#100DaysOfCode in Python Transcripts
Chapter: Days 82-84: Data visualization with Plotly
Lecture: Installing feedparser and plotly
0:00 As usual, I have a Jupyter notebook prepared for this lesson and first, let's actually head over to the terminal
0:09 to install the external modules we're going to use. I'm going to create a directory. cd into it. I was explaining before I use
0:21 a virtual venv with my Python path set to my Anaconda installation. I'm using Anaconda because it comes with all the data science libraries
0:29 and Jupyter notebook and all that. If you're not using Anaconda, you can make a virtual environment just by using the standard
0:37 module in Python, like this but I'm using this to make it all work with my environment. Right. Then I need to enable it. I have an as for that as well
0:49 because I'm using virtual environments for anything because I always want to isolate my dependencies. So, now I'm in the virtual environment
0:58 and you see a nice indication in my prompt. As expected there's nothing installed and it's exactly what we want because we want
1:06 to have all of our stuff in this namespace. I'm going to pip install feedparser serve to parse our blog feed and plotly to do the graphical work.
1:22 That's all now in our virtual environment, so, we can get started. So, I'm heading back to my notebook and let's import the modules we're going to use.
1:35 Right, by the way, one thing I have the virtual environment here enabled that's probably not what happens by default for you.
1:43 So, what I did to get the virtual environment inside my notebook, was to pip install ipykernel so then you run this self install script
1:54 and the name should be your virtual environment. So, in my case, that's venv and after we started the notebook then I have an option here
2:02 to select my virtual environment. So, I put the link here in notebook if you want to work from a similar set up
2:08 as I have, you should go through this link. That's it for set up, in the next video, we're going to use feeds bars to pull data from our PyBites blog.