )\n", " )\n", " .add_xaxis(Faker.choose())\n", " .add_yaxis('组A', Faker.values())\n", " .add_yaxis('组B', Faker.values())\n", " .set_global_opts(\n", " title_opts=opts.TitleOpts(title='主标题', subtitle='副标题'),\n", " xaxis_opts=opts.AxisOpts(name='X轴'),\n", " yaxis_opts=opts.AxisOpts(name='Y轴'),\n", " )\n", ").render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 工具箱配置项" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(\n", " Bar(\n", " init_opts=opts.InitOpts(\n", " animation_opts=opts.AnimationOpts(\n", " animation_delay=1000, animation_easing='elasticOut'\n", " )\n", " )\n", " )\n", " .add_xaxis(Faker.choose())\n", " .add_yaxis('组A', Faker.values())\n", " .add_yaxis('组B', Faker.values())\n", " .set_global_opts(\n", " title_opts=opts.TitleOpts(title='主标题', subtitle='副标题'),\n", " xaxis_opts=opts.AxisOpts(name='X轴'),\n", " yaxis_opts=opts.AxisOpts(name='Y轴'),\n", " toolbox_opts=opts.ToolboxOpts(),\n", " )\n", ").render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 常用的系列配置项" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 标签配置项" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(\n", " Bar()\n", " .add_xaxis(Faker.choose())\n", " .add_yaxis('组A', Faker.values())\n", " .add_yaxis('组B', Faker.values())\n", " .set_global_opts(\n", " title_opts=opts.TitleOpts(title='主标题', subtitle='副标题'),\n", " xaxis_opts=opts.AxisOpts(\n", " name='X轴',\n", " axislabel_opts=opts.LabelOpts(rotate=-90)\n", " ),\n", " yaxis_opts=opts.AxisOpts(\n", " name='Y轴',\n", " axislabel_opts=opts.LabelOpts(formatter='{value}个')\n", " ),\n", " )\n", " .set_series_opts(\n", " label_opts=opts.LabelOpts(\n", "# is_show=False,\n", " font_size=20,\n", " )\n", " )\n", ").render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 标记线配置项" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(\n", " Bar()\n", " .add_xaxis(Faker.choose())\n", " .add_yaxis('组A', Faker.values())\n", " .add_yaxis('组B', Faker.values())\n", " .set_global_opts(\n", " title_opts=opts.TitleOpts(title='主标题', subtitle='副标题'),\n", " )\n", " .set_series_opts(\n", " label_opts=opts.LabelOpts(is_show=False),\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", " opts.MarkLineItem(y=50, name='自定义标记线'),\n", " ]\n", " )\n", " )\n", ").render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 标记点配置项" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(\n", " Bar()\n", " .add_xaxis(Faker.choose())\n", " .add_yaxis('组A', Faker.values())\n", " .add_yaxis('组B', Faker.values())\n", " .set_global_opts(\n", " title_opts=opts.TitleOpts(title='主标题', subtitle='副标题'),\n", " )\n", " .set_series_opts(\n", " label_opts=opts.LabelOpts(is_show=False),\n", " markpoint_opts=opts.MarkPointOpts(\n", " data=[\n", " opts.MarkPointItem(type_='min', name='最小值'),\n", " opts.MarkPointItem(type_='max', name='最大值'),\n", " opts.MarkPointItem(type_='average', name='平均值'),\n", " ]\n", " )\n", " )\n", ").render_notebook()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "data_anal_env", "language": "python", "name": "data_anal_env" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.5" } }, "nbformat": 4, "nbformat_minor": 2 }