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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import plotly.graph_objects as go\n", "go.FigureWidget(); # 初始化\n", "\n", "fig = go.Figure(go.Bar(x=[1, 2, 3], y=[6, 4, 9],\n", " marker_color=\"red\")) # will be overwritten below\n", "\n", "fig.update_traces(overwrite=True, marker={\"opacity\": 0.4})\n", "\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 有条件地更新轨迹\n", "\n", "假设要对轨迹集合进行的更新依赖于某些轨迹属性的当前值。`update_trace()` 方法不能处理这种情况，但是`for_each_trace()`方法可以！\n", "\n", "作为它的第一个参数，`for_each_trace()` 方法每次接受并更新一个轨迹的函数。与 `update_trace()` 一样，`for_each_trace()` 也接受 `selector,`、`row` 和 `col` 参数来控制应该考虑哪些轨迹。\n", "\n", "下面是一个使用 `for_each_trace()` 将“setosa”的唯一标记转换为 Plotly Express 图中的方形符号的示例。\n", "\n", "注意，**这是可能的，因为 Plotly Express 图由输入数据帧中的每个列的单独轨迹组成。**\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "species=setosa
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import plotly.express as px\n", "\n", "df = px.data.iris()\n", "\n", "fig = px.scatter(df, x=\"sepal_width\", y=\"sepal_length\", color=\"species\",\n", " title=\"Conditionally Updating Traces In A Plotly Express Figure With for_each_trace()\")\n", "\n", "fig.for_each_trace(\n", " lambda trace: trace.update(marker_symbol=\"square\") if trace.name == \"setosa\" else (),\n", ")\n", "\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 更新轴\n", "\n", "图对象支持 `update_xaxes()` 和 `update_yaxes()` 方法，它们可用于更新图的一个或多个轴的多个嵌套属性。下面是一个使用 `update_xaxes()` 禁用 Plotly Express 生成的图中所有子图上的垂直网格线的示例。\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "species=setosa
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import plotly.express as px\n", "\n", "df = px.data.iris()\n", "\n", "fig = px.scatter(df, x=\"sepal_width\", y=\"sepal_length\", color=\"species\",\n", " facet_col=\"species\", title=\"Using update_xaxes() With A Plotly Express Figure\")\n", "\n", "fig.update_xaxes(showgrid=False)\n", "\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "还有 `for_each_xaxis()` 和 `for_each_yaxis()` 方法类似于上面描述的 `for_each_trace()` 方法。对于非笛卡儿子图类型(例如 polar)，有额外的 `update_{type}` 和 `for_each_{type}` 方法(例如 `update_polar()`，`for_each_polar()`)。\n", "\n", "## 其他更新方法\n", "\n", "使用 `plot.py` 图库创建的图形也支持：\n", "\n", "* `update_layout_images()` 方法用于 [更新背景布局图像](https://daobook.github.io/plotly-book/tutorial/fundamentals/23_images.html#plotly-images)\n", "\n", "* `update_annotations()` 用来 [更新注释](https://daobook.github.io/plotly-book/tutorial/fundamentals/21_text-and-annotations.html#plotly-text-and-annotations)\n", "\n", "* `update_shapes()` 用来 [更新形状](https://daobook.github.io/plotly-book/tutorial/fundamentals/24_shapes.html#plotly-shapes)\n", "\n", "### 链接图操作\n", "\n", "上面描述的所有图更新操作都是返回对正在修改的图的引用的方法。这使得将多个图形修改操作连接到一个表达式成为可能。\n", "\n", "下面是一个创建链接表达式的示例：\n", "\n", "* 使用 Plotly Express 绘制带有 OLS 趋势线的 faceted scatter plot\n", "\n", "* 使用 `update_layout()` 设置标题字体大小\n", "\n", "* 使用 `update_xaxes()` 禁用垂直网格线\n", "\n", "* 使用 `update_trace()` 更新趋势线的宽度和横线模式\n", "\n", "* 然后使用 `show()` 显示图\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "species=setosa
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"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": { "font": { "size": 24 }, "text": "Chaining Multiple Figure Operations With A Plotly Express Figure" }, "xaxis": { "anchor": "y", "domain": [ 0, 0.31999999999999995 ], "showgrid": false, "title": { "text": "sepal_width" } }, "xaxis2": { "anchor": "y2", "domain": [ 0.33999999999999997, 0.6599999999999999 ], "matches": "x", "showgrid": false, "title": { "text": "sepal_width" } }, "xaxis3": { "anchor": "y3", "domain": [ 0.6799999999999999, 0.9999999999999999 ], "matches": "x", "showgrid": false, "title": { "text": "sepal_width" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "sepal_length" } }, "yaxis2": { "anchor": "x2", "domain": [ 0, 1 ], "matches": "y", "showticklabels": false }, "yaxis3": { "anchor": "x3", "domain": [ 0, 1 ], "matches": "y", "showticklabels": false } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import plotly.express as px\n", "\n", "df = px.data.iris()\n", "\n", "(px.scatter(df, x=\"sepal_width\", y=\"sepal_length\", color=\"species\",\n", " facet_col=\"species\", trendline=\"ols\",\n", " title=\"Chaining Multiple Figure Operations With A Plotly Express Figure\")\n", " .update_layout(title_font_size=24)\n", " .update_xaxes(showgrid=False)\n", " .update_traces(\n", " line=dict(dash=\"dot\", width=4),\n", " selector=dict(type=\"scatter\", mode=\"lines\"))\n", ").show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 属性赋值\n", "\n", "轨迹和布局属性可以使用属性赋值语法进行更新。下面是使用属性赋值设置图标题的示例。" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "type": "bar", "x": [ 1, 2, 3 ], "y": [ 1, 3, 2 ] } ], "layout": { "template": { "data": { "bar": [ { "error_x": { "color": 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"Using Property Assignment Syntax With A Graph Object Figure" } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import plotly.graph_objects as go\n", "\n", "fig = go.Figure(data=go.Bar(x=[1, 2, 3], y=[1, 3, 2]))\n", "fig.layout.title.text = \"Using Property Assignment Syntax With A Graph Object Figure\"\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "下面是使用属性赋值更新条形轮廓的例子。" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "marker": { "line": { "color": "black", "width": 4 } }, "type": "bar", "x": [ 1, 2, 3 ], "y": [ 1, 3, 2 ] } ], "layout": { "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 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