#100DaysOfCode in Python Transcripts
Chapter: Welcome to the course
Lecture: Bob's setup
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I'm using Anaconda for this course,
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a pre-bundled, Python distribution widely used
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in the Data Science community.
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And it comes with a lot of packages already.
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You're not required to use this distribution.
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You can also pip install my requirements,
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which we will see in a bit.
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You can download the full version here.
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We recommend that you use 3.6.
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Really no reason to start a new project in Python 2 anymore.
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You can also install Miniconda, which is a smaller version,
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which only includes the base packages.
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And, mainly, what you need to know is
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for almost all the lessons, I will be using
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Jupyter Notebooks, which is a great way to experiment
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with Python code and more in the browser.
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It's a great tool to both teach and learn Python.
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You can try it out if you want to play a little bit
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at this point by going to try.jupyter.org,
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but I encourage you to install it
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to follow along with my lessons.
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To install it, again, the recommended way is
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to use Anaconda, but you can also use pip install jupyter,
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and that should get it as well.
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And let me show you that quickly.
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So, first I need to clone the 100 Days of Code repo.
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You cd into that.
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At this point you really want to make
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your virtual environment to work
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on the project's requirement in isolation
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to not mess up your global space.
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And in every lesson, I have a video how to pip install
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the requirements for that lesson, but I also
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have 'em all wrapped together in a requirements file.
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So, for all the notebooks, you need Jupyter;
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and ipykernel, which I will explain why in a bit;
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and then I listed out the requirements for each lesson.
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There are various ways to make a virtual environment.
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The classic way is to use pyvenv built in module.
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You can also use pipenv, the new way,
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which should be perfectly fine.
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And Anaconda comes with Conda,
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a utility to manage environments as well.
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However, I am used to virtualenv, just the classic one.
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So, in this course I am making a virtual environment
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with this alias:
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virtualenv -p, pointing to the Python binary
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that comes with my Anaconda installation,
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and the name of my virtual environment.
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So, let's run that now.
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And then you have to activate it.
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And that's what I'm doing, that a lot I have another alias,
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ae, and now I'm in my virtual environment,
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where I don't have anything installed.
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At this point, you can just do it video by video,
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but if you want to have all the packages up-front,
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you can do pip install -r,
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requirements/requirements,
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and that might take a bit because
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it's not only pulling the dependencies,
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but some of the dependencies have other dependencies.
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With that done, you can launch a Jupyter notebook like this.
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And you can go in today's and do any lesson.
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For example, Selenium.
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And you can open the notebook like this,
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and you can follow the lesson.
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And you see that the notebook discovered
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my virtual environment.
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If that is not the case, you might have to tweak it
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a little bit, and that's why I pip install ipykernel
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and Tornado was pulled in as well.
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That should have been enough.
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If that's not the case, you might have to run
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ipythonkernel install <username> <projectname>
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And this will be venv, or the name
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of the virtual environment.
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And then back to the notebook.
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You should have the kernel, venv,
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or whatever you named the virtual environment, here,
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and you can switch to that, but I already have it here.
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So, then your dependency should work.
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It's in your virtual environment and you can follow along
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with the lesson and make any modifications in the code
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and experiment and that's how you learn.
