Python 尝试使用matplotlib（平行坐标？）

我试图可视化一个已排序的表（按列排序）。我的理想结果应该是

关于如何使用matplotlib实现这一目标，有什么建议吗

我已经尝试过给出的建议，但我在附件中寻找一些更喜欢的东西

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提前感谢，

Matplotlib不直接支持此功能，但复制链接到的绘图相当容易

下面的函数在给定2d数据数组时执行类似的操作。它可以排序，也可以不排序，函数并不真正关心

import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np

def sorted_table_plot(data, labels, categories, cmap=None, ax=None):

    # check if an axes was supplied
    if ax is None:
        ax = plt.gca()

    # check if a colormap was supplied
    if cmap is None:
        cmap = plt.cm.jet

    # generate the grid arrays with the coordinates for the annotations
    yy, xx = np.mgrid[:data.shape[0], :data.shape[1]]
    x = xx.flatten()
    y = yy.flatten()
    d = data.flatten()

    # a norm object which we will use with the colorbar
    norm = plt.Normalize(d.min(), d.max())

    # iterate over the data points and draw the labels
    for di, xi, yi in zip(d, x, y):
        color = cmap(norm(di))
        hsv = mcolors.rgb_to_hsv(color[:3])
        fc = 'w' if hsv[2] < 0.7 else 'k'
        ax.annotate(str(di), xy=(xi,yi), xycoords="data",
                va="center", ha="center", color=fc,
                bbox=dict(boxstyle="circle", fc=color))

    # iteratve over all the appearing values and draw the lines
    for i in np.unique(data):
        xi, yi = x[d==i], y[d==i]
        idx = np.argsort(xi)
        plt.plot(xi[idx], yi[idx], color=plt.cm.jet(norm(i)), lw=2)

    # add the axes labels
    ax.set_xticks(xx[0,:])
    ax.set_xticklabels(categories)
    ax.set_yticks(yy[:,0])
    ax.set_yticklabels(labels)

    # adjust the axes ranges
    ax.set_xlim(xx[0,0] - 0.5, xx[-1,-1] + 0.5)
    ax.set_ylim(yy[-1,-1] + 0.5, yy[0,0] - 0.5)

结果:

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Matplotlib不直接支持此功能，但复制链接到的绘图相当容易

下面的函数在给定2d数据数组时执行类似的操作。它可以排序，也可以不排序，函数并不真正关心

import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np

def sorted_table_plot(data, labels, categories, cmap=None, ax=None):

    # check if an axes was supplied
    if ax is None:
        ax = plt.gca()

    # check if a colormap was supplied
    if cmap is None:
        cmap = plt.cm.jet

    # generate the grid arrays with the coordinates for the annotations
    yy, xx = np.mgrid[:data.shape[0], :data.shape[1]]
    x = xx.flatten()
    y = yy.flatten()
    d = data.flatten()

    # a norm object which we will use with the colorbar
    norm = plt.Normalize(d.min(), d.max())

    # iterate over the data points and draw the labels
    for di, xi, yi in zip(d, x, y):
        color = cmap(norm(di))
        hsv = mcolors.rgb_to_hsv(color[:3])
        fc = 'w' if hsv[2] < 0.7 else 'k'
        ax.annotate(str(di), xy=(xi,yi), xycoords="data",
                va="center", ha="center", color=fc,
                bbox=dict(boxstyle="circle", fc=color))

    # iteratve over all the appearing values and draw the lines
    for i in np.unique(data):
        xi, yi = x[d==i], y[d==i]
        idx = np.argsort(xi)
        plt.plot(xi[idx], yi[idx], color=plt.cm.jet(norm(i)), lw=2)

    # add the axes labels
    ax.set_xticks(xx[0,:])
    ax.set_xticklabels(categories)
    ax.set_yticks(yy[:,0])
    ax.set_yticklabels(labels)

    # adjust the axes ranges
    ax.set_xlim(xx[0,0] - 0.5, xx[-1,-1] + 0.5)
    ax.set_ylim(yy[-1,-1] + 0.5, yy[0,0] - 0.5)

结果:

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我认为matplotlib不支持这种绘图。欢迎使用。我回答了你的问题，尽管它实际上不符合SO:SO的标准，但它不是一种编码服务，因此通常你不会得到答案，除非你展示你自己尝试过的内容（是的，发布代码）以及失败的地方。我认为matplotlib不支持这种绘图。欢迎使用SO。我回答了你的问题，尽管它并不真正符合SO的标准：SO不是一种编码服务，所以通常你不会得到答案，除非你展示你自己尝试过的东西（是的，发布你的代码）以及失败的地方。