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"metadata": {},
"source": [
"# Plotly Express in Python\n",
"\n",
"来源:https://mp.weixin.qq.com/s/dg390bbxA_LEoggWg6dGlQ\n",
"\n",
"Plotly Express 是一个新的高级 Python 可视化库:它是 Plotly.py 的高级封装,它为复杂的图表提供了一个简单的语法。 \n",
"\n",
"受 Seaborn 和 ggplot2 的启发,它专门设计为具有简洁,一致且易于学习的 API :只需一次导入,您就可以在一个函数调用中创建丰富的交互式绘图,包括分面绘图(faceting)、地图、动画和趋势线。它带有数据集、颜色面板和主题,就像 Plotly.py 一样。Plotly是新一代的可视化神器,由TopQ量化团队开源。虽然Ploltly功能非常之强大,但是一直没有得到重视,主要原因还是其设置过于繁琐。因此,Plotly推出了其简化接口:Plotly_express,下文中统一简称为px。\n",
"\n",
"Plotly Express 完全免费:凭借其宽松的开源 MIT 许可证,您可以随意使用它(是的,甚至在商业产品中!)。 \n",
"\n",
"最重要的是,Plotly Express 与 Plotly 生态系统的其他部分完全兼容:在您的 Dash 应用程序中使用它,使用 Orca 将您的数据导出为几乎任何文件格式,或使用JupyterLab 图表编辑器在 GUI 中编辑它们!\n",
"\n",
"\n",
"https://plotly.com/python/plotly-express/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 安装plotly_express"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-23T01:57:36.741709Z",
"start_time": "2020-12-23T01:57:31.010418Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting plotly_express\n",
" Downloading plotly_express-0.4.1-py2.py3-none-any.whl (2.9 kB)\n",
"Requirement already satisfied: plotly>=4.1.0 in /opt/anaconda3/lib/python3.7/site-packages (from plotly_express) (4.11.0)\n",
"Requirement already satisfied: patsy>=0.5 in /opt/anaconda3/lib/python3.7/site-packages (from plotly_express) (0.5.1)\n",
"Requirement already satisfied: numpy>=1.11 in /opt/anaconda3/lib/python3.7/site-packages (from plotly_express) (1.18.1)\n",
"Requirement already satisfied: pandas>=0.20.0 in /opt/anaconda3/lib/python3.7/site-packages (from plotly_express) (1.0.1)\n",
"Requirement already satisfied: statsmodels>=0.9.0 in /opt/anaconda3/lib/python3.7/site-packages (from plotly_express) (0.11.0)\n",
"Requirement already satisfied: scipy>=0.18 in /opt/anaconda3/lib/python3.7/site-packages (from plotly_express) (1.4.1)\n",
"Requirement already satisfied: six in /opt/anaconda3/lib/python3.7/site-packages (from plotly>=4.1.0->plotly_express) (1.14.0)\n",
"Requirement already satisfied: retrying>=1.3.3 in /opt/anaconda3/lib/python3.7/site-packages (from plotly>=4.1.0->plotly_express) (1.3.3)\n",
"Requirement already satisfied: python-dateutil>=2.6.1 in /opt/anaconda3/lib/python3.7/site-packages (from pandas>=0.20.0->plotly_express) (2.8.1)\n",
"Requirement already satisfied: pytz>=2017.2 in /opt/anaconda3/lib/python3.7/site-packages (from pandas>=0.20.0->plotly_express) (2019.3)\n",
"Installing collected packages: plotly-express\n",
"Successfully installed plotly-express-0.4.1\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"pip install plotly_express"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 导入数据"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-23T01:59:50.705607Z",
"start_time": "2020-12-23T01:59:50.674233Z"
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"outputs": [
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"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" country | \n",
" continent | \n",
" year | \n",
" lifeExp | \n",
" pop | \n",
" gdpPercap | \n",
" iso_alpha | \n",
" iso_num | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Afghanistan | \n",
" Asia | \n",
" 1952 | \n",
" 28.801 | \n",
" 8425333 | \n",
" 779.445314 | \n",
" AFG | \n",
" 4 | \n",
"
\n",
" \n",
" 1 | \n",
" Afghanistan | \n",
" Asia | \n",
" 1957 | \n",
" 30.332 | \n",
" 9240934 | \n",
" 820.853030 | \n",
" AFG | \n",
" 4 | \n",
"
\n",
" \n",
" 2 | \n",
" Afghanistan | \n",
" Asia | \n",
" 1962 | \n",
" 31.997 | \n",
" 10267083 | \n",
" 853.100710 | \n",
" AFG | \n",
" 4 | \n",
"
\n",
" \n",
" 3 | \n",
" Afghanistan | \n",
" Asia | \n",
" 1967 | \n",
" 34.020 | \n",
" 11537966 | \n",
" 836.197138 | \n",
" AFG | \n",
" 4 | \n",
"
\n",
" \n",
" 4 | \n",
" Afghanistan | \n",
" Asia | \n",
" 1972 | \n",
" 36.088 | \n",
" 13079460 | \n",
" 739.981106 | \n",
" AFG | \n",
" 4 | \n",
"
\n",
" \n",
"
\n",
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"
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"text/plain": [
" country continent year lifeExp pop gdpPercap iso_alpha \\\n",
"0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG \n",
"1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG \n",
"2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG \n",
"3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG \n",
"4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG \n",
"\n",
" iso_num \n",
"0 4 \n",
"1 4 \n",
"2 4 \n",
"3 4 \n",
"4 4 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import plotly.express as px \n",
"\n",
"# 数据集\n",
"gapminder = px.data.gapminder()\n",
"gapminder.head() # 取出前5条数据"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-23T02:00:00.629244Z",
"start_time": "2020-12-23T02:00:00.625226Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(1704, 8)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gapminder.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-23T03:03:02.359200Z",
"start_time": "2020-12-23T03:03:02.357052Z"
}
},
"source": [
"## 可视化示例"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-23T02:00:21.318398Z",
"start_time": "2020-12-23T02:00:20.296931Z"
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"source": [
"# line 图\n",
"fig = px.line(\n",
" gapminder, # 数据集\n",
" x=\"year\", # 横坐标\n",
" y=\"lifeExp\", # 纵坐标\n",
" color=\"continent\", # 颜色的数据\n",
" line_group=\"continent\", # 线性分组\n",
" hover_name=\"country\", # 悬停hover的数据\n",
" line_shape=\"spline\", # 线的形状\n",
" render_mode=\"svg\" # 生成的图片模式\n",
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"fig.show()"
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},
{
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"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-23T02:00:37.894188Z",
"start_time": "2020-12-23T02:00:37.480939Z"
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"source": [
"# area 图\n",
"fig = px.area(\n",
" gapminder, # 数据集\n",
" x=\"year\", # 横坐标\n",
" y=\"pop\", # 纵坐标\n",
" color=\"continent\", # 颜色\n",
" line_group=\"country\" # 线性组别\n",
")\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-23T02:01:10.539358Z",
"start_time": "2020-12-23T02:01:10.441182Z"
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"source": [
"fig = px.scatter(\n",
" gapminder # 绘图使用的数据\n",
" ,x=\"gdpPercap\" # 横纵坐标使用的数据\n",
" ,y=\"lifeExp\" # 纵坐标数据\n",
" ,color=\"continent\" # 区分颜色的属性\n",
" ,size=\"pop\" # 区分圆的大小\n",
" ,size_max=60 # 圆的最大值\n",
" ,hover_name=\"country\" # 图中可视化最上面的名字\n",
" ,animation_frame=\"year\" # 横轴滚动栏的属性year\n",
" ,animation_group=\"country\" # 标注的分组\n",
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" ,log_x=True # 横坐标表取对数\n",
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" ,range_y=[25,90] # 纵轴范围\n",
" ,labels=dict(pop=\"Populations\", # 属性名字的变化,更直观\n",
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"metadata": {},
"source": [
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},
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"execution_count": 52,
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-23T03:01:17.008300Z",
"start_time": "2020-12-23T03:01:16.968382Z"
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},
"outputs": [],
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},
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"cell_type": "code",
"execution_count": 10,
"metadata": {
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"end_time": "2020-12-23T02:01:55.192090Z",
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{
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"metadata": {
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"end_time": "2020-12-23T02:02:16.933915Z",
"start_time": "2020-12-23T02:02:16.683728Z"
}
},
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"fig = px.scatter_geo(\n",
" gapminder, # 数据\n",
" locations=\"iso_alpha\", # 配合颜色color显示\n",
" color=\"continent\", # 颜色\n",
" hover_name=\"country\", # 悬停数据\n",
" size=\"pop\", # 大小\n",
" animation_frame=\"year\", # 数据帧的选择\n",
" projection=\"natural earth\" # 全球地图\n",
" )\n",
"\n",
"fig.show()"
]
},
{
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"end_time": "2020-12-23T02:02:45.218205Z",
"start_time": "2020-12-23T02:02:44.981170Z"
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},
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"source": [
" px.scatter_geo(gapminder, # 数据集\n",
" locations=\"iso_alpha\", # 配和color显示颜色\n",
" color=\"continent\", # 颜色的字段显示\n",
" hover_name=\"country\", # 悬停数据\n",
" size=\"pop\", # 大小\n",
" animation_frame=\"year\" # 数据联动变化的选择\n",
" #,projection=\"natural earth\" # 去掉projection参数\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-23T02:03:05.822421Z",
"start_time": "2020-12-23T02:03:05.753853Z"
}
},
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"start_time": "2021-05-14T13:05:10.515434Z"
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"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
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"zerolinecolor": "white",
"zerolinewidth": 2
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"yaxis": {
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"ticks": "",
"title": {
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"zerolinecolor": "white",
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}
},
"title": {
"text": "London to NYC Great Circle"
}
}
},
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
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],
"source": [
"import plotly.graph_objects as go\n",
"\n",
"fig = go.Figure(data=go.Scattergeo(\n",
" lat = [40.7127, 51.5072],\n",
" lon = [-74.0059, 0.1275],\n",
" mode = 'lines',\n",
" line = dict(width = 2, color = 'blue'),\n",
"))\n",
"\n",
"fig.update_layout(\n",
" title_text = 'London to NYC Great Circle',\n",
" showlegend = False,\n",
" geo = dict(\n",
" resolution = 50,\n",
" showland = True,\n",
" showlakes = True,\n",
" landcolor = 'rgb(204, 204, 204)',\n",
" countrycolor = 'rgb(204, 204, 204)',\n",
" lakecolor = 'rgb(255, 255, 255)',\n",
" projection_type = \"equirectangular\",\n",
" coastlinewidth = 2,\n",
" lataxis = dict(\n",
" range = [20, 60],\n",
" showgrid = True,\n",
" dtick = 10\n",
" ),\n",
" lonaxis = dict(\n",
" range = [-100, 20],\n",
" showgrid = True,\n",
" dtick = 20\n",
" ),\n",
" )\n",
")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-14T12:25:22.080466Z",
"start_time": "2021-05-14T12:25:22.057772Z"
}
},
"outputs": [
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"data": {
"text/html": [
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\n",
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" state | \n",
" country | \n",
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" -84.426944 | \n",
" 21925 | \n",
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" 2 | \n",
" DFW | \n",
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" Dallas-Fort Worth | \n",
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" -97.037200 | \n",
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" PHX | \n",
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" -112.008056 | \n",
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" DEN | \n",
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" Denver | \n",
" CO | \n",
" USA | \n",
" 39.858408 | \n",
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" iata airport city state country \\\n",
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"4 39.858408 -104.667002 13781 "
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},
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],
"source": [
"import plotly.graph_objects as go\n",
"import pandas as pd\n",
"\n",
"df_airports = pd.read_csv('./data/2011_february_us_airport_traffic.csv')\n",
"df_airports.head()"
]
},
{
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"metadata": {
"ExecuteTime": {
"end_time": "2021-05-14T12:26:06.944719Z",
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"outputs": [
{
"data": {
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" AA | \n",
" ORD | \n",
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" AA | \n",
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" start_lat start_lon end_lat end_lon airline airport1 airport2 cnt\n",
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"source": [
"df_flight_paths = pd.read_csv('./data/2011_february_aa_flight_paths.csv')\n",
"df_flight_paths.head()"
]
},
{
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"Long Beach (Daugherty )",
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"Mahlon Sweet ",
"Savannah International",
"Lubbock International",
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"Greenville-Spartanburg",
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"Greater Peoria Regional",
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"Austin-Bergstrom International",
"Abilene Regional",
"Springfield-Branson Regional",
"Bishop",
"Tri-Cities Regional",
"Robert Gray AAF",
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"Austin Straubel International",
"Roberts ",
"Columbus Metropolitan",
"South Bend Regional",
"Boise Air Terminal",
"Myrtle Beach International",
"Minot International",
"University of Illinois-Willard",
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"source": [
"fig = px.scatter(\n",
" iris, # 数据集\n",
" x=\"sepal_width\", # 横坐标\n",
" y=\"sepal_length\" # 纵坐标\n",
" )\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-23T02:04:44.088253Z",
"start_time": "2020-12-23T02:04:44.035316Z"
}
},
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"px.scatter(\n",
" iris, # 数据集\n",
" x=\"sepal_width\", # 横坐标\n",
" y=\"sepal_length\", # 纵坐标\n",
" color=\"species\"\n",
" )"
]
},
{
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"execution_count": 18,
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-23T02:04:56.894933Z",
"start_time": "2020-12-23T02:04:56.755299Z"
}
},
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"source": [
"px.scatter(\n",
" iris, # 数据集\n",
" x=\"sepal_width\", # 横坐标\n",
" y=\"sepal_length\", # 纵坐标\n",
" color=\"species\", # 颜色\n",
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")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-23T02:05:08.862163Z",
"start_time": "2020-12-23T02:05:07.495726Z"
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},
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"source": [
"px.scatter(\n",
" iris, # 数据集\n",
" x=\"sepal_width\", # 横坐标\n",
" y=\"sepal_length\", # 纵坐标\n",
" color=\"species\", # 颜色\n",
" marginal_y=\"violin\", # 纵坐标小提琴图\n",
" marginal_x=\"box\", # 横坐标箱型图\n",
" trendline=\"ols\" # 趋势线\n",
")"
]
},
{
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"execution_count": 20,
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-23T02:05:17.062498Z",
"start_time": "2020-12-23T02:05:16.977546Z"
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"source": [
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" iris, # 数据\n",
" dimensions=[\"sepal_width\",\"sepal_length\",\"petal_width\",\"petal_length\"], # 维度选择\n",
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"start_time": "2020-12-23T02:05:27.090406Z"
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""
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],
"source": [
"# 对当前值加上下两个误差值\n",
"iris[\"e\"] = iris[\"sepal_width\"] / 100\n",
"px.scatter(\n",
" iris, # 绘图数据集\n",
" x=\"sepal_width\", # 横坐标\n",
" y=\"sepal_length\", # 纵坐标\n",
" color=\"species\", # 颜色值\n",
" error_x=\"e\", # 横轴误差\n",
" error_y=\"e\" # 纵轴误差\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-23T02:05:56.044975Z",
"start_time": "2020-12-23T02:05:55.983960Z"
}
},
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{
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"metadata": {
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"start_time": "2020-12-23T02:06:07.417613Z"
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" iris, # 数据集\n",
" x=\"sepal_width\", # 横坐标值\n",
" y=\"sepal_length\", # 纵坐标值\n",
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{
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"start_time": "2020-12-23T02:06:16.537431Z"
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{
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"source": [
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" tips, # 数据集\n",
" x=\"sex\", # 横轴\n",
" y=\"total_bill\", # 纵轴\n",
" color=\"smoker\", # 颜色参数取值\n",
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]
},
{
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"execution_count": 29,
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-23T02:07:29.263699Z",
"start_time": "2020-12-23T02:07:29.157764Z"
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" color=\"smoker\", # 颜色参数取值\n",
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"fig = px.histogram(\n",
" tips, # 绘图数据集\n",
" x=\"sex\", # 横轴为性别\n",
" y=\"tip\", # 纵轴为费用\n",
" histfunc=\"avg\", # 直方图显示的函数\n",
" color=\"smoker\", # 颜色\n",
" barmode=\"group\", # 柱状图模式\n",
" facet_row=\"time\", # 行取值\n",
" facet_col=\"day\", # 列取值\n",
" category_orders={ # 分类顺序\n",
" \"day\":[\"Thur\",\"Fri\",\"Sat\",\"Sun\"],\n",
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"metadata": {
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"start_time": "2020-12-23T02:07:49.102932Z"
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"source": [
"# notched=True显示连接处的锥形部分\n",
"px.box(tips, # 数据集\n",
" x=\"day\", # 横轴数据\n",
" y=\"total_bill\", # 纵轴数据\n",
" color=\"smoker\", # 颜色\n",
" notched=True) # 连接处的锥形部分显示出来"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-23T02:07:59.040232Z",
"start_time": "2020-12-23T02:07:58.991552Z"
}
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" y=\"tip\", # 纵轴坐标 \n",
" color=\"sex\", # 颜色参数取值\n",
" box=True, # box是显示内部的箱体\n",
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{
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"execution_count": 34,
"metadata": {
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"end_time": "2020-12-23T02:08:20.185291Z",
"start_time": "2020-12-23T02:08:20.179280Z"
}
},
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},
{
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"execution_count": 36,
"metadata": {
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"end_time": "2020-12-23T02:09:49.596276Z",
"start_time": "2020-12-23T02:09:49.522724Z"
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"fig = px.scatter_polar(\n",
" wind, # 数据集\n",
" r=\"frequency\", # 半径\n",
" theta=\"direction\", # 角度\n",
" color=\"strength\", # 颜色\n",
" symbol=\"strength\", # 线性闭合\n",
" color_discrete_sequence=px.colors.sequential.Plasma_r) # 颜色变化\n",
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{
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"end_time": "2020-12-23T02:10:00.423887Z",
"start_time": "2020-12-23T02:10:00.338058Z"
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"source": [
"fig = px.line_polar(\n",
" wind, # 数据集\n",
" r=\"frequency\", # 半径\n",
" theta=\"direction\", # 角度\n",
" color=\"strength\", # 颜色\n",
" line_close=True, # 线性闭合\n",
" color_discrete_sequence=px.colors.sequential.Plasma_r) # 颜色变化\n",
"fig.show()"
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},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-23T02:10:10.562950Z",
"start_time": "2020-12-23T02:10:10.491473Z"
}
},
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"#FF6692",
"#B6E880",
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"font": {
"color": "#f2f5fa"
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"geo": {
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"lakecolor": "rgb(17,17,17)",
"landcolor": "rgb(17,17,17)",
"showlakes": true,
"showland": true,
"subunitcolor": "#506784"
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"hoverlabel": {
"align": "left"
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"hovermode": "closest",
"mapbox": {
"style": "dark"
},
"paper_bgcolor": "rgb(17,17,17)",
"plot_bgcolor": "rgb(17,17,17)",
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"gridcolor": "#506784",
"linecolor": "#506784",
"ticks": ""
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"bgcolor": "rgb(17,17,17)",
"radialaxis": {
"gridcolor": "#506784",
"linecolor": "#506784",
"ticks": ""
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"xaxis": {
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"gridcolor": "#506784",
"gridwidth": 2,
"linecolor": "#506784",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#C8D4E3"
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"yaxis": {
"backgroundcolor": "rgb(17,17,17)",
"gridcolor": "#506784",
"gridwidth": 2,
"linecolor": "#506784",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#C8D4E3"
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"gridcolor": "#506784",
"gridwidth": 2,
"linecolor": "#506784",
"showbackground": true,
"ticks": "",
"zerolinecolor": "#C8D4E3"
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},
"shapedefaults": {
"line": {
"color": "#f2f5fa"
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"sliderdefaults": {
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"bordercolor": "rgb(17,17,17)",
"borderwidth": 1,
"tickwidth": 0
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"ternary": {
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"gridcolor": "#506784",
"linecolor": "#506784",
"ticks": ""
},
"baxis": {
"gridcolor": "#506784",
"linecolor": "#506784",
"ticks": ""
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"bgcolor": "rgb(17,17,17)",
"caxis": {
"gridcolor": "#506784",
"linecolor": "#506784",
"ticks": ""
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"title": {
"x": 0.05
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"borderwidth": 0
},
"xaxis": {
"automargin": true,
"gridcolor": "#283442",
"linecolor": "#506784",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "#283442",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "#283442",
"linecolor": "#506784",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "#283442",
"zerolinewidth": 2
}
}
}
}
},
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = px.bar_polar(\n",
" wind, # 数据集\n",
" r=\"frequency\", # 半径\n",
" theta=\"direction\", # 角度\n",
" color=\"strength\", # 颜色\n",
" template=\"plotly_dark\", # 主题\n",
" color_discrete_sequence=px.colors.sequential.Plasma_r) # 颜色变化\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 选择颜色"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-23T02:12:03.254133Z",
"start_time": "2020-12-23T02:12:03.199563Z"
}
},
"outputs": [
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8,
9
],
"hovertemplate": "%{y}[%{customdata}] = %{marker.color}",
"marker": {
"color": [
"#000004",
"#1b0c41",
"#4a0c6b",
"#781c6d",
"#a52c60",
"#cf4446",
"#ed6925",
"#fb9b06",
"#f7d13d",
"#fcffa4"
]
},
"orientation": "h",
"type": "bar",
"x": [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
],
"y": [
"Inferno",
"Inferno",
"Inferno",
"Inferno",
"Inferno",
"Inferno",
"Inferno",
"Inferno",
"Inferno",
"Inferno"
]
},
{
"customdata": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9
],
"hovertemplate": "%{y}[%{customdata}] = %{marker.color}",
"marker": {
"color": [
"#00224e",
"#123570",
"#3b496c",
"#575d6d",
"#707173",
"#8a8678",
"#a59c74",
"#c3b369",
"#e1cc55",
"#fee838"
]
},
"orientation": "h",
"type": "bar",
"x": [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
],
"y": [
"Cividis",
"Cividis",
"Cividis",
"Cividis",
"Cividis",
"Cividis",
"Cividis",
"Cividis",
"Cividis",
"Cividis"
]
},
{
"customdata": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9
],
"hovertemplate": "%{y}[%{customdata}] = %{marker.color}",
"marker": {
"color": [
"#440154",
"#482878",
"#3e4989",
"#31688e",
"#26828e",
"#1f9e89",
"#35b779",
"#6ece58",
"#b5de2b",
"#fde725"
]
},
"orientation": "h",
"type": "bar",
"x": [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
],
"y": [
"Viridis",
"Viridis",
"Viridis",
"Viridis",
"Viridis",
"Viridis",
"Viridis",
"Viridis",
"Viridis",
"Viridis"
]
},
{
"customdata": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12
],
"hovertemplate": "%{y}[%{customdata}] = %{marker.color}",
"marker": {
"color": [
"#0508b8",
"#1910d8",
"#3c19f0",
"#6b1cfb",
"#981cfd",
"#bf1cfd",
"#dd2bfd",
"#f246fe",
"#fc67fd",
"#fe88fc",
"#fea5fd",
"#febefe",
"#fec3fe"
]
},
"orientation": "h",
"type": "bar",
"x": [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
],
"y": [
"Plotly3",
"Plotly3",
"Plotly3",
"Plotly3",
"Plotly3",
"Plotly3",
"Plotly3",
"Plotly3",
"Plotly3",
"Plotly3",
"Plotly3",
"Plotly3",
"Plotly3"
]
}
],
"layout": {
"bargap": 0.5,
"barmode": "stack",
"barnorm": "fraction",
"height": 2600,
"margin": {
"b": 10
},
"showlegend": false,
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"type": "carpet"
}
],
"choropleth": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "choropleth"
}
],
"contour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "contour"
}
],
"contourcarpet": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "contourcarpet"
}
],
"heatmap": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmap"
}
],
"heatmapgl": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmapgl"
}
],
"histogram": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "histogram"
}
],
"histogram2d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2d"
}
],
"histogram2dcontour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2dcontour"
}
],
"mesh3d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "mesh3d"
}
],
"parcoords": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "parcoords"
}
],
"pie": [
{
"automargin": true,
"type": "pie"
}
],
"scatter": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatter"
}
],
"scatter3d": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatter3d"
}
],
"scattercarpet": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattercarpet"
}
],
"scattergeo": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergeo"
}
],
"scattergl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergl"
}
],
"scattermapbox": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattermapbox"
}
],
"scatterpolar": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolar"
}
],
"scatterpolargl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolargl"
}
],
"scatterternary": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterternary"
}
],
"surface": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "surface"
}
],
"table": [
{
"cells": {
"fill": {
"color": "#EBF0F8"
},
"line": {
"color": "white"
}
},
"header": {
"fill": {
"color": "#C8D4E3"
},
"line": {
"color": "white"
}
},
"type": "table"
}
]
},
"layout": {
"annotationdefaults": {
"arrowcolor": "#2a3f5f",
"arrowhead": 0,
"arrowwidth": 1
},
"coloraxis": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"colorscale": {
"diverging": [
[
0,
"#8e0152"
],
[
0.1,
"#c51b7d"
],
[
0.2,
"#de77ae"
],
[
0.3,
"#f1b6da"
],
[
0.4,
"#fde0ef"
],
[
0.5,
"#f7f7f7"
],
[
0.6,
"#e6f5d0"
],
[
0.7,
"#b8e186"
],
[
0.8,
"#7fbc41"
],
[
0.9,
"#4d9221"
],
[
1,
"#276419"
]
],
"sequential": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"sequentialminus": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
]
},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "plotly.colors.sequential"
},
"xaxis": {
"range": [
-0.02,
1.02
],
"showgrid": false,
"showticklabels": false
}
}
},
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"px.colors.sequential.swatches()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 选择template"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-23T02:12:26.026129Z",
"start_time": "2020-12-23T02:12:25.921512Z"
}
},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"hovertemplate": "species=setosa
sepal_width=%{x}
sepal_length=%{y}",
"legendgroup": "setosa",
"marker": {
"color": "#636efa",
"symbol": "circle"
},
"mode": "markers",
"name": "setosa",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
3.5,
3,
3.2,
3.1,
3.6,
3.9,
3.4,
3.4,
2.9,
3.1,
3.7,
3.4,
3,
3,
4,
4.4,
3.9,
3.5,
3.8,
3.8,
3.4,
3.7,
3.6,
3.3,
3.4,
3,
3.4,
3.5,
3.4,
3.2,
3.1,
3.4,
4.1,
4.2,
3.1,
3.2,
3.5,
3.1,
3,
3.4,
3.5,
2.3,
3.2,
3.5,
3.8,
3,
3.8,
3.2,
3.7,
3.3
],
"xaxis": "x",
"y": [
5.1,
4.9,
4.7,
4.6,
5,
5.4,
4.6,
5,
4.4,
4.9,
5.4,
4.8,
4.8,
4.3,
5.8,
5.7,
5.4,
5.1,
5.7,
5.1,
5.4,
5.1,
4.6,
5.1,
4.8,
5,
5,
5.2,
5.2,
4.7,
4.8,
5.4,
5.2,
5.5,
4.9,
5,
5.5,
4.9,
4.4,
5.1,
5,
4.5,
4.4,
5,
5.1,
4.8,
5.1,
4.6,
5.3,
5
],
"yaxis": "y"
},
{
"alignmentgroup": "True",
"hovertemplate": "species=setosa
sepal_width=%{x}",
"legendgroup": "setosa",
"marker": {
"color": "#636efa",
"symbol": "circle"
},
"name": "setosa",
"notched": true,
"offsetgroup": "setosa",
"showlegend": false,
"type": "box",
"x": [
3.5,
3,
3.2,
3.1,
3.6,
3.9,
3.4,
3.4,
2.9,
3.1,
3.7,
3.4,
3,
3,
4,
4.4,
3.9,
3.5,
3.8,
3.8,
3.4,
3.7,
3.6,
3.3,
3.4,
3,
3.4,
3.5,
3.4,
3.2,
3.1,
3.4,
4.1,
4.2,
3.1,
3.2,
3.5,
3.1,
3,
3.4,
3.5,
2.3,
3.2,
3.5,
3.8,
3,
3.8,
3.2,
3.7,
3.3
],
"xaxis": "x3",
"yaxis": "y3"
},
{
"alignmentgroup": "True",
"bingroup": "y",
"hovertemplate": "species=setosa
sepal_length=%{y}
count=%{x}",
"legendgroup": "setosa",
"marker": {
"color": "#636efa"
},
"name": "setosa",
"offsetgroup": "setosa",
"opacity": 0.5,
"showlegend": false,
"type": "histogram",
"xaxis": "x2",
"y": [
5.1,
4.9,
4.7,
4.6,
5,
5.4,
4.6,
5,
4.4,
4.9,
5.4,
4.8,
4.8,
4.3,
5.8,
5.7,
5.4,
5.1,
5.7,
5.1,
5.4,
5.1,
4.6,
5.1,
4.8,
5,
5,
5.2,
5.2,
4.7,
4.8,
5.4,
5.2,
5.5,
4.9,
5,
5.5,
4.9,
4.4,
5.1,
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"source": [
"px.scatter(\n",
" iris, # 数据集\n",
" x=\"sepal_width\", # 横坐标值\n",
" y=\"sepal_length\", # 纵坐标取值\n",
" color=\"species\", # 颜色\n",
" marginal_x=\"box\", # 横坐标为箱型图\n",
" marginal_y=\"histogram\", # 纵坐标为直方图\n",
" height=600, # 高度\n",
" trendline=\"ols\", # 显示趋势线\n",
" template=\"plotly\") # 主题"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-23T02:12:37.823401Z",
"start_time": "2020-12-23T02:12:37.717083Z"
}
},
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"source": [
"px.scatter(\n",
" iris, # 数据集\n",
" x=\"sepal_width\", # 横坐标值\n",
" y=\"sepal_length\", # 纵坐标取值\n",
" color=\"species\", # 颜色\n",
" marginal_x=\"box\", # 横坐标为箱型图\n",
" marginal_y=\"histogram\", # 纵坐标为直方图\n",
" height=600, # 高度\n",
" trendline=\"ols\", # 显示趋势线\n",
" template=\"plotly_white\") # 主题 "
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"ExecuteTime": {
"end_time": "2020-12-23T02:12:48.345479Z",
"start_time": "2020-12-23T02:12:48.239634Z"
}
},
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"source": [
"px.scatter(\n",
" iris, # 数据集\n",
" x=\"sepal_width\", # 横坐标值\n",
" y=\"sepal_length\", # 纵坐标取值\n",
" color=\"species\", # 颜色\n",
" marginal_x=\"box\", # 横坐标为箱型图\n",
" marginal_y=\"histogram\", # 纵坐标为直方图\n",
" height=600, # 高度\n",
" trendline=\"ols\", # 显示趋势线\n",
" template=\"plotly_dark\") # 主题 "
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"end_time": "2020-12-23T02:13:18.104529Z",
"start_time": "2020-12-23T02:13:18.101135Z"
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"source": [
"本文中利用大量的篇幅讲解了如何通过plotly_express来绘制:柱状图、线型图、散点图、小提琴图、极坐标图等各种常见的图形。通过观察上面Plotly_express绘制图形过程,我们不难发现它有三个主要的优点:\n",
"\n",
"- 快速出图,少量的代码就能满足多数的制图要求。基本上都是几个参数的设置我们就能快速出图\n",
"- 图形漂亮,绘制出来的可视化图形颜色亮丽,也有很多的颜色供选择。\n",
"- 图形是动态可视化的。文章中图形都是截图,如果是在Jupyter notebook中都是动态图形"
]
}
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