如何在 matplotlib 中为多列绘制 barplot 3D 投影

Question

我有一个table，根据两个不同的参数包含三个不同的时间特征。我想在 x 和 y 轴上绘制这些参数，并在 z 轴上显示三个不同时间的条形图。我创建了一个简单的条形图，其中我绘制了一个时间特征：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

columns = ['R','Users','A','B','C']

df=pd.DataFrame({'R':[2,2,2,4,4,4,6,6,6,8,8],
              'Users':[80,400,1000,80,400,1000,80,400,1000,80,400],
              'A':[ 0.05381,0.071907,0.08767,0.04493,0.051825,0.05295,0.05285,0.0804,0.0967,0.09864,0.1097],
             'B':[0.04287,0.83652,5.49683,0.02604,.045599,2.80836,0.02678,0.32621,1.41399,0.19025,0.2111],
                'C':[0.02192,0.16217,0.71645, 0.25314,5.12239,38.92758,1.60807,262.4874,8493,11.6025,6288]},
                 columns=columns)



fig = plt.figure()
ax = plt.axes(projection="3d")

num_bars = 11
x_pos = df["R"]
y_pos = df["Users"]
z_pos = [0] * num_bars
x_size = np.ones(num_bars)/4
y_size = np.ones(num_bars)*50
z_size = df["A"]

ax.bar3d(x_pos, y_pos, z_pos, x_size, y_size, z_size, color='aqua')
plt.show()

这会生成一个简单的 3d 条形图：

但是，我想用不同的颜色在其余两列（B 和 C）的现有条形图旁边绘制类似的条形图，并添加图例。我不知道如何实现这一目标。

作为附带问题，是否也可以只显示 df 在 x 轴和 y 轴上的值？值是 2-4-6-8 和 80-400-1000，我不希望 pyplot 在这些轴上添加额外的值。

Answer 1

我自己找到了解决办法。为了解决值问题，我在所有时间都加一（以避免负对数）并在所有时间列上使用 np.log 。这些值以这种方式在 0-10 范围内，并且情节变得更容易阅读。之后，我使用循环遍历每一列并创建相应的值、位置和颜色，我已将它们全部添加到一个列表中。我为每一列移动了 y_pos，因此这些列不会绘制在同一位置。

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

columns = ['R','Users','A','B','C']

df=pd.DataFrame({'R':[2,2,2,4,4,4,6,6,6,8,8],
              'Users':[80,400,1000,80,400,1000,80,400,1000,80,400],
              'A':[ 0.05381,0.071907,0.08767,0.04493,0.051825,0.05295,0.05285,0.0804,0.0967,0.09864,0.1097],
             'B':[0.04287,0.83652,5.49683,0.02604,.045599,2.80836,0.02678,0.32621,1.41399,0.19025,0.2111],
                'C':[0.02192,0.16217,0.71645, 0.25314,5.12239,38.92758,1.60807,262.4874,8493,11.6025,6288]},
                 columns=columns)

fig = plt.figure(figsize=(10, 10))
ax = plt.axes(projection="3d")

df["A"] = np.log(df["A"]+1)
df["B"] = np.log(df["B"]+1)
df["C"] = np.log(df["C"]+1)

colors = ['r', 'g', 'b']

num_bars = 11
x_pos = []
y_pos = []
x_size = np.ones(num_bars*3)/4
y_size = np.ones(num_bars*3)*50
c = ['A','B','C']
z_pos = []
z_size = []
z_color = []
for i,col in enumerate(c):
    x_pos.append(df["R"])
    y_pos.append(df["Users"]+i*50)
    z_pos.append([0] * num_bars)
    z_size.append(df[col])
    z_color.append([colors[i]] * num_bars)
    
x_pos = np.reshape(x_pos,(33,))
y_pos = np.reshape(y_pos,(33,))
z_pos = np.reshape(z_pos,(33,))
z_size = np.reshape(z_size,(33,))
z_color = np.reshape(z_color,(33,))

ax.bar3d(x_pos, y_pos, z_pos, x_size, y_size, z_size, color=z_color)

plt.xlabel('R')
plt.ylabel('Users')
ax.set_zlabel('Time')

from matplotlib.lines import Line2D

legend_elements = [Line2D([0], [0], marker='o', color='w', label='A',markerfacecolor='r', markersize=10),
                  Line2D([0], [0], marker='o', color='w', label='B',markerfacecolor='g', markersize=10),
                   Line2D([0], [0], marker='o', color='w', label='C',markerfacecolor='b', markersize=10)
                  ]
                   
# Make legend
ax.legend(handles=legend_elements, loc='best')
# Set view
ax.view_init(elev=35., azim=35)
plt.show()

最终剧情：

如何在 matplotlib 中为多列绘制 barplot 3D 投影

How to plot barplot 3D projection in matplotlib for multiple columns

python

3d

matplotlib

bar-chart