Python Data Visualization Transcripts
Chapter: Pandas
Lecture: Pandas conclusion
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So let's summarize where pandas fits in the data visualization ecosystem. From a pro's perspective it is your core data analysis tool,
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so you'll be using it anyway and it's helpful to have one place to go and quickly analyze, manipulate your data and then visualize it.
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It is also very customizable with matplot lib. So pretty much anything you can do with matplot lib you can do with pandas
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And there are some specialized plotting types that are only available in pandas that can be useful for certain data analysis problems.
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From a cons perspective, there are some concerns.
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The visualizations that are created by default pandas are not interactive and there are better statistical
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plotting tools out there, which we will cover. So where does that leave us with? How you should use pandas? Well,
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my recommendation is use pandas for your basic exploratory analysis and then when you need to you can customize it with your underlying
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matplot lib API only when it's needed. Finally, I recommend evaluating some of the other tools we're going to talk about for
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more interactive or complex statistical analysis.