Python 实用宝典

Question 1

I’m on Windows 8, using Anaconda 1.7.5 64bit.

I created a new Anaconda environment with

conda create -p ./test python=2.7 pip

from C:\Pr\TEMP\venv\.

This worked well (there is a folder with a new python distribution). conda tells me to type

activate C:\PR\TEMP\venv\test

to activate the environment, however this returns:

No environment named "C:\PR\temp\venv\test" exists in C:\PR\Anaconda\envs

How can I activate the environment? What am I doing wrong?

Question 2

If this happens you would need to set the PATH for your environment (so that it gets the right Python from the environment and Scripts\ on Windows).

Imagine you have created an environment called py33 by using:

conda create -n py33 python=3.3 anaconda

Here the folders are created by default in Anaconda\envs, so you need to set the PATH as:

set PATH=C:\Anaconda\envs\py33\Scripts;C:\Anaconda\envs\py33;%PATH%

Now it should work in the command window:

activate py33

The line above is the Windows equivalent to the code that normally appears in the tutorials for Mac and Linux:

$ source activate py33

More info: https://groups.google.com/a/continuum.io/forum/#!topic/anaconda/8T8i11gO39U

Does `anaconda` create a separate PYTHONPATH variable for each new environment?

Question 3

Use cmd instead of Powershell! I spent 2 hours before I switched to cmd and then it worked!

create Environment:

conda create -n your_environment_name

see list of conda environments:

conda env list

activate your environment:

conda activate your_environment_name

That’s all folks

Question 4

Note that the command for activating an environment has changed in Conda version 4.4. The recommended way of activating an environment is now conda activate myenv instead of source activate myenv. To enable the new syntax, you should modify your .bashrc file. The line that currently reads something like

export PATH="<path_to_your_conda_install>/bin:$PATH"

Should be changed to

. <path_to_your_conda_install>/etc/profile.d/conda.sh

This only adds the conda command to the path, but does not yet activate the base environment (which was previously called root). To do also that, add another line

conda activate base

after the first command. See all the details in Anaconda’s blog post from December 2017. (I think that this page is currently missing a newline between the two lines, it says .../conda.shconda activate base).

(This answer is valid for Linux, but it might be relevant for Windows and Mac as well)

Question 5

All the former answers seem to be outdated.

conda activate was introduced in conda 4.4 and 4.6.

conda activate: The logic and mechanisms underlying environment activation have been reworked. With conda 4.4, conda activate and conda deactivate are now the preferred commands for activating and deactivating environments. You’ll find they are much more snappy than the source activate and source deactivate commands from previous conda versions. The conda activate command also has advantages of (1) being universal across all OSes, shells, and platforms, and (2) not having path collisions with scripts from other packages like python virtualenv’s activate script.

Examples

conda create -n venv-name python=3.6
conda activate -n venv-name
conda deactivate

These new sub-commands are available in “Aanconda Prompt” and “Anaconda Powershell Prompt” automatically. To use conda activate in every shell (normal cmd.exe and powershell), check expose conda command in every shell on Windows.

References

Question 6

As you can see from the error message the paths, that you specified, are wrong. Try it like this:

activate ..\..\temp\venv\test

However, when I needed to install Anaconda, I downloaded it from here and installed it to the default paths (C:\Anaconda), than I put this path to the environment variables, so now Anacondas interpreter is used as default. If you are using PyCharm, for example, you can specify the interpreter there directly.

Question 7

Below is how it worked for me

C:\Windows\system32>set CONDA_ENVS_PATH=d:\your\location
C:\Windows\system32>conda info

Shows new environment path

C:\Windows\system32>conda create -n YourNewEnvironment –clone=root

Clones default root environment

C:\Windows\system32>activate YourNewEnvironment

Deactivating environment “d:\YourDefaultAnaconda3″… Activating environment “d:\your\location\YourNewEnvironment”…

[YourNewEnvironment] C:\Windows\system32>conda info -e

conda environments: #

YourNewEnvironment
* d:\your\location\YourNewEnvironment

root d:\YourDefaultAnaconda3

Question 8

I’ve tried to activate env from Jenkins job (in bash) with conda activate base and it failed, so after many tries, this one worked for me (CentOS 7) :

source /opt/anaconda2/bin/activate base

Question 9

let’s assume your environment name is ‘demo’ and you are using anaconda and want to create a virtual environment:

(if you want python3)

    conda create -n demo python=3

(if you want python2)

    conda create -n demo python=2

After running above command you have to activate the environment by bellow command:

    source activate demo

Question 10

For me, using Anaconda Prompt instead of cmd or PowerShell is the key.

In Anaconda Prompt, all I need to do is activate XXX

Question 11

I was having the same, a fix seems to have been made in the source.

Question 12

just as @Simba has rightly answered alot has changed in the conda env since 4.6. Conda activate (env-name) overthrew source activate (env-name) for good but not without it own challenges. conda activate oftentimes force your environment to base and make u see something like . Therefore throwing loads of error back at you. This can also be because auto_activate_base is set to True.

You can check this by using the following command

conda config --set auto_activate_base False

source ~/.bashrc.

And To reactivate use this

conda config --set auto_activate_base True

source ~/.bashrc

Question 13

Window: conda activate environment_name

Mac: conda activate environment_name

Question 14

I’ve installed and have been using the Anaconda Python distribution, and I have started using the Anaconda (Conda) environment. I can use the standard conda install... command to put packages from the distribution into my environments, but to use anything outside (i.e. Flask-WTF, flask-sqlalchemy, and alembic) I need to use pip install in the active environment. However, when I look at the contents of the environment, either in the directory, or using conda list these pip installed packages don’t show up.

Using pip freeze and pip list just lists every package I’ve ever installed.

Is there a way to keep track of what is in each of my Anaconda envs (both pip and conda installed)?

Question 15

conda-env now does this automatically (if pip was installed with conda).

You can see how this works by using the export tool used for migrating an environment:

conda env export -n <env-name> > environment.yml

The file will list both conda packages and pip packages:

name: stats
channels:
  - javascript
dependencies:
  - python=3.4
  - bokeh=0.9.2
  - numpy=1.9.*
  - nodejs=0.10.*
  - flask
  - pip:
    - Flask-Testing

If you’re looking to follow through with exporting the environment, move environment.yml to the new host machine and run:

conda env create -f path/to/environment.yml

Question 16

conda will only keep track of the packages it installed. And pip will give you the packages that were either installed using the pip installer itself or they used setuptools in their setup.py so conda build generated the egg information. So you have basically three options.

You can take the union of the conda list and pip freeze and manage packages that were installed using conda (that show in the conda list) with the conda package manager and the ones that are installed with pip (that show in pip freeze but not in conda list) with pip.
Install in your environment only the python, pip and distribute packages and manage everything with pip. (This is not that trivial if you’re on Windows…)
Build your own conda packages, and manage everything with conda.

I would personally recommend the third option since it’s very easy to build conda packages. There is a git repository of example recipes on the continuum’s github account. But it usually boils down to:

 conda skeleton pypi PACKAGE
 conda build PACKAGE

or just:

conda pipbuild PACKAGE

Also when you have built them once, you can upload them to https://binstar.org/ and just install from there.

Then you’ll have everything managed using conda.

Question 17

There is a branch of conda (new-pypi-install) that adds better integration with pip and PyPI. In particular conda list will also show pip installed packages and conda install will first try to find a conda package and failing that will use pip to install the package.

This branch is scheduled to be merged later this week so that version 2.1 of conda will have better pip-integration with conda.

Question 18

I followed @Viktor Kerkez’s answer and have had mixed success. I found that sometimes this recipe of

conda skeleton pypi PACKAGE

conda build PACKAGE

would look like everything worked but I could not successfully import PACKAGE. Recently I asked about this on the Anaconda user group and heard from @Travis Oliphant himself on the best way to use conda to build and manage packages that do not ship with Anaconda. You can read this thread here, but I’ll describe the approach below to hopefully make the answers to the OP’s question more complete…

Example: I am going to install the excellent prettyplotlib package on Windows using conda 2.2.5.

1a) conda build --build-recipe prettyplotlib

You’ll see the build messages all look good until the final TEST section of the build. I saw this error

File “C:\Anaconda\conda-bld\test-tmp_dir\run_test.py”, line 23 import None SyntaxError: cannot assign to None TESTS FAILED: prettyplotlib-0.1.3-py27_0

1b) Go into /conda-recipes/prettyplotlib and edit the meta.yaml file. Presently, the packages being set up like in step 1a result in yaml files that have an error in the test section. For example, here is how mine looked for prettyplotlib

test:   # Python imports   imports:
    - 
    - prettyplotlib
    - prettyplotlib

Edit this section to remove the blank line preceded by the – and also remove the redundant prettyplotlib line. At the time of this writing I have found that I need to edit most meta.yaml files like this for external packages I am installing with conda, meaning that there is a blank import line causing the error along with a redundant import of the given package.

1c) Rerun the command from 1a, which should complete with out error this time. At the end of the build you’ll be asked if you want to upload the build to binstar. I entered No and then saw this message:

If you want to upload this package to binstar.org later, type:

$ binstar upload C:\Anaconda\conda-bld\win-64\prettyplotlib-0.1.3-py27_0.tar.bz2

That tar.bz2 file is the build that you now need to actually install.

2) conda install C:\Anaconda\conda-bld\win-64\prettyplotlib-0.1.3-py27_0.tar.bz2

Following these steps I have successfully used conda to install a number of packages that do not come with Anaconda. Previously, I had installed some of these using pip, so I did pip uninstall PACKAGE prior to installing PACKAGE with conda. Using conda, I can now manage (almost) all of my packages with a single approach rather than having a mix of stuff installed with conda, pip, easy_install, and python setup.py install.

For context, I think this recent blog post by @Travis Oliphant will be helpful for people like me who do not appreciate everything that goes into robust Python packaging but certainly appreciate when stuff “just works”. conda seems like a great way forward…

Question 19

This is why I wrote Picky: http://picky.readthedocs.io/

It’s a python package that tracks packages installed with either pip or conda in either virtualenvs and conda envs.

Question 20

I think what’s missing here is that when you do:

>pip install .

to install a local package with a setup.py, it installs a package that is visible to all the conda envs that use the same version of python. Note I am using the conda version of pip!

e.g., if I’m using python2.7 it puts the local package here:

/usr/local/anaconda/lib/python2.7/site-packages

If I then later create a new conda env with python=2.7 (= the default):

>conda create --name new

>source activate new

And then do:

(new)>conda list    // empty - conda is not aware of any packages yet

However, if I do:

(new)>pip list      // the local package installed above is present

So in this case, conda does not know about the pip package, but the package is available to python.

However, If I instead install the local package (again using pip) after I’ve created (and activated) the new conda env, now conda sees it:

(new)>conda list   // sees that the package is there and was installed by pip

So I think the interaction between conda and pip has some issues – ie, using pip to install a local package from within one conda env makes that package available (but not seen via conda list) to all other conda envs of the same python version.

Question 21

conda env export lists all conda and pip packages in an environment. conda-env must be installed in the conda root (conda install -c conda conda-env).

To write an environment.yml file describing the current environment:

conda env export > environment.yml

References:

Question 22

I usually prefix the ‘bin/pip’ folder for the specific environment you want to install the package before the ‘pip’ command. For instance, if you would like to install pymc3 in the environment py34, you should use this command:

~/anaconda/envs/py34/bin/pip install git+https://github.com/pymc-devs/pymc3

You basically just need to find the right path to your environment ‘bin/pip’ folder and put it before the install command.

Question 23

My which pip shows the following path:

$ which pip
/home/kmario23/anaconda3/bin/pip

So, whatever package I install using pip install <package-name> will have to be reflected in the list of packages when the list is exported using:

$ conda list --export > conda_list.txt

But, I don’t. So, instead I used the following command as suggested by several others:

# get environment name by
$ conda-env list

# get list of all installed packages by (conda, pip, etc.,)
$ conda-env export -n <my-environment-name> > all_packages.yml
# if you haven't created any specific env, then just use 'root'

Now, I can see all the packages in my all-packages.yml file.

Question 24

You can start by installing the below given command in the conda environment:

conda install pip

Followed by installing all pip packages you need in the environment.

After installing all the conda and pip packages to export the environment use:

conda env export -n <env-name> > environment.yml

This will create the required file in the folder

Question 25

I am a ruby programmer trying to learn python. I am pretty family with pyenv since it is like a copy and paste from rbenv. Pyenv helps allow to have more than one version of python in a system and also to isolate the python without touching sensitive part of system.

I suppose every python installation come with pip package. What I still don’t understand is, there are many good python libs out there that suggest to use this virtualenv and anaconda. I can even find virtualenv plugin for pyenv.

Now I am getting confused with the purpose of these two pyenv and virtualenv. worse inside pyenv there is a virtualenv plugin.

my questions are:

what is the difference between pyenv and virtualenv?
Is there any difference in using pip command inside both pyenv and virtualenv?
what does this pyenv virutalenv do?

your explanation with example will be highly appreciated.

Question 26

Edit: It’s worth mentioning pip here as well, as conda and pip have similarities and differences that are relevant to this topic.

pip: the Python Package Manager.

You might think of pip as the python equivalent of the ruby gem command
pip is not included with python by default.
You may install Python using homebrew, which will install pip automatically: brew install python
The final version of OSX did not include pip by default. To add pip to your mac system’s version of python, you can sudo easy_install pip
You can find and publish python packages using PyPI: The Python Package Index
The requirements.txt file is comparable to the ruby gemfile
To create a requirements text file, pip freeze > requirements.txt
Note, at this point, we have python installed on our system, and we have created a requirements.txt file that outlines all of the python packages that have been installed on your system.

pyenv: Python Version Manager

From the docs: pyenv lets you easily switch between multiple versions of Python. It’s simple, unobtrusive, and follows the UNIX tradition of single-purpose tools that do one thing well. This project was forked from rbenv and ruby-build, and modified for Python.
Many folks hesitate to use python3.
If you need to use different versions of python, pyenv lets you manage this easily.

virtualenv: Python Environment Manager.

From the docs: The basic problem being addressed is one of dependencies and versions, and indirectly permissions. Imagine you have an application that needs version 1 of LibFoo, but another application requires version 2. How can you use both these applications? If you install everything into /usr/lib/python2.7/site-packages (or whatever your platform’s standard location is), it’s easy to end up in a situation where you unintentionally upgrade an application that shouldn’t be upgraded.
To create a virtualenv, simply invoke virtualenv ENV, where ENV is is a directory to place the new virtual environment.
To initialize the virtualenv, you need to source ENV/bin/activate. To stop using, simply call deactivate.
Once you activate the virtualenv, you might install all of a workspace’s package requirements by running pip install -r against the project’s requirements.txt file.

Anaconda: Package Manager + Environment Manager + Additional Scientific Libraries.

From the docs: Anaconda 4.2.0 includes an easy installation of Python (2.7.12, 3.4.5, and/or 3.5.2) and updates of over 100 pre-built and tested scientific and analytic Python packages that include NumPy, Pandas, SciPy, Matplotlib, and IPython, with over 620 more packages available via a simple conda install <packagename>
As a web developer, I haven’t used Anaconda. It’s ~3GB including all the packages.
There is a slimmed down miniconda version, which seems like it could be a more simple option than using pip + virtualenv, although I don’t have experience using it personally.
While conda allows you to install packages, these packages are separate than PyPI packages, so you may still need to use pip additionally depending on the types of packages you need to install.

See also:

conda vs pip vs virtualenv (section in documentation from anaconda)
the difference between pip and conda (stackoverflow)
the relationship between virtualenv and pyenv (stackoverflow)

Question 27

Simple analogy:

pyenv ~ rbenv
pip ~ bundler
virtual env ~ gemset in rvm. This can be managed by bundler directly without gemset.

Since I use python3 I prefer the python3 built-in virtual environment named venv. venv is simple and easy to use. I would recommend you to read its official docs. The doc is short and concise.

In ruby, we don’t really need a virtual environment because the bundler takes care of it. Both virtual env and bundler are great, however, they have different solutions to solve the same problem.

Question 28

Is there a way (using conda update) that I can list outdated packages and select or bulk update (compatible) packages in Anaconda?

It doesn’t make much sense updating the packages individually as there are hundreds of them.

Question 29

You want conda update --all.

conda search --outdated will show outdated packages, and conda update --all will update them (note that the latter will not update you from Python 2 to Python 3, but the former will show Python as being outdated if you do use Python 2).

Question 30

Before you proceed to conda update --all command, first update conda with conda update conda command if you haven’t update it for a long time. It happent to me (Python 2.7.13 on Anaconda 64 bits).

Question 31

the Conda Package Manager is almost ready for beta testing, but it will not be fully integrated until the release of Spyder 2.4 (https://github.com/spyder-ide/spyder/wiki/Roadmap). As soon as we have it ready for testing we will post something on the mailing list (https://groups.google.com/forum/#!forum/spyderlib). Be sure to subscribe

Cheers!

Question 32

# list packages that can be updated
conda search --outdated

# update all packages prompted(by asking the user yes/no)
conda update --all

# update all packages unprompted
conda update --all -y

Question 33

I recently discovered Conda after I was having trouble installing SciPy, specifically on a Heroku app that I am developing.

With Conda you create environments, very similar to what virtualenv does. My questions are:

If I use Conda will it replace the need for virtualenv? If not, how do I use the two together? Do I install virtualenv in Conda, or Conda in virtualenv?
Do I still need to use pip? If so, will I still be able to install packages with pip in an isolated environment?

Question 34

Conda replaces virtualenv. In my opinion it is better. It is not limited to Python but can be used for other languages too. In my experience it provides a much smoother experience, especially for scientific packages. The first time I got MayaVi properly installed on Mac was with conda.
You can still use pip. In fact, conda installs pip in each new environment. It knows about pip-installed packages.

For example:

conda list

lists all installed packages in your current environment. Conda-installed packages show up like this:

sphinx_rtd_theme          0.1.7                    py35_0    defaults

and the ones installed via pip have the <pip> marker:

wxpython-common           3.0.0.0                   <pip>

Question 35

Short answer is, you only need conda.

Conda effectively combines the functionality of pip and virtualenv in a single package, so you do not need virtualenv if you are using conda.
You would be surprised how many packages conda supports. If it is not enough, you can use pip under conda.

Here is a link to the conda page comparing conda, pip and virtualenv:

https://docs.conda.io/projects/conda/en/latest/commands.html#conda-vs-pip-vs-virtualenv-commands.

Question 36

Virtual Environments and pip

I will add that creating and removing conda environments is simple with Anaconda.

> conda create --name <envname> python=<version> <optional dependencies>

> conda remove --name <envname> --all

In an activated environment, install packages via conda or pip:

(envname)> conda install <package>

(envname)> pip install <package>

These environments are strongly tied to conda’s pip-like package management, so it is simple to create environments and install both Python and non-Python packages.

Jupyter

In addition, installing ipykernel in an environment adds a new listing in the Kernels dropdown menu of Jupyter notebooks, extending reproducible environments to notebooks. As of Anaconda 4.1, nbextensions were added, adding extensions to notebooks more easily.

Reliability

In my experience, conda is faster and more reliable at installing large libraries such as numpy and pandas. Moreover, if you wish to transfer your the preserved state of an environment, you can do so by sharing or cloning an env.

Question 37

Installing Conda will enable you to create and remove python environments as you wish, therefore providing you with same functionality as virtualenv would.

In case of both distributions you would be able to create an isolated filesystem tree, where you can install and remove python packages (probably, with pip) as you wish. Which might come in handy if you want to have different versions of same library for different use cases or you just want to try some distribution and remove it afterwards conserving your disk space.

Differences:

License agreement. While virtualenv comes under most liberal MIT license, Conda uses 3 clause BSD license.

Conda provides you with their own package control system. This package control system often provides precompiled versions (for most popular systems) of popular non-python software, which can easy ones way getting some machine learning packages working. Namely you don’t have to compile optimized C/C++ code for you system. While it is a great relief for most of us, it might affect performance of such libraries.

Unlike virtualenv, Conda duplicating some system libraries at least on Linux system. This libraries can get out of sync leading to inconsistent behavior of your programs.

Verdict:

Conda is great and should be your default choice while starting your way with machine learning. It will save you some time messing with gcc and numerous packages. Yet, Conda does not replace virtualenv. It introduces some additional complexity which might not always be desired. It comes under different license. You might want to avoid using conda on a distributed environments or on HPC hardware.

Question 38

I use both and (as of Jan, 2020) they have some superficial differences that lend themselves to different usages for me. By default Conda prefers to manage a list of environments for you in a central location, whereas virtualenv makes a folder in the current directory. The former (centralized) makes sense if you are e.g. doing machine learning and just have a couple of broad environments that you use across many projects and want to jump into them from anywhere. The latter (per project folder) makes sense if you are doing little one-off projects that have completely different sets of lib requirements that really belong more to the project itself.

The empty environment that Conda creates is about 122MB whereas the virtualenv’s is about 12MB, so that’s another reason you may prefer not to scatter Conda environments around everywhere.

Finally, another superficial indication that Conda prefers its centralized envs is that (again, by default) if you do create a Conda env in your own project folder and activate it the name prefix that appears in your shell is the (way too long) absolute path to the folder. You can fix that by giving it a name, but virtualenv does the right thing by default.

I expect this info to become stale rapidly as the two package managers vie for dominance, but these are the trade-offs as of today :)

Question 39

Another new option and my current preferred method of getting an environment up and running is Pipenv

It is currently the officially recommended Python packaging tool from Python.org

Question 40

Yes, conda is a lot easier to install than virtualenv, and pretty much replaces the latter.

Question 41

I work in corporate, behind several firewall with machine on which I have no admin acces

In my limited experience with python (2 years) i have come across few libraries (JayDeBeApi,sasl) which when installing via pip threw C++ dependency errors error: Microsoft Visual C++ 14.0 is required. Get it with “Microsoft Visual C++ Build Tools”: http://landinghub.visualstudio.com/visual-cpp-build-tools

these installed fine with conda, hence since those days i started working with conda env. however it isnt easy to stop conda from installing dependency inside c.programfiles where i dont have write access.

Question 42

In the Anaconda repository, there are two types of installers:

“Anaconda installers” and “Miniconda installers“.

What are their differences?

Besides, for an installer file, Anaconda2-4.4.0.1-Linux-ppc64le.sh, what does 2-4.4.0.1 stand for?

Question 43

The difference is that miniconda is just shipping the repository management system. So when you install it there is just the management system without packages. Whereas with Anaconda, it is like a distribution with some built in packages.

Like with any Linux distribution, there are some releases which bundles lots of updates for the included packages. That is why there is a difference in version numbering. If you only decide to upgrade Anaconda, you are updating a whole system.

Question 44

Per the original docs (link is now dead):

Choose Anaconda if you:

Are new to conda or Python
Like the convenience of having Python and over 150 scientific packages automatically installed at once
Have the time and disk space (a few minutes and 3 GB), and/or
Don’t want to install each of the packages you want to use individually.

Choose Miniconda if you:

Do not mind installing each of the packages you want to use individually.
Do not have time or disk space to install over 150 packages at once, and/or
Just want fast access to Python and the conda commands, and wish to sort out the other programs later.

I use Miniconda myself. Anaconda is bloated. Many of the packages are never used and could still be easily installed if and when needed.

Note that Conda is the package manager (e.g. conda list displays all installed packages in the environment), whereas Anaconda and Miniconda are distributions. A software distribution is a collection of packages, pre-built and pre-configured, that can be installed and used on a system. A package manager is a tool that automates the process of installing, updating, and removing packages.

Anaconda is a full distribution of the central software in the PyData ecosystem, and includes Python itself along with the binaries for several hundred third-party open-source projects. Miniconda is essentially an installer for an empty conda environment, containing only Conda, its dependencies, and Python. Source.

Once Conda is installed, you can then install whatever package you need from scratch along with any desired version of Python.

2-4.4.0.1 is the version number for your Anaconda installation package. Strangely, it is not listed in their Old Package Lists.

In April 2016, the Anaconda versioning jumped from 2.5 to 4.0 in order to avoid confusion with Python versions 2 & 3. Version 4.0 included the Anaconda Navigator.

Release notes for subsequent versions can be found here.

Question 45

Brief

conda is both a command line tool, and a python package.

Miniconda installer = Python + conda

Anaconda installer = Python + conda + meta package anaconda

meta Python pkg anaconda = about 160 Python pkgs for daily use in data science

Anaconda installer = Miniconda installer + conda install anaconda

Detail

conda is a python manager and an environment manager, which makes it possible to
- install package with conda install flake8
- create an environment with any version of Python with conda create -n myenv python=3.6
Miniconda installer = Python + conda

conda, the package manager and environment manager, is a Python package. So Python is installed. Cause conda distribute Python interpreter with its own libraries/dependencies but not the existing ones on your operating system, other minimal dependencies like openssl, ncurses, sqlite, etc are installed as well.

Basically, Miniconda is just conda and its minimal dependencies. And the environment where conda is installed is the “base” environment, which is previously called “root” environment.
Anaconda installer = Python + conda + meta package anaconda

meta Python package anaconda = about 160 Python pkgs for daily use in data science

Meta packages, are packages that do NOT contain actual softwares and simply depend on other packages to be installed.

Download an anaconda meta package from Anaconda Cloud and extract the content from it. The actual 160+ packages to be installed are listed in info/recipe/meta.yaml.

package:
    name: anaconda
    version: '2019.07'
build:
    ignore_run_exports:
        - '*'
    number: '0'
    pin_depends: strict
    string: py36_0
requirements:
    build:
        - python 3.6.8 haf84260_0
    is_meta_pkg:
        - true
    run:
        - alabaster 0.7.12 py36_0
        - anaconda-client 1.7.2 py36_0
        - anaconda-project 0.8.3 py_0
        # ...
        - beautifulsoup4 4.7.1 py36_1
        # ...
        - curl 7.65.2 ha441bb4_0
        # ...
        - hdf5 1.10.4 hfa1e0ec_0
        # ...
        - ipykernel 5.1.1 py36h39e3cac_0
        - ipython 7.6.1 py36h39e3cac_0
        - ipython_genutils 0.2.0 py36h241746c_0
        - ipywidgets 7.5.0 py_0
        # ...
        - jupyter 1.0.0 py36_7
        - jupyter_client 5.3.1 py_0
        - jupyter_console 6.0.0 py36_0
        - jupyter_core 4.5.0 py_0
        - jupyterlab 1.0.2 py36hf63ae98_0
        - jupyterlab_server 1.0.0 py_0
        # ...
        - matplotlib 3.1.0 py36h54f8f79_0
        # ...
        - mkl 2019.4 233
        - mkl-service 2.0.2 py36h1de35cc_0
        - mkl_fft 1.0.12 py36h5e564d8_0
        - mkl_random 1.0.2 py36h27c97d8_0
        # ...
        - nltk 3.4.4 py36_0
        # ...
        - numpy 1.16.4 py36hacdab7b_0
        - numpy-base 1.16.4 py36h6575580_0
        - numpydoc 0.9.1 py_0
        # ...
        - pandas 0.24.2 py36h0a44026_0
        - pandoc 2.2.3.2 0
        # ...
        - pillow 6.1.0 py36hb68e598_0
        # ...
        - pyqt 5.9.2 py36h655552a_2
        # ...
        - qt 5.9.7 h468cd18_1
        - qtawesome 0.5.7 py36_1
        - qtconsole 4.5.1 py_0
        - qtpy 1.8.0 py_0
        # ...
        - requests 2.22.0 py36_0
        # ...
        - sphinx 2.1.2 py_0
        - sphinxcontrib 1.0 py36_1
        - sphinxcontrib-applehelp 1.0.1 py_0
        - sphinxcontrib-devhelp 1.0.1 py_0
        - sphinxcontrib-htmlhelp 1.0.2 py_0
        - sphinxcontrib-jsmath 1.0.1 py_0
        - sphinxcontrib-qthelp 1.0.2 py_0
        - sphinxcontrib-serializinghtml 1.1.3 py_0
        - sphinxcontrib-websupport 1.1.2 py_0
        - spyder 3.3.6 py36_0
        - spyder-kernels 0.5.1 py36_0
        # ...

The pre-installed packages from meta pkg anaconda are mainly for web scraping and data science. Like requests, beautifulsoup, numpy, nltk, etc.

If you have a Miniconda installed, conda install anaconda will make it same as an Anaconda installation, except that the installation folder names are different.

Miniconda2 v.s. Miniconda. Anaconda2 v.s. Anaconda.

2 means the bundled Python interpreter for conda in the “base” environment is Python 2, but not Python 3.

Question 46

Miniconda gives you the Python interpreter itself, along with a command-line tool called conda which operates as a cross-platform package manager geared toward Python packages, similar in spirit to the apt or yum tools that Linux users might be familiar with.

Anaconda includes both Python and conda, and additionally bundles a suite of other pre-installed packages geared toward scientific computing. Because of the size of this bundle, expect the installation to consume several gigabytes of disk space.

Source: Jake VanderPlas’s Python Data Science Handbook

Question 47

The 2 in Anaconda2 means that the main version of Python will be 2.x rather than the 3.x installed in Anaconda3. The current release has Python 2.7.13.

The 4.4.0.1 is the version number of Anaconda. The current advertised version is 4.4.0 and I assume the .1 is a minor release or for other similar use. The Windows releases, which I use, just say 4.4.0 in the file name.

Others have now explained the difference between Anaconda and Miniconda, so I’ll skip that.

Question 48

Anaconda is a very large installation ~ 2 GB and is most useful for those users who are not familiar with installing modules or packages with other package managers.

Anaconda seems to be promoting itself as the official package manager of Jupyter. It’s not. Anaconda bundles Jupyter, R, python, and many packages with its installation.

Anaconda is not necessary for installing Jupyter Lab or the R kernel. There is plenty of information available elsewhere for installing Jupyter Lab or Notebooks. There is also plenty of information elsewhere for installing R studio. The following shows how to install the R kernel directly from R Studio:

To install the R kernel, without Anaconda, start R Studio. In the R terminal window enter these three commands:

install.packages("devtools")
devtools::install_github("IRkernel/IRkernel")
IRkernel::installspec()

Done. The next time Jupyter is opened, the R kernel will be available and available.

Question 49

Both Anaconda and miniconda use the conda package manager. The chief differece between between Anaconda and miniconda,however,is that

The Anaconda distribution comes pre-loaded with all the packages while the miniconda distribution is just the management system without any pre-loaded packages. If one uses miniconda, one has to download individual packages and libraries separately.

I personally use Anaconda distribution as I dont really have to worry much about individual package installations.

A disadvantage of miniconda is that installing each individual package can take a long amount of time. Compared to that installing and using Anaconda takes a lot less time.

However, there are some packages in anaconda (QtConsole, Glueviz,Orange3) that I have never had to use. I dont even know their purpose. So a disadvantage of anaconda is that it occupies more space than needed.

Question 50

I installed Python Anaconda on Mac (OS Mavericks). I wanted to revert to the default version of Python on my Mac. What’s the best way to do this? Should I delete the ~/anaconda directory? Any other changes required?

Currently when I run which python I get this path:

/Users/username/anaconda/bin/python

Question 51

From the docs:

To uninstall Anaconda open a terminal window and remove the entire anaconda install directory: rm -rf ~/anaconda. You may also edit ~/.bash_profile and remove the anaconda directory from your PATH environment variable, and remove the hidden .condarc file and .conda and .continuum directories which may have been created in the home directory with rm -rf ~/.condarc ~/.conda ~/.continuum.

Further notes:

Python3 installs may use a ~/anaconda3 dir instead of ~/anaconda.
You might also have a ~/.anaconda hidden directory that may be removed.
Depending on how you installed, it is possible that the PATH is modified in one of your runcom files, and not in your shell profile. So, for example if you are using bash, be sure to check your ~/.bashrc if you don’t find the PATH modified in ~/.bash_profile.

Question 52

The anaconda installer adds a line in your ~/.bash_profile script that prepends the anaconda bin directory to your $PATH environment variable. Deleting the anaconda directory should be all you need to do, but it’s good housekeeping to remove this line from your setup script too.

Question 53

Package “anaconda clean“, available from Anaconda platform, should uninstall safely.

conda install anaconda-clean   # install the package anaconda clean
anaconda-clean --yes           # clean all anaconda related files and directories 
rm -rf ~/anaconda3             # removes the entire anaconda directory

rm -rf ~/.anaconda_backup       # anaconda clean creates a back_up of files/dirs, remove it 
                                # (conda list; cmd shouldn't respond after the clean up)

Refer: https://docs.anaconda.com/anaconda/install/uninstall for more details.

Question 54

Removing the Anaconda directory helps, but I don’t think that’s a good idea as you might need to use anaconda sometimes in near future. So, as suggested by mwaskom, anaconda installer automatically adds PATH variable which points to anaconda/bin directory in the ~/.bashrc file.

It looks like this

PATH="/home/linuxsagar/anaconda3/bin:$PATH

So, just comment out the line (add # in the beginning of the line). Then reload the ~/.bashrc file executing source ~/.bashrc

Now, verify the changes executing which python in the new terminal.

Question 55

rm -rf ~/anaconda

It was pretty easy. It switched my pointer to Python: https://docs.continuum.io/anaconda/install#os-x-uninstall

Question 56

If you’re uninstalling Anaconda to be able to use the base Python installation in the system, you could temporarily disable the path by following these steps and not uninstalling Anaconda.

Go to your home directory. Just a cd command will do.

Edit the file .bashrc.

Look for something like export PATH="/home/ubuntu/anaconda3/bin:$PATH" in the file.

Put a # at the beginning to comment it from the script.

#export PATH="/home/ubuntu/anaconda3/bin:$PATH"

Open a new terminal and you should be running the base python installation. This works on Linux systems. Should work on Mac too.

Question 57

Uninstalling Anaconda

To uninstall Anaconda, you can do a simple remove of the program. This will leave a few files behind, which for most users is just fine. See Option A.

If you also want to remove all traces of the configuration files and directories from Anaconda and its programs, you can download and use the Anaconda-Clean program first, then do a simple remove. See Option B.

Option A.

Use simple remove to uninstall Anaconda:

macOS–Open the Terminal.app or iTerm2 terminal application, and then remove your entire Anaconda directory, which has a name such as anaconda2 or anaconda3, by entering rm -rf ~/anaconda3.

Option B.

Full uninstall using Anaconda-Clean and simple remove.

NOTE: Anaconda-Clean must be run before simple remove.

Install the Anaconda-Clean package from Anaconda Prompt or a terminal window:

conda install anaconda-clean

In the same window, run one of these commands:

Remove all Anaconda-related files and directories with a confirmation prompt before deleting each one:

anaconda-clean

Or, remove all Anaconda-related files and directories without being prompted to delete each one:

anaconda-clean --yes

Anaconda-Clean creates a backup of all files and directories that might be removed, such as .bash_profile, in a folder named .anaconda_backup in your home directory. Also note that Anaconda-Clean leaves your data files in the AnacondaProjects directory untouched. After using Anaconda-Clean, follow the instructions above in Option A to uninstall Anaconda. Removing Anaconda path from .bash_profile

If you use Linux or macOS, you may also wish to check the .bash_profilefile in your home directory for a line such as:

export PATH="/Users/jsmith/anaconda3/bin:$PATH"

NOTE: Replace /Users/jsmith/anaconda3/ with your actual path.

This line adds the Anaconda path to the PATH environment variable. It may refer to either Anaconda or Miniconda. After uninstalling Anaconda, you may delete this line and save the file.

by official uninstalling way

Question 58

rm -rf ~/anaconda3

nano ~/.bashrc

Ctrl+W to search for “Anaconda”

Delete or comment out the following lines:

/home/sammuel/.bashrc
# added by Anaconda3 4.2.0 installer
export PATH="/home/sammuel/anaconda3/bin:$PATH"

When you’re done editing the file, type Ctrl+X to exit and y to save changes.

Anaconda is now removed from your server.

Question 59

In case you have multiple version of anaconda,

rm -rf ~/anaconda2 [for version 2]

rm -rf ~/anaconda3 [for version 3]

Open .bashrc file in a text editor

vim .bashrc

remove anaconda directory from your PATH.

export PATH=”/home/{username}/anaconda2/bin:$PATH” [for version 2]

export PATH=”/home/{username}/anaconda3/bin:$PATH” [for version 3]

Question 60

I simply:

rm -rf ~/anaconda3

…this removed conda also.

Then:

mousepad ~/.bashrc

…and removed the path line added at the very bottom (clearly identified by Anaconda as ‘added by Anaconda’.

Worth noting that anaconda3 created a backup of my .bashrc file before modification, and named it as:

./bashrc-anaconda3.bak

…so I could always have just renamed this and deleted my modified .bashrc

Question 61

To uninstall anaconda you have to:

1) Remove the entire anaconda install directory with:

rm -rf ~/anaconda2

2) And (OPTIONAL):

->Edit ~/.bash_profile to remove the anaconda directory from your PATH environment variable.

->Remove the following hidden file and folders that may have been created in the home directory:

rm -rf ~/.condarc ~/.conda ~/.continuum

source

Question 62

To uninstall Anaconda Fully from your System :

Open Terminal
rm -rf ~/miniconda
rm -rf ~/.condarc ~/.conda ~/.continuum

Question 63

rm -rf ~/anaconda3

It was enough

Question 64

I always try to follow the developers advice, since they are usually the ones that now how it would affect your system. Theoretically this should be the safest way:

Install the Anaconda-Clean package from Anaconda Prompt (terminal on Linux or macOS):

conda install anaconda-clean

In the same window, run one of these commands:

Remove all Anaconda-related files and directories with a confirmation prompt before deleting each one:

anaconda-clean

Remove all Anaconda-related files and directories without being prompted to delete each one:

anaconda-clean --yes

Anaconda-Clean creates a backup of all files and directories that might be removed in a folder named .anaconda_backup in your home directory. Also note that Anaconda-Clean leaves your data files in the AnacondaProjects directory untouched.

https://docs.anaconda.com/anaconda/install/uninstall/

问题：如何激活Anaconda环境

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问题：如何在Anaconda（Conda）环境中跟踪点子安装的软件包？

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问题：pyenv，virtualenv，anaconda有什么区别？

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问题：使用Conda进行批量包装更新

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问题：康达能否取代对virtualenv的需求？

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差异：

判决：

Differences:

Verdict:

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问题：水蟒vs.迷你康达

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简要

详情

Brief

Detail

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问题：Python Anaconda-如何安全卸载

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卸载Anaconda

选项A。

选项B。

Uninstalling Anaconda

Option A.

Option B.

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