大熊猫连续数据的平行坐标图

Question

pandas的parallel_coordinates函数非常有用:

import pandas
import matplotlib.pyplot as plt
from pandas.tools.plotting import parallel_coordinates
sampdata = read_csv('/usr/local/lib/python3.3/dist-packages/pandas/tests/data/iris.csv')
parallel_coordinates(sampdata, 'Name')

但是当你有连续的数据时,它的行为并不是你所期望的:

mypos = np.random.randint(10, size=(100, 2))
mydata = DataFrame(mypos, columns=['x', 'y'])
myres = np.random.rand(100, 1)
mydata['res'] = myres
parallel_coordinates(mydata, 'res')

我希望线条的颜色能够反映连续变量的大小,例如从白色到黑色的渐变,最好还有一些透明度(alpha值)的可能性,旁边还有一个颜色条.

Answer 1

我今天遇到了同样的问题.我的解决方案是从pandas复制parallel_coordinates并根据我的特殊需要进行调整.我认为它对其他人有用,这是我的实现:

def parallel_coordinates(frame, class_column, cols=None, ax=None, color=None,
                     use_columns=False, xticks=None, colormap=None,
                     **kwds):
    import matplotlib.pyplot as plt
    import matplotlib as mpl

    n = len(frame)
    class_col = frame[class_column]
    class_min = np.amin(class_col)
    class_max = np.amax(class_col)

    if cols is None:
        df = frame.drop(class_column, axis=1)
    else:
        df = frame[cols]

    used_legends = set([])

    ncols = len(df.columns)

    # determine values to use for xticks
    if use_columns is True:
        if not np.all(np.isreal(list(df.columns))):
            raise ValueError('Columns must be numeric to be used as xticks')
        x = df.columns
    elif xticks is not None:
        if not np.all(np.isreal(xticks)):
            raise ValueError('xticks specified must be numeric')
        elif len(xticks) != ncols:
            raise ValueError('Length of xticks must match number of columns')
        x = xticks
    else:
        x = range(ncols)

    fig = plt.figure()
    ax = plt.gca()

    Colorm = plt.get_cmap(colormap)

    for i in range(n):
        y = df.iloc[i].values
        kls = class_col.iat[i]
        ax.plot(x, y, color=Colorm((kls - class_min)/(class_max-class_min)), **kwds)

    for i in x:
        ax.axvline(i, linewidth=1, color='black')

    ax.set_xticks(x)
    ax.set_xticklabels(df.columns)
    ax.set_xlim(x[0], x[-1])
    ax.legend(loc='upper right')
    ax.grid()

    bounds = np.linspace(class_min,class_max,10)
    cax,_ = mpl.colorbar.make_axes(ax)
    cb = mpl.colorbar.ColorbarBase(cax, cmap=Colorm, spacing='proportional', ticks=bounds, boundaries=bounds, format='%.2f')

    return fig

我不知道它是否适用于pandas原始功能提供的每个选项.但是对于你的例子,它给出了这样的东西:

parallel_coordinates(mydata, 'res', colormap="binary")

您可以通过在上一个函数中更改此行来添加alpha值:

ax.plot(x, y, color=Colorm((kls - class_min)/(class_max-class_min)), alpha=(kls - class_min)/(class_max-class_min), **kwds)

对于pandas原始示例,删除名称并将最后一列用作值:

sampdata = read_csv('iris_modified.csv')
parallel_coordinates(sampdata, 'Value')

我希望这能帮到您!

克里斯托夫