Python Data Visualization Transcripts
Chapter: Matplotlib
Lecture: System setup
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I'm a firm believer that the best way to learn this content is to follow along on your own system. So I'm going to discuss how to get your system set
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up so that you can experiment with some of the code on your own. I assume you have at least a little bit of familiarity with the Python package index
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or Conda and some of the other tools for installing modules on your system and managing
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environments. So one thing that I want to make sure you do is have a
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virtual environment or a conda environment set up so that the content that we're gonna walk
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through is separate from the other environments that you might have on your system.
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For this course, I'm using Python 3.8 but I don't use anything that is too specific to that version. So anything probably from a python 3.6 up to as of
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this recording 3.10 is coming out soon, should work. So as long as you're using a modern version of Python3 you
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should be good for most of the modules you can use pip or conda for installing
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I'm gonna use pip for the majority of the examples because I think that's a little more universal than Conda and for each chapter,
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I'll walk through how to install those modules. So for this chapter we're gonna focus on getting pandas,
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mat plot lib and the Jupyter notebook installed and then I am gonna use stats models to show how to do a regression line and plot that with matplotlib,
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I did run into some issues and have in the past when installing on Windows. Sometimes Pywin 32 can be a little challenging to install with pip
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So, if you do have issues, I recommend using conda for some of these Binaries. like PyWin 32. So you can install doing conda installed pywin32 and then
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in future chapters, we're gonna install some additional modules that you will need for the visualizations during each chapter. I'll walk through this.
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But if you are a little more advanced and want to take a look at installing these on your own, you can but for now,
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just focus on pandas, matplot lib notebook and stats models. All the code I'm going to run through is in a Jupyter notebook.
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Towards the end of the course, I will be generating some code in VS code. Finally, if you have any errors getting these modules installed or getting
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your environment set up, I highly encourage you to look at the individual package documentation
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because that will have the most recent information and tips and tricks we're getting these modules set up on your system.