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"(plotly:graph-objects)=\n",
"# 图对象\n",
"\n",
"参考：[Graph Objects | Python | Plotly](https://plotly.com/python/graph-objects/)\n",
"\n",
"## 什么是图对象?\n",
"\n",
"`plotly.graph_objects` 模块(通常按顺序导入)包含一个自动生成的 Python类层次结构，这些类在这个图模式中表示非叶节点。术语“图对象”指的是这些类的实例。`plotly.graph_objects` 模块中有两大主类：[`Figure`](https://plotly.com/python-api-reference/generated/plotly.graph_objects.Figure.html) 和 [`FigureWidget`](plotly:figurewidget)（兼容 `ipywidgets` 的变体），它们都表示整个图。这些类的实例有许多方便的方法来用 Python 操作它们的属性(例如 `.update_layout()` 或 `.add_trace()`，它们都接受“魔法下划线”表示法)，以及渲染它们(例如`.show()`)并将它们导出为各种格式(例如 `.to_json()` 或 `.write_image()` 或 `.write_html()`)。\n",
"\n",
"注意：`Plotly Express` 中的函数(这是 Plotly 库的推荐入口点)都建立在图对象之上，并且全返回 `plotly.graph_objects.Figure` 实例。\n",
"\n",
"图的每个非叶子属性都由 `plotly` 中的一个 `plotly.graph_objects` 层次结构的类实例表示。例如，一个 `fig` 可以有一个属性`layout.margin`包含属性 `t`、`l`、`b` 和 `r`，它们是树的叶子：它们没有子节点。`fig.layout` 的字段是 `plotly.graph_objects.Layout` 类的一个对象，并且 `fig.layout.margin` 表示 `margin` 节点，它有字段 `t`、`l`、`b` 和 `r`，其中包含各自叶节点的值。注意，指定所有这些值不需要使用“魔法下划线”符号创建中间对象： `go.Figure(layout_margin=dict(t=10, b=10, r=10, l=10))`。\n",
"\n",
"列表中包含的对象是属性`data`的值，称为“轨迹”，可以是 40 多种类型中的一种，每种类型在`plotly.graph_objects`中都有相应的类。例如，`scatter`类型的轨迹由`plotly.graph_objects.Scatter`类的实例表示。这意味着一个以`go.Figure(data=[go.Scatter(x=[1,2], y=[3,4)])`将JSON 表示 `{\"data\": [{\"type\": \"scatter\", \"x\": [1,2], \"y\": [3,4]}]}`。\n",
"\n",
"(plotly:when-use-graph-objects)=\n",
"## 何时直接使用图对象\n",
"\n",
"创建图的推荐方法是使用`plotly.express`模块中的函数，统称为 Plotly express，它都返回 `plot.graph_objects.Figure` 的实例，所以使用 `plotly` 库生成的每个图，实际上都使用了图对象，除非是手工从字典中构建的。也就是说，某些类型的图形还不能用 Plotly Express 创建，比如使用特定的 3D 轨迹类型(如网格或等值面)的图。此外，从使用 Plotly Express 创建的图形开始创建某些图形非常麻烦，例如具有[不同类型的子图](plotly:mixed-subplots)、[双轴图](plotly:multiple-axes) 或具有多个不同类型轨迹的[分面图](https://plotly.com/python/facet-plots/)。\n",
"\n",
"注意，Plotly Express 在一个函数调用中生成的图[很容易在创建时定制](plotly:styling-plotly-express)，并且在创建后使用`update_*`和`add_*`方法进行[操作](plotly:creating-and-updating-figures)。Plotly Express 生成的图总是可以使用图对象从头开始构建，但这种方法通常需要 5-100 行代码，而不是 1 行。下面是一个简单的例子，演示如何从相同的数据生成相同的图形对象，一次使用 Plotly Express，另一次不使用。本例中的数据是 \"long form\" 的，但 Plotly Express 也接受 [\"wide form\"](https://plotly.com/python/wide-form/) 的数据，而且 Plotly Express 相对于图对象节省的行数是可以比较的。更复杂的图形，如 [sunbursts](https://plotly.com/python/sunburst-charts/), [parallel coordinates](https://plotly.com/python/parallel-coordinates-plot/), [facet plots](https://plotly.com/python/facet-plots/) 或 [animations](https://plotly.com/python/animations/) 需要更多的图对象代码行，而使用 Plotly Express 从一种表示转换到另一种表示通常只需要更改几个字符。"
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"source": [
"import pandas as pd\n",
"\n",
"df = pd.DataFrame({\n",
" \"Fruit\": [\"Apples\", \"Oranges\", \"Bananas\", \"Apples\", \"Oranges\", \"Bananas\"],\n",
" \"Contestant\": [\"Alex\", \"Alex\", \"Alex\", \"Jordan\", \"Jordan\", \"Jordan\"],\n",
" \"Number Eaten\": [2, 1, 3, 1, 3, 2],\n",
"})\n",
"\n",
"import plotly.express as px\n",
"\n",
"fig = px.bar(df, x=\"Fruit\", y=\"Number Eaten\", color=\"Contestant\", barmode=\"group\")\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"id": "5c251335",
"metadata": {},
"source": [
"不使用 `px`：\n",
"\n",
"```python\n",
"import plotly.graph_objects as go\n",
"\n",
"fig = go.Figure()\n",
"for contestant, group in df.groupby(\"Contestant\"):\n",
" fig.add_trace(go.Bar(x=group[\"Fruit\"], y=group[\"Number Eaten\"], name=contestant,\n",
" hovertemplate=\"Contestant=%s
Fruit=%%{x}
Number Eaten=%%{y}\"% contestant))\n",
"fig.update_layout(legend_title_text = \"Contestant\")\n",
"fig.update_xaxes(title_text=\"Fruit\")\n",
"fig.update_yaxes(title_text=\"Number Eaten\")\n",
"fig.show()\n",
"```\n",
"\n",
"(plotly:mixed-subplots)=\n",
"## 混合子图\n",
"\n",
"```{important}\n",
"Plotly Express 不支持创建具有任意混合子图的图形，即具有不同类型子图的图形。Plotly Express 仅支持 [facet 图](https://plotly.com/python/facet-plots/)和[边际分布子图](https://plotly.com/python/marginal-plots/)。要制作带有混合子图的图形，请将 [`make_subplots()`](https://plotly.com/python/subplots/) 函数与下图所述的图对象结合使用。\n",
"```"
]
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"import plotly.graph_objects as go\n",
"from plotly.subplots import make_subplots\n",
"\n",
"import pandas as pd\n",
"go.FigureWidget();\n",
"\n",
"# read in volcano database data\n",
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" \"https://raw.githubusercontent.com/plotly/datasets/master/volcano_db.csv\",\n",
" encoding=\"iso-8859-1\",\n",
")\n",
"\n",
"# frequency of Country\n",
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"\n",
"# read in 3d volcano surface data\n",
"df_v = pd.read_csv(\"https://raw.githubusercontent.com/plotly/datasets/master/volcano.csv\")\n",
"\n",
"# Initialize figure with subplots\n",
"fig = make_subplots(\n",
" rows=2, cols=2,\n",
" column_widths=[0.6, 0.4],\n",
" row_heights=[0.4, 0.6],\n",
" specs=[[{\"type\": \"scattergeo\", \"rowspan\": 2}, {\"type\": \"bar\"}],\n",
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"# Add scattergeo globe map of volcano locations\n",
"fig.add_trace(\n",
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" lon=df[\"Longitude\"],\n",
" mode=\"markers\",\n",
" hoverinfo=\"text\",\n",
" showlegend=False,\n",
" marker=dict(color=\"crimson\", size=4, opacity=0.8)),\n",
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"\n",
"# Add locations bar chart\n",
"fig.add_trace(\n",
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" row=1, col=2\n",
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"\n",
"# Add 3d surface of volcano\n",
"fig.add_trace(\n",
" go.Surface(z=df_v.values.tolist(), showscale=False),\n",
" row=2, col=2\n",
")\n",
"\n",
"# Update geo subplot properties\n",
"fig.update_geos(\n",
" projection_type=\"orthographic\",\n",
" landcolor=\"white\",\n",
" oceancolor=\"MidnightBlue\",\n",
" showocean=True,\n",
" lakecolor=\"LightBlue\"\n",
")\n",
"\n",
"# Rotate x-axis labels\n",
"fig.update_xaxes(tickangle=45)\n",
"\n",
"# Set theme, margin, and annotation in layout\n",
"fig.update_layout(\n",
" template=\"plotly_dark\",\n",
" margin=dict(r=10, t=25, b=40, l=60),\n",
" annotations=[\n",
" dict(\n",
" text=\"Source: NOAA\",\n",
" showarrow=False,\n",
" xref=\"paper\",\n",
" yref=\"paper\",\n",
" x=0,\n",
" y=0)\n",
" ]\n",
")\n",
"\n",
"fig.show()"
]
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