pandas dataframe 以 3D 方式绘制多个帧

Question

我想在一张 3D 图中绘制两个数据框

data1 = {'numbers': [1,2,3,4,5,6,7,8,9,10], 'frequency': [5,2,1,6,9,3,8,2,0,5]} 
data2 = {'numbers': [1,2,3,4,5,6,7,8,9,10], 'frequency': [19,12,1,26,19,33,28,28,10,5]} 
newdf = pd.DataFrame(data1)
newdf2= pd.DataFrame(data2)
fig = plt.figure(figsize=(10, 5))
ax = fig.add_subplot(111, projection='3d')
newdf.plot(kind='bar',x ='numbers', y='frequency',figsize=(10,5), color='thistle', width=.4, legend=True, alpha=0.8, ax=ax)
newdf2.plot(kind='bar',x ='numbers', y='frequency',figsize=(10,5), color='navy', width=.2,legend=True, alpha=1,ax=ax) 
plot.show()

这会将两个图绘制在一个图中，但 y 轴和 z 轴被调换了。我想在 z 平面中绘制每个数据集，其中数字形成 x 轴，频率形成 y 轴。我从所有示例中都无法理解如何实现这一目标。我还想将条形图绘制为 3d 条形图。我很感激任何帮助，请

Answer 1

我只是在猜测，因为我没有我希望看到的那种输出示例，但是您希望实现以下示例的 3D 图形：y 轴是数据框类型，z轴为频率。

import matplotlib.pyplot as plt

data1 = {'numbers': [1,2,3,4,5,6,7,8,9,10], 'frequency': [5,2,1,6,9,3,8,2,0,5]} 
data2 = {'numbers': [1,2,3,4,5,6,7,8,9,10], 'frequency': [19,12,1,26,19,33,28,28,10,5]} 
newdf = pd.DataFrame(data1)
newdf2 = pd.DataFrame(data2)

fig = plt.figure(figsize=(10,5))
ax = fig.add_subplot(111, projection='3d')

yticks = [4,3,2,1,0]
ax.bar(newdf['numbers'], newdf['frequency'], zs=3, zdir='y', color='b', alpha=0.8)
ax.bar(newdf2['numbers'], newdf2['frequency'], zs=1, zdir='y', color='r', alpha=0.8)

ax.set_xlabel('number')
ax.set_ylabel('df_type')
ax.set_zlabel('frequency')

ax.set_yticks(yticks)

plt.show()

bar3d 类型

# ax.bar3d(xpos, ypos, zpos, dx, dy, dz)
ax.bar3d(newdf['numbers'], 3, 0, dx=1, dy=1, dz=newdf['frequency'], color='b', alpha=0.6)
ax.bar3d(newdf2['numbers'], 0, 0, dx=1, dy=1, dz=newdf2['frequency'], color='r', alpha=0.3)

Answer 2

这是我手头的解决方案（找不到原始链接）。

import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import pandas as pd

# datasets
data1 = {'numbers': [1,2,3,4,5,6,7,8,9,10], 'frequency': [5,2,1,6,9,3,8,2,0,5]} 
data2 = {'numbers': [1,2,3,4,5,6,7,8,9,10], 'frequency': [19,12,1,26,19,33,28,28,10,5]} 

newdf = pd.DataFrame(data1)
newdf2= pd.DataFrame(data2)

# put all the data in one place
# can use data1['frequency'] and data2['frequency'] directly
data = np.array([
        newdf['frequency'].values,
        newdf2['frequency'].values,
        ])

fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(111, projection='3d')
colors = ["r","g","b"]*5  # for up to 15 sets of bars

# Draw 3D bars 
ncnt, nbins = data.shape[:2]
xs = np.arange(nbins)
for i in range(ncnt):
    ys = data[i]
    cs = [colors[i]] * nbins
    ax.bar(xs, ys.ravel(), zs=i, zdir='x', color=cs, alpha=0.8)

ax.set_xlabel('data_frame')
ax.set_ylabel('numbers')
ax.set_zlabel('frequency')

ax.set_xticks(range(data.shape[0]))    # 2 dataframes
ax.set_yticks(newdf['numbers'].values) # from 'numbers' column

plt.show()

输出图：