{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 5.1 折线图/面积图" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pyecharts.faker import Faker" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 折线图" ] }, { "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 Line" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(\n", " Line()\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='基本折线图')\n", " )\n", ").render_notebook()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "aqi_data = pd.read_csv('./city_aqi.csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "aqi_data.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(\n", " Line()\n", " .add_xaxis(aqi_data['日期'].tolist())\n", " .add_yaxis('', aqi_data['AQI'].tolist())\n", " .set_global_opts(\n", " title_opts=opts.TitleOpts(title='空气质量指数'),\n", " datazoom_opts=opts.DataZoomOpts()\n", " )\n", ").render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 面积图" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(\n", " Line()\n", " .add_xaxis(Faker.choose())\n", " .add_yaxis('组A', Faker.values(), areastyle_opts=opts.AreaStyleOpts(opacity=0.5))\n", " .add_yaxis('组B', Faker.values(), areastyle_opts=opts.AreaStyleOpts(opacity=0.5))\n", " .set_global_opts(\n", " title_opts=opts.TitleOpts(title='基本面积图')\n", " )\n", ").render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 5.2 K 线图" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pyecharts.charts import Candlestick" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x_data = [\"2017-10-24\", \"2017-10-25\", \"2017-10-26\", \"2017-10-27\"]\n", "y_data = [[20, 30, 10, 35], [40, 35, 30, 55], [33, 38, 33, 40], [40, 40, 32, 42]]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(\n", " Candlestick()\n", " .add_xaxis(x_data)\n", " .add_yaxis(series_name='', y_axis=y_data)\n", ").render_notebook()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "stock_data = pd.read_csv('./stock.csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "stock_data.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(\n", " Candlestick()\n", " .add_xaxis(stock_data['Date'].tolist())\n", " .add_yaxis(series_name='', y_axis=stock_data[['Open', 'Close', 'Low', 'High']].values.tolist())\n", " .set_global_opts(\n", " yaxis_opts=opts.AxisOpts(\n", " is_scale=True,\n", " splitline_opts=opts.SplitLineOpts(\n", " is_show=True, linestyle_opts=opts.LineStyleOpts(width=1)\n", " )\n", " )\n", " )\n", ").render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 5.3 其他常用图表" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 阶梯图" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(\n", " Line()\n", " .add_xaxis(Faker.days_attrs)\n", " .add_yaxis('', Faker.days_values, is_step=True)\n", " .set_global_opts(\n", " title_opts=opts.TitleOpts(title='阶梯图')\n", " )\n", ").render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 平滑曲线图" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(\n", " Line()\n", " .add_xaxis(Faker.days_attrs)\n", " .add_yaxis('', Faker.days_values, is_smooth=True)\n", " .set_global_opts(\n", " title_opts=opts.TitleOpts(title='平滑曲线图')\n", " )\n", ").render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 堆叠折线图" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pv_data = pd.read_csv('./pv_data.csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pv_data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(\n", " Line()\n", " .add_xaxis(xaxis_data=pv_data['日期'].tolist())\n", " .add_yaxis(\n", " series_name='邮件营销',\n", " stack='stack1',\n", " y_axis=pv_data['邮件营销'].tolist(),\n", " label_opts=opts.LabelOpts(is_show=False),\n", " )\n", " .add_yaxis(\n", " series_name='联盟广告',\n", " stack='stack1',\n", " y_axis=pv_data['联盟广告'].tolist(),\n", " label_opts=opts.LabelOpts(is_show=False),\n", " )\n", " .add_yaxis(\n", " series_name='视频广告',\n", " stack='stack1',\n", " y_axis=pv_data['视频广告'].tolist(),\n", " label_opts=opts.LabelOpts(is_show=False),\n", " )\n", " .add_yaxis(\n", " series_name='直接访问',\n", " stack='stack1',\n", " y_axis=pv_data['直接访问'].tolist(),\n", " label_opts=opts.LabelOpts(is_show=False),\n", " )\n", " .add_yaxis(\n", " series_name='搜索引擎',\n", " stack='stack1',\n", " y_axis=pv_data['搜索引擎'].tolist(),\n", " label_opts=opts.LabelOpts(is_show=False),\n", " )\n", " .set_global_opts(\n", " title_opts=opts.TitleOpts(title='堆叠折线图'),\n", " tooltip_opts=opts.TooltipOpts(trigger=\"axis\"),\n", " yaxis_opts=opts.AxisOpts(\n", " splitline_opts=opts.SplitLineOpts(is_show=True),\n", " ),\n", " xaxis_opts=opts.AxisOpts(type_=\"category\", boundary_gap=False),\n", " )\n", ").render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 堆叠面积图" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(\n", " Line()\n", " .add_xaxis(xaxis_data=pv_data['日期'].tolist())\n", " .add_yaxis(\n", " series_name='邮件营销',\n", " stack='stack1',\n", " y_axis=pv_data['邮件营销'].tolist(),\n", " areastyle_opts=opts.AreaStyleOpts(opacity=0.5),\n", " label_opts=opts.LabelOpts(is_show=False),\n", " )\n", " .add_yaxis(\n", " series_name='联盟广告',\n", " stack='stack1',\n", " y_axis=pv_data['联盟广告'].tolist(),\n", " areastyle_opts=opts.AreaStyleOpts(opacity=0.5),\n", " label_opts=opts.LabelOpts(is_show=False),\n", " )\n", " .add_yaxis(\n", " series_name='视频广告',\n", " stack='stack1',\n", " y_axis=pv_data['视频广告'].tolist(),\n", " areastyle_opts=opts.AreaStyleOpts(opacity=0.5),\n", " label_opts=opts.LabelOpts(is_show=False),\n", " )\n", " .add_yaxis(\n", " series_name='直接访问',\n", " stack='stack1',\n", " y_axis=pv_data['直接访问'].tolist(),\n", " areastyle_opts=opts.AreaStyleOpts(opacity=0.5),\n", " label_opts=opts.LabelOpts(is_show=False),\n", " )\n", " .add_yaxis(\n", " series_name='搜索引擎',\n", " stack='stack1',\n", " y_axis=pv_data['搜索引擎'].tolist(),\n", " areastyle_opts=opts.AreaStyleOpts(opacity=0.5),\n", " label_opts=opts.LabelOpts(is_show=False),\n", " )\n", " .set_global_opts(\n", " title_opts=opts.TitleOpts(title='堆叠面积图'),\n", " tooltip_opts=opts.TooltipOpts(trigger=\"axis\"),\n", " yaxis_opts=opts.AxisOpts(\n", " splitline_opts=opts.SplitLineOpts(is_show=True),\n", " ),\n", " xaxis_opts=opts.AxisOpts(type_=\"category\", boundary_gap=False),\n", " )\n", ").render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 5.4 常用的配置项" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 提示框配置项" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(\n", " Line()\n", " .add_xaxis(Faker.choose())\n", " .add_yaxis('组A', Faker.values())\n", " .add_yaxis('组B', Faker.values())\n", " .set_global_opts(\n", " tooltip_opts=opts.TooltipOpts(trigger='axis')\n", " )\n", ").render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 视觉映射配置项" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(\n", " Line()\n", " .add_xaxis(aqi_data['日期'].tolist())\n", " .add_yaxis('', \n", " aqi_data['AQI'].tolist(), \n", " is_smooth=True,\n", " is_symbol_show=False\n", " )\n", " .set_global_opts(\n", " title_opts=opts.TitleOpts(title='空气质量指数'),\n", " tooltip_opts=opts.TooltipOpts(trigger='axis'),\n", " visualmap_opts=opts.VisualMapOpts(\n", " pos_top=\"10\",\n", " pos_right=\"10\",\n", " is_piecewise=True,\n", " pieces=[\n", " {\"min\": 0, \"max\": 50, \"color\": \"#096\"},\n", " {\"min\": 50, \"max\": 100, \"color\": \"#ffde33\"},\n", " {\"min\": 100, \"max\": 150, \"color\": \"#ff9933\"},\n", " {\"min\": 150, \"max\": 200, \"color\": \"#cc0033\"},\n", " {\"min\": 200, \"max\": 300, \"color\": \"#660099\"},\n", " {\"min\": 300, \"color\": \"#7e0023\"},\n", " ],\n", " out_of_range={\"color\": \"#999\"},\n", " ),\n", " )\n", ").render_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 折线样式" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(\n", " Line()\n", " .add_xaxis(Faker.choose())\n", " .add_yaxis(\n", " '组A', \n", " Faker.values(),\n", " symbol='triangle',\n", " symbol_size=20,\n", " linestyle_opts=opts.LineStyleOpts(color=\"green\", width=4, type_=\"dashed\"),\n", " itemstyle_opts=opts.ItemStyleOpts(\n", " border_width=3, border_color=\"yellow\", color=\"blue\"\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 }