#100DaysOfCode in Python Transcripts
Chapter: Welcome to the course
Lecture: Bob's setup
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0:00
I'm using Anaconda for this course, a pre-bundled, Python distribution widely used in the Data Science community.
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And it comes with a lot of packages already. You're not required to use this distribution. You can also pip install my requirements,
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which we will see in a bit. You can download the full version here. We recommend that you use 3.6.
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Really no reason to start a new project in Python 2 anymore. You can also install Miniconda, which is a smaller version,
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which only includes the base packages. And, mainly, what you need to know is for almost all the lessons, I will be using
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Jupyter Notebooks, which is a great way to experiment with Python code and more in the browser. 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 at this point by going to try.jupyter.org, but I encourage you to install it
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to follow along with my lessons. To install it, again, the recommended way is to use Anaconda, but you can also use pip install jupyter,
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and that should get it as well. And let me show you that quickly. So, first I need to clone the 100 Days of Code repo. You cd into that.
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At this point you really want to make your virtual environment to work on the project's requirement in isolation to not mess up your global space.
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And in every lesson, I have a video how to pip install the requirements for that lesson, but I also
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have 'em all wrapped together in a requirements file. So, for all the notebooks, you need Jupyter; and ipykernel, which I will explain why in a bit;
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and then I listed out the requirements for each lesson. There are various ways to make a virtual environment.
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The classic way is to use pyvenv built in module. You can also use pipenv, the new way, which should be perfectly fine. And Anaconda comes with Conda,
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a utility to manage environments as well. However, I am used to virtualenv, just the classic one. So, in this course I am making a virtual environment
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with this alias: virtualenv -p, pointing to the Python binary that comes with my Anaconda installation, and the name of my virtual environment.
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So, let's run that now. And then you have to activate it. 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, where I don't have anything installed. 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, you can do pip install -r, requirements/requirements, and that might take a bit because
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it's not only pulling the dependencies, but some of the dependencies have other dependencies.
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With that done, you can launch a Jupyter notebook like this. And you can go in today's and do any lesson. For example, Selenium.
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And you can open the notebook like this, and you can follow the lesson. And you see that the notebook discovered my virtual environment.
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If that is not the case, you might have to tweak it a little bit, and that's why I pip install ipykernel and Tornado was pulled in as well.
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That should have been enough. If that's not the case, you might have to run iPythonkernel install <username> <projectname>
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And this will be venv, or the name of the virtual environment. And then back to the notebook. You should have the kernel, venv,
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or whatever you named the virtual environment, here, and you can switch to that, but I already have it here. So, then your dependency should work.
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It's in your virtual environment and you can follow along with the lesson and make any modifications in the code
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and experiment and that's how you learn.