Anaconda IDE 介绍

Anaconda IDE 开发环境简介。

Anaconda

Unleash Al Innovation and Value

The World's Most Popular Python/R Data Science Platform

The open-source Anaconda Distribution is the easiest way to perform Python/R data science and machine learning on Linux, Windows, and Mac OS X.

Anaconda - Anaconda Documentation - Anaconda Distribution

Anaconda 是一个包含诸多常用科学包及其依赖项的 Python 发行版本，其开源跨平台，解决了 Python 原生包管理器 Pip 的依赖冲突问题，极大地方便了 Python 环境的管理。

如果你使用 Python 的场景属于数据科学领域，则 Anaconda 可以被看作是标配。

install#

在 Windows 下，可通过包管理器 Chocolatey 或 Scoop 进行安装：

# scoop bucket add scoopet https://github.com/integzz/scoopet
scoop install miniconda-cn
choco install miniconda3

对 macOS 用户，可用 Homebrew 安装：

brew cask install anaconda

brew 安装日志提示其安装位置：

PREFIX=/usr/local/anaconda3

macOS 通过 brew 安装的 python3 目前的版本为 3.9.0：

$ python3 -V
Python 3.9.0

$ pip3 -V
pip 20.2.4 from /usr/local/lib/python3.9/site-packages/pip (python 3.9)

cd 进入 anaconda3 命令行工具包目录（/usr/local/anaconda3/bin），执行 ./python3 -V 可知，anaconda3 内置的 python3 版本为 3.7.6：

$ cd /usr/local/anaconda3/bin
$ ./python3 -V
Python 3.7.6

$ ./pip -V
pip 20.0.2 from /usr/local/anaconda3/lib/python3.7/site-packages/pip (python 3.7)

执行 ./pip list 可以查看安装的第三方包：

$ ./pip list | more
Package                            Version
---------------------------------- -------------------
alabaster                          0.7.12
anaconda-client                    1.7.2
anaconda-navigator                 1.9.12
anaconda-project                   0.8.3

$ ./pip list | grep 'pandas'
pandas                             1.0.1

conda#

conda is a tool for managing and deploying applications, environments and packages.

conda 是 Anaconda 内置的命令行工具包，macOS 安装 Anaconda 后无法在终端使用 conda 命令怎么办？

cd 进入 anaconda3 命令行工具包目录（/usr/local/anaconda3/bin），即可执行 ./conda 相关命令。

查看 conda 版本：

$ cd /usr/local/anaconda3/bin
$ ./conda -V
conda 4.8.2

help#

查看 conda 帮助：

$ ./conda -h
usage: conda [-h] [-V] command ...

conda is a tool for managing and deploying applications, environments and packages.

Options:

positional arguments:
  command
    clean        Remove unused packages and caches.
    config       Modify configuration values in .condarc. This is modeled
                 after the git config command. Writes to the user .condarc
                 file (/Users/faner/.condarc) by default.
    create       Create a new conda environment from a list of specified
                 packages.
    help         Displays a list of available conda commands and their help
                 strings.
    info         Display information about current conda install.
    init         Initialize conda for shell interaction. [Experimental]
    install      Installs a list of packages into a specified conda
                 environment.
    list         List linked packages in a conda environment.
    package      Low-level conda package utility. (EXPERIMENTAL)
    remove       Remove a list of packages from a specified conda environment.
    uninstall    Alias for conda remove.
    run          Run an executable in a conda environment. [Experimental]
    search       Search for packages and display associated information. The
                 input is a MatchSpec, a query language for conda packages.
                 See examples below.
    update       Updates conda packages to the latest compatible version.
    upgrade      Alias for conda update.

执行 conda activate -h 查看 activate 子命令的帮助：

$ ./conda activate -h
usage: conda activate [-h] [--[no-]stack] [env_name_or_prefix]

Activate a conda environment.

Options:

positional arguments:
  env_name_or_prefix    The environment name or prefix to activate. If the
                        prefix is a relative path, it must start with './'
                        (or '.\' on Windows).

info#

执行 ./conda info 命令可查看当前安装的 conda 相关配置信息。
conda 有个子环境的概念，默认为 active environment : base。

$ ./conda info

     active environment : base
    active env location : /usr/local/anaconda3
            shell level : 1
       user config file : /Users/faner/.condarc
 populated config files : /Users/faner/.condarc
          conda version : 4.8.2
    conda-build version : 3.18.11
         python version : 3.7.6.final.0
       virtual packages : __osx=10.16
       base environment : /usr/local/anaconda3  (writable)
           channel URLs : https://repo.anaconda.com/pkgs/main/osx-64
                          https://repo.anaconda.com/pkgs/main/noarch
                          https://repo.anaconda.com/pkgs/r/osx-64
                          https://repo.anaconda.com/pkgs/r/noarch
          package cache : /usr/local/anaconda3/pkgs
                          /Users/faner/.conda/pkgs
       envs directories : /usr/local/anaconda3/envs
                          /Users/faner/.conda/envs
               platform : osx-64
             user-agent : conda/4.8.2 requests/2.22.0 CPython/3.7.6 Darwin/20.1.0 OSX/10.16
                UID:GID : 501:20
             netrc file : None
           offline mode : False

list#

列举 conda 默认的 base 环境（/usr/local/anaconda3）集成的工具包：

$ ./conda list | wc -l
     305

$ ./conda list | more
# packages in environment at /usr/local/anaconda3:
#
# Name                    Version                   Build  Channel
_ipyw_jlab_nb_ext_conf    0.1.0                    py37_0
alabaster                 0.7.12                   py37_0
anaconda                  2020.02                  py37_0
anaconda-client           1.7.2                    py37_0
anaconda-navigator        1.9.12                   py37_0
anaconda-project          0.8.4                      py_0

$ ./conda list | grep 'pandas'
pandas                    1.0.1            py37h6c726b0_0

Anaconda Toolset Suite

Our repository features over 8,000 open-source data science and machine learning packages, Anaconda-built and compiled for all major operating systems and architectures.

Anaconda-Suite

Jupyter: Jupyter is a non-profit, open-source project, born out of the IPython Project in 2014 as it evolved to support interactive data science and scientific computing across all programming languages.

Analyze data with scalability and performance:

NumPy: NumPy is the fundamental package for scientific computing with Python.
SciPy: SciPy is a Python-based ecosystem of open-source software for mathematics, science, and engineering.
Numba: Numba is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code.
pandas: pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
Dask: Dask provides advanced parallelism for analytics, enabling performance at scale for the tools you love. It is developed in coordination with other community projects like Numpy, Pandas, and Scikit-Learn.

Visualize Toolset:

Matplotlib: Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.
Bokeh: Bokeh is an interactive visualization library that targets modern web browsers for presentation.
Datashader: Datashader is a graphics pipeline system for creating meaningful representations of large datasets quickly and flexibly.
Holoviews: HoloViews is an open-source Python library designed to make data analysis and visualization seamless and simple.

conda envs#

Anaconda介绍、安装及使用教程

base#

执行 ./conda activate 尝试激活 base 环境，报错 CommandNotFoundError：

$ ./conda activate

CommandNotFoundError: Your shell has not been properly configured to use 'conda activate'.
To initialize your shell, run

    $ conda init <SHELL_NAME>

Currently supported shells are:
  - bash
  - fish
  - tcsh
  - xonsh
  - zsh
  - powershell

See 'conda init --help' for more information and options.

IMPORTANT: You may need to close and restart your shell after running 'conda init'.

init shell#

根据提示执行 ./conda init zsh，vim ~/.zshrc 可看到 ZSH 配置文件末尾添加了 conda initialize，主要是将 conda bin 添加到环境变量：

export PATH="/usr/local/anaconda3/bin:$PATH"

执行 . ~/.zshrc 或 source ~/.zshrc 或重开一个新的 zsh 终端窗口，即可进入 conda base 环境，可直接运行 conda 系列命令。

$ conda -V
conda 4.8.2
(base)

$ python3 -V
Python 3.7.6
(base)

activate#

将 conda 集成进当前 shell（zsh）之后，再次执行 conda activate 可进入 conda base 环境。

默认的 base 环境携带了所有集成的组件库（Toolset Suite），可以直接导入引用，新建的子环境则需要自行按需安装。
如无特殊的环境隔离需求，普通简单的需求直接在 base 环境运行调试即可。

activate 后进入 conda base prompt 环境，查看内置的 python 版本和路径：

$ conda activate
$ (base) python -V
Python 3.9.7
$ (base) which python
/usr/local/anaconda3/bin/python

deactivate#

在 conda base 环境下，执行 conda deactivate 可退出 base 环境，回到系统默认的 zsh-python 环境。

$ conda deactivate

$ python3 -V
Python 3.9.0

auto_activate_base#

由于执行 ./conda init zsh 时改动了 ZSH 配置文件（~/.zshrc），导致每次启动 zsh 终端窗口，都会自动进入 conda base 环境。

怎样取消每次自动进入 conda base 环境呢？

可通过以下三种方式：

方法一：注释掉 ZSH 配置文件（~/.zshrc）中的 conda initialize 相关脚本；

方法二：每次在命令行通过 conda deactivate 退出 base 环境；

方法三：推荐方式

通过将 auto_activate_base 参数设置为 false 实现：

conda config --set auto_activate_base false

如果要进入的话可执行通过 conda activate 默认进入 base 环境

如果反悔了，可以再次恢复 auto_activate_base 参数值：

conda config --set auto_activate_base true

conda config 实际上是修改 user config file : ~/.condarc。

create env#

执行 conda create -h 查看 create 子命令帮助。

# 创建
conda create -n [env_name] [package_spec [package_spec ...]]
# 删除
conda env remove -n [env_name]

输入 conda create -n Py376 python=3.7.6，创建一个名为 Py376 的 python 3.7.6 子环境。

Preparing transaction: done
Verifying transaction: \ WARNING conda.core.path_actions:verify(963): Unable to create environments file. Path not writable.
  environment location: /Users/faner/.conda/environments.txt

done
Executing transaction: / WARNING conda.core.envs_manager:register_env(52): Unable to register environment. Path not writable or missing.
  environment location: /usr/local/anaconda3/envs/Py376
  registry file: /Users/faner/.conda/environments.txt
done
#
# To activate this environment, use
#
#     $ conda activate Py376
#
# To deactivate an active environment, use
#
#     $ conda deactivate

# 中间省略

Preparing transaction: done
Verifying transaction: \ WARNING conda.core.path_actions:verify(963): Unable to create environments file. Path not writable.
  environment location: /Users/faner/.conda/environments.txt

done
Executing transaction: / WARNING conda.core.envs_manager:register_env(52): Unable to register environment. Path not writable or missing.
  environment location: /usr/local/anaconda3/envs/Py376
  registry file: /Users/faner/.conda/environments.txt
done
#
# To activate this environment, use
#
#     $ conda activate Py376
#
# To deactivate an active environment, use
#
#     $ conda deactivate

Path not writable#

执行 conda 相关命令时，提示 WARNING：

Unable to create environments file. Path not writable.
Unable to register environment. Path not writable or missing.

当然可以已 sudo 身份重新执行一遍，但是最好按照以下方式为 conda 工作目录添加必要的写权限。

How does one fix the issue of not writable paths with conda?

ls 查看 ~/.conda 权限为755，属组成员不可写：

$ ls -lhFA | grep '\.conda/'
drwxr-xr-x    3 faner  staff    96B Nov 15 18:53 .conda/

为属组成员添加权限，chmod -R g+w 变更权限为 775：

$ # sudo chmod -R 775 .conda
$ sudo chmod -R g+w .conda
Password:

$ ls -lhFA | grep '\.conda/'
drwxrwxr-x    3 faner  staff    96B Nov 15 18:53 .conda/

env list#

创建了新的子环境后，再执行 conda env list 可以看到环境列表多了一项：

$ conda env list
# conda environments:
#
base                  *  /usr/local/anaconda3
Py376                    /usr/local/anaconda3/envs/Py376

(base)

environment location: /Users/faner/.conda/environments.txt

$ cat ~/.conda/environments.txt
/usr/local/anaconda3
/usr/local/anaconda3/envs/Py376

新建的 Anaconda 子环境被安装在 /usr/local/anaconda3/envs 目录下。

activate#

创建好子环境之后，按照提示执行 conda activate Py376，激活名为 Py376 的子环境。

必须执行过 conda init，否则执行 activate 时提示报错 CommandNotFoundError！

此时执行 conda activate Py376 切换到 Py376 子环境，可再次执行 conda info 确认相关配置信息：

$ conda activate Py376
(Py376)

$ conda info

     active environment : Py376
    active env location : /usr/local/anaconda3/envs/Py376

用完之后执行 conda deactivate 退回 base 环境。

$ conda deactivate
(base)

install#

执行 conda install -h 查看 install 子命令帮助。

usage: conda install [-h] [--revision REVISION] [-n ENVIRONMENT | -p PATH]
                     [--freeze-installed | --update-deps | -S | --update-all | --update-specs]
                     [package_spec [package_spec ...]]

在 Py376 下执行 conda list | grep 'pandas' 可知，新建的子环境并没有自带 base 下的 pandas 等 Toolset Suite，需要自行按需安装。

conda install -n Py376 pandas

常用包管理相关的命令如下：

# 搜索某个包，会列举所有可安装版本
conda search [package_name]
# 为指定环境安装指定版本的包
conda install [-n ENVIRONMENT] [package_spec] [--revision REVISION]
# 查看已安装列表
conda list
# 更新某个包
conda update [package_name]
# 更新所有包
conda update --all
# 删除已安装的包
conda remove [package_name] # remove alias as uninstall

conda update#

执行 conda update -h 查看 update 子命令帮助。

在 base 环境下执行 conda update 提示没有提供要升级的包名，并给出了升级 anaconda 自身的命令。

$ conda update

CondaValueError: no package names supplied
# If you want to update to a newer version of Anaconda, type:
#
# $ conda update --prefix /usr/local/anaconda3 anaconda

执行 conda update -n Py376 scipy 更新 Py376 下的 scipy 包。
执行 conda update python 更新当前子环境（base）下的 python 包。
执行 conda update --all 更新当前子环境（base）下的所有包。

$ conda update --all
Collecting package metadata (current_repodata.json): done
Solving environment: done

# All requested packages already installed.

Anaconda IDE 介绍

install#

conda#

help#

info#

list#

conda envs#

base#

init shell#

activate#

deactivate#

auto_activate_base#

create env#

Path not writable#

env list#

activate#

install#

conda update#

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