anaconda - Windows 中的路径环境变量

anaconda - path environment variable in windows(anaconda - Windows 中的路径环境变量)
本文介绍了anaconda - Windows 中的路径环境变量的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着跟版网的小编来一起学习吧!

问题描述

我正在尝试从 windows 命令提示符 (windows 10) 运行 python.所以结果是没有配置path环境变量时的典型结果

I am trying to run python from the windows command prompt (windows 10). So the result is the typical one when the path environment variable is not configured

 c:windowssystem32>python
'python' is not recognized as an internal or external command, operable
 program or batch file

但是,我不确定应该在路径变量中设置哪个目录.

however, I am not sure which is the right directory I should set up in the path variable.

我尝试了一些变体,但都不起作用,包括:

I tried a few variations, and none of them work, including:

c:usersxxxanaconda3
c:usersxxxanaconda3Scripts
c:usersxxxanaconda3libspython34

它们都不起作用.

有没有人对这个特定的系统星座(windows、anaconda)有经验.谢谢.

Does anyone have experience with this particular system constellation (windows, anaconda). Thanks.

推荐答案

原来我错了.

解决方案是:在anaconda(以及其他实现)中,将path环境变量设置为'python.exe'的安装目录.

Solution is: in anaconda (as well as in other implementations), set the path environment variable to the directory where 'python.exe' is installed.

默认情况下,anaconda 中的 python.exe 文件位于:

As a default, the python.exe file in anaconda is in:

c:.....anaconda

在你这样做之后,很明显,python 命令在我的例子中起作用,产生以下结果.

after you do that, obviously, the python command works, in my case, yielding the following.

python
Python 3.4.3 |Anaconda 2.2.0. (64|bit)|(default, Nov 7 2015), etc, etc

这篇关于anaconda - Windows 中的路径环境变量的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持跟版网!

本站部分内容来源互联网,如果有图片或者内容侵犯了您的权益,请联系我们,我们会在确认后第一时间进行删除!

相关文档推荐

groupby multiple coords along a single dimension in xarray(在xarray中按单个维度的多个坐标分组)
Group by and Sum in Pandas without losing columns(Pandas中的GROUP BY AND SUM不丢失列)
Group by + New Column + Grab value former row based on conditionals(GROUP BY+新列+基于条件的前一行抓取值)
Groupby and interpolate in Pandas(PANDA中的Groupby算法和插值算法)
Pandas - Group Rows based on a column and replace NaN with non-null values(PANAS-基于列对行进行分组,并将NaN替换为非空值)
Grouping pandas DataFrame by 10 minute intervals(按10分钟间隔对 pandas 数据帧进行分组)