请求依赖项警告

Request Dependency Warning

提问人:John P 提问时间:12/21/2022 最后编辑:John P 更新时间:12/21/2022 访问量:69

问:

在线自学python。编写了一个简单的 python 脚本(如下),该脚本生成了预期结果和依赖项警告(如下)。Python 3.11 和 3.10 会产生此依赖项警告。

<导入请求

引用 = requests.get(url=“https://api.kanye.rest”) quote.raise_for_status()

侃爷 = quote.json()

print(侃爷)>

显示代码和警告的整个控制台输出:

C:\Users\John\AppData\Local\Programs\Python\Python310\python.exe E:\Python_Projects\Kanye\scratch.py
C:\Users\John\AppData\Local\Programs\Python\Python310\lib\site-packages\requests\_init_.py:109:RequestsDependencyWarning:urllib3 (1.26.13) 或 chardet (None)/charset_normalizer (3.0.1) 与支持的版本不匹配!
warnings.warn(
{'quote': '我们将改变范式'}

进程已完成,退出代码为 0

Windows 11 Pycharm PyCharm 2022.3(社区版)
Build #PC-223.7571.203,构建于 2022 年 11 月 30 日
运行时版本:17.0.5+1-b653.14 amd64
VM:JetBrains s.r.o. 开发的 OpenJDK 64 位服务器虚拟机
Windows 11 10.0
GC:G1 年轻一代,G1 老一代
内存:2030M
核心数:12
注册表:
debugger.new.tool.window.layout=true ide.experimental.ui=true

非捆绑插件:
izhangzhihao.rainbow.brackets (2022.3.5)

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)

Installed python modules

I installed python 3.10.9 with the same results. At this point I realized mismatch in modules but I lack expertise to resolve.

Asking for assistance in solving the dependency warning.

Curious, in spite of the mismatch the code works as intended. Any clue why?

python-3.x 请求 警告

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0赞 U13-Forward 12/21/2022
Please trim your code to make it easier to find your problem. Follow these guidelines to create a minimal reproducible example.

答: 暂无答案