Develop Reference

Installing packages in python and setting up the working environment

I've been coding with R for quite a while but I want to start learning and using python more for its machine learning applications. However, I'm quite confused as to how to properly install packages and set up the whole working environment. Unlike R where I suppose most people just use RStudio and directly install packages with install.packages(), there seems to be a variety of ways this can be done in python, including pip install conda install and there is also the issue of doing it in the command prompt or one of the IDEs. I've downloaded python 3.8.5 and anaconda3 and some of my most burning questions right now are:
When to use which command for installing packages? (and also should I always do it in the command prompt aka cmd on windows instead of inside jupyter notebook)
How to navigate the cmd syntax/coding (for example the python documentation for installing packages has this piece of code: py -m pip install "SomeProject" but I am completely unfamiliar with this syntax and how to use it - so in the long run do I also have to learn what goes on in the command prompt or does most of the operations occur in the IDE and I mostly don't have to touch the cmd?)
How to set up a working directory of sorts (like setwd() in R) such that my .ipynb files can be saved to other directories or even better if I can just directly start my IDE from another file destination?
I've tried looking at some online resources but they mostly deal with coding basics and the python language instead of these technical aspects of the set up, so I would greatly appreciate some advice on how to navigate and set up the python working environment in general. Thanks a lot!

Python uses a different way of installing packages. Python has a thing named venv which stands for Virtual Environment. You install all of your packages in venv. Usually for each new project you make a new venv.
By using Anaconda on windows you install everything within the anaconda environment that you have specified.
python -m pip install "modulename" is a command that will install modulename to your default venv. You will be able to use this module when no other venv is specified. Here is the docs page. And here is a tutorial on how to use venv
By default python uses the same directory you have your code in. e.g. C:/Users/me/home/mypythonfile.py will run in C:/Users/me/home/ and will be able to access files in this directory. However you can use ../ to navigate directories or you can specify an absolute path to file you want to open e.g. with open("C:/system32/somesystemfile.sys") as file

Going over the technical differences of conda and pip:
So Conda is a packaging tool and installer that aims to do more than what pip does; handle library dependencies outside of the Python packages as well as the Python packages themselves. Both have many similar functionalities as well, you can install packages or create virtual environments with both.
It is generally advisable to generally have both conda and pip installed since there are some packages which might not be available with conda but with pip and vice versa.
The commands to install in both the ways is easy enough, but one thing to keep in mind is that
conda stores packages in the anaconda/pkgs directory
pip stores it in directory under /usr/local/bin/ for a Unix-based system, or \Program Files\ for Windows
You can use both pip or conda inside the jupyter notebook, it will work just fine, but it may be possible that you get multiple versions of the same package.
Most of the times, you will use cmd only to install a module used in your code, or to create environments, py -m pip install "SomeProject" here basically means that the module "SomeProject" will be downloaded in base env.

You could think of conda as python with a variety of additional functionalities, such as certain pre-installed packages and tools, such as spyder and jupyter. Hence, you must be precise when you say:
I've downloaded python 3.8.5 and anaconda3
Does it mean you installed python in your computer and then also anaconda?
In general, or at least in my opinion, using anaconda has advantages for development, but typically you'll just use a simple python installation in production (if that applies to you).
Anaconda has it's own package registry/repository . When you call conda install <package>, it will search for the package there and install it if available. You would better search it first, for instance matplotlib.
pip is a package manager for the Python Package Index. pip also ships with anaconda. Hence, in an anaconda environment you may install packages from either sources (either using pip install or conda install). For instance, pandas from PyPI and pandas from conda. There is no guarantee that packages exist in both sources. You must either search it first or simply try it.
In your first steps, I would suggest you to stick to only one dev env (either simple python or anaconda, recommend the second). Because that simplifies the question: "which python and which pip is executed in the cmd line?". That said, those commands should work as expected in any terminal, it be a simple cmd or an embedded one like in PyCharm or VS Code.
You could inspect that by running (on windows and linux at least):
which python, which pip.
Honestly, this is a question/answer that falls outside the scope of SO and for more info you would better check official websites, such as for anaconda or search for python vs anaconda blogs.

Should I pip install python inside virtualenv?

I need python3.6 for tensorflow installation, so I downloaded python3.6.12.tar. And I found that I should pip install tarfile. However, in this case it is an older version of python. FYI, In my computer(laptop) I installed python3.9.
My question is: can I pip install python.tar inside a virtualenv?

This is not how virtual environments work. I suggest you to do a little bit more research on virtual environments in Python.
Virtual Environments and Packages
Basically you need to install the necessary python version onto your machine. Then go ahead and use that specific python (which is version 3.6 in your case), to create a virtual environment with the command
~ /usr/bin/<path-to-python3.6> -m venv venv
This command will create a folder called venv. Now you need to source the activation script inside this folder to activate your environment.
Handy note: if you are dealing with different versions of python, a more robust way of handling such situations is via using a tool called pyenv.

Does miniconda installation affect standard python installation?

I had first installed python using the standard python distribution available on their official website and I would be using pip to install all necessary packages.
However, now I wish to use miniconda, since it is a better choice for data science.
But, it installs python along with It and I don't want to disturb my earlier setup of pip+Python.
Will installing miniconda affect my python installation.
Is there a way of installing it without disturbing the python installation?
I am on a Windows operating system.

You can safely install Anaconada (or Miniconda) on top of other Python installations. It goes into a completely different folder on your local disk. But leave the default installation options on default, especially don't add Python to your path.
The important thing is that you activate your environment before you use it via
conda activate
and then start Python from there (or let your IDE do that for you).
(base)> python
Without activatation conda doesn't work and calling python from the command prompt will start your 'standard installation' again.
The advantage of Anaconda is that it guarantees maximumum consistency for the 'scientific stack' and in case you are still missing some 3rd party packages you can always install them aditionally via `pip install' into an activated conda environment.

Should Anaconda be use to manage system python? Or, is it just to create an isolated environment?

I already have Python 2.7 installed in Windows. I have normally used pip to install packages. However, Pandas recommends using Anaconda and it appears that it has many benefits so I wanted to try it.
I installed miniconda and it just reinstalled Python under its own directory. Does Anaconda always duplicate the python libraries or can it be used to manage the system's python.
I use python to develop and also wanted to use Pandas to analyse data. However, I would like to avoid have two copies of Python. I want to have one python environment that is constant with all the packages that I intend to have. Otherwise, I feel that I will have to install the same packages multiple times.
I know that Anaconda is to separate different environments. Does this mean that I am trying to do something it is not its purpose or have I installed it incorrectly?

Anaconda has a root environment that includes a bit more than 100 of the most popular Python packages.
Yes, you can use the root Python as your system's Python executable.
The anaconda installation comes with Conda, which is a robust environment manager. If you want to keep your root environment stable, you can use Conda to create new environments for each project, and Conda handles the dependencies of each environment as well.
You can create a new environment named "analysis" that has Python, IPython, and Pandas using:
conda create --name analysis python ipython pandas
After installing all of the packages, you can use the environment by running (from the CMD prompt):
conda activate analysis

Scikit-learn - installing development version (0.20)

I currently have scikit-learn 0.19 installed. I'd like to test my code using the latest development version as there seems to be a fix for Incremental PCA.
How do I go about installing this new version if I've previously installed scikit-learn using anaconda?
Also, how would I revert back to the stable release in the event that 0.20 does not solve my problem?
I am in need of some hand holding here, as I've read the docs on the website and not sure I completely understand the process (especially being able to revert back to the stable version if needed).

The whole point of the Anaconda Python distribution (apart from the convenience of having a bunch of useful packages included) is that you get the conda environment manager, which exists to meet exactly this sort of requirement.
What you want to do is to create a new conda environment by launching the Anaconda prompt and typing
conda create -n myenv scikit-learn other-package other-package2 etc
where myenv is the name you want to give the new environment and other-package other-package2 etc are the names of any other packages you will want to use (import) in your code. conda will figure out any dependencies of these packages and show you a list of what is going to be installed before it proceeds.
If you want to specify that a package should be a particular version, add that to the package name e.g. other-package=1.1.0, otherwise conda will install the latest versions of each package that are mutually compatible. You can also specify a particular version of Python by including it in the package list, e.g. python=3.4. You can check what versions of a package are available with conda search package-name (where package-name is the name of the package you want, obviously).
To run your code in the newly created environment, first activate the environment at the Anaconda prompt. If you use the Spyder IDE, launch it after activating the correct environment, or use the start menu shortcut specific to that environment if you have one. Other IDEs may have their own method of selecting a specific environment to work in.
To revert to the version(s) you were using before, activate the environment containing those versions - if you've never created a new environment before, that'll be root.

Just in case someone comes here looking for a solution without conda:
The website recommends that you download the latest code via
git clone git://github.com/scikit-learn/scikit-learn.git
and then include it in pip via (after changing to the directory)
pip install --editable .
You can also add the --user flag to have pip install to a local directory. Then, uninstalling should be as easy as pip uninstall sklearn.