散点直方图（可定位轴）

以直方图的形式在图的两侧显示散点的边缘分布。

为了使主轴与边缘对齐，轴位置由 Divider ，通过 make_axes_locatable .

生成类似图形的另一种方法如中所示 直方图散点图 例子。下面所示的可定位轴方法的优点是边缘轴遵循主轴的固定纵横比。

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable

# Fixing random state for reproducibility
np.random.seed(19680801)

# the random data
x = np.random.randn(1000)
y = np.random.randn(1000)


fig, ax = plt.subplots(figsize=(5.5, 5.5))

# the scatter plot:
ax.scatter(x, y)

# Set aspect of the main axes.
ax.set_aspect(1.)

# create new axes on the right and on the top of the current axes
divider = make_axes_locatable(ax)
# below height and pad are in inches
ax_histx = divider.append_axes("top", 1.2, pad=0.1, sharex=ax)
ax_histy = divider.append_axes("right", 1.2, pad=0.1, sharey=ax)

# make some labels invisible
ax_histx.xaxis.set_tick_params(labelbottom=False)
ax_histy.yaxis.set_tick_params(labelleft=False)

# now determine nice limits by hand:
binwidth = 0.25
xymax = max(np.max(np.abs(x)), np.max(np.abs(y)))
lim = (int(xymax/binwidth) + 1)*binwidth

bins = np.arange(-lim, lim + binwidth, binwidth)
ax_histx.hist(x, bins=bins)
ax_histy.hist(y, bins=bins, orientation='horizontal')

# the xaxis of ax_histx and yaxis of ax_histy are shared with ax,
# thus there is no need to manually adjust the xlim and ylim of these
# axis.

ax_histx.set_yticks([0, 50, 100])
ax_histy.set_xticks([0, 50, 100])

plt.show()

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工具书类

本例中显示了以下函数、方法和类的使用：

import matplotlib
import mpl_toolkits
mpl_toolkits.axes_grid1.axes_divider.make_axes_locatable
matplotlib.axes.Axes.set_aspect
matplotlib.axes.Axes.scatter
matplotlib.axes.Axes.hist

出：

<function Axes.hist at 0x7faa00dc7f28>