相关图

一个分类图，显示了在法医工作中获得的 214 个玻璃碎片样本的矿物含量之间的相关性。

该数据集包含七个变量，测量了每个玻璃碎片中发现的镁 (Mg)、钙 (Ca)、铁 (Fe)、钾 (K)、钠 (Na)、铝 (Al) 和钡 (Ba) 的含量。彩色圆圈代表这些变量对之间的相关性。每个相关性的幅度用圆圈的大小编码。

详情

样本数据:

bokeh.sampledata.forensic_glass

Bokeh API:

figure.scatter

更多信息:

相关图

关键词:

散点图, 相关图

from itertools import combinations

import numpy as np
import pandas as pd

from bokeh.models import ColumnDataSource, FixedTicker
from bokeh.plotting import figure, show
from bokeh.sampledata.forensic_glass import data as df
from bokeh.transform import linear_cmap

elements = ("Mg", "Ca", "Fe", "K", "Na", "Al", "Ba")
pairs = list(combinations(elements, 2))

correlations = []
for x, y in pairs:
    matrix = np.corrcoef(df[x], df[y])
    correlations.append(matrix[0, 1])

x, y = list(zip(*pairs))

new_df = pd.DataFrame({
    "oxide_1": x,
    "oxide_2": y,
    "correlation": correlations,
    "dot_size": [(1+ 10 * abs(corr)) * 10 for corr in correlations],
})

x_range = new_df["oxide_1"].unique()
y_range = list(new_df["oxide_2"].unique())

source = ColumnDataSource(new_df)

p = figure(x_axis_location="above", toolbar_location=None, x_range=x_range, y_range=y_range, background_fill_color="#fafafa")

c = p.scatter(x="oxide_1", y="oxide_2", size="dot_size", source=source, fill_color=linear_cmap("correlation", "RdYlGn9", -0.5, 0.5), line_color="#202020")

color_bar = c.construct_color_bar(
    location=(200, 0),
    ticker=FixedTicker(ticks=[-0.5, 0.0, 0.5]),
    title="correlation",
    major_tick_line_color=None,
    width=150,
    height=20,
)

p.add_layout(color_bar, "below")

p.axis.major_tick_line_color = None
p.axis.major_tick_out = 0
p.axis.axis_line_color = None
p.grid.grid_line_color = None
p.outline_line_color = None

show(p)