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掌握这五种高级可视化图表将使数据可视化变得容易。这些库互为补充，以最大化数据表达。和弦图创造性地展示了数据点之间复杂的关系。节点围绕一个圆圈排列，通过弧线连接。弧线的长度反映了连接值，其粗细表示关系的重要性。颜色对数据进行分类，使比较变得容易。广泛应用于各个领域，特别是在可视化遗传数据方面。以下是一个使用 Holoviews & Bokeh 创建显示五个国家之间贸易关系的和弦图的示例。import holoviews as hvfrom holoviews import optsimport pandas as pdimport numpy as nphv.extension('bokeh')
# Sample matrix representing the export volumes between 5 countriesexport_data = np.array([[0, 50, 30, 20, 10], [10, 0, 40, 30, 20], [20, 10, 0, 35, 25], [30, 20, 10, 0, 40], [25, 15, 30, 20, 0]])
labels = ['USA', 'China', 'Germany', 'Japan', 'India']
# Creating a pandas DataFramedf = pd.DataFrame(export_data, index=labels, columns=labels)df = df.stack().reset_index()
df.columns = ['source', 'target', 'value']
# Creating a Chord objectchord = hv.Chord(df)
# Styling the Chord diagramchord.opts( opts.Chord( cmap='Category20', edge_cmap='Category20', labels='source', label_text_font_size='10pt', edge_color='source', node_color='index', width=700, height=700 )).select(value=(5, None))
# Display the plotchord
https://holoviews.org/reference/elements/matplotlib/Chord.html
https://github.com/moshi4/pyCirclizeSunburst Chart 通过清晰展示层次数据，超越了传统的饼图和环图。它使用同心圆，每个圆代表层次中的一级。中心是根，扇形表示节点。每个扇形的大小反映了它的值，直观地理解数据的重要性。在可视化文件系统层次结构、用户导航路径、市场细分和遗传数据方面非常有用。以下是一个使用 Plotly 库创建Sunburst Chart 的示例。import plotly.express as pximport numpy as np
df = px.data.gapminder().query("year == 2007")
fig = px.sunburst(df, path=['continent', 'country'], values='pop', color='lifeExp', hover_data=['iso_alpha'], color_continuous_scale='RdBu', color_continuous_midpoint=np.average(df['lifeExp'], weights=df['pop']))fig.show()
https://plotly.com/python/sunburst-charts/六边形分箱图，或称六边形分箱，对于可视化二维数据分布非常有效，特别是当数据点密集时。它将数据空间划分为六边形箱，颜色表示每个箱中的点数，清晰地表示数据分布。以下是一个使用 Python 和 Matplotlib 创建六边形分箱图的示例，展示了空气质量指数 (AQI) 与医院访问之间的相关性。import numpy as npimport matplotlib.pyplot as pltfrom mplhexbin import HexBin
np.random.seed(0) n_points = 10000x = np.random.rand(n_points) * 100 y = 5 * np.sin(x * np.pi / 50) + np.random.randn(n_points) * 15
fig, ax = plt.subplots(figsize=(10, 8))
hb = HexBin(ax, gridsize=20, cmap='viridis', extent=[0, 100, -30, 50]) hb.hexbin(x, y, mincnt=1)
ax.set_title('Relationship between Air Quality Index (AQI) and Hospital Visits')ax.set_xlabel('Air Quality Index (AQI)')ax.set_ylabel('Hospital Visits')
plt.colorbar(hb.cmap, ax=ax, label='Number of Data Points') plt.show()
https://matplotlib.org/stable/gallery/statistics/hexbin_demo.html桑基图可视化数据流，非常适合能源、材料和财务数据。以 Matthew Henry Phineas Riall Sankey 命名，它显示了系统各阶段或部分之间的流量。节点宽度与流量数量成比例，易于理解数据规模和方向。以下是一个使用 Python 创建桑基图的示例，展示了从生产源头到小城市消费者的能量流。import plotly.graph_objects as go
labels = ["Coal", "Solar", "Wind", "Nuclear", "Residential", "Industrial", "Commercial"]
source = [0, 1, 2, 3, 0, 1, 2, 3] target = [4, 4, 4, 4, 5, 5, 5, 5] value = [25, 10, 40, 20, 30, 15, 25, 35]
fig = go.Figure(data=[go.Sankey( node=dict( pad=15, thickness=20, line=dict(color="black", width=0.5), label=labels ), link=dict( source=source, target=target, value=value ))])
fig.update_layout(title_text="Energy Flow in Model City", font_size=12)fig.show()
https://plotly.com/python/sankey-diagram/流图类似于河流，描绘了随时间的变化。颜色区分类别，而“河流”的宽度表示每个类别的值。它直观地展示趋势和关系，易于理解数据动态。import altair as altfrom vega_datasets import data
source = data.unemployment_across_industries.url
alt.Chart(source).mark_area().encode( alt.X('yearmonth(date):T', axis=alt.Axis(format='%Y', domain=False, tickSize=0) ), alt.Y('sum(count):Q', stack='center', axis=None), alt.Color('series:N', scale=alt.Scale(scheme='category20b') )).interactive()
https://altair-viz.github.io/gallery/streamgraph.html🌟 想要变身计算机视觉小能手？快来「小白玩转Python」公众号！回复“Python视觉实战项目”，解锁31个超有趣的视觉项目大礼包！🎁
