Windows+Jupyter环境下'matplotlib.axes._subplots'模块缺失问题求助

环境信息 #

• Windows 11 Home（版本10.0.22631，Dell G15 5520 x64）
• Python 3.12，pip与Python已配置到系统PATH，基础命令运行正常
• 使用Jupyter Notebook开发

依赖包情况 #

项目使用的包及安装方式：

from timeseries_generator import LinearTrend, Generator, WhiteNoise, RandomFeatureFactor # 安装方式：pip install git+https://github.com/Nike-Inc/timeseries-generator.git 或 pip install jupyter git+https://github.com/Nike-Inc/timeseries-generator.git
import pandas as pd
import matplotlib # 因报错单独安装过
import keras # 后续用于机器学习

报错详情 #

运行示例代码时出现如下错误：

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[3], line 1
----> 1 from timeseries_generator import LinearTrend, Generator, WhiteNoise, RandomFeatureFactor
      2 import pandas as pd
      3 import matplotlib

File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\timeseries_generator\__init__.py:1
----> 1 from .base_factor import BaseFactor
      2 from .errors import *
      3 from .generator import Generator

File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\timeseries_generator\base_factor.py:5
      2 from typing import List, Dict, Optional, Union, Tuple
      4 from matplotlib.figure import Figure
----> 5 from matplotlib.axes._subplots import SubplotBase
      6 from matplotlib.pyplot import subplots
      7 from pandas import DataFrame, date_range, DatetimeIndex

ModuleNotFoundError: No module named 'matplotlib.axes._subplots'

已尝试的无效解决方法 #

• 仅保留单个Python版本
• 更新所有已安装的依赖包
• 单独重新安装matplotlib（安装时提示依赖已满足：

Requirement already satisfied: types-python-dateutil>=2.8.10 in c:PATH-To-Python\python312\lib\site-packages (from arrow>=0.15.0->isoduration->jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook->jupyter) (2.9.0.20240316)  

• 在Jupyter单元格内直接执行安装命令

运行的Jupyter代码 #

from timeseries_generator import LinearTrend, Generator, WhiteNoise, RandomFeatureFactor
import pandas as pd
import matplotlib
import keras

# 构建线性趋势
lt = LinearTrend(coef=2.0, offset=1., col_name="my_linear_trend")
g = Generator(factors={lt}, features=None, date_range=pd.date_range(start="01-01-2020", end="01-20-2020"))
g.generate()
g.plot()

# 为生成器添加白噪声
wn = WhiteNoise(stdev_factor=0.05)
g.update_factor(wn)
g.generate()
g.plot()

需求 #

需要生成用于机器学习的合成时序数据图表，请求可行的解决方案。

内容的提问来源于stack exchange，提问作者aibe0815