在matplotlib中是否有创建散点图矩阵的函数? [英] Is there a function to make scatterplot matrices in matplotlib?

问题描述

散点图矩阵示例

matplotlib.pyplot中有这样的功能吗?

Is there such a function in matplotlib.pyplot?

推荐答案

通常来说，matplotlib通常不包含对多个轴对象(在这种情况下为子图)进行操作的绘图函数.期望您可以编写一个简单的函数将事物串在一起，但是您希望如此.

Generally speaking, matplotlib doesn't usually contain plotting functions that operate on more than one axes object (subplot, in this case). The expectation is that you'd write a simple function to string things together however you'd like.

我不太确定您的数据是什么样子，但是仅构建一个函数来从头开始执行此操作非常简单.如果您始终要使用结构化或Rec数组，则可以简化此操作. (即，每个数据系列始终都有一个名称，因此您无需指定名称.)

I'm not quite sure what your data looks like, but it's quite simple to just build a function to do this from scratch. If you're always going to be working with structured or rec arrays, then you can simplify this a touch. (i.e. There's always a name associated with each data series, so you can omit having to specify names.)

例如:

import itertools
import numpy as np
import matplotlib.pyplot as plt

def main():
    np.random.seed(1977)
    numvars, numdata = 4, 10
    data = 10 * np.random.random((numvars, numdata))
    fig = scatterplot_matrix(data, ['mpg', 'disp', 'drat', 'wt'],
            linestyle='none', marker='o', color='black', mfc='none')
    fig.suptitle('Simple Scatterplot Matrix')
    plt.show()

def scatterplot_matrix(data, names, **kwargs):
    """Plots a scatterplot matrix of subplots.  Each row of "data" is plotted
    against other rows, resulting in a nrows by nrows grid of subplots with the
    diagonal subplots labeled with "names".  Additional keyword arguments are
    passed on to matplotlib's "plot" command. Returns the matplotlib figure
    object containg the subplot grid."""
    numvars, numdata = data.shape
    fig, axes = plt.subplots(nrows=numvars, ncols=numvars, figsize=(8,8))
    fig.subplots_adjust(hspace=0.05, wspace=0.05)

    for ax in axes.flat:
        # Hide all ticks and labels
        ax.xaxis.set_visible(False)
        ax.yaxis.set_visible(False)

        # Set up ticks only on one side for the "edge" subplots...
        if ax.is_first_col():
            ax.yaxis.set_ticks_position('left')
        if ax.is_last_col():
            ax.yaxis.set_ticks_position('right')
        if ax.is_first_row():
            ax.xaxis.set_ticks_position('top')
        if ax.is_last_row():
            ax.xaxis.set_ticks_position('bottom')

    # Plot the data.
    for i, j in zip(*np.triu_indices_from(axes, k=1)):
        for x, y in [(i,j), (j,i)]:
            axes[x,y].plot(data[x], data[y], **kwargs)

    # Label the diagonal subplots...
    for i, label in enumerate(names):
        axes[i,i].annotate(label, (0.5, 0.5), xycoords='axes fraction',
                ha='center', va='center')

    # Turn on the proper x or y axes ticks.
    for i, j in zip(range(numvars), itertools.cycle((-1, 0))):
        axes[j,i].xaxis.set_visible(True)
        axes[i,j].yaxis.set_visible(True)

    return fig

main()

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