How to plot barplot 3D projection for multiple columns

Question

I have a table that contains three different time characteristics according to two different parameters. I want to plot those parameters on x and y-axis and show bars of the three different times on the z-axis. I have created a simple bar plot where I plot one of the time characteristics:

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()

This produces a simple 3d barplot:

However, I would like to plot similar bars next to the existing ones for the rest two columns (B and C) in a different color and add a plot legend as well. I could not figure out how to achieve this.

As a side question, is it as well possible to show only values from df at x- and y-axis? The values are 2-4-6-8 and 80-400-1000, I do not wish pyplot to add additional values on those axis.

Answer 1

I have managed to find a solution myself. To solve the problem with values I have added one to all times (to avoid negative log) and used np.log on all time columns. The values got on scale 0-10 this way and the plot got way easier to read. After that I used loop to go over each column and create corresponding values, positions and colors which I have added all to one list. I moved y_pos for each column so the columns do not plot on same position.

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()

Final plot: