更新：您现在已经更新了您的问题以更清楚地说明您的目标，让我演示三种不同的方法来绘制此类数据，它们都有很多优点和缺点。
一般的要点（至少对我而言！）是 matplotlib 在 3D 中糟糕，尤其是在创建可发布的人物时（ 我个人认为，您的里程可能会有所不同。）
我做了什么：我使用了您发布的第二张图片背后的原始数据。在所有情况下，我都使用了zorder 并添加了多边形数据（在 2D 中：fill_between()，在 3D 中：PolyCollection）来增强“3D 效果”，即启用“在彼此前面绘图”。下面的代码显示：
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plot_2D_a() 使用颜色来表示角度，因此保持原来的y轴；虽然这在技术上现在只能用来读出最前面的线图，但它仍然给读者一种对 y 刻度的“感觉”。
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plot_2D_b() 删除不必要的刺/刻度，而是将角度添加为文本标签；这与您发布的第二张图片最接近
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plot_3D() 使用mplot3d 制作“3D”图；虽然现在可以旋转它来分析数据，但在尝试缩放时它会中断（至少对我而言），产生截止数据和/或隐藏轴。
在matplotlib 中，最终有许多方法 来实现瀑布图，你必须自己决定你要做什么。就个人而言，我可能大部分时间都是plot_2D_a()，因为它允许在或多或少“所有 3 个维度”中轻松重新缩放，同时还保持适当的轴（+colorbar）允许读者获取所有相关信息，一旦您将其发布在某处作为静态图像。
代码：
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.collections import PolyCollection
import numpy as np
def offset(myFig,myAx,n=1,yOff=60):
dx, dy = 0., yOff/myFig.dpi
return myAx.transData + mpl.transforms.ScaledTranslation(dx,n*dy,myFig.dpi_scale_trans)
## taken from
## http://www.gnuplotting.org/data/head_related_impulse_responses.txt
df=pd.read_csv('head_related_impulse_responses.txt',delimiter="\t",skiprows=range(2),header=None)
df=df.transpose()
def plot_2D_a():
""" a 2D plot which uses color to indicate the angle"""
fig,ax=plt.subplots(figsize=(5,6))
sampling=2
thetas=range(0,360)[::sampling]
cmap = mpl.cm.get_cmap('viridis')
norm = mpl.colors.Normalize(vmin=0,vmax=360)
for idx,i in enumerate(thetas):
z_ind=360-idx ## to ensure each plot is "behind" the previous plot
trans=offset(fig,ax,idx,yOff=sampling)
xs=df.loc[0]
ys=df.loc[i+1]
## note that I am using both .plot() and .fill_between(.. edgecolor="None" ..)
# in order to circumvent showing the "edges" of the fill_between
ax.plot(xs,ys,color=cmap(norm(i)),linewidth=1, transform=trans,zorder=z_ind)
## try alpha=0.05 below for some "light shading"
ax.fill_between(xs,ys,-0.5,facecolor="w",alpha=1, edgecolor="None",transform=trans,zorder=z_ind)
cbax = fig.add_axes([0.9, 0.15, 0.02, 0.7]) # x-position, y-position, x-width, y-height
cb1 = mpl.colorbar.ColorbarBase(cbax, cmap=cmap, norm=norm, orientation='vertical')
cb1.set_label('Angle')
## use some sensible viewing limits
ax.set_xlim(-0.2,2.2)
ax.set_ylim(-0.5,5)
ax.set_xlabel('time [ms]')
def plot_2D_b():
""" a 2D plot which removes the y-axis and replaces it with text labels to indicate angles """
fig,ax=plt.subplots(figsize=(5,6))
sampling=2
thetas=range(0,360)[::sampling]
for idx,i in enumerate(thetas):
z_ind=360-idx ## to ensure each plot is "behind" the previous plot
trans=offset(fig,ax,idx,yOff=sampling)
xs=df.loc[0]
ys=df.loc[i+1]
## note that I am using both .plot() and .fill_between(.. edgecolor="None" ..)
# in order to circumvent showing the "edges" of the fill_between
ax.plot(xs,ys,color="k",linewidth=0.5, transform=trans,zorder=z_ind)
ax.fill_between(xs,ys,-0.5,facecolor="w", edgecolor="None",transform=trans,zorder=z_ind)
## for every 10th line plot, add a text denoting the angle.
# There is probably a better way to do this.
if idx%10==0:
textTrans=mpl.transforms.blended_transform_factory(ax.transAxes, trans)
ax.text(-0.05,0,u'{0}º'.format(i),ha="center",va="center",transform=textTrans,clip_on=False)
## use some sensible viewing limits
ax.set_xlim(df.loc[0].min(),df.loc[0].max())
ax.set_ylim(-0.5,5)
## turn off the spines
for side in ["top","right","left"]:
ax.spines[side].set_visible(False)
## and turn off the y axis
ax.set_yticks([])
ax.set_xlabel('time [ms]')
#--------------------------------------------------------------------------------
def plot_3D():
""" a 3D plot of the data, with differently scaled axes"""
fig=plt.figure(figsize=(5,6))
ax= fig.gca(projection='3d')
"""
adjust the axes3d scaling, taken from https://stackoverflow.com/a/30419243/565489
"""
# OUR ONE LINER ADDED HERE: to scale the x, y, z axes
ax.get_proj = lambda: np.dot(Axes3D.get_proj(ax), np.diag([1, 2, 1, 1]))
sampling=2
thetas=range(0,360)[::sampling]
verts = []
count = len(thetas)
for idx,i in enumerate(thetas):
z_ind=360-idx
xs=df.loc[0].values
ys=df.loc[i+1].values
## To have the polygons stretch to the bottom,
# you either have to change the outermost ydata here,
# or append one "x" pixel on each side and then run this.
ys[0] = -0.5
ys[-1]= -0.5
verts.append(list(zip(xs, ys)))
zs=thetas
poly = PolyCollection(verts, facecolors = "w", edgecolors="k",linewidth=0.5 )
ax.add_collection3d(poly, zs=zs, zdir='y')
ax.set_ylim(0,360)
ax.set_xlim(df.loc[0].min(),df.loc[0].max())
ax.set_zlim(-0.5,1)
ax.set_xlabel('time [ms]')
# plot_2D_a()
# plot_2D_b()
plot_3D()
plt.show()