diff --git a/ch-plotly/basiccharts.ipynb b/ch-plotly/basiccharts.ipynb new file mode 100644 index 0000000..4935c5e --- /dev/null +++ b/ch-plotly/basiccharts.ipynb @@ -0,0 +1,5483 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 基本图表" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import plotly.graph_objects as go\n", + "#import plotly.express as px\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "在 Matplotlib 章节中，我们介绍了散点图、柱状条形图、饼图、直方图、箱线图等基本图表的作图方法，它们在 Plotly 库中也有对应的函数来作出 `trace` 。\n", + "## 散点图\n", + "\n", + "`go.Scatter` 是 Plotly 中用于创建散点图（scatter plot）的类。以下是一些常用的参数：\n", + "\n", + "- `x`: 用于指定数据点在 x 轴上的坐标，可以是单个值、列表或数组。\n", + "- `y`: 用于指定数据点在 y 轴上的坐标，可以是单个值、列表或数组。\n", + "- `mode`: 定义散点图的模式，常见的包括 'markers'（仅显示标记）、'lines'（仅显示线）、'lines+markers'（同时显示线和标记）等，因此该类也可以作出折线图。\n", + "- `name`: 为 `trace` 指定一个名称，用于图例等，默认名称为“trace x”，x从0开始计数。\n", + "- `marker`: 一个字典，包含有关标记的属性，例如颜色、大小等。\n", + "- `line`: 一个字典，包含有关线的属性，例如颜色、宽度等。\n", + "\n", + "例：向图形对象添加一个 mode 为 'markers' 的散点图。 x 值为 [1, 2, 3, 4]，对应的 y 值为 [10, 11, 12, 13]。" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "mode": "markers", + "type": "scatter", + "x": [ + 1, + 2, + 3, + 4 + ], + "y": [ + 10, + 11, + 12, + 13 + ] + } + ], + "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 + }, + "pattern": { + "fillmode": "overlay", 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"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 + } + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_values = [1, 2, 3, 4]\n", + "y_values = [8, 9, 10, 11]\n", + "\n", + "trace = go.Scatter(\n", + " x=x_values,\n", + " y=y_values,\n", + " mode='markers+lines',\n", + " name='Scatter Plot',\n", + " marker=dict(\n", + " color='blue',\n", + " size=10,\n", + " symbol='circle'\n", + " ),\n", + " line=dict(\n", + " color='red',\n", + " width=2\n", + " )\n", + ")\n", + "\n", + "fig.add_trace(trace)\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 条形图\n", + "`go.Bar` 是 Plotly 中用于创建条形图（Bar chart）的类。以下是一些常用的参数：\n", + "\n", + "`x`: 用于指定条形的位置，可以是单个值、列表或数组。每个位置上都会绘制一条条形。\n", + "`y`: 用于指定条形的高度，可以是单个值、列表或数组。每个高度值对应一个位置上的条形。\n", + "`name`: 为 trace 指定一个名称，用于图例等。\n", + "`orientation`: 指定条形图的方向，可以是 'v'（垂直）或 'h'（水平），默认为垂直型条形图。\n", + "`marker`: 一个字典，包含有关条形的属性，例如颜色、透明度等。\n", + "`text`: 用于在条形上显示的文本，可以是单个值、列表或数组；可以用来在图中展示具体的类别。\n", + "\n", + "例：作出条形图，条形的颜色为蓝色，透明度为0.7，同时在每个条形上显示对应的 A、B、C、D 类别。" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "marker": { + "color": "blue", + "opacity": 0.7 + }, + "name": "Bar Chart", + "orientation": "v", + "text": [ + "A", + "B", + "C", + "D" + ], + "type": "bar", + "x": [ + "A", + "B", + "C", + "D" + ], + "y": [ + 10, + 11, + 12, + 13 + ] + } + ], + "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", + 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"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 + } + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_values = [\"A\",\"B\",\"C\",\"D\"]\n", + "y_values = [10, 11, 12, 13]\n", + "\n", + "trace = go.Bar(\n", + " x=x_values,\n", + " y=y_values,\n", + " name='Bar Chart',\n", + " orientation='v',\n", + " marker=dict(\n", + " color='blue',\n", + " opacity=0.7\n", + " ),\n", + " text=['A', 'B', 'C', 'D']\n", + ")\n", + "\n", + "fig = go.Figure(trace)\n", + "fig.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 饼图\n", + "\n", + "`go.Pie` 是 Plotly 中用于创建饼图（Pie chart）的类。以下是一些常用的参数：\n", + "\n", + "- `labels`: 用于指定每个扇形的标签，可以是单个值、列表或数组。\n", + "- `values`: 用于指定每个扇形的数值，表示占比或数量，可以是单个值、列表或数组。\n", + "- `textinfo`: 用于控制在每个扇形上显示的文本信息，例如百分比、数值等。\n", + "- `hoverinfo`: 指定饼图上鼠标悬停时显示的信息。\n", + "- `hole`: 设置饼图中心的空洞大小，取值范围为 [0, 1]，默认为0，通过调整该参数，可以作出环形图。\n", + "- `name`: 为 trace 指定一个名称，用于图例等。\n", + "- `marker`: 一个字典，包含有关扇形的属性，例如颜色、透明度等。\n", + "\n", + "例：对给定的数据作饼图。\n", + "\n", + "1. 控制饼图中每个部分上显示的文本信息，这里设置为显示百分比和标签。\n", + "\n", + "2. 设置悬停（hover）信息，当鼠标悬停在饼图的某个部分时显示的信息，这里设置为显示标签和百分比。\n", + "\n", + "3. 控制饼图中间的空洞的大小，取值范围为 [0, 1]，这里设置为 0.3，表示空洞占整个饼图的 30%（即变为环形图）。\n", + "\n", + "4. 配置饼图的颜色和边界线样式：在`marker`中用`colors`参数包含四种颜色的列表，分别对应饼图的四个部分；`line`参数用字典，定义边界线的样式，包括颜色和宽度。这里设置为白色边界线，宽度为2。" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hole": 0.3, + "hoverinfo": "label+percent", + "labels": [ + "Category A", + "Category B", + "Category C", + "Category D" + ], + "marker": { + "colors": [ + "red", + "green", + "blue", + "purple" + ], + "line": { + "color": "white", + "width": 2 + } + }, + "name": "Pie Chart", + "textinfo": "percent+label", + "type": "pie", + "values": [ + 40, + 30, + 20, + 10 + ] + } + ], + "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 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "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" + }, + 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"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 + } + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## 定义数据\n", + "labels = ['Category A', 'Category B', 'Category C', 'Category D'] #包含了饼图每个部分的标签，表示不同的类别\n", + "values = [40, 30, 20, 10] #包含了每个部分对应的数值\n", + "\n", + "trace = go.Pie(\n", + " labels=labels,\n", + " values=values,\n", + " textinfo='percent+label',\n", + " hoverinfo='label+percent',\n", + " hole=0.3,\n", + " name='Pie Chart',\n", + " marker=dict(\n", + " colors=['red', 'green', 'blue', 'purple'],\n", + " line=dict(color='white', width=2)\n", + " )\n", + ")\n", + "\n", + "#fig = go.Figure(trace) # 可以直接作图\n", + "fig = go.Figure()\n", + "fig.add_trace(trace)\n", + "fig.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 直方图\n", + "\n", + "\n", + "`go.Histogram` 是 Plotly 中用于创建饼图（Pie chart）的类。以下是一些常用的参数：\n", + "\n", + "- `x`: 用于指定数据点在 x 轴上的坐标，表示待绘制直方图的数据，可以是单个值、列表或数组。\n", + "- `nbinsx`: 设置直方图的箱体数量，即直方图中显示的条形数量。\n", + "- `histnorm`: 用于指定直方图的归一化方式，可以是 'percent'（百分比）、'probability'（概率）等。\n", + "- `name`: 为 trace 指定一个名称，用于图例等。\n", + "- `orientation`: 设置直方图的方向，可以是 'v'（垂直）或 'h'（水平），默认为垂直。\n", + "- `opacity`: 设置直方图的透明度，取值范围为 [0, 1]。\n", + "- `marker`: 一个字典，包含有关直方图的条形的属性，例如颜色、透明度等。\n", + "\n", + "例：对给定一组连续的数据作直方图，分为5个箱子，显示为百分比的方式，透明度设置为0.7，整体颜色为蓝色。" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "histnorm": "percent", + "marker": { + "color": "blue" + }, + "name": "Histogram", + "nbinsx": 5, + "opacity": 0.7, + "type": "histogram", + "x": [ + 1, + 2, + 2, + 3, + 3, + 3, + 4, + 4, + 5, + 5 + ] + } + ], + "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 + }, + "pattern": { + "fillmode": "overlay", 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'all'（所有数据点）、'outliers'（离群值）、'all' + 'outliers' 等。不设置该参数时，则不显示具体的点位。\n", + "- `name`: 为 trace 指定一个名称，用于图例等。\n", + "- `boxmean`: 设置是否在箱体内显示均值线。\n", + "- `marker`: 一个字典，包含有关具体点的属性，例如颜色、透明度等。\n", + "- `line`: 一个字典，包含有关中位线和边缘线的属性，例如颜色、宽度等，也控制了箱体颜色。\n", + "\n", + "\n", + "例：对给定的一组连续数据作箱线图，展示所有数据点为散点，显示均值线，数据点颜色为绿色，异常值颜色为红色，并使用蓝色线条描绘。" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "boxmean": true, + "boxpoints": "all", + "line": { + "color": "blue" + }, + "marker": { + "color": "green", + "outliercolor": "red" + }, + "name": "boxplot", + "type": "box", + "y": [ + 1, + 2, + 2, + 3, + 3, + 3, + 4, + 4, + 5, + 5 + ] + } + ], + "layout": { + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 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"language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ch-plotly/elements.ipynb b/ch-plotly/elements.ipynb new file mode 100644 index 0000000..4752dbd --- /dev/null +++ b/ch-plotly/elements.ipynb @@ -0,0 +1,5845 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plotly 画图元素" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import plotly.graph_objects as go\n", + "#import plotly.express as px\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotly 提供了丰富的画图元素，使得用户能够创建各种交互性图形。本节对 Plotly 中常用的画图元素进行详细说明。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 图形 Figure \n", + "\n", + "与 Matplotlib 类似， Plotly 中的 `Figure` 对象也是整个图形的容器，包含了数据的集合轨迹 `traces` 和图形样式 `Layout` 。可以创建 `Figure` 并在其中添加多个 `traces`，然后通过修改 `Layout` 来调整外观；也可以在一个 `Figure` 对象中创建多个子图，再分别添加对应的 `traces`。\n", + "\n", + "在 Plotly 的第一节，我们已经介绍了如何创建一个子图。这里主要以创建多个子图为例。我们用 `plotly.subplots` 模块中的 `.make_subplots()` 方法来创建多个子图的图表，可类比于 Matplotlib 中的`subplots()`函数，常用 `rows` 和 `cols` 参数设置子图的行数和列数排布， `subplot_titles` 参数设置子图的名字，用列表传入。 \n", + "\n", + "例：创建以一行两列形式包含两个子图的图表，第一个子图为散点图，第二个子图为折线图，分别命名为 Subplot 1 和 Subplot 2。\n", + "\n", + "这里先创建好 `trace`，再传入 `.add_trace()`，并用 `row` 和 `column` 参数设置好对应的子图位置。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "mode": "markers", + "type": "scatter", + "x": [ + 1, + 2, + 3, + 4 + ], + "xaxis": "x", + "y": [ + 10, + 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go.Scatter(x=[1, 2, 3, 4], y=[5, 4, 3, 2], mode='lines')\n", + "fig.add_trace(line_trace, row=1, col=2)\n", + "\n", + "# 更新布局信息\n", + "fig.update_layout(title='Multiple Subplots')\n", + "\n", + "# 显示图表\n", + "fig.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 布局 `Layout`\n", + "`Layout` 是图形的外观和样式的控制元素， `layout` 对象包含在 `Figure` 对象中，通常通过设置 `.update_layout()` 来调整图形的布局，一张图可以多次调整，即可以多次使用该函数。常见的具体设置如下。\n", + "\n", + "### 修改标题 \n", + "\n", + "使用 `title` 参数设置新标题即可。\n", + "\n", + "例：将上面的 `Figure` 的标题改为 Subplots，并居中显示。\n", + "\n", + "```{note}\n", + "Plotly 中，图片标题不会默认居中，需要设置 `title_x=0.5` 来调整。\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "mode": "markers", + "type": "scatter", + "x": [ + 1, + 2, + 3, + 4 + ], + "xaxis": "x", + "y": [ + 10, + 11, + 12, + 13 + ], + 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"baxis": { + "gridcolor": "#506784", + "linecolor": "#506784", + "ticks": "" + }, + "bgcolor": "rgb(17,17,17)", + "caxis": { + "gridcolor": "#506784", + "linecolor": "#506784", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "updatemenudefaults": { + "bgcolor": "#506784", + "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 + } + } + }, + "title": { + "text": "Subplots", + "x": 0.5 + }, + "width": 600, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 0.45 + ], + "title": { + "text": "X1轴标题" + } + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0.55, + 1 + ], + "title": { + "text": "X2轴标题" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Y1轴标题" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Y2轴标题" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 设置子图1的轴标题\n", + "fig.update_xaxes(title_text='X1轴标题', row=1, col=1)\n", + "fig.update_yaxes(title_text='Y1轴标题', row=1, col=1)\n", + "\n", + "# 设置子图2的轴标题\n", + "fig.update_xaxes(title_text='X2轴标题', row=1, col=2)\n", + "fig.update_yaxes(title_text='Y2轴标题', row=1, col=2)\n", + "\n", + "# 显示图形\n", + "fig.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 设置模版 `template`\n", + "`template` 参数用于设置图形布局的模板，这可以应用特定的风格和主题。常用的参数选择有：\n", + "- \"plotly\" (默认): 默认模板，采用 Plotly 经典的白色背景和颜色样式。\n", + "- \"plotly_dark\": 深色主题的模板，适合在暗色环境中查看。\n", + "- \"ggplot2\": 类似于 R 语言中的 ggplot2 图形系统的模板，采用灰色背景。\n", + "- \"seaborn\": 模拟 seaborn Python 库的外观，具有淡蓝色的背景和灰色网格线。\n", + "- \"simple_white\": 简单的白色背景，适用于注重简洁性的图形。\n", + "- \"plotly_white\": 与 \"plotly\" 模板类似，采用白色背景，但可能有轻微差异。\n", + "\n", + "例：对上面的 `Figure` 使用深色主题。\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "mode": "markers", + "type": "scatter", + "x": [ + 1, + 2, + 3, + 4 + ], + "xaxis": "x", + "y": [ + 10, + 11, + 12, + 13 + ], + "yaxis": "y" + }, + { + "mode": "lines", + "type": "scatter", + "x": [ + 1, + 2, + 3, + 4 + ], + "xaxis": "x2", + "y": [ + 5, + 4, + 3, + 2 + ], + "yaxis": "y2" + } + ], + "layout": { + "annotations": [ + { + "font": { + "size": 16 + }, + "showarrow": false, + "text": "Subplot 1", + "x": 0.225, + "xanchor": "center", + "xref": "paper", + "y": 1, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 16 + }, + "showarrow": false, + "text": "Subplot 2", + "x": 0.775, + "xanchor": "center", + "xref": "paper", + "y": 1, + 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"source": [ + "fig.update_layout(\n", + " height=400, # 设置图形高度\n", + " width=600 # 设置图形宽度\n", + ")\n", + "fig.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pyds", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ch-plotly/financialcharts.ipynb b/ch-plotly/financialcharts.ipynb new file mode 100644 index 0000000..2233bf9 --- /dev/null +++ b/ch-plotly/financialcharts.ipynb @@ -0,0 +1,7781 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 金融图表" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import plotly.graph_objects as go\n", + "#import plotly.express as px\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Plotly 的金融图表库提供了丰富的功能，可以用于分析和可视化金融数据。本小节介绍一些基本的经济金融相关图表。\n", + "\n", + "## 蜡烛图\n", + "\n", + "蜡烛图是一种用于展示股票或其他金融资产价格的图表类型。每个蜡烛表示一段时间内的开盘价、收盘价、最高价和最低价，股市的k线图就是一个蜡烛图。Plotly 的 `candlestick` 函数可用于创建这种类型的图表。参数有：\n", + "\n", + "- x: x 轴上的数据。通常是时间轴上的日期数据，表示蜡烛图中每个蜡烛的位置。\n", + "- `open`: 每个蜡烛的开盘价数据。\n", + "- `high`: 每个蜡烛的最高价数据。\n", + "- `low`: 每个蜡烛的最低价数据。\n", + "- `close`: 每个蜡烛的收盘价数据。\n", + "\n", + "日内收盘价高于开盘价则绿色，反之蜡烛图显示为红色。还可以在 `.update_layout` 中设置 `xaxis_rangeslider_visible` 参数决定是否有时间区间的滑块，设置为 False 则不显示滑块，默认为显示。\n", + "\n", + "例：作出苹果公司（AAPL）2015-2917年股市的蜡烛图。数据包含了每日的开盘价、最高价、最低价和收盘价。作图后，可以通过交互按钮放大特定时间区间的具体表现。\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + 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high=df['AAPL.High'],\n", + " low=df['AAPL.Low'],\n", + " close=df['AAPL.Close']))\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 开-高-低-收图（OHLC Charts）\n", + "OHLC 图表与蜡烛图类似，但用线段表示开盘和收盘价，通过 `Ohlc` 函数创建。" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "close": [ + 127.830002, + 128.720001, + 128.449997, + 129.5, + 133, + 132.169998, + 128.789993, + 130.419998, + 128.460007, + 129.089996, + 129.360001, + 128.539993, + 126.410004, + 126.599998, + 127.139999, + 124.510002, + 122.239998, + 124.449997, + 123.589996, + 124.949997, + 127.040001, + 128.470001, + 127.5, + 125.900002, + 127.209999, + 126.690002, + 123.379997, + 124.239998, + 123.25, + 126.370003, + 124.43, + 124.25, + 125.32, + 127.349998, + 126.010002, + 125.599998, + 126.559998, + 127.099998, + 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"title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = go.Figure()\n", + "fig.add_trace(go.Ohlc(x=df['Date'],\n", + " open=df['AAPL.Open'],\n", + " high=df['AAPL.High'],\n", + " low=df['AAPL.Low'],\n", + " close=df['AAPL.Close']))\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 瀑布图 \n", + "\n", + "瀑布图是一种用于展示数值数据中各项增减对总体变化的贡献的图表类型。它以垂直方向的条形表示数据的累积效果，展示了从一个起始点到最终结果的各个过程和因素的贡献。瀑布图通常用于呈现财务数据中的收入、支出、利润等变化。\n", + "\n", + "`go.Waterfall` 是 Plotly 中用于创建瀑布图的图表对象。主要参数包括：\n", + "\n", + "- `measure` : 每个柱的测量类型，可以是 \"relative\"（相对值，即相对于前一个柱的增量）或 \"total\"（总值，即当前柱的绝对值）。\n", + "- `y`: 每个柱的数值，代表各个因素对总体变化的贡献。\n", + "- `name`(optional): 该瀑布图的名称，用于在图例中标识。\n", + "- `orientation`(optional): 瀑布图的方向，可以是 \"v\"（垂直）或 \"h\"（水平）。\n", + "- `x` (optional): 每个柱的标签，用于表示瀑布图中的各个过程或因素。\n", + "- `textposition` (optional): 柱上文本标签的位置，可以是 \"inside\"（内部）或 \"outside\"（外部）。\n", + "- `text`(optional): 与每个柱相关联的文本标签，用于显示在柱的旁边。\n", + "- `connector` (optional): 连接线的样式，是一个包含 \"line\" 字典的字典。可以设置颜色等样式。\n", + "\n", + "例：创建一个垂直方向的瀑布图，表示了不同因素对于损益表的贡献，包括销售额、咨询费用、净收入、采购成本、其他费用以及税前利润。" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "connector": { + "line": { + "color": "rgb(63, 63, 63)" + } + }, + "measure": [ + "relative", + "relative", + "total", + "relative", + "relative", + "total" + ], + "orientation": "v", + "text": [ + "+60", + "+80", + "", + "-40", + "-20", + "Total" + ], + "textposition": "outside", + "type": "waterfall", + "x": [ + "Sales", + "Consulting", + "Net revenue", + "Purchases", + "Other expenses", + "Profit before tax" + ], + "y": [ + 60, + 80, + 0, + -40, + -20, + 0 + ] + } + ], + "layout": { + "template": { + "data": { + "bar": 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@@ -0,0 +1,5928 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plotly 简介\n", + "\n", + "Plotly 是一个用于创建$\\textbf{交互性}$可视化图表的 Python 库。它支持多种图表类型，包括散点图、线图、条形图、饼图、3D 图等，并且提供了丰富的交互性功能，使用户能够在图表上进行缩放、平移、旋转等操作，也支持悬停显示信息、点击事件等。\n", + "\n", + "它是 Matplotlib 画图库的一个补充，有以下一些区别：\n", + "\n", + "- 使用 Matplotlib 需要较多的代码来创建和定制图表，需要显式地指定图表的各个部分，如标题、轴标签等；Plotly 是一个交互式的绘图库，可以用较少的代码生成交互式图表。\n", + "\n", + "- 默认情况下，Matplotlib 生成的图表通常是静态的，缺乏交互性。虽然可以通过一些插件或工具实现一些交互性，但不如 Plotly 易用和直观。\n", + "\n", + "- Matplotlib 是一个功能强大的通用绘图库，支持许多图表类型，包括线图、散点图、柱状图等；Plotly 同时提供一些 Matplotlib 不太容易实现的高级图表类型，如3D图表、地理图表等。\n", + "\n", + "## 安装库和导入包\n", + "\n", + "使用 `pip` 安装：\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Requirement already satisfied: plotly in /Users/xu/anaconda3/envs/pyds/lib/python3.11/site-packages (5.18.0)\n", + "Requirement already satisfied: tenacity>=6.2.0 in /Users/xu/anaconda3/envs/pyds/lib/python3.11/site-packages (from plotly) (8.2.3)\n", + "Requirement already satisfied: packaging in /Users/xu/anaconda3/envs/pyds/lib/python3.11/site-packages (from plotly) (23.2)\n" + ] + } + ], + "source": [ + "## 安装包\n", + "!pip install plotly -i https://pypi.tuna.tsinghua.edu.cn/simple" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Requirement already satisfied: kaleido in /Users/xu/anaconda3/envs/pyds/lib/python3.11/site-packages (0.2.1)\n" + ] + } + ], + "source": [ + "!pip install -U kaleido -i https://pypi.tuna.tsinghua.edu.cn/simple" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "如下所示，导入 Plotly (并导入其他相关库)：\n", + "\n", + "```{note}\n", + "plotly.graph_object 模块包含了用于创建图表的类和方法，而使用as go的别名后，可以使用更简洁的go来调用这些类和方法，使得代码更为简洁。\n", + "\n", + "plotly.express 是一种更高级的 api 接口，通常需要更少的代码，因为它使用了一些默认设置，而且很多配置都是隐式的，适用于快速创建简单图表，而 plotly.graph_objects 更适合需要更多控制和自定义的情况。\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import plotly.graph_objects as go\n", + "import plotly.express as px\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 常用绘图步骤\n", + "\n", + "Plotly 的绘图通常遵循一定的步骤和格式。以下是一个简单的示例，展示了创建一个基本折线图的流程：\n", + "\n", + "### 类似于 Matplotlib，首先创建 `Figure` 对象。\n", + "\n", + "使用 `go.Figure()` 创建一个 `Figure` 对象，它是 Plotly 绘图的基础。可以选择添加一些初始布局信息（用`.update_layout()`方法添加，后续详细说明）。" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + 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中，trace 是一个基本的绘图元素，用于描述图表中的数据和其样式。\n", + "```\n", + "使用 `add_trace` 方法向 `Figure` 对象添加具体的图表，比如散点图、折线图等（可以在一张图上添加多个）。\n", + "\n", + "该例使用 `go.Scatter()` 表示作散点图或折线图：`x=x_data`指定了图表的x轴数据；`y=y_data`指定了图表的y轴数据；`mode='lines+markers'`设置了 `trace` 的模式，在这里，它设置为 lines+markers，表示想要在图表上同时显示线和标记；`name='My Line'`为这条 `trace` 分配一个名称。当在一个图表中有多个 `trace` （即多组数据多组图表）时，`name` 参数可以便于作出图例。" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "mode": "lines+markers", + "name": "My Line", + "type": "scatter", + "x": [ + 1, + 2, + 3, + 4 + ], + "y": [ + 10, + 11, + 12, + 13 + ] + } + ], + "layout": { + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + 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pd.date_range(start='2022-01-01', end='2022-12-31', freq='B')\n", + "data = pd.DataFrame(date_rng, columns=['date'])\n", + "data['price'] = 100 + (pd.Series(range(len(date_rng))) + pd.Series(range(len(date_rng))) * 0.1).cumsum()\n", + "data['returns'] = data['price'].pct_change()\n", + "\n", + "# 创建折线图\n", + "fig = go.Figure()\n", + "\n", + "# 基础绘图 - 价格走势\n", + "fig.add_trace(go.Scatter(x=data['date'], y=data['price'], mode='lines', name='Price'))\n", + "\n", + "# 交互性 - 添加悬停信息\n", + "fig.update_traces(hoverinfo='x+y', line=dict(width=2))\n", + "\n", + "# 添加布局信息\n", + "fig.update_layout(\n", + " title='金融数据可视化',\n", + " xaxis_title='日期',\n", + " yaxis_title='价格',\n", + " template='plotly_dark' # 使用暗色主题\n", + ")\n", + "\n", + "# 显示图表\n", + "fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "close": [ + 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+ "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": "股票蜡烛图" + }, + "xaxis": { + "title": { + "text": "日期" + } + }, + "yaxis": { + "title": { + "text": "价格" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.graph_objects as go\n", + "\n", + "# 模拟蜡烛图数据\n", + "candle_data = {\n", + " 'date': ['2022-01-01', '2022-01-02', '2022-01-03'],\n", + " 'open': [150, 155, 160],\n", + " 'high': [160, 162, 165],\n", + " 'low': [145, 152, 158],\n", + " 'close': [155, 158, 163]\n", + "}\n", + "\n", + "fig = go.Figure(data=[go.Candlestick(x=candle_data['date'],\n", + " open=candle_data['open'],\n", + " high=candle_data['high'],\n", + " low=candle_data['low'],\n", + " close=candle_data['close'])])\n", + "\n", + "fig.update_layout(title='股票蜡烛图', xaxis_title='日期', yaxis_title='价格')\n", + "fig.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pyds", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": 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