很多时候，文学一张炫酷图就足以胜过千言万语。作种对于数学科学家来说，炫酷当想阐述自己的态图观点、劳动成果时，文学我们需要直接有效的作种沟通。单调乏味的炫酷文本和数字，很难抓住别人的态图眼球，飘飘亮亮的文学可视化动态图是必不可少的，至少是作种一个加分项。
本文将基于Python的炫酷Plotly图形库，介绍几种工作中常用的态图动画图和交互式图标。在使用之前看一下是文学否安装了 Plotly。
1. 朝阳图
层次结构数据通常存储为矩形数据框，作种其中不同的炫酷列对应于层次结构的不同级别。px.sunburst可以采用path与列列表相对应的参数。请注意，云服务器提供商如果给出id，则parent不应提供path。
import plotly.express as px df = px.data.tips() fig = px.sunburst(df, path=[day, time, sex], values=total_bill) fig.show() 2. 桑基图
桑基图通过定义可视化到流动的贡献源来表示源节点，目标为目标节点，数值以设置流volum，和标签，显示了节点名称,在流量分析中常用。
import plotly.graph_objects as go import urllib, json url = https://raw.githubusercontent.com/plotly/plotly.js/master/test/image/mocks/sankey_energy.json response = urllib.request.urlopen(url) data = json.loads(response.read()) # override gray link colors with source colors opacity = 0.4 # change magenta to its rgba value to add opacity data[data][0][node][color] = [rgba(255,0,255, 0.8) if color == "magenta" else color for color in data[data][0][node][color]] data[data][0][link][color] = [data[data][0][node][color][src].replace("0.8", str(opacity)) for src in data[data][0][link][source]] fig = go.Figure(data=[go.Sankey( valueformat = ".0f", valuesuffix = "TWh", # Define nodes node = dict( pad = 15, thickness = 15, line = dict(color = "black", width = 0.5), label = data[data][0][node][label], color = data[data][0][node][color] ), # Add links link = dict( source = data[data][0][link][source], target = data[data][0][link][target], value = data[data][0][link][value], label = data[data][0][link][label], color = data[data][0][link][color] ))]) fig.update_layout(title_text="Energy forecast for 2050<br>Source: Department of Energy & Climate Change, Tom Counsell via <a href=https://bost.ocks.org/mike/sankey/>Mike Bostock</a>", font_size=10) fig.show() 效果图
3. 雷达图
雷达图(也称为蜘蛛情节或情节星)显示器在从中心轴始发表示定量变量的二维图的形式多变量数据。轴的相对位置和角度通常是无用的。它等效于轴沿径向排列的平行坐标图。
import plotly.graph_objects as go import urllib, json url = https://raw.githubusercontent.com/plotly/plotly.js/master/test/image/mocks/sankey_energy.json response = urllib.request.urlopen(url) data = json.loads(response.read()) # override gray link colors with source colors opacity = 0.4 # change magenta to its rgba value to add opacity data[data][0][node][color] = [rgba(255,0,255, 0.8) if color == "magenta" else color for color in data[data][0][node][color]] data[data][0][link][color] = [data[data][0][node][color][src].replace("0.8", str(opacity)) for src in data[data][0][link][source]] fig = go.Figure(data=[go.Sankey( valueformat = ".0f", valuesuffix = "TWh", # Define nodes node = dict( pad = 15, thickness = 15, line = dict(color = "black", width = 0.5), label = data[data][0][node][label], color = data[data][0][node][color] ), # Add links link = dict( source = data[data][0][link][source], target = data[data][0][link][target], value = data[data][0][link][value], label = data[data][0][link][label], color = data[data][0][link][color] ))]) fig.update_layout(title_text="Energy forecast for 2050<br>Source: Department of Energy & Climate Change, Tom Counsell via <a href=https://bost.ocks.org/mike/sankey/>Mike Bostock</a>", font_size=10) fig.show() 效果图
4. 漏斗图
漏斗图通常用于表示业务流程不同阶段的数据。在商业智能中，这是识别流程潜在问题区域的重要机制。例如，它用于观察销售过程中每个阶段的收入或损失，并显示逐渐减小的值。每个阶段均以占所有值的百分比表示。网站模板
from plotly import graph_objects as go fig = go.Figure() fig.add_trace(go.Funnel( name = Montreal, y = ["Website visit", "Downloads", "Potential customers", "Requested price"], x = [120, 60, 30, 20], textinfo = "value+percent initial")) fig.add_trace(go.Funnel( name = Toronto, orientation = "h", y = ["Website visit", "Downloads", "Potential customers", "Requested price", "invoice sent"], x = [100, 60, 40, 30, 20], textposition = "inside", textinfo = "value+percent previous")) fig.add_trace(go.Funnel( name = Vancouver, orientation = "h", y = ["Website visit", "Downloads", "Potential customers", "Requested price", "invoice sent", "Finalized"], x = [90, 70, 50, 30, 10, 5], textposition = "outside", textinfo = "value+percent total")) fig.show() 效果图
5. 3D表面图
具有轮廓的曲面图，使用contours属性显示和自定义每个轴的轮廓数据。
import plotly.graph_objects as go import pandas as pd # Read data from a csv z_data = pd.read_csv(https://raw.githubusercontent.com/plotly/datasets/master/api_docs/mt_bruno_elevation.csv) fig = go.Figure(data=[go.Surface(z=z_data.values)]) fig.update_traces(contours_z=dict(show=True, usecolormap=True, highlightcolor="limegreen", project_z=True)) fig.update_layout(title=Mt Bruno Elevation, autosize=False, scene_camera_eye=dict(x=1.87, y=0.88, z=-0.64), width=500, height=500, margin=dict(l=65, r=50, b=65, t=90) ) fig.show() 6. 动画图
一些Plotly Express函数支持通过animation_frame和animation_group参数创建动画人物。这是使用Plotly Express创建的动画散点图的示例。请注意，您应始终修复x_range和，y_range以确保您的数据在整个动画中始终可见。
import plotly.express as px df = px.data.gapminder() px.scatter(df, x="gdpPercap", y="lifeExp", animation_frame="year", animation_group="country", size="pop", color="continent", hover_name="country", log_x=True, size_max=55, range_x=[100,100000], range_y=[25,90]) 结论
可视化的图形在日常工作中经常实用，其中Plotly是用过的体验比较好的，本篇文章分享给大家一些案例，Plotly可视化远不止这些，在后续的文章中，涉及可视化部分的，将介绍更多酷炫的可视化图形，喜欢点个在看分享，收藏以备不时之需。
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