使用matplotlib.pyplot.hist2d触发ValueError: too many values to unpack (expected 2)

问题详情 #

我正在学习Python绘图函数，使用matplotlib.pyplot.hist2d创建二维直方图时出现报错：ValueError: too many values to unpack (expected 2)。散点图可以正常显示，但二维直方图无法生成。

我的完整代码如下：

import numpy as np 
from numpy.random import seed 
from numpy.random import rand 
import matplotlib.pyplot as pit 
from mpl_toolkits.mplot3d import Axes3D 

X = [] 
y = []

X.append(rand(1)*100)
Y.append(rand(1)*100)

for i in range(1,10000):
    X.append(rand(1)*100) 
    Y.append(X[i-1])

mu = np.mean(X) 
sigma = np.std(X) 

fig = pit.figure() 
axes = fig.subplots() 
axes.scatter(X, Y, s=.5) 
pit.show() 
print("X mean = {}\nX std dev = {}".format(mu, sigma)) 
x = np.array(X) 
 
plt.hist2d(X,Y, bins='auto') 
pit.show()

散点图输出结果：

X mean = 50.271585907067916
X std dev = 28.926289139289707

尝试改用axes.hist2d(X,Y, bins='auto')后仍出现相同错误，完整回溯信息：

AttributeError Traceback (most recent call last) 
/usr/share/jupyter/venvs/cognos-5.12.4/lib/python3.7/site-packages/numpy/lib/histograms.py in histogramdd(sample, bins, range, normed, weights, density) 
 1015   # Sample is an ND-array.
 1016   N, D = sample.shape 
 1017   except (AttributeError, ValueError): 

AttributeError: 'list' object has no attribute 'shape' 
 During handling of the above exception, another exception occurred:
 
 ValueError      Traceback (most recent call last) 
 /tmp/ipykernel_452/1275939754.py in <module> 
 ---> 36    plt.hist2d(X,Y, bins='auto') 
 37    plt.show()
 
 /usr/share/jupyter/venvs/cognos-5.12.4/lib/python3.7/site-packages/matplotlib/pyplot.py in hist2d(x, y, bins, range, density, weights, cmin, cmax, data, **kwargs) 
2625    x, y, bins=bins, range=range, density=density, 
2626    weights=weights, cmin=cmin,cmax=cmax, 
---> 2627   **({"data": data} if data is not None else {}), **kwargs) 
2628    sci(_ret[-1]) 
2629    return _ret 
 
 /usr/share/jupyter/venvs/cognos-5.12.4/lib/python3.7/site-packages/matplotlib/__init__.py in inner(ax, data, *args, **kwargs) 
1412    def inner(ax, *args, data=None, **kwargs): 
1413    if data is None: 
---> 1414    return func(ax,*map(sanitize_sequence, args), **kwargs) 
1415
1416    bound = new_sig.bind(ax, *args, **kwargs) 
 
 /usr/share/jupyter/venvs/cognos-5.12.4/lib/python3.7/site-packages/matplotlib/axes/_axes.py in hist2d(self, x, y, bins, range, density, weights, cmin, cmax, **kwargs) 
6981
6982    h, xedges, yedges = np.histogram2d(x, y, bins=bins, range=range, 
---> 6983        density=density, weights=weights) 
6984
6985    if cmin is not None: 
 
<_array_function_ internals> in histogram2d(*args, **kwargs) 
 
 /usr/share/jupyter/venvs/cognos-5.12.4/lib/python3.7/site-packages/numpy/lib/twodim_base.py in histogram2d(x, y, bins, range, normed, weights, density) 
749    xedges = yedges = asarray(bins) 
750    ins = [xedges, yedges] 
---> 751    hist, edges = histogramdd([x, y], bins, range, normed, weights, density) 
752    return hist, edges[0], edges[1] 
 
<_array_function_ internals> in histogramdd(*args, **kwargs) 
 
 /usr/share/jupyter/venvs/cognos-5.12.4/lib/python3.7/site-packages/numpy/lib/histograms.py in histogramdd(sample, bins, range, normed, weights, density) 
1018    # Sample is a sequence of 1D arrays. 
1019    sample = np.atleast_2d(sample).T 
---> 1020    N, D = sample.shape 
1021
1022    nbin = np.empty(D, int) 
 
ValueError: too many values to unpack (expected 2) 

错误原因 #

• 数据结构错误：rand(1)返回的是长度为1的numpy数组，直接存入列表后，X和Y变成了“数组的列表”，而非一维数值列表。hist2d无法正确解析这种嵌套结构，导致numpy在处理形状时触发错误。
• 别名混用：代码中导入matplotlib.pyplot时用了pit作为别名，但后面调用plt.hist2d，属于未定义变量（实际运行中可能是笔误，但不影响核心错误）。

修复步骤 #

• 提取rand(1)生成的标量值：用float()将数组转成单个数值，避免列表嵌套数组。
• 将列表转换为一维numpy数组：hist2d对numpy数组的兼容性更好，能正确识别数据形状。
• 统一matplotlib别名：修正pit为plt，避免变量混淆。

修复后完整代码 #

import numpy as np 
from numpy.random import rand 
import matplotlib.pyplot as plt 

X = [] 
Y = []

# 提取rand(1)的标量值，而非保留数组
X.append(float(rand(1)*100))
Y.append(float(rand(1)*100))

for i in range(1,10000):
    X.append(float(rand(1)*100)) 
    Y.append(X[i-1])

# 转换为一维numpy数组
X = np.array(X)
Y = np.array(Y)

mu = np.mean(X) 
sigma = np.std(X) 

fig = plt.figure() 
axes = fig.subplots() 
axes.scatter(X, Y, s=.5) 
plt.show() 
print("X mean = {}\nX std dev = {}".format(mu, sigma)) 

# 生成二维直方图并添加颜色条
plt.hist2d(X,Y, bins='auto') 
plt.colorbar()
plt.show()

内容的提问来源于stack exchange，提问作者Kevin