Python Data Visualization Transcripts
Chapter: Visualization Concepts
Lecture: Visualization variables

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0:00 Now let's bring together continuous and discrete data and compare it to quantitative and qualitative data
0:06 and give some specific examples. We have quantitative or numerical data that's discreet.
0:13 It would look like a set of numbers like 1234 or specific dates. We could also have discrete categorical values and those can be broken down into an
0:24 Unordered list like the pet list of dog, lizard or monkey where there's really no difference between them or an ordered list.
0:32 Or there is some concept where there is a relationship between the order of the items in the list. If we look at continuous numerical values,
0:41 those will be arbitrary numbers such as 5.5 a percentage or maybe currency as well as time that includes hours and minutes.
0:51 The reason I cover this is that your visualization is going to be more effective if
0:57 you treat for example an ordered qualitative value differently than a continuous quantitative value.
1:04 And in fact some of the modules that we'll talk about can infer types of visualization based on the data type of your pandas data frame.

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